Water supply risk assessment method under salt tide invasion in tidal estuary region based on fresh water prevailing and storage scheduling

Abstract

The invention discloses a water supply risk assessment method, system and device for tidal estuary area salt tide invasion based on fresh water prevailing and storage, and a storage medium. The method comprises the following steps: acquiring day-by-day target site data and historical salt tide disaster simulation data of a research area in the same time sequence; training the historical salt tide disaster simulation model by using the target site data, and inputting the historical salt tide disaster simulation data into the trained historical salt tide disaster simulation model to obtain a long-sequence historical salinity simulation value; generalizing a water supply system of the research area, and constructing a water supply risk comprehensive evaluation index system based on fresh water grabbing and storage scheduling; based on the water supply risk comprehensive evaluation index system, calculating a comprehensive water supply risk index according to the long-sequence historical salinity simulation value; and performing frequency analysis on the comprehensive water supply risk index, and dividing a water supply risk degree and an early warning level based on a set water supply risk threshold. According to the method, the comprehensive water supply risk index is quantified based on fresh water preemptive storage scheduling, the salt tide risk degree and the early warning level are scientifically divided from the angles of the influence degree and the occurrence frequency of long-sequence historical salt tide disasters, and an effective decision basis is provided for water supply safety study and judgment of estuary coastal areas depending on tidal river reaches.

Description

Technical Field

[0001] The present invention relates to the field of saltwater intrusion risk assessment, and in particular to a method, system, terminal device and computer-readable storage medium for assessing water supply risk under saltwater intrusion in a tidal estuary area based on emergency freshwater storage scheduling. Background Art

[0002] Estuarine and coastal areas are key areas for human survival and economic and social development. Saltwater intrusion is a common natural phenomenon in this area. Especially when upstream runoff weakens, tidal action will intensify the mixing of salt and fresh water, causing the salt-fresh water interface to advance upstream along the river channel, thereby polluting the freshwater resources in tidal river sections. Under the dual influence of global climate change and human activities, sea level rise and frequent extreme climate events are expected to make the situation of saltwater intrusion in estuaries around the world increasingly severe, which will pose greater challenges to water supply security and social stability in estuary and coastal areas.

[0003] From the current research status at home and abroad, the current research mainly focuses on the mechanism and simulation of saltwater intrusion, while there are few discussions on the risk assessment and early warning methods of saltwater intrusion threatening water supply security. On the one hand, due to the limitations of historical salinity observation data, the existing saltwater mechanism research and simulation research generally involve a short period of time, which is not enough to scientifically evaluate the risk level and threshold of saltwater from the perspective of the impact degree and frequency of long-sequence historical saltwater disasters. On the other hand, when the main water intake cannot be normally drawn due to excessive salinity, emergency dispatch measures are generally taken to increase the flow of downstream rivers by using large upstream reservoirs to alleviate the saltwater intrusion at the estuary, increase the probability of freshwater intake, and make full use of the regulation and storage capacity of local reservoirs to maximize the storage of freshwater resources. Although existing studies have judged the impact degree and early warning level of saltwater from the perspectives of saltwater upstream distance, duration, maximum salinity, and the number of hours when the water intake exceeds the standard, they have failed to consider the regulation and storage role of local reservoirs. The water stored in local reservoirs serves as a supplementary freshwater resource in the early stages of saltwater intrusion. When the water from upstream is severely depleted and the saltwater intrusion lasts for a long time, the limited reservoir capacity may not be able to guarantee sufficient and continuous freshwater supply, resulting in the risk of water shortage. Summary of the invention

[0004] In order to solve at least one technical problem in the above-mentioned prior art, the present invention provides a water supply risk assessment method, system, terminal device and computer-readable storage medium under saltwater intrusion in tidal estuary areas based on emergency freshwater scheduling. By comprehensively assessing the risk of saltwater intrusion from the perspectives of natural characteristics of saltwater tides, reservoir regulation and the degree and frequency of impact of historical disasters, different risk levels are clarified, and a fast and reliable decision-making basis is provided for saltwater risk management, which is of great significance for improving the water security guarantee capability in estuary and coastal areas.

[0005] The first object of the present invention is to provide a method for assessing water supply risk under saltwater intrusion in tidal estuary areas based on emergency freshwater storage scheduling.

[0006] The second object of the present invention is to provide a water supply risk assessment system under saltwater intrusion in tidal estuary areas based on emergency freshwater storage scheduling.

[0007] The third object of the present invention is to provide a terminal device.

[0008] A fourth object of the present invention is to provide a computer-readable storage medium.

[0009] The first object of the present invention can be achieved by adopting the following technical solutions:

[0010] A method for assessing water supply risk under saltwater intrusion in tidal estuary areas based on emergency freshwater storage scheduling, the method comprising:

[0011] Obtain daily target site data and historical saltwater disaster simulation data in the same time series in the study area; the target site data include measured salinity data, flow data, tide data and wind data, and the historical saltwater disaster simulation data include daily flow data, tide data and wind data in a long time series;

[0012] The historical saltwater disaster simulation model is trained using the target site data, and the historical saltwater disaster simulation data is input into the trained historical saltwater disaster simulation model to obtain a long sequence of historical salinity simulation values;

[0013] The water supply system in the study area is generalized, and a comprehensive water supply risk evaluation index system based on emergency freshwater scheduling is constructed; based on the comprehensive water supply risk evaluation index system, the comprehensive water supply risk index is calculated according to the long-sequence historical salinity simulation value; the water supply system includes the main water intake of the river, local water storage reservoirs and water supply objects;

[0014] Conduct frequency analysis on the comprehensive water supply risk index, and divide the water supply risk degree and warning level based on the set water supply risk threshold.

[0015] Furthermore, the comprehensive water supply risk evaluation index system is based on which the comprehensive water supply risk index is calculated according to the long-sequence historical salinity simulation value, including:

[0016] Based on the comprehensive evaluation index system of water supply risk, the values ​​of each quantitative evaluation index in the comprehensive evaluation index system of water risk are calculated according to the long-sequence historical salinity simulation values, the maximum water intake capacity of the main water intakes of the river, the adjustable storage capacity of the local water storage reservoirs and the water demand of the water supply objects;

[0017] The symbol conversion method is used to process the values ​​of each quantitative evaluation index to obtain the normalized positive index value of each quantitative evaluation index;

[0018] According to the processed values ​​of each quantitative evaluation index, the judgment matrix is ​​constructed by using the hierarchical analysis method and the pairwise judgment method and the corresponding weight matrix is ​​calculated; if the consistency test index of the weight matrix is ​​greater than or equal to the first set threshold, the judgment matrix is ​​reconstructed;

[0019] The comprehensive water supply risk index is calculated based on the corresponding weight matrix of the determined judgment matrix and the values ​​of each quantitative evaluation index after processing.

[0020] Furthermore, the water supply system in the study area is generalized, and a comprehensive evaluation index system for water supply risk based on emergency storage of fresh water is constructed, including:

[0021] Quantitative evaluation indicators are selected from the probability of fresh water intake at the main river intake, the degree of dependence on local water storage reservoirs and the degree of water shortage of water supply objects, including:

[0022] From the perspective of the probability of taking fresh water from the main water intake of the river, the average probability of taking fresh water R1, the minimum probability of taking fresh water R2 and the number of days where fresh water cannot be taken R3 of the main water intake of the river in the next k days are selected; k is a positive integer greater than or equal to 1;

[0023] From the perspective of local water storage reservoir dependence, the difference between the average water demand and average water withdrawal in the next k days, R4, and the average storage rate in the next k days, R5, are selected;

[0024] In terms of water shortage degree, the average substantial water shortage amount R6 and the number of substantial water shortage days R7 in the next k days are selected;

[0025] The calculation formulas for each quantitative evaluation index are as follows:

[0026]

[0027] in:

[0028]

[0029] Where, T hour is the number of hours when the salinity of the main water intake of the river exceeds the standard, f hour (S) is the function obtained by fitting the empirical relationship between the daily salinity exceeding the standard and the daily average salinity in the measured salinity data, and S is the long-series historical salinity simulation value; ave(T hour ) is the average daily exceeding-limit hours of the main water intake of the river in the next k days; min(T hour ) represents the longest daily exceeding-limit hours at the main water intake of the river in the next k days; sum(n) represents the number of days when fresh water cannot be taken at the main water intake of the river in the next k days. Considering that the actual water pump cannot be started and stopped quickly, the fresh water taking probability is 1-Thour / 24 is less than 0.1, which means that it is impossible to take short positions within one day, that is, n = 1; D a is the daily water demand of the water supply object, ave(D a ) is the average daily water demand in the next k days; W a is the daily water intake of the main water intake of the river, ave(W a ) is the average daily water intake in the next k days; the storage rate P t is the effective water storage capacity V on that day t and dead storage capacity V s The sum of the normal storage capacity of the reservoir V total The ratio of ave(P t ) represents the average filling rate of the local water reservoir in the future k days; W d is the substantial water deficit, ave(W d ) represents the average water shortage in the next k days; sum(m) represents the number of days with substantial water shortage in the next k days. d >0, it is considered that substantial water shortage occurs on that day, that is, m=1; W c V is the maximum water intake capacity of the main water intake of the river; a Indicates the amount of excess water used to supplement local water storage reservoirs; W s The amount of water required to supplement the water supply from local water storage reservoirs; V t and V t-1 They represent the effective water storage capacity of the local water storage reservoir on the current day and the previous day respectively; k is a positive integer greater than or equal to 1;

[0030] The rules for emergency freshwater storage and scheduling under saltwater intrusion are as follows: when saltwater intrusion occurs, the main water intakes of each river channel will draw water from the river channel to the maximum extent during the water-drawing period, and the water intake will give priority to meeting the needs of the water supply objects. If there is still surplus water, the excess water intake will be used to supplement the local water storage reservoir. The effective water storage capacity of the reservoir after replenishment should not exceed the regulating capacity of the reservoir; if the water intake cannot meet the needs of the water supply objects, water will be released from the local reservoir for supplementary water supply, and the effective water storage capacity of the reservoir after supplementary water supply should not be less than 0; if the water intake and the reservoir supplementary water supply still cannot meet the needs of the water supply objects, a substantial water shortage will occur.

[0031] Furthermore, the frequency analysis of the comprehensive water supply risk index is performed to divide the water supply risk degree and warning level based on the set water supply risk threshold, including:

[0032] Firstly, the comprehensive water supply risk index less than the second set threshold is eliminated from the comprehensive water supply risk index;

[0033] Then, the frequency analysis of the remaining long-sequence daily comprehensive water supply risk index was carried out to count the cumulative frequencies corresponding to different comprehensive water supply risk indexes;

[0034] Finally, based on the set water supply risk threshold, the water supply risk degree and warning level are divided according to the cumulative frequency.

[0035] Furthermore, the historical saltwater disaster simulation model is a multi-layer BP neural network model;

[0036] The method of training the historical saltwater disaster simulation model using the target site data includes:

[0037] The measured flow data, tide data and wind data are used as saltwater influencing factors to input into the historical saltwater disaster simulation model, and the salinity simulation values ​​of the main water intakes of each river are output;

[0038] According to the measured salinity data of the main water intakes of each river and the long-sequence historical salinity simulation values, the performance of the historical saltwater disaster simulation model was evaluated, and the optimal lag time of each saltwater influencing factor was obtained. The corresponding BP neural network model is the trained historical saltwater disaster simulation model.

[0039] Furthermore, the optimal lag time of each saltwater tide influencing factor is obtained by the following process:

[0040] Using the particle swarm optimization algorithm, the lag time of each saltwater influencing factor, the connection weight and threshold of the multi-layer BP neural network are taken as the position value of the particle.

[0041] The performance index value of the BP neural network model is used as the fitness value of the particle. The target site data is used for iterative calculation to determine the position value that maximizes the particle fitness value, that is, the optimal lag time and optimal neural network parameters of each influencing factor are obtained. The corresponding BP neural network model is the BP neural network model with the best performance.

[0042] Furthermore, the long time series is no less than 20 years.

[0043] Furthermore, it is characterized in that, after frequency analysis of the comprehensive water supply risk index, the method further includes determining early warning indicative indicators from the flow of the upstream control hydrological station, the probability of fresh water withdrawal from the main water intake of the river, and the average filling rate of the local water storage reservoir; and dividing the risk level warning according to the early warning indicative indicators, specifically including:

[0044] Based on the set water supply risk threshold, the comprehensive water supply risk index RI is divided into corresponding categories;

[0045] Using the Mann-Whitney U nonparametric test method, we selected three indicators: the average flow data for the next k days, the average probability of fresh water intake at the main water intake of the main river in the next k days, and the average fullness rate of local water storage reservoirs in the next k days. We analyzed the differences between the three indicators in different categories and used the indicators with significant differences as early warning indicators.

[0046] If the warning indicator M is greater than the judgment threshold, it is classified into the corresponding warning level;

[0047] Wherein, the determination threshold is:

[0048]

[0049] Where M Aave and M Bave They are taken from the two types of RI that the indicator M is intended to distinguish; the indicator M between the two types of RI has significant differences and is marked as range A and range B, respectively, where range B corresponds to a higher level of warning level; M Aave is the average in range A; M Bave is the average in range B; M A-Bth It is the judgment threshold of indicator M between range A and range B, and serves as the judgment criterion for the warning level corresponding to the indicator M in range A entering range B.

[0050] Furthermore, after obtaining the daily target station data in the study area within the same time series, the method further includes:

[0051] The saltwater intrusion prediction model is trained using the target site data, and the monitored target site data is input into the trained saltwater intrusion prediction model to obtain the salinity forecast value for the next f days; the saltwater intrusion prediction model and the historical saltwater disaster simulation model both adopt a multi-layer BP neural network model; wherein f is a positive integer greater than or equal to 1;

[0052] Based on the comprehensive evaluation index system of water supply risk, the comprehensive water supply risk index is calculated according to the future salinity forecast value;

[0053] Water supply risk levels and warning levels are divided according to the comprehensive water supply risk index and set thresholds.

[0054] Furthermore, the use of target site data to train the saltwater intrusion prediction model includes:

[0055] The daily average salinity in the salinity data is used as the cumulative effect of historical salinity, and together with the flow data and wind data, is input into the saltwater intrusion forecast model as a factor affecting saltwater intrusion to obtain the future salinity forecast value.

[0056] According to the salinity data and future salinity forecast values, the performance of the saltwater intrusion forecast model is evaluated, and the optimal lag time of each saltwater influencing factor is obtained. The corresponding BP neural network model is the trained saltwater intrusion forecast model.

[0057] The second object of the present invention can be achieved by adopting the following technical solutions:

[0058] A water supply risk assessment system for saltwater intrusion in tidal estuary areas based on emergency freshwater storage scheduling, the system comprising obtaining:

[0059] The acquisition module is used to obtain the target site data and historical saltwater disaster simulation data of the study area on a daily basis in the same time series; the target site data include the measured salinity data, flow data, tide data and wind data, and the historical saltwater disaster simulation data include the daily flow data, tide data and wind data in a long time series;

[0060] A training module is used to train the historical saltwater disaster simulation model using the target site data, input the historical saltwater disaster simulation data into the trained historical saltwater disaster simulation model, and obtain a long sequence of historical salinity simulation values;

[0061] The construction and calculation module is used to generalize the water supply system in the study area and construct a comprehensive evaluation index system for water supply risk based on emergency storage of fresh water scheduling; based on the comprehensive evaluation index system for water supply risk, the comprehensive water supply risk index is calculated according to the long-sequence historical salinity simulation value; the water supply system includes the main water intake of the river, local water storage reservoirs and water supply objects;

[0062] The classification module is used to conduct frequency analysis on the comprehensive water supply risk index and to classify the water supply risk degree and warning level based on the set water supply risk threshold.

[0063] The third object of the present invention can be achieved by adopting the following technical solutions:

[0064] A terminal device comprises a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the above-mentioned water supply risk assessment method under saltwater intrusion in a tidal estuary area based on emergency storage of fresh water scheduling is implemented.

[0065] The fourth object of the present invention can be achieved by adopting the following technical solutions:

[0066] A computer-readable storage medium stores a program, which, when executed by a processor, implements the above-mentioned water supply risk assessment method under saltwater intrusion in tidal estuary areas based on emergency storage of fresh water scheduling.

[0067] The present invention has the following beneficial effects compared with the prior art:

[0068] The present invention is based on the rules for rushing to store fresh water under saltwater intrusion conditions, combined with saltwater intrusion simulation, local reservoir rushing to store fresh water scheduling and regional water supply risk comprehensive evaluation index system, quantifies the regional water supply risk under saltwater intrusion with a comprehensive water supply risk index, and clarifies different water supply risk levels and early warning levels by analyzing the frequency distribution of the comprehensive water supply risk index under long-sequence historical saltwater simulation; the flow of upstream control hydrological stations, the probability of fresh water intake at major water intakes, and the average storage rate of local reservoirs (groups) are used as early warning indicative indicators to divide risk level early warning. The present invention provides an effective analysis idea and evaluation technology for the water supply safety assessment of estuary and coastal areas that rely on tidal river sections as water sources, and has a promoting effect on improving the water security guarantee capacity of estuary and coastal areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0070] Figure 1 It is a flow chart of a method for assessing water supply risk under saltwater intrusion in tidal estuary areas based on emergency freshwater storage scheduling according to Example 1 of the present invention;

[0071] Figure 2 It is a schematic diagram of a water supply risk assessment method under saltwater intrusion in tidal estuary areas based on emergency freshwater storage scheduling according to Example 1 of the present invention;

[0072] Figure 3 is the daily saltwater tide risk warning result of the Zhuhai-Macao water supply system in the dry season from 1979 to 2019 according to an embodiment of the present invention;

[0073] Figure 4 This is the daily saltwater tide risk warning result of the Zhuhai-Macao water supply system in the dry season in 2022 based on the early warning indicative index determination method according to Example 1 of the present invention;

[0074] Figure 5 It is a structural block diagram of a water supply risk assessment system for saltwater intrusion in tidal estuary areas based on emergency freshwater storage scheduling according to Embodiment 2 of the present invention;

[0075] Figure 6 This is a structural block diagram of the terminal device of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should be understood that the specific embodiments described are only used to explain the present application and are not used to limit the present application.

[0077] Embodiment 1:

[0078] refer to Figure 1 , 2 This embodiment provides a method for assessing water supply risk under saltwater intrusion in tidal estuary areas based on emergency freshwater storage scheduling, including the following steps:

[0079] S101, obtaining daily target site data and historical saltwater disaster simulation data in the same time series in the study area; wherein the target site data include measured salinity data, flow data, tide data and wind data, and the historical saltwater disaster simulation data include daily flow data, tide data and wind data in a long time series;

[0080] S102, using the target site data to train the historical saltwater disaster simulation model and the saltwater intrusion prediction model respectively; inputting the historical saltwater disaster simulation data into the trained historical saltwater disaster simulation model to obtain a long sequence of historical salinity simulation values; inputting the monitored target site data into the trained saltwater intrusion prediction model to obtain future salinity prediction values;

[0081] S103. Generalize the water supply system in the study area and construct a comprehensive water supply risk evaluation index system based on emergency freshwater storage scheduling; based on the comprehensive water supply risk evaluation index system, calculate the comprehensive water supply risk index according to the long-sequence historical salinity simulation value or future salinity prediction value; the water supply system at least includes the main water intake of the river, local water storage reservoirs and water supply objects;

[0082] S104. Conduct frequency analysis on the comprehensive water supply risk index calculated based on the long-sequence historical salinity simulation values, and classify the water supply risk degree and warning level; and determine the warning indicative indicators from the aspects of the flow of the upstream controlling hydrological station, the probability of taking fresh water from the main water intake of the river, and the average storage rate of the local reservoir group; classify the warning into corresponding levels based on the comprehensive water supply risk index calculated based on the future salinity forecast value or its warning indicative indicators.

[0083] This embodiment takes the Modaomen waterway at the Pearl River Estuary and the Zhuhai-Macao water supply system as the research objects. First, determine the target sites in the study area, which include the main water intakes of the river, the upstream controlling hydrological stations, the estuary tides and meteorological stations, and collect data such as the salinity of the main water intakes of the river, the flow of the hydrological stations, the tide level of the tidal stations, and the wind force of the meteorological stations; then, based on the machine learning method, construct a historical saltwater disaster simulation model and a saltwater intrusion forecast model for the study area to obtain the long-series historical salinity simulation values ​​and future salinity forecast values ​​of the main water intakes of the Modaomen waterway respectively; combined with the characteristics of the Zhuhai-Macao water supply system, construct a water resources supply and demand balance model and a comprehensive evaluation index system for water supply risks in the study area; based on the water supply wind A comprehensive water supply risk evaluation index system is established, and the comprehensive water supply risk index of the region is calculated according to the long-sequence historical salinity simulation values ​​or the future salinity prediction values; then, a frequency analysis is conducted on the comprehensive water supply risk index calculated according to the long-sequence historical salinity simulation values, and the water supply risk threshold and early warning level of the Zhuhai-Macao water supply system under saltwater intrusion are determined, and early warning indicative indicators are determined from aspects such as the flow of the upstream controlling hydrological station, the probability of taking fresh water from the main water intakes of the main rivers, and the average storage rate of the local reservoir group; finally, an early warning of the corresponding level is issued according to the comprehensive water supply risk index or early warning indicative index value calculated according to the future salinity prediction value.

[0084] Furthermore, step S101 specifically includes:

[0085] (1) Collect target site data as model training and testing datasets.

[0086] The measured salinity data, flow data, tide data and wind data of the target station in the same time series are obtained daily, which are mainly used for training and testing machine learning models. The three main water intakes of Zhuzhoutou, Pinggang and Guangchang pumping stations of Modaomen waterway are selected to collect hourly chlorine content data of each water intake from 2019 to 2023. After inspection and processing, the daily exceeding hours and daily average salinity data are obtained; the daily flow data of Wuzhou hydrological station of Xijiang control station and Shijiao hydrological station of Beijiang control station of Modaomen waterway are collected from 2019 to 2023; the daily average sea level and daily maximum tidal range data of Crucian Bay Station in Hong Kong outside the estuary of Modaomen waterway are collected from 2019 to 2023; the daily average wind speed and prevailing wind direction data of Wanglan Island Station in Hong Kong outside the estuary of Modaomen waterway are collected from 2019 to 2023; the above data are quality controlled to obtain the model training and testing data sets.

[0087] (2) Collect historical saltwater disaster simulation data.

[0088] The measured flow data, tide level data and wind speed data of the target station are obtained on a daily basis over a long time series, which are mainly used for long-sequence historical saltwater disaster simulation.

[0089] The purpose of obtaining the measured flow data of the hydrological station sequence in this embodiment is to obtain the natural runoff process through runoff restoration calculation, so as to provide input for simulating historical saltwater disasters without human interference. Through the "Global 3-hour River Flood Reanalysis Data GRFR V1.0" (data range is 1979-2019) of the National Qinghai-Tibet Plateau Science Data Center, the daily natural flow data of the river section where Wuzhou Station and Shijiao Station are located from 1979 to 2019 are extracted; the daily average sea level and daily maximum tidal range data of the Hong Kong Crucian Carp Bay Station outside the Modaomen Waterway estuary from 1979 to 2019 are collected; the daily average wind speed and prevailing wind direction data of the Hong Kong Henglan Island Station outside the Modaomen Waterway estuary from 1979 to 2019 are collected; the above data are quality controlled to obtain historical saltwater disaster simulation data.

[0090] Furthermore, step S102 specifically includes:

[0091] (1) Construct a historical saltwater disaster simulation model and a saltwater intrusion forecast model.

[0092] A multi-layer BP neural network model was selected to construct a historical saltwater disaster simulation model. The upstream flow, estuary tide level and wind force were used as saltwater influencing factors to obtain the salinity simulation values ​​of each main water intake. The flow of Wuzhou Station and Shijiao Station was used as two flow factors, the average sea level and maximum tidal range of the tidal station outside the estuary were used as two tidal factors, and the average wind speed and prevailing wind direction of the meteorological station outside the estuary were used as two wind factors, totaling 6 model input factors; the daily average salinity of Zhuzhoutou, Pinggang and Guangchang pumping stations was used as the model simulation object to construct a historical saltwater disaster simulation model with a multi-layer neural network structure.

[0093] A multi-layer BP neural network model was selected to construct a saltwater intrusion forecast model. The daily average salinity of each major water intake in the past was used to represent the cumulative effect of historical salinity, and the upstream flow, estuary tide level and wind force were used as saltwater influencing factors to obtain the salinity prediction values ​​of each major water intake. Considering the influence of the cumulative effect of past salinity, the daily average salinity of each water intake in the past was used as the salinity factor, together with the flow factor, tidal factor and wind force factor as the model input factor, a total of 9 factors; the daily average salinity of Zhuzhoutou, Pinggang and Guangchang pumping stations was used as the model simulation object to construct a saltwater intrusion forecast model with a multi-layer neural network structure. Compared with the historical saltwater disaster simulation model, the saltwater intrusion forecast model has higher simulation accuracy because it takes into account the cumulative effect of previous salinity; but because of this (the salinity factor is required as input), it cannot be used for long-series historical saltwater disaster simulation without salinity observation data.

[0094] (2) The target site data are used to train the historical saltwater disaster simulation model and the saltwater intrusion prediction model respectively.

[0095] Using the model training and test data sets, the two models were iteratively trained. According to the measured salinity data of the main water intakes and the output results of the neural network model, the model performance was evaluated, the optimal lag time of each influencing factor was obtained, and the neural network model with the best performance was determined.

[0096] Furthermore, step (2) specifically includes:

[0097] (2-1) Divide the model training set and test data set into a training set and a test set.

[0098] In this embodiment, the daily measured data from 2019 to 2023 are divided into two subsets, 2019 to 2021 and 2022 to 2023, which are used as training sets and test sets respectively.

[0099] (2-2) Use the training set and test set to train and test the two models respectively.

[0100] Using the particle swarm optimization algorithm, the lag time of each influencing factor, the connection weights and thresholds of the multi-layer neural network are taken as the position value of the particle, and the performance index value of the neural network model is taken as the fitness value of the particle. The training set is used for repeated iterative calculation to determine the position value that maximizes the fitness value of the particle, that is, the optimal lag time and optimal neural network parameters of each influencing factor are obtained, and the corresponding neural network model is the neural network model with the best performance; the optimal neural network model is tested using the test set. The salinity changes at each water intake are closely related to external factors such as upstream flow, estuary tides, wind and waves, and generally there is a lag effect for a period of time (in days), so it is necessary to clarify the optimal lag time of each influencing factor.

[0101] In this embodiment, the lag time of four types of influencing factors (salinity factor, flow factor, tidal factor, wind factor) and the neural network connection weight and threshold are used as model optimization parameters, and the particle swarm algorithm is used to optimize the parameters. The minimum root mean square difference between the model simulation sequence and the observation data sequence is used as the optimization goal of the particle swarm algorithm. It is iterated 1000 times until the optimal lag time of each influencing factor and the neural network model with the best performance are finally obtained. Based on this, the expression of the long sequence simulation value and the future salinity prediction value of the main water intake is obtained as follows:

[0102] Y f =f(D(X))=f(D(X q ,X z ,X w ))

[0103] Y h =h(D(X))=h(D(X s ,X q ,X z ,X w))

[0104] In the above formula, f(·) represents the relationship function between the salinity simulation and the influencing factor generated by the historical saltwater disaster simulation model with the best performance, Y f represents the simulated value of salinity at the water intake; h(·) represents the relationship function between the salinity prediction and the influencing factor generated by the best performance saltwater intrusion forecasting model, Y h represents the predicted value of water intake salinity; D(X) represents the influencing factor data under the optimal lag time; X s represents the characteristic set of salinity influencing factors, X q represents the feature set of traffic influencing factors, X z represents the characteristic set of tidal influence factors, X w Represents the feature set of wind influencing factors.

[0105] (3) Input the historical saltwater disaster simulation data into the trained historical saltwater disaster simulation model to obtain a long sequence of historical salinity simulation values; input the monitored target station data into the trained saltwater intrusion prediction model to obtain the future salinity prediction value.

[0106] The historical saltwater disaster simulation data collected in step S101 is input into the trained historical saltwater disaster simulation model to obtain the daily salinity simulation values ​​of Zhuzhoutou, Pinggang and Guangchang pumping stations of Modaomen waterway from 1979 to 2019.

[0107] The collected monitoring data of hydrology, salinity, tide, meteorology, etc. of each target station of the Modaomen waterway are input into the trained saltwater intrusion forecast model to obtain the salinity forecast values ​​of the Zhuzhoutou, Pinggang, and Guangchang pumping stations in the future k days; k can be any positive integer greater than or equal to 1. In this embodiment, the value of k is 3.

[0108] Furthermore, step S103 specifically includes:

[0109] (1) Establish a comprehensive evaluation index system for water supply risks.

[0110] (1-1) Define the scope of the study area, determine the main water intakes affected by saltwater tides, local reservoirs (groups) that can be used to store fresh water, and water supply targets; obtain the maximum water intake capacity of the main water intakes, the adjustable storage capacity of the local reservoirs (groups) (the difference between normal water storage and dead storage capacity), and the water demand of the water supply targets.

[0111] This example takes the Zhuhai-Macao water supply system as the object. This system uses the three pumping stations (Zhuzhoutou, Pinggang, and Guangchang pumping stations) of the Modaomen waterway as the main water intake pumping stations, forming an interconnected system consisting of pumping station projects, pipe network projects, and water supply reservoir groups. The total water demand of its water supply objects in the dry season is about 1.23 million m 3The current water intake capacity of Zhuzhoutou, Pinggang and Guangchang pumping stations is 2.7 million m 3 / d, 1.4 million m 3 / d and 800,000 m 3 / d. The local reservoirs that provide water supply in the Zhuhai-Macao water supply system include the Zhuyin Reservoir, the North Reservoir Group and the South Reservoir Group, of which the Zhuyin Reservoir has a total storage capacity of 40.18 million m 3 , regulating reservoir capacity 38.11 million m 3 The total storage capacity of the supporting project Yuekeng Reservoir is 3.15 million m 3 , regulating reservoir capacity 2.8 million m 3 The North Reservoir Group includes three reservoirs: Dajingshan, Fenghuangshan and Meixi, with a total storage capacity of 25.56 million m 3 , regulating reservoir capacity 18.19 million m 3 The Nanku Group includes four reservoirs: Nanping, Zhuxiandong, Shedikeng, and Yinkeng, with a total storage capacity of 8.27 million m 3 , regulating reservoir capacity 5.45 million m 3 . Through the pipeline network project and the transfer pump station, the reservoir (group) can be replenished through the Zhuzhoutou, Pinggang and Guangchang pump stations, with basically no replenishment pressure; theoretically, only when the Zhuzhoutou pump station located at the uppermost reaches of the Modaomen waterway is unable to draw water due to excessive salinity, the water use of Zhuhai and Macao will be completely dependent on the local reservoir group for water supply. The data in this example is referenced from the "Implementation Plan for Water Scheduling of the Pearl River Dry Season in 2022-2023" of the Pearl River Water Conservancy Commission of the Ministry of Water Resources.

[0112] (1-2) According to the main water intakes, local reservoirs that can be used to store fresh water, and water supply objects, a regional water resources supply and demand balance model based on fresh water storage scheduling is constructed.

[0113] The specific expression of the regional water resources supply and demand balance model is:

[0114]

[0115] In the above formula:

[0116] T hour represents the number of hours when the salinity at the water intake exceeds the standard, calculated as a function of the daily average salinity S at the water intake, f hour (S); According to the empirical relationship between the daily salinity exceeding the standard and the daily average salinity in the measured salinity data of the water intake, the function f is fitted hour (S); This embodiment obtains the piecewise fitting function of the daily exceeding hours and daily average salinity of each water intake based on the measured salinity data of Zhuzhoutou, Pinggang and Guangchang pumping stations of Modaomen Waterway from 2019 to 2023, as follows:

[0117] Zhuzhoutou Pumping Station is:

[0118]

[0119] Pinggang Pumping Station is:

[0120]

[0121] Guangchang Pumping Station is:

[0122]

[0123] W a Indicates the daily water intake of the water intake. When the salinity of the water intake exceeds the standard, the water intake cannot take water; W a According to the maximum water intake capacity W c and the number of hours T where the daily average salinity exceeds the standard hour calculate;

[0124] Clarify the rules for emergency storage of fresh water under saltwater intrusion: (1) When saltwater intrusion occurs, each water intake shall take water from the river to the maximum extent during the water-drawing period, and the water intake shall give priority to meeting the needs of the water supply objects. If there is still surplus water, the excess water intake shall be used to supplement the local reservoir. The effective water storage capacity of the reservoir after replenishment shall not exceed the reservoir regulation capacity (that is, the reservoir water level shall not exceed the normal water storage level); (2) If the water intake cannot meet the needs of the water supply objects, water shall be released from the local reservoir for supplementary water supply. The effective water storage capacity of the reservoir after supplementary water supply shall not be less than 0 (that is, the reservoir water level shall not be lower than the dead water level); (3) If the water intake and the reservoir supplementary water supply still cannot meet the needs of the water supply objects, a substantial water shortage will occur;

[0125] D a Indicates the daily water demand of the water supply object; V c Represents the regulating storage capacity of the local reservoir, that is, the storage capacity between the normal water level and the dead water level; V t and V t-1 Respectively represent the effective water storage capacity of the local reservoir on the current day and the previous day; V a Indicates the amount of excess water used to supplement the local reservoir; W s Indicates the amount of water that needs to be supplemented by the local reservoir; W d Indicates the substantial water shortage; In this embodiment, the regional water resources supply and demand balance model based on the rush storage of fresh water scheduling is used for the dry season of the Modaomen waterway at the Pearl River Estuary, and is calculated from September of the current year to April of the following year; Referring to the Zhuhai Water Resources Bulletin, the initial effective water storage capacity of the local reservoir (group) in the dry season is set to 1 / 3 of the regulating storage capacity. In this embodiment, the three water intakes are calculated according to their respective water intake capacity and the number of daily excess hours, and then the daily water intake W is obtained by summing up a ; Zhuyin Reservoir (including its supporting projects), North Reservoir Group and South Reservoir Group are combined as a whole, and the supply and demand balance calculation is carried out based on the combined regulating storage capacity as a whole.

[0126] (1-3) Select and calculate the quantitative evaluation indicators in the comprehensive evaluation index system of water supply risk.

[0127] Seven quantitative evaluation indicators were selected from three aspects: the probability of fresh water intake, the degree of dependence on local reservoirs and the degree of water shortage, including:

[0128] First, in terms of the probability of fresh water intake, the average fresh water intake probability R1 of the main water intake in the next three days, the minimum fresh water intake probability R2, and the number of days where fresh water cannot be taken R3 are selected, totaling 3 quantitative evaluation indicators, representing the degree of influence of saltwater intrusion on the water intake capacity of the river. In this embodiment, the average values ​​of Zhuzhoutou, Pinggang, and Guangchang pumping stations are taken to calculate R1, R2, and R2.

[0129] Secondly, in terms of the degree of dependence on local reservoirs, two quantitative evaluation indicators are selected: the difference between the average water demand and the average water intake in the next three days, R4, and the average storage rate of the local reservoir (group) in the next three days, R5, representing the dependence and vulnerability of the water supply system on the water supply of local reservoirs. In this embodiment, R4 is calculated based on the total water intake of Zhuzhoutou, Pinggang, and Guangchang pumping stations; and R5 is calculated by combining Zhuyin Reservoir (including its supporting projects), North Reservoir Group, and South Reservoir Group as a whole.

[0130] Then, in terms of the degree of water shortage, two quantitative evaluation indicators were selected, namely the average substantial water shortage in the next three days R6 and the number of substantial water shortage days R7, which represent the severity of substantial water shortage caused by saltwater intrusion.

[0131] Calculate the values ​​of each quantitative evaluation index:

[0132]

[0133] In the above formula, ave(T hour ) represents the average daily exceeding-limit hours of the water intake in the next three days; min(T hour ) represents the longest daily exceeding hours of the water intake in the next 3 days; sum(n) represents the number of days when fresh water cannot be taken from the water intake in the next 3 days. Considering that the actual water pump cannot be started and stopped quickly, the case where the probability of taking fresh water is less than 0.1 ((1-T hour / 24)<0.1) is considered as being unable to be depreciated within one day (n=1); ave(D a ) represents the average daily water demand for the next three days; ave(W a ) represents the average daily water intake in the next three days; ave(P t ) represents the average storage rate of the local reservoir in the next three days, where the storage rate P t Calculated as the total amount of water stored on that day (effective water storage V t and dead storage capacity V s The sum of the water volume and the normal storage capacity of the reservoir V total(normal water level corresponding to the storage capacity); ave(W d ) represents the average water shortage in the next three days; sum(m) represents the number of days with substantial water shortage in the next three days. d >0, it is considered that substantial water shortage occurs on that day (m=1).

[0134] (2) The symbol conversion method is used to process the quantitative evaluation indicators in the comprehensive evaluation index system of water supply risk so that they all become positive indicators.

[0135] Specifically, the larger the values ​​of the indicators R1, R2, and R5 are, the smaller the water supply risk is. They are negative indicators and should be converted from their original positive values ​​to negative values, thereby becoming positive indicators. The larger the positive indicator value is, the greater the water supply risk is. In this embodiment, the average freshwater probability R1, the minimum freshwater probability R2, and the average reservoir fullness rate R2 are converted from their original positive values ​​to negative values, thereby converting them from negative indicators to positive indicators. Then, they are normalized together with other indicators to obtain normalized positive evaluation indicator values.

[0136] (3) Calculate the comprehensive water supply risk index based on the values ​​of each quantitative evaluation index after processing.

[0137] The analytic hierarchy process and the pairwise judgment method are used to construct the judgment matrix of the seven quantitative evaluation indicators and calculate the corresponding weight matrix, and the consistency test index CR of the weight matrix is ​​calculated; when the CR is less than 0.1, the matrix consistency is acceptable, otherwise the judgment matrix is ​​reconstructed; according to the determined weight matrix and the values ​​of each evaluation index, the comprehensive water supply risk index RI is calculated. In this embodiment, the judgment matrix of each indicator is constructed by the pairwise judgment method, as shown in Table 1; after calculation, the consistency index CR value of the judgment matrix is ​​0.095, which is less than 0.1, and meets the consistency requirements.

[0138] Table 1 Judgment matrix of comprehensive evaluation indicators of water supply risk

[0139]

[0140] Furthermore, step S104 specifically includes:

[0141] (1) Conduct frequency analysis on the comprehensive water supply risk index calculated based on the long-series historical salinity simulation values ​​to classify the water supply risk degree and warning level.

[0142] This embodiment is based on the historical saltwater disaster simulation data set from 1979 to 2019 collected in step S101, and uses the historical saltwater disaster simulation model based on machine learning trained in step S102 to obtain the daily salinity simulation values ​​of Zhuzhoutou, Pinggang and Guangchang pumping stations from 1979 to 2019; on this basis, the regional water resources supply and demand balance model based on the emergency storage of fresh water scheduling in step S103 is used to obtain the water resources supply and demand balance state of the Zhuhai-Macao water supply system in the dry season from 1979 to 2019, and calculate the comprehensive evaluation index values ​​of various water supply risks on a daily basis, and finally obtain the comprehensive water supply risk index of the Zhuhai-Macao water supply system in the dry season from 1979 to 2019.

[0143] The frequency analysis of the daily comprehensive water supply risk index in the long-sequence dry season is carried out: First, the cases of RI < 0.1 are eliminated from the daily comprehensive water supply risk index in the long-sequence dry season, because RI < 0.1 means that the water supply risk is very small. Such cases are expected to account for most of the long-sequence daily RI. After elimination, it will be beneficial to further subdivide the distribution of the long-sequence daily RI; then, the frequency analysis of the remaining long-sequence daily RI is carried out, and the cumulative frequency P corresponding to different RI values ​​is counted. cum (n); If the cumulative frequency of a certain RI value is P cum (n), then the probability that the daily RI of the long sequence is less than or equal to this value is n; finally, according to the percentile method, P cum (30%), P cum (60%), P cum (80%), P cum (90%), as the water supply risk threshold, corresponds to general, large, major, and extremely major water supply risks, respectively, corresponding to four warning levels of blue (Ⅳ), yellow (Ⅲ), orange (Ⅱ), and red (Ⅰ). In this embodiment, by statistically analyzing the daily comprehensive water supply risk index during the dry season from 1979 to 2019, it was found that the number of days with RI < 0.1 accounted for as high as 72.9%, indicating that the water supply risk in the Zhuhai-Macao water supply system is very small most of the time; further, the situation of RI ≥ 0.1 was analyzed, and it was found that the frequency of RI values ​​not exceeding 0.2 was about 30%, the frequency of not exceeding 0.3 was about 60%, the frequency of not exceeding 0.5 was about 80%, and the frequency of not exceeding 0.8 was about 90%, thereby determining P cum (30%), P cum (60%), P cum (80%), P cum(90%) The corresponding RI values ​​are 0.2, 0.3, 0.5, and 0.8 respectively; accordingly, the regional water supply risk warning level under saltwater intrusion is divided as follows: ①0.2≤RI<0.3, at this time, the saltwater intrusion is weak, the water supply risk is general, and it corresponds to the blue (Ⅳ) warning; ②0.3≤RI<0.5, at this time, the saltwater intrusion is obvious, the water supply risk is large, and it corresponds to the yellow (Ⅲ) warning; ③0.5≤RI<0.8, at this time, the saltwater intrusion is serious, the water supply risk is serious, and it corresponds to the orange (Ⅱ) warning; ④RI≥0.8, at this time, the saltwater intrusion is serious, the water supply risk is particularly serious, and it corresponds to the red (Ⅰ) warning. In summary, the water supply risk warning and warning level under the saltwater intrusion of the Modaomen waterway Zhuhai-Macao water supply system are obtained in this embodiment as shown in Table 2.

[0144] Table 2 Risk thresholds and warning levels of the Zhuhai-Macao water supply system of the Modaomen waterway

[0145]

[0146] (2) Determine the early warning indicative indicators based on the flow of the upstream controlling hydrological station, the probability of fresh water extraction at the main water intake, and the average storage rate of local reservoirs (groups); and divide the risk level warning according to the early warning indicative indicators.

[0147] According to the risk threshold, the daily RI of the long sequence is divided into 5 categories (0≤RI <RI 30％ ,RI 30％ ≤RI <RI 60％ ,RI 60％ ≤RI <RI 80％ ,RI 80％ ≤RI <RI 90％ ,RI 90％ ≤RI≤1), using the Mann-Whitney U non-parametric test method, select the average flow of the upstream control hydrological station in the next three days, the average probability of taking fresh water at the main water intake, and the average storage rate of local reservoirs (groups) and other indicators, analyze whether there are significant differences between different RI ranges, and then use the indicators with significant differences as early warning indicative indicators. In this embodiment, the daily RI of the dry season from 1979 to 2019 is divided into 5 categories: 0.1≤RI<0.2, 0.2≤RI<0.3, 0.3≤RI<0.5, 0.5≤RI<0.8, 0.8≤RI≤1; the total flow of the upstream Wuzhou Station and Shijiao Station, the average probability of taking fresh water at the main water intake, and the average storage rate of the local reservoir (group) are selected as indicators, and the Mann-Whitney U non-parametric test is performed between different RI ranges, and the significance test results are shown in Table 3.

[0148] Table 3 Comparison of characteristics of early warning indicators at different warning levels

[0149]

[0150] Note: In Table 3, categories O, IV, III, II and I respectively represent 0.1≤RI<0.2, 0.2≤RI<0.3, 0.3≤RI<0.5, 0.5≤RI<0.8, 0.8≤RI≤1. Letters A / B / C / D are used to indicate the difference between groups. There is a significant difference between two categories with different letters. The numbers in the table represent the mean of each category. The flow unit is m 3 / s.

[0151] In this embodiment, according to the results of Table 3, the total flow of Wuzhou Station and Shijiao Station and the average storage rate of local reservoirs (groups) are selected as the warning indicative indicators of blue warning (level IV) (such as number ④ in Table 3); further, the average probability of taking fresh water at the main water intake and the average storage rate of local reservoirs (groups) are selected as the warning indicative indicators of yellow warning (level III) (such as number ③ in Table 3); further, the average probability of taking fresh water at the main water intake is selected as the warning indicative indicator of orange warning (level II) (such as number ② in Table 3); further, the total flow of Wuzhou Station and Shijiao Station, the average probability of taking fresh water at the main water intake, and the average storage rate of local reservoirs (groups) are used as the warning indicative indicators of red warning (level I) (such as number ① in Table 3).

[0152] Calculate the judgment threshold corresponding to the early warning indicative indicator, as follows:

[0153]

[0154] Where M Aave and M Bave The two RI ranges are taken from the warning indicative indicator M to be distinguished; the indicator M between the two RI ranges has significant differences and is marked as range A and range B respectively, where range B corresponds to a higher level of warning level; M Aave represents the average in range A; M Bave represents the average in range B; M A-Bth The threshold value representing the index M between range A and range B is used as the criterion for determining the warning level corresponding to entering range B. In this embodiment, the calculation is performed according to the mean value shown in Table 3, and some results are corrected to make it easier to distinguish, and the warning indicative indicators and threshold values ​​of each level are obtained, which are further:

[0155] Blue warning (Level IV): The combined flow of the upstream Wuzhou Station and Shijiao Station is less than 3000m3 / s, and the average storage rate of the local reservoir (group) is less than 0.8;

[0156] Orange warning (Level III): The average probability of fresh water withdrawal at Zhuzhoutou, Pinggang and Guangchang pumping stations is less than 0.6, and the average storage rate of local reservoirs (groups) is less than 0.5;

[0157] Yellow warning (Level II): The average probability of fresh water withdrawal at Zhuzhoutou, Pinggang and Guangchang pumping stations is less than 0.3, and the average storage rate of local reservoirs (groups) is less than 0.5;

[0158] Red alert (Level I): The combined flow of the upstream Wuzhou Station and Shijiao Station is less than 1700m3 / s or the average probability of fresh water withdrawal at the Zhuzhoutou, Pinggang and Guangchang pumping stations is less than 0.1, and the average storage rate of the local reservoir (group) is less than 0.2.

[0159] (3) If the comprehensive water supply risk index or its warning indicator calculated based on the future salinity forecast exceeds the set threshold, a warning of the corresponding level will be issued.

[0160] First, according to the monitoring data of hydrology, salinity, tide, meteorology, etc. of each target station of Modaomen waterway, the saltwater intrusion forecasting model trained in step S102 is used to obtain the salinity forecast values ​​of the water intakes of Zhuzhoutou, Pinggang and Guangchang pumping stations in the next three days; then, the regional water resources supply and demand balance model based on emergency storage of fresh water scheduling and the comprehensive evaluation index system of water supply risk established in step S103 are used to calculate and obtain the comprehensive water supply risk index value and the corresponding early warning indicative index value; finally, the comprehensive water supply risk index determination method can be used to determine the current water supply risk degree and early warning level according to the calculated comprehensive water supply risk index and related thresholds; the early warning indicative index determination method can also be used to determine the current early warning level according to the relevant early warning indicative index values ​​and related thresholds; the comprehensive water supply risk index determination method and the early warning indicative index determination method can be used simultaneously and can verify each other.

[0161] Specifically, this embodiment divides the warning levels of long-sequence historical saltwater disasters from 1979 to 2019 based on the comprehensive water supply risk index determination method and the early warning indicative index determination method. The results are shown in Figure 3 Based on the observation data of the dry season in 2022, the saltwater intrusion forecast model was used to obtain the salinity forecast value of each water intake, and the saltwater intrusion risk warning was carried out based on the early warning indicative index judgment method. The results are shown in Figure 4 .like Figure 4 As shown in the figure, compared with the comprehensive water supply risk index determination method, the classification of saltwater intrusion risk levels based on early warning indicative indicators such as the average flow of hydrological stations, the average probability of fresh water intake at major water intakes, and the average storage rate of local reservoirs (groups) is not only more intuitive, but also more conducive to achieving target-oriented human regulation, and can provide a scientific basis for rapid response to saltwater intrusion in multiple decision-making stages such as monitoring, evaluation and adjustment.

[0162] This embodiment should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description, and therefore it is intended that all changes falling within the meaning and scope of the equivalent elements of the application documents are included in the present invention. It can be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a program, and the corresponding program can be stored in a computer-readable storage medium.

[0163] It should be noted that although the method operations of the above embodiments are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0164] Embodiment 2:

[0165] like Figure 5 As shown, this embodiment provides a water supply risk assessment system for saltwater intrusion in tidal estuary areas based on emergency freshwater storage scheduling, the system includes an acquisition module 501, a training module 502, a construction and calculation module 503 and a division module 504, wherein:

[0166] The acquisition module 501 is used to acquire the target site data and historical saltwater disaster simulation data of the study area on a daily basis in the same time series; wherein the target site data include the measured salinity data, flow data, tide data and wind data, and the historical saltwater disaster simulation data include the daily flow data, tide data and wind data in a long time series;

[0167] The training module 502 is used to train the historical saltwater disaster simulation model using the target site data, input the historical saltwater disaster simulation data into the trained historical saltwater disaster simulation model, and obtain a long sequence of historical salinity simulation values;

[0168] The construction and calculation module 503 is used to generalize the water supply system in the study area and construct a comprehensive evaluation index system for water supply risk based on the emergency storage of fresh water scheduling; based on the comprehensive evaluation index system for water supply risk, the comprehensive water supply risk index is calculated according to the long-sequence historical salinity simulation value; wherein the water supply system includes the main water intake of the river, the local water storage reservoir and the water supply object;

[0169] The classification module 504 is used to perform frequency analysis on the comprehensive water supply risk index and classify the water supply risk degree and warning level based on the set water supply risk threshold.

[0170] The specific implementation of each module in this embodiment can refer to the above-mentioned embodiment 1, which will not be described one by one here; it should be noted that the system provided in this embodiment is only illustrated by the division of the above-mentioned functional modules. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0171] Embodiment 3:

[0172] This embodiment provides a terminal device, which can be a computer. Figure 6 As shown, a processor 602, a memory, an input device 603, a display 604 and a network interface 605 connected by a system bus 601, the processor is used to provide computing and control capabilities, the memory includes a non-volatile storage medium 606 and an internal memory 607, the non-volatile storage medium 606 stores an operating system, a computer program and a database, the internal memory 607 provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium, when the processor 602 executes the computer program stored in the memory, the water supply risk assessment method under saltwater intrusion in tidal estuary areas based on rush storage of fresh water scheduling of the above-mentioned embodiment 1 is implemented as follows:

[0173] Obtain daily target site data and historical saltwater disaster simulation data in the same time series in the study area; the target site data include measured salinity data, flow data, tide data and wind data, and the historical saltwater disaster simulation data include daily flow data, tide data and wind data in a long time series;

[0174] The historical saltwater disaster simulation model is trained using the target site data, and the historical saltwater disaster simulation data is input into the trained historical saltwater disaster simulation model to obtain a long sequence of historical salinity simulation values;

[0175] The water supply system in the study area is generalized, and a comprehensive water supply risk evaluation index system based on emergency freshwater scheduling is constructed; based on the comprehensive water supply risk evaluation index system, the comprehensive water supply risk index is calculated according to the long-sequence historical salinity simulation value; the water supply system includes the main water intake of the river, local water storage reservoirs and water supply objects;

[0176] Conduct frequency analysis on the comprehensive water supply risk index, and divide the water supply risk degree and warning level based on the set water supply risk threshold.

[0177] Embodiment 4:

[0178] This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for assessing water supply risk under saltwater intrusion in tidal estuary areas based on emergency freshwater storage scheduling of the above-mentioned embodiment 1 is implemented as follows:

[0179] Obtain daily target site data and historical saltwater disaster simulation data in the same time series in the study area; the target site data include measured salinity data, flow data, tide data and wind data, and the historical saltwater disaster simulation data include daily flow data, tide data and wind data in a long time series;

[0180] The historical saltwater disaster simulation model is trained using the target site data, and the historical saltwater disaster simulation data is input into the trained historical saltwater disaster simulation model to obtain a long sequence of historical salinity simulation values;

[0181] The water supply system in the study area is generalized, and a comprehensive water supply risk evaluation index system based on emergency freshwater scheduling is constructed; based on the comprehensive water supply risk evaluation index system, the comprehensive water supply risk index is calculated according to the long-sequence historical salinity simulation value; the water supply system includes the main water intake of the river, local water storage reservoirs and water supply objects;

[0182] Conduct frequency analysis on the comprehensive water supply risk index, and divide the water supply risk degree and warning level based on the set water supply risk threshold.

[0183] It should be noted that the computer-readable storage medium of the present embodiment may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0184] In summary, the water supply risk assessment method, system, equipment and storage medium for saltwater intrusion in tidal estuary areas based on emergency freshwater scheduling provided by the present invention include: obtaining data such as water intake salinity, hydrological station flow, tidal station tide level, and meteorological station wind force of the target station in the study area; using the acquired data to train a historical saltwater disaster simulation model based on machine learning, and obtaining a long-sequence historical salinity simulation value of the main water intake based on the historical saltwater disaster simulation model; generalizing the water supply system in the study area, and constructing a comprehensive evaluation index system for water supply risk based on emergency freshwater scheduling; based on the comprehensive evaluation index system for water supply risk based on emergency freshwater scheduling, the comprehensive water supply risk index of the study area is calculated according to the salinity simulation value; the comprehensive water supply risk index under the long-sequence historical saltwater simulation is frequency analyzed, and the water supply risk threshold and warning level are divided. The present invention quantifies the regional water supply risk based on the emergency freshwater scheduling rule under saltwater intrusion, and scientifically divides the saltwater risk level from the perspective of the impact degree and frequency of long-sequence historical saltwater disasters. In addition, early warning indicative indicators are determined from aspects such as the flow of upstream controlling hydrological stations, the probability of taking fresh water from the main water intakes, and the average storage rate of the local reservoir group; and corresponding level warnings are issued based on the early warning indicative indicators. The results of this technical solution are not only more intuitive, but also more conducive to the realization of goal-oriented human regulation, and can provide a scientific basis for rapid response to saltwater intrusion in multiple decision-making stages such as monitoring, evaluation and adjustment. In addition, the present invention also constructs a saltwater intrusion forecast model to predict the future salinity forecast value of the main water intake, and then predict the water supply risk level under future saltwater intrusion. The present invention provides an effective technical method for the assessment of water supply safety in estuary and coastal areas that rely on tidal river sections as water sources, and can be widely used in the field of saltwater intrusion risk assessment technology.

[0185] The above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and inventive concept of the present invention within the scope disclosed by the present invention, which shall fall within the protection scope of the present invention.

Claims

1. A water supply risk assessment method for saltwater intrusion in tidal estuaries based on emergency freshwater storage scheduling, characterized in that: The method comprises: Obtain daily target site data and historical saltwater disaster simulation data in the same time series in the study area; the target site data include measured salinity data, flow data, tide data and wind data, and the historical saltwater disaster simulation data include daily flow data, tide data and wind data in a long time series; The historical saltwater disaster simulation model is trained using the target site data, and the historical saltwater disaster simulation data is input into the trained historical saltwater disaster simulation model to obtain a long sequence of historical salinity simulation values; The water supply system in the study area is generalized, and a comprehensive water supply risk evaluation index system based on emergency freshwater scheduling is constructed; based on the comprehensive water supply risk evaluation index system, the comprehensive water supply risk index is calculated according to the long-sequence historical salinity simulation value; the water supply system includes the main water intake of the river, local water storage reservoirs and water supply objects; Conduct frequency analysis on the comprehensive water supply risk index, and divide the water supply risk degree and warning level based on the set water supply risk threshold.

2. The method for assessing water supply risk under saltwater intrusion in tidal estuary areas according to claim 1 is characterized in that: The comprehensive water supply risk evaluation index system is based on which the comprehensive water supply risk index is calculated according to the long-sequence historical salinity simulation value, including: Based on the comprehensive evaluation index system of water supply risk, the values ​​of each quantitative evaluation index in the comprehensive evaluation index system of water risk are calculated according to the long-sequence historical salinity simulation values, the maximum water intake capacity of the main water intakes of the river, the adjustable storage capacity of the local water storage reservoirs and the water demand of the water supply objects; The symbol conversion method is used to process the values ​​of each quantitative evaluation index to obtain the normalized positive index value of each quantitative evaluation index; According to the processed values ​​of each quantitative evaluation index, the judgment matrix is ​​constructed by using the hierarchical analysis method and the pairwise judgment method and the corresponding weight matrix is ​​calculated; if the consistency test index of the weight matrix is ​​greater than or equal to the first set threshold, the judgment matrix is ​​reconstructed; The comprehensive water supply risk index is calculated based on the corresponding weight matrix of the determined judgment matrix and the values ​​of each quantitative evaluation index after processing.

3. The method for assessing water supply risk under saltwater intrusion in tidal estuaries according to any one of claims 1 and 2, characterized in that: The water supply system in the study area is generalized, and a comprehensive evaluation index system for water supply risk based on emergency storage of fresh water is constructed, including: Quantitative evaluation indicators are selected from the probability of fresh water intake at the main river intake, the degree of dependence on local water storage reservoirs and the degree of water shortage of water supply objects, including: From the perspective of the probability of taking fresh water from the main water intake of the river, the average probability of taking fresh water R1, the minimum probability of taking fresh water R2 and the number of days where fresh water cannot be taken R3 of the main water intake of the river in the next k days are selected; k is a positive integer greater than or equal to 1; From the perspective of local water storage reservoir dependence, the difference between the average water demand and average water withdrawal in the next k days, R4, and the average storage rate in the next k days, R5, are selected; In terms of water shortage degree, the average substantial water shortage amount R6 and the number of substantial water shortage days R7 in the next k days are selected; The calculation formulas for each quantitative evaluation index are as follows: in: Where, T hour is the number of hours when the salinity of the main water intake of the river exceeds the standard, f hour (S) is the function obtained by fitting the empirical relationship between the daily salinity exceeding the standard and the daily average salinity in the measured salinity data, and S is the long-series historical salinity simulation value; ave(T hour ) is the average daily exceeding-limit hours of the main water intake of the river in the next k days; min(T hour ) represents the longest daily exceeding-limit hours at the main water intake of the river in the next k days; sum(n) represents the number of days when fresh water cannot be taken at the main water intake of the river in the next k days. Considering that the actual water pump cannot be started and stopped quickly, the fresh water taking probability is 1-T hour / 24 is less than 0.1, which means that it is impossible to take short positions within one day, that is, n = 1; D a is the daily water demand of the water supply object, ave(D a ) is the average daily water demand in the next k days; W a is the daily water intake of the main water intake of the river, ave(W a ) is the average daily water intake in the next k days; the storage rate P t is the effective water storage capacity V on that day t and dead storage capacity V s The sum of the normal storage capacity of the reservoir V total The ratio of ave(P t ) represents the average filling rate of the local water reservoir in the future k days; W d is the substantial water deficit, ave(W d ) represents the average water shortage in the next k days; sum(m) represents the number of days with substantial water shortage in the next k days. d >0, it is considered that substantial water shortage occurs on that day, that is, m=1; W c V is the maximum water intake capacity of the main water intake of the river; a Indicates the amount of excess water used to supplement local water storage reservoirs; W s The amount of water required to supplement the water supply from local water storage reservoirs; V t and V t-1 They represent the effective water storage capacity of the local water storage reservoir on the current day and the previous day respectively; k is a positive integer greater than or equal to 1; The rules for emergency freshwater storage and scheduling under saltwater intrusion are as follows: when saltwater intrusion occurs, the main water intakes of each river channel will draw water from the river channel to the maximum extent during the water-drawing period, and the water intake will give priority to meeting the needs of the water supply objects. If there is still surplus water, the excess water intake will be used to supplement the local water storage reservoir. The effective water storage capacity of the reservoir after replenishment should not exceed the regulating capacity of the reservoir; if the water intake cannot meet the needs of the water supply objects, water will be released from the local reservoir for supplementary water supply, and the effective water storage capacity of the reservoir after supplementary water supply should not be less than 0; if the water intake and the reservoir supplementary water supply still cannot meet the needs of the water supply objects, a substantial water shortage will occur.

4. The method for assessing water supply risk under saltwater intrusion in tidal estuary areas according to claim 1, characterized in that: The frequency analysis of the comprehensive water supply risk index is carried out to divide the water supply risk degree and warning level based on the set water supply risk threshold, including: Firstly, the comprehensive water supply risk index less than the second set threshold is eliminated from the comprehensive water supply risk index; Then, the frequency analysis of the remaining long-sequence daily comprehensive water supply risk index was carried out to count the cumulative frequencies corresponding to different comprehensive water supply risk indexes; Finally, based on the set water supply risk threshold, the water supply risk degree and warning level are divided according to the cumulative frequency.

5. The method for assessing water supply risk under saltwater intrusion in tidal estuary areas according to claim 1, characterized in that: The historical saltwater disaster simulation model is a multi-layer BP neural network model; The method of training the historical saltwater disaster simulation model using the target site data includes: The measured flow data, tide data and wind data are used as saltwater influencing factors to input into the historical saltwater disaster simulation model, and the salinity simulation values ​​of the main water intakes of each river are output; According to the measured salinity data of the main water intakes of each river and the long-sequence historical salinity simulation values, the performance of the historical saltwater disaster simulation model was evaluated, and the optimal lag time of each saltwater influencing factor was obtained. The corresponding BP neural network model is the trained historical saltwater disaster simulation model.

6. The method for assessing water supply risk under saltwater intrusion in tidal estuaries according to claim 5, characterized in that: The optimal lag time of each saltwater tide influencing factor is obtained by the following process: Using the particle swarm optimization algorithm, the lag time of each saltwater influencing factor, the connection weight and threshold of the multi-layer BP neural network are taken as the position value of the particle. The performance index value of the BP neural network model is used as the fitness value of the particle. The target site data is used for iterative calculation to determine the position value that maximizes the particle fitness value, that is, the optimal lag time and optimal neural network parameters of each influencing factor are obtained. The corresponding BP neural network model is the BP neural network model with the best performance.

7. The method for assessing water supply risk under saltwater intrusion in tidal estuary areas according to claim 1, characterized in that: The long time series is no less than 20 years.

8. The method for assessing water supply risk under saltwater intrusion in tidal estuary areas according to any one of claims 1, 2, 4 to 7, characterized in that: After conducting a frequency analysis of the comprehensive water supply risk index, the method also includes determining early warning indicative indicators from the flow of upstream control hydrological stations, the probability of fresh water intake at the main river intake, and the average filling rate of local water storage reservoirs; According to the early warning indicative indicators, risk level warnings are divided, including: Based on the set water supply risk threshold, the comprehensive water supply risk index RI is divided into corresponding categories; Using the Mann-Whitney U nonparametric test method, we selected three indicators: the average flow data for the next k days, the average probability of fresh water intake at the main water intake of the main river in the next k days, and the average fullness rate of local water storage reservoirs in the next k days. We analyzed the differences between the three indicators in different categories and used the indicators with significant differences as early warning indicators. If the warning indicator M is greater than the judgment threshold, it is classified into the corresponding warning level; Wherein, the determination threshold is: Where M Aave and M Bave They are taken from the two types of RI that the indicator M is intended to distinguish; the indicator M between the two types of RI has significant differences and is marked as range A and range B, respectively, where range B corresponds to a higher level of warning level; M Aave is the average in range A; M Bave is the average in range B; M A-Bth It is the judgment threshold of indicator M between range A and range B, and serves as the judgment criterion for the warning level corresponding to the indicator M in range A entering range B.

9. The method for assessing water supply risk under saltwater intrusion in tidal estuary areas according to claims 1, 2, 4 to 7, characterized in that: After obtaining the daily target station data in the same time series in the study area, the method further includes: The saltwater intrusion prediction model is trained using the target site data, and the monitored target site data is input into the trained saltwater intrusion prediction model to obtain the salinity forecast value for the next f days; the saltwater intrusion prediction model and the historical saltwater disaster simulation model both adopt a multi-layer BP neural network model; wherein f is a positive integer greater than or equal to 1; Based on the comprehensive evaluation index system of water supply risk, the comprehensive water supply risk index is calculated according to the future salinity forecast value; Water supply risk levels and warning levels are divided according to the comprehensive water supply risk index and set thresholds.

10. The method for assessing water supply risk under saltwater intrusion in tidal estuary areas according to claim 9, characterized in that: The method of training the saltwater intrusion prediction model using the target site data includes: The daily average salinity in the salinity data is used as the cumulative effect of historical salinity, and together with the flow data and wind data, is input into the saltwater intrusion forecast model as a factor affecting saltwater intrusion to obtain the future salinity forecast value. According to the salinity data and future salinity forecast values, the performance of the saltwater intrusion forecast model is evaluated, and the optimal lag time of each saltwater influencing factor is obtained. The corresponding BP neural network model is the trained saltwater intrusion forecast model.

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