Ultra-long water-reducing river reach ecological scheduling optimization method based on prototype observation

Abstract

The invention relates to the technical field of ecological scheduling, and discloses a prototype observation-based ultra-long water-reducing river reach ecological scheduling optimization method, which comprises the following steps: S1, obtaining observation indexes in real time, and obtaining river reach basic data by using monitoring equipment; s3, establishing an ecological scheduling scheme; S4, carrying out simulation calculation simulation; S5, evaluating the ecological scheduling scheme; and S6, carrying out practical application and long-term monitoring. According to the method, a plurality of prototype observation points are arranged at the ultra-long water-reducing river reach, long-term continuous observation is carried out, and technical means such as an intelligent sensor network, an unmanned aerial vehicle and an unmanned ship are utilized, so that comprehensive and accurate basic data are obtained, a reliable basis is provided for subsequent model establishment and scheduling scheme formulation, and the scientificity of ecological scheduling is improved. Meanwhile, due to collection of multivariate data and application of a data mining technology, more potential ecological laws are disclosed, and the cognitive level of an ecological system is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological regulation, and specifically provides an optimization method for ecological regulation of an extremely long river reach with reduced flow based on prototype observation. Background Technique

[0002] Ecological regulation of an extremely long river reach with reduced flow is a water resource management method. After building water conservancy projects such as dams on a river, a river reach with reduced flow will be formed. When this river reach with reduced flow is very long, ecological regulation is required to intervene. This kind of regulation mainly considers maintaining the basic functions of the river ecosystem.

[0003] In practical applications, the above ecological regulation process involves controlling the discharged water volume according to certain rules. For example, during the dry season, a certain ecological base flow is guaranteed to allow sufficient water to flow in the extremely long river reach with reduced flow to avoid river drying and flow interruption. At the same time, water quality maintenance should be taken into account to ensure a good living environment for aquatic organisms, and river morphology stability should also be considered to prevent bank erosion. Through these comprehensive ecological regulation measures, the negative impacts of water conservancy project construction on the ecological system of the extremely long river reach with reduced flow are alleviated, and the integrity and biodiversity of the river ecosystem are protected.

[0004] However, at present, diversion-type hydropower projects mainly use diversion structures to concentrate the natural river channel drop to form a hydropower head for hydropower stations. After the formation of the hydropower station, the original natural river channel eco-hydrological situation will be changed, resulting in a river reach with reduced or dewatered flow. Currently, there are extensive extremely long large-scale river reaches with reduced / dewatered flow in China. For this type of river reach, its basic ecological functions are mainly guaranteed through the ecological regulation measures of water conservancy projects. At present, the formulation of ecological regulation plans for this type of river reach with reduced flow is mostly based on theoretical research, and combines two methods: the minimum discharged flow constraint and the ecological regulation process constraint (artificial rising water process) according to the minimum discharged flow management method of water conservancy projects. There are few reasonable and appropriate ecological regulation plans formulated considering the actual needs of the river reach with reduced flow and coordinating the water resource utilization efficiency and the needs of ecological environment protection. In view of this, we propose an optimization method for ecological regulation of an extremely long river reach with reduced flow based on prototype observation. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides an optimization method for ecological regulation of an extremely long river reach with reduced flow based on prototype observation, which solves the problem that the existing ecological regulation plans for this type of river reach with reduced flow rarely consider the actual needs of the river reach with reduced flow and coordinate the water resource utilization efficiency and the needs of ecological environment protection.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An optimization method for ecological regulation of an extremely long river reach with reduced flow based on prototype observation, including the following steps:

[0007] S1: Real-time acquisition of observation indicators

[0008] Set up multiple prototype observation points in the ultra-long river reach with reduced water flow, determine the observation indicators, including water level, flow rate, water quality, types and quantities of aquatic organisms, and conduct long-term continuous observations using a variety of monitoring devices to obtain basic data;

[0009] S2: Data analysis and model establishment

[0010] Sort out and analyze the obtained basic data, establish a water flow-ecological response model, and analyze the response laws of the aquatic ecosystem under different water flow conditions;

[0011] S3: Establish an ecological regulation plan

[0012] According to the established water flow-ecological response model and combined with the ecological protection objectives, set multiple ecological regulation plans, and each plan includes different flow processes and water level regulation range parameters;

[0013] S4: Simulation calculation and simulation

[0014] Use numerical simulation software to simulate each ecological regulation plan, predict the impact of each plan on the ecological environment of the ultra-long river reach with reduced water flow after implementation, including the impact on aquatic biological habitats, water quality improvement, etc.;

[0015] S5: Evaluation of ecological regulation plan

[0016] According to the simulation results, use a multi-objective evaluation method to evaluate each ecological regulation plan and determine the optimal ecological regulation plan;

[0017] S6: Practical application and long-term monitoring

[0018] Apply the optimal ecological regulation plan to the actual regulation of the ultra-long river reach with reduced water flow, and continuously monitor the implementation effect through the prototype observation points, and dynamically adjust the regulation plan according to the monitoring results.

[0019] Preferably, in the real-time acquisition of the S1 observation indicators, the observation indicators include geological conditions, vegetation coverage of the river bank zone, and river trend conditions.

[0020] Preferably, in the real-time acquisition of the S1 observation indicators, use an intelligent sensor network and Internet of Things technology to realize automatic calibration, self-diagnosis of monitoring devices and real-time transmission of data to the cloud server; at the same time, use drones to conduct regular remote sensing monitoring and unmanned boats to conduct close-range observations of specific areas to supplement the data blind spots of fixed observation points.

[0021] Preferably, in the S2 data analysis and model establishment, when establishing the water flow-ecological response model, in addition to using machine learning algorithms to train the data, deep learning algorithms are also introduced, including convolutional neural networks to process river spatial data and long short-term memory networks to process time series data; a generative adversarial network is used to expand the data set to improve the generalization ability of the model; at the same time, considering multiple data including precipitation patterns and surrounding land use changes, data mining techniques are used to discover the hidden correlations between different observation indicators.

[0022] Preferably, in the S3 ecological regulation plan establishment, the ecological protection objectives include protecting the living environment of rare aquatic biological species, maintaining the biodiversity of the river ecosystem, and ensuring the stability of the riparian ecosystem; at the same time, the value of ecosystem services is taken into consideration, the service values such as water conservation, climate regulation, and recreation are evaluated, and they are quantified as evaluation indicators for the regulation plan.

[0023] Preferably, in the S3 ecological regulation plan establishment, the ecological regulation plan also considers the operation requirements of water conservancy projects and the social and economic water use demands; by establishing a water resources supply-demand balance model, the relationship between ecological water use, industrial water use, agricultural water use, and domestic water use is coordinated.

[0024] Preferably, in the S4 simulation calculation and simulation, the numerical simulation software uses the finite element method to simulate the water flow and ecological processes; a coupled ecological-hydrodynamic-water quality model is constructed to comprehensively simulate the interactions between water flow, nutrient transport, aquatic organism growth and reproduction, and water quality changes.

[0025] Preferably, in the S4 simulation calculation and simulation, when establishing the water flow-ecological response model, the influence of different seasonal factors on the water ecosystem is considered, and sub-models are established for different seasons respectively, and the annual comprehensive simulation is realized through seasonal weight adjustment.

[0026] Preferably, in the S5 ecological regulation plan evaluation, the multi-objective evaluation method adopts the combination of the analytic hierarchy process and the fuzzy comprehensive evaluation method; at the same time, a real-time evaluation system is established, and the implementation effect of the plan is timely feedback using real-time monitoring data and model prediction results, which is used as the reference basis for plan evaluation.

[0027] Preferably, in the S6 actual application and long-term monitoring, the basis for dynamic adjustment includes the deviation between the actually monitored ecological indicators and the expected goals, and the change of the river water inflow situation; an adaptive regulation strategy is developed to automatically adjust the regulation plan according to the real-time state of the ecosystem and the preset ecological thresholds; scenario simulations are carried out for extreme climate events or human activity interferences and emergency plans are formulated.

[0028] The present invention provides an ecological regulation optimization method for an ultra-long river reach with reduced water flow based on prototype observation. It has the following beneficial effects:

[0029] 1. Through the setting of multiple prototype observation points in the ultra-long river reach with reduced water volume and long-term continuous observation, and by using technical means such as intelligent sensor networks, unmanned aerial vehicles, and unmanned boats, the present invention has obtained comprehensive and accurate basic data, providing a reliable basis for subsequent model establishment and formulation of scheduling plans, and improving the scientific nature of ecological scheduling. At the same time, the collection of multi-source data and the application of data mining technology have revealed more potential ecological laws, further enhancing the understanding level of the ecological system.

[0030] 2. The present invention uses numerical simulation software to simulate and predict multiple ecological scheduling plans, constructs a coupled ecological-hydrodynamic-water quality model, and optimizes the model using advanced deep learning algorithms, enabling more accurate simulation of the complex change process of the ecological system. At the same time, incorporating the value of ecosystem services into the evaluation index of the scheduling plan and using a multi-objective evaluation method to comprehensively consider various factors effectively balance the interests of multiple parties such as ecological protection, operation of water conservancy projects, and water demand for social and economic use, and determines a more comprehensive and optimized ecological scheduling plan.

[0031] 3. After applying the optimal ecological scheduling plan to actual scheduling through the establishment of the present invention, through measures such as a real-time evaluation system, an adaptive scheduling strategy, and an emergency plan, the implementation effect is continuously monitored, dynamically adjusted, and emergencies are responded to. It can not only promptly adapt to changes in the river ecological environment and incoming water conditions, ensure that ecological scheduling always proceeds in a direction conducive to ecological protection and sustainable development, but also quickly respond in the face of extreme situations, minimize damage to the ecological system to the greatest extent, and enhance the adaptability, effectiveness, and risk resistance ability of ecological scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flow chart of an ecological scheduling optimization method for an ultra-long river reach with reduced water volume based on prototype observation;

[0033] Figure 2 is a schematic diagram of the real-time acquisition process of the S1 observation index of the present invention;

[0034] Figure 3 is a schematic diagram of the S6 actual application and long-term monitoring of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] Embodiment:

[0037] Please refer to the appendix Figure 1 - Appendix Figure 3 The embodiment of the present invention provides an ecological regulation optimization method for an extremely long river reach with reduced water flow based on prototype observation, including the following steps:

[0038] S1: Real-time acquisition of observation indicators

[0039] Set multiple prototype observation points in the extremely long river reach with reduced water flow, determine the observation indicators, including water level, flow rate, water quality, types and quantities of aquatic organisms, and use a variety of monitoring devices for long-term continuous observation to obtain basic data;

[0040] S2: Data analysis and model establishment

[0041] Sort out and analyze the obtained basic data, establish a water flow-ecological response model, and analyze the response laws of the aquatic ecosystem under different water flow conditions;

[0042] S3: Establish an ecological regulation plan

[0043] According to the established water flow-ecological response model, combined with the ecological protection objectives, set multiple ecological regulation plans, and each plan includes different flow processes and water level regulation range parameters;

[0044] S4: Simulation calculation and simulation

[0045] Use numerical simulation software to simulate each ecological regulation plan, and predict the impact of each plan on the ecological environment of the extremely long river reach with reduced water flow after implementation, including the impact on aquatic organism habitats, water quality improvement, etc.;

[0046] S5: Evaluation of ecological regulation plans

[0047] According to the simulation results, use a multi-objective evaluation method to evaluate each ecological regulation plan and determine the optimal ecological regulation plan;

[0048] S6: Practical application and long-term monitoring

[0049] Apply the optimal ecological regulation plan to the actual regulation of the extremely long river reach with reduced water flow, and continuously monitor the implementation effect through the prototype observation points, and dynamically adjust the regulation plan according to the monitoring results.

[0050] In the real-time acquisition of the observation indicators in S1, in the extremely long river reach with reduced water flow, according to the topographic and geomorphic features, hydrological characteristics and ecological distribution status of the river, scientifically and reasonably set multiple prototype observation points, and the observation indicators include geological conditions, vegetation coverage of the river bank zone and the river trend.

[0051] In the real-time acquisition of the S1 observation indicators, the intelligent sensor network and Internet of Things technology are used to achieve automatic calibration, self-diagnosis of monitoring devices and real-time transmission of data to the cloud server; at the same time, drones are regularly used for remote sensing monitoring and unmanned boats are used for close-range observation of specific areas to supplement the data blind spots of fixed observation points, including the following steps:

[0052] 1. Data preparation stage

[0053] Data collection: Collect various types of data from multiple prototype observation points in the ultra-long river reach with reduced flow, including water level, flow rate, water quality, types and quantities of aquatic organisms, vegetation coverage of the river bank zone, precipitation patterns, changes in surrounding land use, etc. These data come from multiple sources such as intelligent sensor networks, drone remote sensing monitoring, and unmanned boat close-range observation;

[0054] Data cleaning: Since the actually collected data has noise, missing values or incorrect values, it is necessary to clean the collected data. For example, for water level data, if there are obvious outliers outside the normal range, it is necessary to correct or supplement them by data interpolation method or by comparing with data of surrounding observation points; for missing water quality data, a missing value filling algorithm based on machine learning is used for processing;

[0055] Data transformation: Convert the collected original data into a form for algorithm processing. Usually, the data is organized into a transaction set, and each transaction contains a combination of multiple observation indicators. For example, combine the information such as high water level, large flow rate, good water quality, and large quantity of a certain type of aquatic organism at each observation point at a certain moment into a transaction;

[0056] 2. Frequent item set generation stage

[0057] Set the minimum support: The minimum support is a percentage value, indicating the lowest frequency at which an item set appears in the transaction set. For example, set the minimum support to 20%, which means that in all transactions, an item set must appear in at least 20% of the transactions to be considered a frequent item set;

[0058] Generate candidate 1-item sets: Extract all single observation indicators from the dataset as candidate 1-item sets. For example, {high water level}, {large flow rate}, {good water quality}; then calculate the support of each candidate 1-item set, that is, the number of transactions containing this item set divided by the total number of transactions. Assume the total number of transactions is 1000, and the number of transactions containing {high water level} is 300, then the support of {high water level} is 300 / 1000 = 30%;

[0059] Generate frequent 1-item sets: Screen out the candidate 1-item sets whose support is greater than or equal to the minimum support to form frequent 1-item sets. In the above example, if the minimum support is 20%, then {high water level} will be included in the frequent 1-item sets;

[0060] Generate candidate k-item sets (k > 1): Generate candidate k-item sets based on frequent (k - 1)-item sets. For example, there are frequent 1-item sets {High water level}, {Large flow rate}, {Good water quality}. When generating candidate 2-item sets, through combination operations, we get {High water level, Large flow rate}, {High water level, Good water quality}, {Large flow rate, Good water quality}, etc., and then calculate the support of each candidate 2-item set; assume there are 250 transactions containing {High water level, Large flow rate}, then its support is 250 / 1000 = 25%.

[0061] Generate frequent k-item sets (k > 1): Similarly, select candidate k-item sets with support greater than or equal to the minimum support to form frequent k-item sets, and repeat this process until no new frequent item sets can be generated.

[0062] 3. Association rule generation stage

[0063] Set the minimum confidence: Used to measure the reliability of association rules. For example, set the minimum confidence to 60%, which means that among the transactions that satisfy the antecedent, at least 60% of the transactions also satisfy the consequent;

[0064] Generate candidate association rules: Generate candidate association rules from frequent item sets; for example, from the frequent 2-item set {High water level, Large flow rate}, we can generate candidate association rules "High water level → Large flow rate" and "Large flow rate → High water level";

[0065] Calculate the confidence: For each candidate association rule, calculate its confidence. The formula is:

[0066]

[0067] For example, for "High water level → Large flow rate", assume the support of {High water level} is 30% and the support of {High water level, Large flow rate} is 25%, then its confidence is 25% / 30% ≈ 83.3%;

[0068] Filter valid association rules: Select candidate association rules with confidence greater than or equal to the minimum confidence as valid association rules. In the above example, if the minimum confidence is 60%, then "High water level → Large flow rate" will be considered a valid association rule. Through these valid association rules, we can discover the potential relationships between different observation indicators in the ecosystem of the ultra-long river reach with reduced water flow, such as the relationships between water level and flow rate, water quality and the number of aquatic organisms, etc., so as to provide a more in-depth basis for ecological regulation.

[0069] In the S2 data analysis and model establishment, when establishing the water flow-ecological response model, in addition to using machine learning algorithms to train the data, deep learning algorithms are also introduced, including convolutional neural networks to process river spatial data and long short-term memory networks to process time series data; generative adversarial networks are used to expand the dataset to improve the generalization ability of the model; at the same time, considering multiple data such as precipitation patterns and surrounding land use changes, data mining techniques are used to discover the hidden correlations between different observation indicators. The operations of the convolutional neural network and long short-term memory network established here are as follows:

[0070] Data input and preprocessing: Convert spatial data related to rivers, such as river channel topography and riparian vegetation distribution information, into a matrix form suitable for CNN processing. For example, for river channel topography data, it can be divided into regular grids, and each grid corresponds to an element in the matrix. The element value can be topographic feature parameters such as the elevation and slope of the grid. For the riparian vegetation distribution, through remote sensing image recognition, different vegetation types and coverage degrees are encoded as matrix element values;

[0071] Convolution layer operation: The core of the CNN is the convolution layer, which contains multiple convolution kernels. Taking the processing of river channel topography data as an example, assume that the size of the input topographic matrix X is 100×100, and the size of the convolution kernel W is 3×3. When calculating the output feature map, the convolution kernel slides on the input matrix with a certain stride, and the elements at the corresponding positions are multiplied and summed each time, and then the bias term b is added;

[0072] For example, for the value at the upper left corner (1,1) of the output feature map, the calculation method is:

[0073]

[0074] Through this convolution operation, the convolution kernel can extract local topographic features, such as steep areas and gentle areas. Multiple convolution kernels with different parameters can extract various different local features, enriching the description of the river channel topography;

[0075] Pooling layer operation: A pooling layer is connected after the convolution layer, and common ones are max pooling or average pooling;

[0076] Taking max pooling as an example, assume that the size of the pooling window is 2×2 and the stride is 2. For the feature map output by the convolution layer, the maximum value in each 2×2 sub-region is taken as the output after pooling. For example, in a 2×2 sub-region if d is the largest, the output value after pooling is d;

[0077] Pooling operations can reduce the amount of data, reduce the computational complexity of the model, retain the main features at the same time, and prevent overfitting;

[0078] Application of Long Short-Term Memory Network (LSTM) in Ecological Models

[0079] Time Series Data Preparation: Collect time series data related to the ecosystem, such as river flow at different times, water quality indicators (such as changes in dissolved oxygen content and pH over time), and temporal changes in the number of aquatic organisms. Organize this data into a sequence in chronological order, with the data at each time step serving as the input to the LSTM;

[0080] Internal Mechanism of LSTM Unit: An LSTM unit consists of an input gate, a forget gate, an output gate, and a memory cell. Taking river flow prediction as an example, let the current time be t and the previous time be t - 1. The input gate i t determines how much information from the current input x t can enter the memory cell, and the calculation formula is:

[0081] i t = σ(W i ·[h t-1 , x t + b i )

[0082] where σ is the sigmoid function, W i is the weight matrix, h t-1 is the hidden state at the previous time step, and b i is the bias term;

[0083] The forget gate f t controls which information from the previous time step in the memory cell needs to be retained, and the formula is:

[0084] f t = σ(W f ·[h t-1 , x t + b f )

[0085] The candidate memory cell is calculated through the current input and the previous time step's hidden state, and the formula is:

[0086]

[0087] The memory cell C t then combines the effects of the forget gate and the input gate, and the update formula is:

[0088]

[0089] The output gate o t determines which information in the memory cell will be output to the hidden state h t at the current time step, and the calculation formula is:

[0090] o t = σ(W o · [h t-1 , x t + b o )

[0091] The calculation formula for the hidden state h t is as follows:

[0092] h t = o t · tanh(C t )

[0093] Long-term dependency learning: LSTM can effectively learn long-term dependencies in time series data. For example, when simulating the law of river flow changing with seasons, it can remember the flow change information of multiple past seasons. Even if there are some short-term fluctuations or interferences in the middle, it can accurately predict future flows. When the spring precipitation increases and causes the flow to rise, LSTM can combine the flow change patterns in past springs for many years and the flow data of the previous winter (reflecting the initial state of the river) to accurately predict the changing trends of subsequent summer and autumn flows.

[0094] Multivariate time series processing: In an actual ecosystem, time series of multiple variables influence each other. LSTM can process multiple time series data simultaneously. For example, it can input time series data such as river flow, water temperature, and dissolved oxygen content at the same time. Through internal weight connections and gating mechanisms, it learns the interrelationships and dynamic change laws among these variables. For example, when the flow increases, it may affect the distribution of water temperature and the content of dissolved oxygen. LSTM can capture this complex multivariate interaction relationship, thereby more comprehensively and accurately simulating the evolution process of the ecosystem in the time dimension and providing a more accurate prediction basis for formulating ecological scheduling plans.

[0095] In the step S3 of establishing the ecological scheduling plan, the ecological protection objectives include protecting the living environments of rare aquatic biological species, maintaining the biodiversity of the river ecosystem, and ensuring the stability of the riparian ecosystem; at the same time, the value of ecosystem services is taken into consideration, and the service values such as water conservation, climate regulation, and recreation are evaluated and quantified as evaluation indicators for the scheduling plan.

[0096] In the step S3 of establishing the ecological scheduling plan, the ecological scheduling plan also considers the operation requirements of water conservancy projects and the social and economic water use demands; by establishing a water resource supply-demand balance model, the relationships among ecological water use, industrial water use, agricultural water use, and domestic water use are coordinated.

[0097] In the S4 simulation calculation, the numerical simulation software uses the finite element method to simulate the water flow and ecological processes; a coupled ecological-hydrodynamic-water quality model is constructed to comprehensively simulate the interactions among water flow, nutrient transport, aquatic organism growth and reproduction, and water quality changes.

[0098] In the S4 simulation calculation, when establishing the water flow-ecological response model, the impacts of different seasonal factors on the water ecosystem are considered. Sub-models are established for different seasons respectively, and comprehensive annual simulation is achieved through seasonal weight adjustment.

[0099] In the S5 ecological regulation scheme evaluation, the multi-objective evaluation method combines the analytic hierarchy process and the fuzzy comprehensive evaluation method; meanwhile, a real-time evaluation system is established to use real-time monitoring data and model prediction results to timely feedback the implementation effect of the scheme, which is used as the reference basis for scheme evaluation.

[0100] In the S6 actual application and long-term monitoring, the basis for dynamic adjustment includes the deviation between the actually monitored ecological indicators and the expected targets, and the changes in the river water inflow; an adaptive regulation strategy is developed to automatically adjust the regulation scheme according to the real-time state of the ecosystem and the preset ecological thresholds; scenario simulations are carried out for extreme climate events or human activity interferences and emergency plans are formulated. The adaptive regulation strategy established here is as follows:

[0101] Threshold-based triggering mechanism: With the adaptive algorithm based on threshold comparison as the core, corresponding allowable deviation ranges (thresholds) are set for different ecological indicators;

[0102] For example, for the river flow, the set target value is Q0, and the allowable deviation range is ±ΔQ. When the real-time monitored flow Q satisfies |Q - Q0| > ΔQ, the system automatically triggers the regulation scheme adjustment mechanism. Assume Q0 = 500m 3 / s, ΔQ = 50m 3 / s. If the real-time monitored flow is 420m 3 / s, since |420 - 500| = 80 > 50, the adjustment is immediately triggered;

[0103] Calculation and decision of the adjustment amount: Once the adjustment mechanism is triggered, the specific adjustment amount needs to be determined. The adjustment amount is usually related to the deviation degree and is calculated through a preset adjustment coefficient k. For example, the adjustment amount ΔS = k × |E - E0|, where E is the currently monitored ecological indicator value and E0 is the expected target value;

[0104] Assume that when adjusting the river flow, the adjustment coefficient k = 0.5, the current flow Q = 420m 3 / s, and the target flow Q0 = 500m 3If the flow rate is / s, then the adjustment amount ΔS = 0.5×|420 - 500| = 40 m 3 / s. Based on this adjustment amount, combined with the actual water conservancy project facilities and operation plan, specific operations are decided, such as whether to increase or decrease the water discharge flow rate of the reservoir, adjust the opening and closing degree of the sluice gate, etc.

[0105] Dynamic adjustment and feedback optimization: After implementing the adjustment operation, the system continuously monitors the changes in ecological indicators in real time. By comparing the adjusted indicator data with the target value, it is judged whether the adjustment measures are effective. If the indicators after adjustment still do not return to the target range, the system will further analyze the reasons and determine whether it is due to insufficient adjustment amount, wrong adjustment direction, or other factors not considered;

[0106] According to the analysis results, the adjustment strategy is optimized, the adjustment amount is calculated again and the new adjustment measures are implemented, forming a closed-loop dynamic adjustment process;

[0107] For example, after increasing the water discharge flow rate of the reservoir, it is found that the dissolved oxygen content in the water quality does not increase as expected. After analysis, it may be that the increased water flow velocity leads to the oxygen reaeration efficiency of the water body not reaching the expected value. At this time, it may be necessary to adjust the way of discharging water from the reservoir, such as using intermittent water discharge, to increase the dissolved oxygen content until the ecological indicators are stable within the target range.

[0108] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An ecological dispatch optimization method for ultra-long water reduction river sections based on prototype observation, characterized in that: The following steps are involved: S1: Real-time acquisition of observation indicators Set up multiple prototype observation points in the super-long water reduction river section, determine the observation indicators, including water level, flow, water quality, aquatic organism species and quantity, use a variety of monitoring equipment to conduct long-term continuous observation, and obtain basic data; S2: Data analysis and model building Organize and analyze the basic data obtained, establish a water flow-ecological response model, and analyze the response laws of the water ecosystem under different water flow conditions; S3: Establish an ecological scheduling plan According to the established water flow-ecological response model and combined with ecological protection goals, multiple ecological scheduling schemes are set, each of which contains different flow process and water level control range parameters; S4: Simulation Use numerical simulation software to simulate various ecological scheduling schemes and predict the impact of each scheme on the ecological environment of the ultra-long water reduction river section after implementation, including the impact on aquatic habitats, water quality improvement, etc.; S5: Ecological scheduling scheme evaluation According to the simulation results, a multi-objective evaluation method is used to evaluate each ecological scheduling scheme and determine the optimal ecological scheduling scheme; S6: Practical application and long-term monitoring The optimal ecological scheduling scheme is applied to the actual scheduling of ultra-long water reduction river sections, and the implementation effect is continuously monitored through prototype observation points, and the scheduling scheme is dynamically adjusted according to the monitoring results.

2. The method for optimizing ecological dispatch of an ultra-long water reduction river section based on prototype observation according to claim 1 is characterized in that: The S1 observation indicators are obtained in real time, and the observation indicators include geological conditions, riverbank vegetation coverage and river direction.

3. The method for optimizing ecological dispatch of ultra-long water reduction river sections based on prototype observation according to claim 1 is characterized in that: In the real-time acquisition of the S1 observation indicators, intelligent sensor networks and Internet of Things technologies are used to realize automatic calibration and self-diagnosis of monitoring equipment and real-time transmission of data to the cloud server. At the same time, drones are used for regular remote sensing monitoring and unmanned ships are used for close-range observation of specific areas to supplement the data blind spots of fixed observation points.

4. The method for optimizing ecological dispatching of ultra-long water reduction river sections based on prototype observation according to claim 1 is characterized in that: In the S2 data analysis and model establishment, when establishing the water flow-ecological response model, in addition to using machine learning algorithms to train the data, deep learning algorithms are also introduced, including convolutional neural networks to process river spatial data and long short-term memory networks to process time series data; generative adversarial networks are used to expand the data set to improve the generalization ability of the model; at the same time, considering multivariate data including precipitation patterns and surrounding land use changes, data mining techniques are used to discover hidden correlations between different observation indicators.

5. The method for optimizing ecological dispatching of ultra-long water reduction river sections based on prototype observation according to claim 1 is characterized in that: In the S3 ecological scheduling plan, the ecological protection goals include protecting the living environment of rare aquatic species, maintaining the biodiversity of river ecosystems, and ensuring the stability of riparian ecosystems; at the same time, the value of ecosystem services is taken into consideration, and the value of services such as water conservation, climate regulation, and leisure and entertainment are evaluated and quantified as evaluation indicators of the scheduling plan.

6. The method for optimizing ecological dispatch of ultra-long water reduction river sections based on prototype observation according to claim 1 is characterized in that: In the S3, the ecological scheduling plan is established, which also takes into account the operation requirements of water conservancy projects and the social and economic water demand; by establishing a water resources supply and demand balance model, the relationship between ecological water use, industrial water use, agricultural water use and domestic water use is coordinated.

7. The method for optimizing ecological dispatch of ultra-long water reduction river sections based on prototype observation according to claim 1 is characterized in that: In the S4 simulation calculation, the numerical simulation software uses the finite element method or simulates the water flow and ecological processes; constructs a coupled ecological-hydrodynamic-water quality model to comprehensively simulate the interaction between water flow, nutrient transport, growth and reproduction of aquatic organisms, and water quality changes.

8. The method for optimizing ecological regulation of ultra-long water reduction river sections based on prototype observation according to claim 1 is characterized in that: In the S4 simulation, when establishing the water flow-ecological response model, the impact of different seasonal factors on the water ecosystem is taken into account. At the same time, sub-models are established for different seasons, and comprehensive simulation throughout the year is achieved through seasonal weight adjustment.

9. The method for optimizing ecological dispatching of ultra-long water reduction river sections based on prototype observation according to claim 1 is characterized in that: In the evaluation of the S5 ecological scheduling scheme, the multi-objective evaluation method adopts a combination of hierarchical analysis method and fuzzy comprehensive evaluation method; at the same time, a real-time evaluation system is established to use real-time monitoring data and model prediction results to promptly feedback the implementation effect of the scheme, which is used as a reference for scheme evaluation.

10. The method for optimizing ecological regulation of ultra-long water reduction river sections based on prototype observation according to claim 1, characterized in that: In the actual application and long-term monitoring of the S6, the basis for dynamic adjustment includes the deviation between the actually monitored ecological indicators and the expected targets, and the changes in river water conditions; developing an adaptive scheduling strategy to automatically adjust the scheduling plan according to the real-time status of the ecosystem and the preset ecological thresholds; conducting scenario simulations and formulating emergency plans for extreme climate events or human activity interference.

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