River and lake ecological flow self-adaptive regulation system and method

Abstract

The present application belongs to the technical field of river and lake flow regulation, and provides a river and lake ecological flow self-adaptive regulation system and method. The method comprises the following steps: determining the irrigation water threshold of each agricultural planting area in a target period based on crop type information and crop growth cycle data; calculating the first dynamic regulation coefficient of each agricultural planting area based on current flow data, actual irrigation water consumption and the irrigation water threshold, and calculating the corresponding second dynamic regulation coefficient based on natural water data of each agricultural planting area; fusing the first dynamic regulation coefficient and the second dynamic regulation coefficient to obtain a dynamic regulation coefficient, and issuing an instruction based on the dynamic regulation coefficient to adjust the water supply of each agricultural planting area. The present application can solve the problem of mismatch between traditional river and lake flow static regulation and dynamic agricultural irrigation water demand, and realize the collaborative optimization of ecological flow guarantee and agricultural water demand.

Description

Technical Field

[0001] This invention relates to the field of river and lake flow regulation technology, and more specifically, to an adaptive regulation system and method for river and lake ecological flow. Background Technology

[0002] The scientific regulation of river and lake ecological flow has become a core element in maintaining the ecological balance of watersheds and ensuring the sustainable use of water resources. How to coordinate water demand from agriculture, industry, and daily life while ensuring the ecological base flow of rivers and lakes has become a critical issue that urgently needs to be addressed in the field of water resource management.

[0003] Agriculture, as a major water user, exhibits significant spatial and temporal variations in its irrigation water requirements, closely related to crop type, growth cycle, and regional climate conditions. Different regions, due to varying planting structures, show marked differences in irrigation water requirements at specific times. For example, region A primarily grows winter wheat, which matures from late May to early June each year. At this time, the wheat has completed grain filling, and the root system's need for water absorption decreases significantly, making it not a critical irrigation period. Therefore, region A requires less irrigation water during this specific time. In contrast, region B primarily grows rice, which is in the jointing and booting stage during the same period. This is the most water-sensitive stage in rice growth, requiring a shallow water layer in the field to meet the needs of young panicle development. The crop's high water demand during this period results in higher irrigation water requirements in region B. This dynamic variation in irrigation water requirements between regions due to differences in crop growth cycles necessitates refined and differentiated allocation and regulation of river and lake water flows.

[0004] However, existing methods for regulating river and lake ecological flow are mostly based on fixed ecological flow thresholds and rely on static scheduling through water conservancy facilities, neglecting the spatiotemporal fluctuations in agricultural irrigation water demand across different regions. In practical applications, when some areas are in periods of low water demand, existing methods fail to scientifically allocate surplus water to areas of high water demand, resulting in low water resource utilization efficiency. Conversely, when multiple areas simultaneously enter periods of high water demand, the lack of dynamic regulation mechanisms can easily lead to conflicts between ecological flow assurance and agricultural irrigation water use, which can range from affecting crop yields to severely damaging the stability of river and lake ecosystems.

[0005] Therefore, adaptive regulation of river and lake ecological flow based on the dynamic changes in agricultural water demand is a core technical issue for improving the efficiency of water resource management. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method, system, electronic device, non-transient computer-readable storage medium, and computer program product for adaptive regulation of river and lake ecological flow.

[0007] This invention provides a method for adaptive regulation of river and lake ecological flow, comprising the following steps:

[0008] Construct a flow association control area group comprising several agricultural planting areas that satisfy the flow coordination control relationship, including crop type information, crop growth cycle data and corresponding geographic spatial coordinates of each agricultural planting area;

[0009] Based on crop type information and crop growth cycle data, the irrigation water demand threshold for each agricultural planting area during the target period is determined; and the current flow data of rivers, lakes and tributaries and the actual irrigation water consumption of each agricultural planting area are collected in real time.

[0010] The first dynamic control coefficient for each agricultural planting area is calculated based on the current flow data, actual irrigation water consumption, and irrigation water demand threshold. The corresponding second dynamic control coefficient is calculated based on the natural water inflow data of each agricultural planting area. The natural water inflow data includes precipitation data and input water data from non-controlled rivers.

[0011] The first and second dynamic control coefficients are fused to obtain the dynamic control coefficient. Based on the dynamic control coefficients, instructions are generated and sent to the river and lake water conservancy control facilities to adjust the water supply of each agricultural planting area.

[0012] This invention also provides a river and lake ecological flow adaptive regulation system, comprising:

[0013] The construction unit constructs a flow association control area group consisting of several agricultural planting areas that satisfy the flow coordination and control relationship, including crop type information, crop growth cycle data and corresponding geographic spatial coordinates of each agricultural planting area;

[0014] The first calculation unit determines the irrigation water demand threshold for each agricultural planting area during the target period based on crop type information and crop growth cycle data; and collects the current flow data of rivers, lakes and tributaries and the actual irrigation water consumption of each agricultural planting area in real time.

[0015] The second calculation unit calculates the first dynamic control coefficient for each agricultural planting area based on the current flow data, actual irrigation water consumption, and irrigation water demand threshold, and calculates the corresponding second dynamic control coefficient based on the natural water inflow data of each agricultural planting area; wherein, the natural water inflow data includes precipitation data and input water data from non-controlled rivers;

[0016] The control unit integrates the first dynamic control coefficient and the second dynamic control coefficient to obtain the dynamic control coefficient. Based on the dynamic control coefficient, it generates and sends instructions to the river and lake water conservancy control facilities to adjust the water supply of each agricultural planting area.

[0017] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method described in the preceding invention.

[0018] The present invention also provides a non-transient computer-readable storage medium having instructions embodied thereon that can be executed by one or more processors to perform a method as described in any of the preceding claims.

[0019] The present invention also provides a computer program product comprising an executable computer program that, when executed, implements the method as described in any of the preceding claims.

[0020] This invention constructs a flow-related regulation area group to accurately match regional water demand characteristics with hydrological correlations. It dynamically calculates water demand thresholds based on crop growth cycles and integrates real-time flow and natural inflow data to generate regulation coefficients, thus avoiding water supply imbalances caused by single factors. This solves the problem of mismatch between traditional static river and lake flow regulation and dynamic agricultural irrigation water demand, achieving synergistic optimization of ecological flow security and agricultural water demand. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating an adaptive regulation method for river and lake ecological flow disclosed in an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of the structure of a river and lake ecological flow adaptive regulation system disclosed in an embodiment of the present invention. Detailed Implementation

[0023] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0026] like Figure 1 As shown in the figure, an embodiment of the present invention discloses an adaptive regulation method 100 for river and lake ecological flow, which includes the following steps:

[0027] 100. Construct a flow association control area group that includes several agricultural planting areas that meet the flow coordination control relationship, including crop type information, crop growth cycle data and corresponding geographic spatial coordinates of each agricultural planting area.

[0028] In this step, the natural flow characteristics of water resources in the basin (mainly the vertical flow direction) and the control and radiation range of water conservancy projects are combined to divide multiple agricultural planting areas in the basin into the same flow-related control area group, and then carry out subsequent coordinated flow control.

[0029] By collecting the geospatial coordinates of each agricultural planting area through a Geographic Information System (GIS), the geographical location of the area in the watershed can be accurately determined, and its spatial distance and hydraulic connection with surrounding rivers, lakes and tributaries can be clarified.

[0030] In addition, information on crop types (such as wheat, rice, and corn) in various agricultural planting areas can be collected, based on which the water requirements of crops can be determined; at the same time, crop growth cycle data are collected, including the start and end times and duration of key growth stages such as sowing, tillering, grain filling, and maturity.

[0031] 200. Based on crop type information and crop growth cycle data, determine the irrigation water demand threshold for each agricultural planting area during the target period; and collect the current flow data of rivers, lakes and tributaries in real time and the actual irrigation water consumption of each agricultural planting area.

[0032] In this step, based on the crop type information and crop growth cycle data obtained in step 100, and combined with methods such as the crop coefficient method and evapotranspiration calculation models (such as the Penman-Monteith model), the irrigation water demand threshold for each agricultural planting area during the target period (such as monthly, quarterly, or specific growth stage) is determined. This irrigation water demand threshold represents the minimum amount of irrigation water required to ensure normal crop growth in the agricultural planting area during the target period, and needs to comprehensively consider factors such as crop transpiration, soil moisture loss, and effective precipitation.

[0033] Meanwhile, real-time flow data is collected by hydrological monitoring stations (such as water level gauges and flow meters) deployed on the main streams and tributaries of rivers and lakes, thereby enabling a grasp of the real-time supply capacity of river and lake water resources; actual irrigation water consumption data is collected in various agricultural planting areas through agricultural irrigation metering facilities (such as smart water meters and soil moisture sensors), reflecting the current water consumption situation. These two types of data together constitute the basic input for dynamic regulation.

[0034] 300, the first dynamic control coefficient of each agricultural planting area is calculated based on the current flow data, actual irrigation water consumption and irrigation water demand threshold, and the corresponding second dynamic control coefficient is calculated based on the natural water inflow data of each agricultural planting area; wherein, the natural water inflow data includes precipitation data and input water data of non-controlled rivers.

[0035] In this step, after obtaining the irrigation water demand threshold and the actual irrigation water consumption, the calculation stage of the regulation parameters begins. The regulation coefficients are calculated from two dimensions: the difference between water demand and water consumption, and the natural water supply, as detailed below:

[0036] The first dynamic control coefficient is calculated based on current flow data, actual irrigation water consumption, and irrigation water demand threshold. Specifically, it quantifies the deviation between actual irrigation water consumption and irrigation water demand threshold, as well as the matching degree of river and lake water supply capacity. For example, when the actual irrigation water consumption is lower than the irrigation water demand threshold and the current river and lake flow is sufficient, the first dynamic control coefficient is set to favor increasing the water supply; if the actual irrigation water consumption is close to the irrigation water demand threshold but the river and lake flow is insufficient, the first dynamic control coefficient is set to favor limiting the water supply.

[0037] The second dynamic regulation coefficient is calculated based on natural water inflow data from various agricultural planting areas. Specifically, natural water inflow data includes precipitation data (collected through rain gauges) within the agricultural planting areas and input water data from non-regulated rivers (obtained through cross-border hydrological monitoring). This natural water inflow can partially replace irrigation water. When natural water inflow is sufficient, the second dynamic regulation coefficient reduces the reliance on river and lake regulation for water supply; conversely, it requires increasing the weight of water supply regulation.

[0038] 400, the first dynamic control coefficient and the second dynamic control coefficient are fused to obtain the dynamic control coefficient, and instructions are generated and sent to the river and lake water conservancy control facilities based on the dynamic control coefficient to adjust the water supply of each agricultural planting area.

[0039] In this step, weighted fusion, neural network fusion, or fuzzy logic fusion methods are used to fuse the first dynamic control coefficient and the second dynamic control coefficient to obtain a dynamic control coefficient that comprehensively reflects the urgency of water demand, the feasibility of water supply, and the contribution of natural water inflow.

[0040] Based on the dynamic regulation coefficients of each agricultural planting area, instructions are generated and sent to river and lake water conservancy regulation facilities (such as gates, pumping stations, and water diversion hubs). By adjusting the gate opening and pumping station operating power, the water supply to each agricultural planting area is precisely adjusted. For example, the water supply is increased for agricultural planting areas with high dynamic regulation coefficients, and the water supply is appropriately reduced for agricultural planting areas with low dynamic regulation coefficients, ensuring that agricultural irrigation needs are maximized while protecting the ecological base flow of rivers and lakes.

[0041] This invention constructs a flow-related regulation area group to accurately match regional water demand characteristics with hydrological correlations. It dynamically calculates water demand thresholds based on crop growth cycles and integrates real-time flow and natural inflow data to generate regulation coefficients, thus avoiding water supply imbalances caused by single factors. This solves the problem of mismatch between traditional static river and lake flow regulation and dynamic agricultural irrigation water demand, achieving synergistic optimization of ecological flow security and agricultural water demand.

[0042] As an example, the construction includes a group of agricultural planting areas that satisfy the flow-coordinated regulation relationship, including:

[0043] 101. By simulating the water flow trajectory through a watershed digital twin model and combining the topological relationships of rivers, lakes, and tributaries, the main water supply river and lake units for each agricultural planting area are determined.

[0044] In this step, a watershed digital twin model (a virtual simulation system that integrates topographic data, hydrological parameters, and water conservancy project layout) is used to simulate the movement trajectory of water flow within the watershed, including the confluence path of water flow from the source to tributaries and then to the main stream, as well as the water distribution process.

[0045] Simultaneously, based on the topological relationships of rivers and tributaries (such as the location of tributaries flowing into the main stream and the hierarchical structure of the water system), the main river and lake units (such as specific tributaries, lakes, or reservoirs) on which irrigation water for each agricultural planting area depends can be determined. For example, if simulation reveals that 80% of the irrigation water in region A comes from tributary A, then tributary A can be identified as the main water supply river and lake unit for region A.

[0046] 102. Based on the water resource carrying capacity of the main water source supplying river and lake units and the hydraulic connection strength between each agricultural planting area and the water source, preliminary regional grouping is obtained; wherein, the hydraulic connection strength is determined comprehensively based on water conveyance distance, water conveyance efficiency and loss rate along the way.

[0047] In this step, after identifying the main water source supply river and lake units, preliminary grouping is conducted based on the water resource carrying capacity of these units and the hydraulic connection strength between each agricultural planting area and the water source. The water resource carrying capacity of the main water source supply river and lake unit refers to the maximum amount of water available for irrigation under the premise of ensuring ecological base flow, which can be determined through methods such as hydrological frequency analysis and ecological flow threshold calculation. The hydraulic connection strength between each agricultural planting area and the water source is comprehensively assessed based on the water conveyance distance (the length of the pipeline / channel from the area to the water source), water conveyance efficiency (the water retention rate during water conveyance), and loss rate along the route (the proportion of losses such as evaporation and leakage). For example, the shorter the water conveyance distance, the higher the water conveyance efficiency, and the lower the loss rate along the route, the higher the corresponding hydraulic connection strength.

[0048] Furthermore, agricultural planting areas within the water resource carrying capacity and with similar hydraulic connection strength to the same water source are grouped into the same preliminary regional group to ensure that the water supply conditions of each area within the group are consistent.

[0049] 103. Based on the overlap of peak water demand periods in the crop growth cycle and the similarity of water demand characteristics of crop types within each preliminary regional group, the preliminary regional groups are optimized and adjusted to ultimately form several flow-related regulation regional groups. Among them, agricultural planting areas with peak water demand periods overlapping less than a preset overlap threshold or water demand characteristics similarity higher than a preset similarity threshold are assigned to the same flow-related regulation regional group.

[0050] In this step, the preliminary regional groups are finely optimized based on the overlap of peak water demand periods in the crop growth cycle and the similarity of water demand characteristics among crop types in each preliminary regional group. Specifically, the percentage of overlap of peak water demand periods in each region within a group is calculated, and this percentage is used as the overlap of peak water demand periods in the crop growth cycle. If the overlap of peak water demand periods is lower than a preset overlap threshold (e.g., 30%), it indicates that the peak water demand periods in the regions are significantly staggered, and water supply pressure can be balanced through coordinated scheduling, making them suitable for grouping together.

[0051] Similarity is calculated based on parameters such as crop drought resistance, irrigation cycle, and water requirement per unit area, and this similarity is used as the similarity of water requirement characteristics for crop types. If the similarity of water requirement characteristics is higher than a preset similarity threshold (e.g., 70%), it indicates that the water requirement patterns in the region are similar, making it convenient to adopt a unified control strategy and suitable to be grouped into the same group.

[0052] For preliminary regional groups that are not suitable for inclusion in the same group, regrouping is performed and the above analysis is repeated. Through the above optimization and adjustment, the final flow-related control regional groups not only meet the requirements of hydrological and hydraulic correlation, but also conform to the coordinated scheduling adaptability of water demand characteristics.

[0053] As an example, the first dynamic regulation coefficient for each agricultural planting area, calculated based on current flow data, actual irrigation water consumption, and irrigation water demand threshold, includes:

[0054] 301. Calculate the difference between the irrigation water demand threshold and the actual irrigation water consumption in each agricultural planting area as the water demand gap value, and calculate the difference between the current flow data and the ecological base flow threshold as the adjustable flow margin.

[0055] In this step, for each agricultural planting area, the actual irrigation water consumption of that area is subtracted from the irrigation water demand threshold determined in step 200 to obtain the water demand gap value. A positive water demand gap value indicates that the current actual water consumption has not yet reached the minimum requirement for normal crop growth, and a water demand gap exists; the larger the value, the more significant the gap. Conversely, a negative water demand gap value indicates that the actual irrigation water consumption has exceeded the irrigation water demand threshold, resulting in a water surplus. In this case, it is necessary to consider reducing the water supply to avoid waste. For example, the irrigation water demand threshold for area A is 1 million m³. 3 The actual irrigation water consumption was 700,000 cubic meters. 3 The water shortage is then 300,000 m³. 3 This reflects a clear need for water replenishment.

[0056] Furthermore, the current flow data of a river or tributary is subtracted from the ecological baseflow threshold (the minimum flow required to maintain the basic functions of the river or lake ecosystem) to obtain the controllable flow margin. A positive controllable flow margin indicates that after meeting basic ecological needs, there is still surplus water available for irrigation regulation; the larger the value, the more ample the available regulation space. A negative controllable flow margin indicates that the current flow is below the ecological baseflow threshold, in which case ecological water use should be prioritized, and irrigation water supply should be reduced or suspended. For example, the current flow of River A is 50 m³ / s. 3 / s, with an ecological base current threshold of 30m. 3 / s, then the adjustable flow margin is 20m. 3 / s indicates the presence of a regulated water volume available for irrigation.

[0057] 302. The water demand gap value and the adjustable flow margin are normalized to obtain the relative gap ratio and the relative margin ratio, respectively. The first dynamic control coefficient is calculated based on the relative gap ratio and the relative margin ratio.

[0058] In this step, after obtaining the water demand gap value and the adjustable flow margin, the urgency of water demand and the feasibility of water supply are combined, and the first dynamic control coefficient is calculated through a quantitative model.

[0059] Specifically, the water demand deficit and the adjustable flow margin are first normalized to eliminate dimensional differences. For example, the water demand deficit is divided by the irrigation water demand threshold to obtain the relative deficit ratio; the adjustable flow margin is divided by the ecological base flow threshold to obtain the relative margin ratio. A weighted summation model is then used to calculate the first dynamic control coefficient K1, and the calculation formula can be expressed as follows:

[0060] K1 = ω1 × P1 + ω2 × P2

[0061] Wherein, P1 is the relative deficit ratio, P2 is the relative surplus ratio, ω1 is the water demand deficit weight (value 0.5-0.7), ω2 is the flow surplus weight (value 0.3-0.5), and ω1+ω2=1, reflecting the regulatory orientation of prioritizing water demand over water supply.

[0062] In addition, boundary values ​​need to be set for the water demand deficit and the adjustable flow margin. Specifically, when the relative deficit ratio is negative (water surplus), a lower limit boundary (such as -0.2) is set to avoid excessively lowering the first dynamic control coefficient. For example, if the actual calculated relative deficit ratio is -0.4 (severe surplus), the boundary value of -0.2 is substituted into the above calculation formula to limit its downward pull on K1 and retain appropriate control flexibility.

[0063] When the relative margin ratio is negative (current flow data is below the ecological base flow threshold), a stricter lower limit boundary (such as -0.5) is set to prioritize ecological water use. For example, if the actual calculated relative margin ratio is -0.6 (significantly below the ecological base flow), the boundary value of -0.5 is substituted into the calculation, and the K1 value is significantly lowered to strengthen the restriction on irrigation water supply.

[0064] As an example, the second dynamic control coefficient calculated based on the natural water inflow data of each agricultural planting area includes:

[0065] 303. Predict the total natural water inflow of each agricultural planting area during the target period. The total natural water inflow is the sum of effective precipitation utilization and effective utilization of non-regulated river input water. The effective precipitation utilization is calculated based on predicted precipitation data combined with regional runoff coefficient and soil infiltration rate. The effective utilization of non-regulated river input water is calculated based on input water data combined with water conveyance loss rate.

[0066] In this step, for each agricultural planting area, the total amount of natural water that can be actually utilized during the target period is calculated, taking into account both precipitation and unregulated river inflow. The specific calculation method is as follows:

[0067] (1) Effective utilization of precipitation: Based on precipitation data collected by regional rain gauges (such as cumulative precipitation during the target period), combined with the regional runoff coefficient (the proportion of precipitation that forms surface runoff, which is related to topography and vegetation cover) and soil infiltration rate (the proportion of precipitation that seeps into the soil and is utilized by crops), the effective utilization of precipitation is calculated using the formula: Predicted precipitation × (1 - runoff coefficient) × soil infiltration rate. For example, if the predicted precipitation in a certain area during the target period is 100 mm, the runoff coefficient is 0.3, and the soil infiltration rate is 0.6, then the effective utilization of precipitation = 100 × (1 - 0.3) × 0.6 = 42 mm (the water volume value is obtained after converting it to the regional area).

[0068] (2) Effective utilization of unregulated river input water: Based on the unregulated river input water data obtained from cross-border hydrological monitoring (such as the total input water volume during the target period), combined with the loss rate during the water conveyance process (the proportion of losses due to evaporation, seepage, etc.), it is calculated using the formula: Effective utilization of unregulated river input water = Total input water volume × (1 - Water conveyance loss rate). For example, if the total input water volume is 500,000 m³... 3 If the water loss rate is 20%, then the effective utilization volume = 50 × (1 - 20%) = 400,000 m³ 3 .

[0069] Adding the effective utilization of the above-mentioned precipitation to the effective utilization of non-regulated river input water, we obtain the total amount of natural water that can be directly utilized in the agricultural planting area during the target period, which reflects the actual replenishment capacity of natural water sources for irrigation.

[0070] 304. Calculate the ratio of the total natural water inflow to the irrigation water demand threshold to obtain the natural water inflow replenishment coefficient, and calculate the second dynamic control coefficient based on the natural water inflow replenishment coefficient.

[0071] In this step, after obtaining the total amount of natural water inflow, the second dynamic regulation coefficient is calculated through a quantitative model based on the degree of substitution of natural water inflow for irrigation water demand and the dependence on river and lake regulation for water supply.

[0072] Specifically, the total natural water inflow obtained in step 303 is divided by the irrigation water demand threshold determined in step 200 to obtain the natural water inflow replenishment coefficient. A higher natural water inflow replenishment coefficient indicates a higher coverage ratio of irrigation water demand by natural water inflow. For example, a coefficient of 0.6 means that 60% of the irrigation water demand can be met by natural water inflow. The second dynamic regulation coefficient K2 is calculated using a reverse mapping model, as shown in the following formula:

[0073] K2=1-min(δ·P3,0.8)

[0074] Wherein, δ is the natural water inflow efficiency correction coefficient (value range 0.9-1.1), used to fine-tune the actual effective contribution of natural water inflow (such as considering the impact of uneven precipitation distribution on utilization efficiency); P3 is the natural water inflow replenishment coefficient.

[0075] By setting an upper limit constraint min(δ·P3, 0.8), excessive coverage by natural water inflow can be avoided, which would lead to an excessively low coefficient. The final value range of the second dynamic regulation coefficient K2 is [0.2, 1.0]. A higher value indicates a weaker natural water supply capacity (such as in arid areas or periods of natural water scarcity), and a more urgent need for river and lake regulation and water supply. A lower value indicates a more abundant natural water inflow, which can reduce dependence on river and lake water resources and achieve the regulation goal of prioritizing the use of natural water inflow.

[0076] As an example, the second dynamic control coefficient calculated based on the natural water inflow coefficient includes:

[0077] 3041, The natural water inflow efficiency correction coefficient is determined based on the uniformity of precipitation distribution, soil texture type and crop root depth during the target period;

[0078] In this step, the natural water inflow efficiency correction coefficient δ is used to quantify the actual effectiveness of crop utilization of natural water inflow. It is determined comprehensively based on the following core factors within the target period, and the value of δ must be strictly controlled between 0.9 and 1.1:

[0079] (1) Evenness of precipitation distribution: Evenness is calculated by the ratio of the standard deviation of daily precipitation to the mean during the target period (the smaller the ratio, the more even the distribution). If the evenness is higher than the historical average for the region, the value of δ is increased by 0.05-0.1 (e.g., from 1.0 to 1.05) from the base value, because even precipitation can reduce runoff loss and improve utilization efficiency; if the evenness is lower than the historical average, δ remains unchanged from the base value. For example, if the evenness of precipitation in a certain region during the target period is better than the historical level, δ is increased by 0.08 from the base value of soil texture.

[0080] (2) Soil texture type: The basic value is set according to the difference in soil water retention capacity: sandy soil (poor water retention, easy leakage of natural water) has a basic value of 0.9; loamy soil (moderate water retention, the benchmark type) has a basic value of 1.0; clay soil (strong water retention, high utilization rate of natural water) has a basic value of 1.1. For example, the basic value of δ is directly taken as 1.1 for clay soil areas.

[0081] (3) Crop root depth: The deeper the roots, the stronger the crop's ability to absorb natural water from deeper soil layers. It is set that for every 10cm increase in root depth, δ increases by 0.02 from the current value, up to a maximum of 1.1. For example, if the wheat root depth is 60cm, an increase of 20cm from the baseline depth (40cm), δ will increase by an additional 0.04.

[0082] By combining the above three factors, the actual value of δ is finally determined, which can more accurately reflect the effective utilization efficiency of natural water under specific conditions.

[0083] 3042, the second dynamic control coefficient is calculated based on the natural water supply coefficient and the natural water efficiency correction coefficient.

[0084] In this step, based on the synergistic effect of the natural water inflow replenishment coefficient and the natural water inflow efficiency correction coefficient, the second dynamic control coefficient K2 is calculated through a quantitative model, as follows:

[0085] Multiply the natural water supply coefficient (P3) obtained in step 304 by the δ determined in step 3041 to obtain the corrected supply coefficient. For example, if P3 = 0.6 and δ = 1.05, then the corrected natural water supply coefficient = 0.6 × 1.05 = 0.63, which reflects both the coverage ratio of natural water and incorporates the correction for actual utilization efficiency.

[0086] The second dynamic control coefficient is calculated using the formula K2 = 1 - min(P4, 0.8), where P4 is the corrected natural water inflow coefficient. An upper limit constraint is set using the min function, meaning that when the corrected natural water inflow coefficient is ≥ 0.8, the calculation is forced to be based on 0.8 to avoid the control failure caused by a K2 value that is too low (below 0.2).

[0087] For example, if the corrected replenishment coefficient is 0.7 (70% natural water coverage), then K2 = 1 - 0.7 = 0.3 (low dependence on river and lake water supply); if the corrected natural water replenishment coefficient is 0.9, then K2 = 1 - 0.8 = 0.2 (minimum value, indicating sufficient natural water supply); if the corrected natural water replenishment coefficient is 0.3 (30% natural water coverage), then K2 = 1 - 0.3 = 0.7 (high dependence on river and lake water supply).

[0088] The final K2 value (0.2-1.0) can more accurately reflect the actual contribution of natural water inflow and the degree of dependence of irrigation on river and lake water supply.

[0089] As an example, the first dynamic control coefficient and the second dynamic control coefficient are fused to obtain a dynamic control coefficient. Instructions are generated based on these dynamic control coefficients and sent to river and lake water conservancy control facilities, including:

[0090] 401. Determine the first fusion weight of the first dynamic regulation coefficient and the second fusion weight of the second dynamic regulation coefficient for each agricultural planting area in the flow correlation regulation area group according to the core irrigation area and non-core irrigation area.

[0091] In this step, based on the differences in importance among agricultural planting areas within the flow-related control area group, the fusion weights of the first and second dynamic control coefficients are set according to different categories, reflecting the priority orientation of control, as follows:

[0092] Core irrigation area: refers to key areas for planting high-value crops and ensuring food security, requiring priority in responding to the matching degree of water demand gap and water supply capacity. Therefore, the first fusion weight ω a The value ranges from 0.6 to 0.7, and the second fusion weight ω b The value ranges from 0.3 to 0.4, and ω a +ω b =1. For example, ω in major grain-producing areas a Set to 0.65, ωb Set to 0.35 to enhance the impact of the first dynamic control coefficient K1 on water supply feasibility.

[0093] Non-core irrigation areas: These refer to areas where cash crops are grown or where secondary irrigation is needed, requiring a balance between water demand and the efficiency of natural water use. Therefore, the first fusion weight ω a The value ranges from 0.5 to 0.6, and the second fusion weight ω b The value ranges from 0.4 to 0.5. For example, ω in fruit and vegetable growing areas a Set to 0.55, ω b Set to 0.45 to increase the contribution weight of natural water flow reflected by the second dynamic control coefficient K2.

[0094] 402. The first dynamic control coefficient and the second dynamic control coefficient are weighted and fused based on the first fusion weight and the second fusion weight to obtain the dynamic control coefficient;

[0095] In this step, based on the fusion weights determined in step 401, K1 and K2 are merged into a comprehensive dynamic control coefficient K using a weighted summation model. The merged K value comprehensively reflects the urgency of water demand, the feasibility of water supply, and the contribution of natural water inflow.

[0096] 403. Determine the control level of the corresponding agricultural planting area based on the dynamic control coefficient, and issue instructions to the river and lake water conservancy control facilities in the corresponding area based on the control level; wherein, the instructions involve the gate opening degree and / or pump station power.

[0097] In this step, the control levels are divided according to the range of values ​​for the dynamic control coefficient K, and the operating parameters of the water conservancy facilities are adjusted accordingly. Specifically:

[0098] Regulation level classification:

[0099] Level 1 regulation: K≥0.8, corresponding to scenarios with urgent water demand, sufficient water supply and insufficient natural water inflow, where priority should be given to ensuring water supply;

[0100] Secondary regulation: 0.5≤K<0.8, corresponding to the normal scenario where water demand and supply are basically matched, maintaining a stable water supply;

[0101] Level 3 regulation: K<0.5 corresponds to scenarios of water surplus or ecological flow shortage, requiring a reduction in water supply.

[0102] Instruction execution logic:

[0103] Level 1 control: Sending instructions to the gate to increase the opening by 10%-20%, and / or sending instructions to the pumping station to increase the power by 15%-25%, thereby increasing the water supply;

[0104] Level 2 control: Issue a maintenance command to keep the current gate opening or pump station power unchanged;

[0105] Level 3 regulation: Issue instructions to reduce gate opening by 10%-30% or reduce pump station power by 20%-40% to reduce water supply and prioritize the protection of river and lake ecological base flow.

[0106] This embodiment improves the adaptability of the second dynamic control coefficient to actual hydrological conditions by refining the determination logic of the natural water efficiency correction coefficient, making the assessment of the contribution of natural water to irrigation more accurate.

[0107] like Figure 2 As shown, this embodiment of the invention also discloses a river and lake ecological flow adaptive regulation system 20, comprising:

[0108] Construction Unit 11 constructs a flow association control area group comprising several agricultural planting areas that satisfy the flow coordination control relationship, including crop type information, crop growth cycle data and corresponding geographic spatial coordinates of each agricultural planting area;

[0109] The first calculation unit 12 determines the irrigation water demand threshold for each agricultural planting area during the target period based on crop type information and crop growth cycle data; and collects the current flow data of rivers, lakes and tributaries and the actual irrigation water consumption of each agricultural planting area in real time.

[0110] The second calculation unit 13 calculates the first dynamic control coefficient of each agricultural planting area based on the current flow data, actual irrigation water consumption and irrigation water demand threshold, and calculates the corresponding second dynamic control coefficient based on the natural water inflow data of each agricultural planting area; wherein, the natural water inflow data includes precipitation data and input water data of non-controlled rivers;

[0111] The control unit 14 integrates the first dynamic control coefficient and the second dynamic control coefficient to obtain the dynamic control coefficient. Based on the dynamic control coefficient, it generates and sends instructions to the river and lake water conservancy control facilities to adjust the water supply of each agricultural planting area.

[0112] As an example, the building unit 11 specifically:

[0113] By simulating the water flow trajectory using a watershed digital twin model and combining it with the topological relationships of rivers, lakes, and tributaries, the main water supply river and lake units for each agricultural planting area are determined.

[0114] Based on the water resource carrying capacity of the main water source supplying river and lake units and the hydraulic connection strength between each agricultural planting area and the water source, preliminary regional grouping is obtained; wherein, the hydraulic connection strength is determined comprehensively based on water conveyance distance, water conveyance efficiency and loss rate along the way;

[0115] Based on the overlap of peak water demand periods in the crop growth cycle and the similarity of water demand characteristics among crop types within each preliminary regional group, the preliminary regional groups are optimized and adjusted to ultimately form several flow-related regulation regional groups. Among them, agricultural planting areas with peak water demand periods overlapping less than a preset overlap threshold or water demand characteristics similarity higher than a preset similarity threshold are assigned to the same flow-related regulation regional group.

[0116] As an example, the second computational unit 13 specifically:

[0117] The difference between the irrigation water demand threshold and the actual irrigation water consumption in each agricultural planting area is calculated as the water demand gap value, and the difference between the current flow data and the ecological base flow threshold is calculated as the adjustable flow margin.

[0118] The water demand gap value and the adjustable flow margin are normalized to obtain the relative gap ratio and the relative margin ratio, respectively. The first dynamic control coefficient is calculated based on the relative gap ratio and the relative margin ratio.

[0119] As an example, the second computational unit 13 specifically:

[0120] The total natural inflow of each agricultural planting area is predicted within a target period. The total natural inflow is the sum of effective precipitation utilization and effective utilization of unregulated river input water. The effective precipitation utilization is calculated based on predicted precipitation data combined with regional runoff coefficient and soil infiltration rate. The effective utilization of unregulated river input water is calculated based on input water data combined with water conveyance loss rate.

[0121] The ratio of the total natural water inflow to the irrigation water demand threshold is calculated to obtain the natural water inflow replenishment coefficient. Based on the natural water inflow replenishment coefficient, the second dynamic control coefficient is calculated.

[0122] As an example, the second computational unit 13 specifically:

[0123] The natural water inflow efficiency correction coefficient is determined based on the uniformity of precipitation distribution, soil texture type and crop root depth within the target period.

[0124] The second dynamic control coefficient is calculated based on the natural water supply coefficient and the natural water efficiency correction coefficient.

[0125] As an example, the control unit 14 specifically:

[0126] The first fusion weight of the first dynamic regulation coefficient and the second fusion weight of the second dynamic regulation coefficient are determined according to the core irrigation area and non-core irrigation area in the flow correlation regulation area group for each agricultural planting area.

[0127] The first dynamic control coefficient and the second dynamic control coefficient are weighted and fused based on the first fusion weight and the second fusion weight to obtain the dynamic control coefficient.

[0128] The control level of the corresponding agricultural planting area is determined according to the dynamic control coefficient, and instructions are issued to the river and lake water conservancy control facilities in the corresponding area based on the control level; wherein, the instructions involve the gate opening degree and / or pump station power.

[0129] This invention also discloses an electronic device, including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method described in the preceding embodiment.

[0130] This invention also discloses a non-transient computer-readable storage medium having instructions embodied thereon that can be executed by one or more processors to perform the method described in any of the preceding embodiments.

[0131] The present invention also provides a computer program product comprising an executable computer program that, when executed, implements the method as described in any of the preceding claims.

[0132] Although the invention has been specifically shown and described with reference to preferred embodiments, those skilled in the art will understand that various modifications in form and detail may be made without departing from the spirit and scope of the invention. Accordingly, the disclosed invention should be considered merely illustrative and limited only by the scope specified in the appended claims.

Claims

1. A method for adaptive regulation of river and lake ecological flow, characterized in that, The process includes the following steps: Constructing a flow-related control area group comprising several agricultural planting areas that satisfy the flow coordination and control relationship, including crop type information, crop growth cycle data, and corresponding geographic coordinates of each agricultural planting area; and determining the irrigation water demand threshold for each agricultural planting area during the target period based on the crop type information and crop growth cycle data. In addition, the system collects real-time current flow data of rivers and tributaries, as well as actual irrigation water consumption in each agricultural planting area; calculates a first dynamic control coefficient for each agricultural planting area based on the current flow data, actual irrigation water consumption, and irrigation water demand threshold; and calculates a corresponding second dynamic control coefficient based on the natural water inflow data of each agricultural planting area; wherein, the natural water inflow data includes precipitation data and input water data from non-controlled rivers; the first and second dynamic control coefficients are fused to obtain a dynamic control coefficient; and instructions are generated and sent to river and lake water conservancy control facilities based on the dynamic control coefficients to adjust the water supply to each agricultural planting area. A flow-related regulation regional group is constructed, comprising several agricultural planting areas that satisfy the flow coordination and regulation relationship. This includes: simulating water flow trajectories using a watershed digital twin model and determining the main water source supply river and lake units for each agricultural planting area based on the topological relationships of river and lake tributaries; deriving preliminary regional groupings based on the water resource carrying capacity of the main water source supply river and lake units and the hydraulic connection strength between each agricultural planting area and the water source; wherein the hydraulic connection strength is determined comprehensively based on water conveyance distance, water conveyance efficiency, and loss rate along the way; optimizing and adjusting the preliminary regional groupings by combining the overlap of peak water demand periods during crop growth cycles and the similarity of water demand characteristics of crop types within each preliminary regional group, ultimately forming several flow-related regulation regional groups; wherein agricultural planting areas with peak water demand period overlap below a preset overlap threshold or water demand characteristic similarity above a preset similarity threshold are grouped into the same flow-related regulation regional group.

2. The adaptive regulation method for river and lake ecological flow according to claim 1, characterized in that: The first dynamic control coefficient for each agricultural planting area is calculated based on the current flow data, actual irrigation water consumption, and irrigation water demand threshold. This includes: calculating the difference between the irrigation water demand threshold and the actual irrigation water consumption for each agricultural planting area as the water demand gap value; calculating the difference between the current flow data and the ecological base flow threshold as the controllable flow margin; normalizing the water demand gap value and the controllable flow margin to obtain the relative gap ratio and the relative margin ratio, respectively; and calculating the first dynamic control coefficient based on the relative gap ratio and the relative margin ratio.

3. The adaptive regulation method for river and lake ecological flow according to claim 2, characterized in that: The second dynamic regulation coefficient is calculated based on the natural water inflow data of each agricultural planting area. This includes: predicting the total natural water inflow of each agricultural planting area during the target period, wherein the total natural water inflow is the sum of effective precipitation utilization and effective utilization of non-regulated river input water; wherein the effective precipitation utilization is calculated based on the predicted precipitation data combined with the regional runoff coefficient and soil infiltration rate, and the effective utilization of non-regulated river input water is calculated based on the input water data combined with the water conveyance loss rate; calculating the ratio of the total natural water inflow to the irrigation water demand threshold to obtain the natural water inflow replenishment coefficient, and calculating the second dynamic regulation coefficient based on the natural water inflow replenishment coefficient.

4. The adaptive regulation method for river and lake ecological flow according to claim 3, characterized in that: The second dynamic control coefficient is calculated based on the natural water supply coefficient, including: determining the natural water supply efficiency correction coefficient based on the precipitation distribution uniformity, soil texture type and crop root depth within the target period; and calculating the second dynamic control coefficient based on the natural water supply coefficient and the natural water supply efficiency correction coefficient.

5. The adaptive regulation method for river and lake ecological flow according to claim 1, characterized in that: The first and second dynamic control coefficients are fused to obtain a dynamic control coefficient. Instructions are generated and sent to river and lake water conservancy control facilities based on these dynamic control coefficients. This includes: determining the first fusion weight of the first dynamic control coefficient and the second fusion weight of the second dynamic control coefficient for each agricultural planting area in the flow-related control area group according to core irrigation areas and non-core irrigation areas; weighting and fusing the first and second dynamic control coefficients based on the first and second fusion weights to obtain the dynamic control coefficient; determining the control level for the corresponding agricultural planting area based on the dynamic control coefficient; and sending instructions to the river and lake water conservancy control facilities in the corresponding area based on the control level. The instructions involve gate opening and / or pump station power.

6. A river and lake ecological flow adaptive regulation system, characterized in that, include: The construction unit constructs a flow-related control area group comprising several agricultural planting areas that satisfy the flow-coordinated control relationship, including crop type information, crop growth cycle data, and corresponding geographic coordinates of each agricultural planting area; the first calculation unit determines the irrigation water demand threshold of each agricultural planting area during the target period based on the crop type information and crop growth cycle data. In addition, it collects real-time data on the current flow of rivers and tributaries, as well as the actual irrigation water consumption in various agricultural planting areas; The second calculation unit calculates the first dynamic control coefficient for each agricultural planting area based on the current flow data, actual irrigation water consumption, and irrigation water demand threshold, and calculates the corresponding second dynamic control coefficient based on the natural water inflow data of each agricultural planting area; wherein, the natural water inflow data includes precipitation data and input water data from non-controlled rivers; the control unit merges the first and second dynamic control coefficients to obtain the dynamic control coefficient, generates and sends instructions to the river and lake water conservancy control facilities based on the dynamic control coefficients, and adjusts the water supply of each agricultural planting area. The building unit specifically includes: By simulating water flow trajectories using a watershed digital twin model and combining the topological relationships of rivers, lakes, and tributaries, the main water supply river and lake units for each agricultural planting area are determined. Based on the water resource carrying capacity of the main water supply river and lake units and the hydraulic connection strength between each agricultural planting area and the water source, preliminary regional groupings are derived. The hydraulic connection strength is determined comprehensively based on water conveyance distance, water conveyance efficiency, and loss rate along the way. Combining the overlap of peak water demand periods during crop growth cycles and the similarity of water demand characteristics among crop types within each preliminary regional group, the preliminary regional groupings are optimized and adjusted, ultimately forming several flow-related regulation regional groups. Agricultural planting areas with peak water demand period overlap below a preset overlap threshold or water demand characteristic similarity above a preset similarity threshold are grouped into the same flow-related regulation regional group.

7. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method of any one of claims 1-5.

8. A non-transient computer-readable storage medium having instructions embodied thereon, characterized in that, The instructions can be executed by one or more processors to perform the method for implementing any one of claims 1-5.

9. A computer program product comprising an executable computer program, characterized in that, When the computer program is executed, it implements the method as described in any one of claims 1-5.

Images