Intelligent control method and system for irrigation area diversion gate

Abstract

The invention discloses an intelligent control method and system for an irrigation area diversion gate, and relates to the technical field of water conservancy projects. The method comprises the following steps: obtaining operation data of all gates in a target branch canal scheduling region, carrying out cross-region long-term water distribution global optimization calculation by relying on a cloud center, obtaining the total distribution water quantity of the region, and issuing the total distribution water quantity to a corresponding edge calculation node; the communication state of the gate terminals and the cloud center or the edge computing node is judged, if communication of all the gate terminals is normal, the edge computing node conducts predictive cooperative control on all the gates in the area, and the opening and closing sequence and the optimal opening degree sequence of all the gates are determined; and if the gate terminal communication interruption exists, independently adjusting the opening degree of the target interruption gate to obtain an optimal opening degree value. According to the method, reasonable distribution of water resources can be realized through global optimization, and the regional scheduling problem caused by gate communication interruption can be avoided, so that the regulation and control safety of the water conservancy system under the condition of abnormal communication is improved.

Description

Technical Field

[0001] This invention belongs to the field of water conservancy engineering technology, specifically relating to an intelligent control method and system for irrigation diversion gates. Background Technology

[0002] Water resources are a core element of agricultural production, and irrigation districts, as key carriers of agricultural irrigation, directly affect crop yield and quality through efficient water resource allocation. Water diversion gates are the core implementing components of water resource scheduling in irrigation districts, playing a crucial role in precisely allocating water according to the needs of different irrigated areas, such as crop type, growth stage, and soil moisture.

[0003] Patent application CN117850487A discloses a gate intelligent control method and system based on water conservancy monitoring data, relating to the field of water conservancy management technology. The method includes: acquiring operational data of all gates in the water conservancy system and determining the analysis duration of each gate based on the operational data; forming a gate analysis duration matrix from the analysis durations of all gates; real-time monitoring of water level information in the water conservancy system and predicting the gate opening / closing degree of each gate at the next analysis duration, denoted as the gate control opening / closing degree; generating gate control signals based on the gate control opening / closing degree and summarizing all gate control signals to form a gate control signal matrix; and controlling the gate control opening / closing degree from the central control terminal.

[0004] However, this method does not have a mechanism to deal with the interruption of communication signals between the gate terminal and the control center. When communication is interrupted, the gate will maintain its original state and cannot flexibly adjust the opening degree according to local water level changes. Furthermore, there is no mechanism for state synchronization and historical data retransmission after fault recovery. This will not only cause the control of a single gate to fail, but may also cause the global water level control to become unbalanced due to the inconsistent states of multiple gates, thereby causing equipment damage or safety risks and resulting in low safety of water conservancy system control. Summary of the Invention

[0005] The purpose of this invention is to solve the problem of low security in water conservancy system regulation caused by communication signal interruption in intelligent gate control systems, and to propose an intelligent control method and system for irrigation diversion gates.

[0006] In a first aspect of this invention, a method for intelligent control of irrigation diversion gates is first proposed, the method comprising: Obtain operational data for all gates within the target branch canal's scheduling area; the operational data includes water level, flow rate, rainfall, and gate status information; Based on the operational data, the total allocated water volume for the target branch canal scheduling area is obtained through global optimization calculation of long-term cross-regional water allocation via the cloud hub; the cloud hub is a computing platform for allocating water resources in all branch canal scheduling areas. The total allocated water volume is sent to the edge computing node of the target branch canal scheduling area; the edge computing node is a control unit that controls the opening and closing sequence and opening degree of multiple gates on the target branch canal scheduling area; Determine the communication status between the gate terminal and the cloud hub or edge computing node; If the communication status of all gate terminals is normal, the edge computing node performs predictive collaborative control on all gates within the target branch canal scheduling area to determine the opening and closing sequence and optimal opening degree sequence of each gate. If the communication status of a gate terminal is interrupted, the optimal opening value is obtained by independently adjusting the opening of the target interrupted gate; the target interrupted gate is any one of the gates among all communication interrupted gates.

[0007] This solution first uses a cloud hub to perform macro-level water allocation based on global data, providing edge nodes with clear total adjustment targets. The edge nodes, acting as an intermediate layer, dynamically switch between two fundamentally different control modes based on the key variable of communication status: when communication is fully operational, they perform collaborative optimization based on global information to achieve optimal system efficiency; when a communication interruption is detected, they immediately switch to an autonomous control mode based on local terminal information, initiating an independent adjustment algorithm for the interruption gate. This ensures a smooth transfer of control and seamless functional integration, allowing the system to leverage the advantages of centralized optimization under normal conditions while ensuring basic security and functional continuity through terminal autonomy during communication failures. Thus, it achieves a unity of global optimization and local robustness at the architectural level.

[0008] Optionally, based on the operational data, the total allocated water volume for the target branch canal scheduling area is obtained through global optimization calculation of cross-regional long-term water volume allocation via the cloud hub, including: Step 1: Calculate the real-time hydraulic load of the target branch canal scheduling area at the current moment based on the water level and flow rate in the operational data; Step 2: Based on the rainfall data in the operational data and combined with short-term weather forecasts, calculate the expected net inflow increase of the target branch canal scheduling area during the future scheduling period; Step 3: Based on the gate status information in the operational data, determine the availability and current opening of the key control gates within the target branch canal scheduling area to obtain the maximum adjustable capacity; Step 4: Construct a feasible range for the total water allocation based on the real-time hydraulic load, the expected net inflow, and the maximum adjustable capacity; Step 5: Select initial calculation points for the feasible interval using the golden section search method; Step 6: Input the candidate value of the total allocated water volume corresponding to the initial calculation point into the preset evaluation model, and determine whether the candidate value of the total allocated water volume causes any key node water level to exceed the limit or the channel flow to exceed the limit to obtain the safety evaluation result; Step 7: Update the initial calculation point according to the golden section search method and the safety assessment result to obtain an updated calculation point. Use the updated calculation point as the new initial calculation point and repeat step 6 until the interval length between the updated calculation point and the initial calculation point is less than a preset threshold. Finally, select the candidate total water allocation value with the largest value from all safety assessment results that meet the safety conditions as the total water allocation value.

[0009] Steps 1 to 3 of this scheme assess the system status of the dispatch area from three dimensions: load, inflow, and capacity, respectively, defining the physical boundaries for decision-making. Step 4 integrates the key outputs of the first three steps into a feasible interval, achieving unified quantification of multiple constraints and transforming complex hydraulic, meteorological, and engineering constraints into a clear decision space. Step 5 and subsequent steps employ the golden ratio search method within this defined interval, using a pre-set evaluation model as the criterion to efficiently and systematically find the optimal solution. Each iteration updates the search point based on the safety assessment results of the previous step, ensuring that the optimization process always takes place within a safe and feasible solution space, ultimately converging to the optimal solution that satisfies both the global water allocation objective and the local operational safety.

[0010] Optionally, the edge computing node performs predictive collaborative control of all gates within the target branch canal scheduling area, determining the opening and closing sequence and optimal opening degree sequence of each gate, including: The system monitors in real time the communication delay and gate response delay between the edge computing node and the target gate terminal within the target branch canal scheduling area, and predicts the total delay time required for the next control command to take effect from generation to the target gate terminal; the target gate is any one of all gates. Based on the total allocated water volume, real-time water level, and flow rate, calculate the amount of water that the target gate needs to regulate in the next scheduling cycle; The preset scheduling cycle of the target gate is divided into control sub-windows of equal length according to the total delay time; Query the preset opening degree water volume mapping table, and obtain the initial opening degree value based on the adjusted water volume and the opening degree value of the target gate; In the first control sub-window, with the initial opening value as the opening value, the optimal opening sequence of the target gate is obtained by calculating the opening of each subsequent control sub-window based on the remaining regulating water volume and the total delay time using a preset dynamic programming algorithm. By statistically analyzing all optimal planned opening sequences, the coordinated opening and closing sequence of all gates within the target branch canal scheduling area and the optimal opening sequence corresponding to each gate are determined.

[0011] This scheme first precisely quantifies the time delay effect of command transmission by real-time monitoring of communication and response latency, providing a crucial time scale basis for subsequent predictive control. Then, the scheduling cycle is subdivided into control sub-windows based on this total delay, cleverly transforming the adverse factor of time delay into a predictable and plannable control phase, thereby converting traditional open-loop scheduling into closed-loop predictive control with look-ahead compensation. Next, based on the initial opening degree query of the adjusted water volume, a reliable starting point and objective are provided for the dynamic programming algorithm, ensuring that the optimization process converges to a physically feasible solution. Finally, the dynamic programming algorithm performs rolling optimization within each sub-window to generate the optimal opening degree sequence for a single gate considering the delay effect. Then, the independent sequences of all gates are coordinated and arranged on the time axis, ultimately forming a globally coordinated and time-precise collaborative control scheme.

[0012] Optionally, the optimal opening sequence for independently adjusting the target interruption gate includes: The target interrupt gate last received the total regulating water volume and the real-time water level measurement value of the local water level sensor at the target interrupt gate terminal before the communication interruption. The actual water storage volume is obtained by converting the real-time water level measurement value using a preset water level and water storage volume mapping table. The difference between the actual water storage volume and the target total regulating water volume is calculated to obtain the water volume deviation value; The ratio of the target total regulating water volume to the actual water storage volume is used as the regulating intensity coefficient; The optimal opening value is obtained by calculating the opening degree of the target interruption gate based on the water volume deviation value, the target total regulating water volume, and the regulating intensity coefficient.

[0013] This scheme uses water volume commands and real-time water levels as core inputs. Then, through a water level-storage meter conversion, the water level information is mapped to controllable storage parameters. Next, the water volume deviation is calculated to clarify the adjustment target, and an adaptive adjustment coefficient is generated using the ratio of the target to the actual water volume, dynamically reflecting the urgency of the task. Finally, the deviation, total water volume, and adjustment coefficient are substituted into a dedicated control formula. This formula coordinates physical constraints and task status, directly calculating the current optimal opening value. The conversion from raw data to control commands is redundant, ensuring the accuracy and adaptability of control decisions under limited information.

[0014] Optionally, calculating the optimal opening value of the target interruption gate based on the water volume deviation value, the target total regulating water volume, and the regulating intensity coefficient includes: pass The opening value is calculated; where, This is the opening value. This represents the maximum permissible opening of the gate. This represents the water volume deviation value. The target total regulating water volume, To adjust the intensity coefficient, H is the real-time water level value. This is the upper limit of the water level.

[0015] This scheme utilizes only a single water volume command locked before communication interruption and the local real-time water level to construct a control law with a simple structure and clear physical meaning. The numerator of the formula undergoes nonlinear processing of the water volume deviation based on a coefficient. The value of this coefficient allows for flexible adjustment of the system response's aggressiveness or conservatism, achieving adaptive switching of the control strategy. The denominator directly uses the target total water volume as a normalization benchmark, ensuring that the adjustment intensity automatically matches the task scale and avoiding the introduction of additional calibration parameters. The square root term embeds the basic physical relationship between gate flow and water level, and combines the real-time water level with the safety upper limit, naturally achieving output limiting under hydraulic constraints. The entire formula integrates target deviation, adaptive adjustment, and physical constraints, requiring no online optimization or complex parameter tuning, with minimal computational load, making it highly suitable for specific scenarios where gate terminals have limited computing power and scarce sensor information.

[0016] In a second aspect of this invention, an intelligent control system for irrigation diversion gates is proposed, comprising: The data acquisition module is used to acquire the operational data of all gates within the target branch canal scheduling area; the operational data includes water level, flow rate, rainfall, and gate status information. The total water volume calculation module is used to calculate the total allocated water volume of the target branch canal scheduling area by performing global optimization calculations on long-term cross-regional water volume allocation through the cloud hub based on the operation data; the cloud hub is a calculation platform for allocating water resources in all branch canal scheduling areas. The sending module is used to send the total allocated water volume to the edge computing node of the target branch canal scheduling area; the edge computing node is a control unit that controls the opening and closing sequence and opening degree of multiple gates on the target branch canal scheduling area; The judgment module is used to determine the communication status between the gate terminal and the cloud hub or edge computing node; The first condition module is used to determine the opening and closing sequence and optimal opening degree sequence of each gate if the communication status of all gate terminals is normal. The second condition module is used to independently adjust the opening of the target interrupted gate to obtain the optimal opening value if the communication status of a gate terminal is interrupted; the target interrupted gate is any one of the gates among all communication interrupted gates.

[0017] Optionally, the total water volume calculation module includes: The load calculation module is used to calculate the real-time hydraulic load of the target branch canal scheduling area at the current moment based on the water level and flow rate in the operation data. The incremental calculation module is used to calculate the expected net inflow increment of the target branch canal scheduling area during the future scheduling period based on the rainfall data in the operation data and combined with short-term weather forecasts. The capacity calculation module is used to determine the availability and current opening of key control gates within the target branch canal scheduling area based on the gate status information in the operation data to obtain the maximum adjustable capacity. The interval construction module is used to construct a feasible interval for the total water allocation based on the real-time hydraulic load, the expected net inflow, and the maximum adjustable capacity. The calculation point selection module is used to select initial calculation points from the feasible interval using the golden section search method. The result generation module is used to input the candidate value of the total allocated water volume corresponding to the initial calculation point into the preset evaluation model, and determine whether the candidate value of the total allocated water volume causes any key node water level to exceed the limit or channel flow to exceed the limit to obtain the safety evaluation result. The loop module is used to update the initial calculation point according to the golden section search method and the safety assessment result to obtain an updated calculation point. The updated calculation point is used as the new initial calculation point. The result generation module is repeatedly executed until the interval length between the updated calculation point and the initial calculation point is less than a preset threshold. Finally, the candidate value of the total allocated water volume that meets the safety conditions from all safety assessment results is selected as the total allocated water volume.

[0018] Optionally, the first condition module includes: The delay prediction module is used to monitor in real time the communication delay and gate response delay between the edge computing node and the target gate terminal within the target branch canal scheduling area, and to predict the total delay time required for the next control command to take effect from generation to the target gate terminal; the target gate is any one of all gates. The water volume adjustment calculation module is used to calculate the amount of water volume that the target gate needs to adjust in the next scheduling cycle based on the total allocated water volume, real-time water level and flow rate. The partitioning module is used to divide the preset scheduling cycle of the target gate into control sub-windows of equal length according to the total delay time; The opening value acquisition module is used to query a preset opening water volume mapping table and obtain the initial opening value based on the adjusted water volume and the opening value of the target gate. The first opening value calculation module is used in the first control sub-window to calculate the opening of each subsequent control sub-window based on the initial opening value, the remaining regulating water volume and the total delay time, and to obtain the optimal opening sequence of the target gate by using a preset dynamic programming algorithm. The statistics module is used to count all optimal planned opening sequences and determine the coordinated opening and closing sequence of all gates within the target branch canal scheduling area and the optimal opening sequence corresponding to each gate.

[0019] Optionally, the second condition module includes: The parameter acquisition module is used to acquire the total regulating water volume of the target gate last received before the communication interruption and the real-time water level measurement value of the local water level sensor at the terminal of the target interruption gate. The water storage acquisition module is used to convert the real-time water level measurement value into the actual water storage value through a preset water level and water storage mapping table; The difference calculation module is used to calculate the difference between the actual water storage volume and the target total regulating water volume to obtain the water volume deviation value; The coefficient calculation module is used to use the ratio of the target total regulating water volume to the actual water storage volume as the regulating intensity coefficient. The second opening value calculation module is used to calculate the optimal opening value of the target interruption gate based on the water volume deviation value, the target total regulating water volume, and the regulating intensity coefficient.

[0020] Optionally, the second opening value calculation module includes: pass The opening value is calculated; where, This is the opening value. This represents the maximum permissible opening of the gate. This represents the water volume deviation value. The target total regulating water volume, To adjust the intensity coefficient, H is the real-time water level value. This is the upper limit of the water level.

[0021] The beneficial effects of this invention are as follows: This invention proposes an intelligent control method for irrigation district gates. It first acquires operational data such as water level, flow rate, rainfall, and gate status of all gates within the target branch canal's scheduling area. Then, relying on a cloud-based central control system, it performs global optimization calculations for long-term cross-regional water allocation and calculates the total allocated water volume, which is then distributed to edge computing nodes. Simultaneously, it assesses the communication status of the gate terminals. When communication is normal, the edge computing nodes implement predictive collaborative control over all gates within the area; when communication is interrupted, the opening of the interrupted gate is adjusted individually. This method can achieve rational allocation of water resources through global optimization and avoid regional scheduling problems caused by gate communication interruptions, thereby improving the security of water system regulation under communication anomalies. Attached Figure Description

[0022] The present invention will now be further described with reference to the accompanying drawings.

[0023] Figure 1 A flowchart illustrating an intelligent control method for irrigation diversion gates provided in an embodiment of the present invention; Figure 2 A flowchart illustrating the total water allocation calculation process provided in this embodiment of the invention. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0025] 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.

[0026] This invention provides an intelligent control method for irrigation ditches and gates. See also... Figure 1 , Figure 1 A flowchart illustrating an intelligent control method for irrigation diversion gates provided in an embodiment of the present invention. The method includes the following steps: S101, Obtain the operating data of all gates within the target branch canal scheduling area; S102, Based on the operational data, the total allocated water volume of the target branch canal scheduling area is obtained by performing global optimization calculations on long-term cross-regional water volume allocation through the cloud hub; S103, sends the total allocated water volume to the edge computing node of the target branch canal scheduling area; S104, determine the communication status between the gate terminal and the cloud hub or edge computing node; S105, if the communication status of all gate terminals is normal, the edge computing node performs predictive collaborative control on all gates within the target branch canal scheduling area to determine the opening and closing sequence and optimal opening degree sequence of each gate. S106, If the communication status of a gate terminal is interrupted, the target interrupted gate is independently adjusted to obtain the optimal opening value. The operational data includes water level, flow rate, rainfall, and gate status information; The cloud hub is a computing platform for allocating water resources in the dispatching area of ​​all branch canals; Edge computing nodes are control units that control the opening and closing sequence and opening degree of multiple gates in the target branch canal scheduling area; The target interrupt gate is any one of the communication interrupt gates.

[0027] This invention provides an intelligent control method for irrigation district gates. The method first collects operational data such as water level, flow rate, rainfall, and gate status of all gates within the target branch canal's scheduling area. Then, relying on a cloud-based central hub, it performs global optimization calculations for long-term cross-regional water allocation and generates a total allocated water volume. This total allocated water volume is then distributed to the corresponding edge computing nodes. The communication status of each gate terminal is assessed. If all gate terminals are communicating normally, the edge computing nodes perform predictive collaborative control over all gates within the area. If a gate terminal communication is interrupted, the opening of that interrupted gate is adjusted individually. This method can achieve rational allocation of water resources through global optimization and avoid regional scheduling problems caused by gate communication interruptions, thereby improving the security of water system regulation under communication anomalies.

[0028] In one embodiment, reference Figure 2 , Figure 2 This is a flowchart of the total water allocation calculation process provided in this embodiment of the invention. Based on operational data, the total water allocation for the target branch canal scheduling area is obtained through global optimization calculation of long-term cross-regional water allocation via a cloud hub, including: S201, calculate the real-time hydraulic load of the target branch canal scheduling area at the current moment based on the water level and flow rate in the operation data; S202, based on rainfall data in the operational data and combined with short-term weather forecasts, calculate the expected net inflow of water to the target branch canal scheduling area during the future scheduling period; S203, based on the gate status information in the operation data, determine the availability and current opening of the key control gates in the target branch canal scheduling area to obtain the maximum adjustable capacity; S204. Construct a feasible range for the total water allocation volume based on the real-time hydraulic load, the expected net inflow, and the maximum adjustable capacity. S205, the initial calculation points are obtained by selecting calculation points in the feasible interval through the golden section search method; S206, input the candidate value of the total allocated water volume corresponding to the initial calculation point into the preset evaluation model, and determine whether the candidate value of the total allocated water volume causes any key node water level to exceed the limit or the channel flow to exceed the limit to obtain the safety evaluation result. S207, the initial calculation point is updated according to the golden section search method and the safety assessment results to obtain the updated calculation point. The updated calculation point is used as the new initial calculation point. S206 is repeated until the interval length between the updated calculation point and the initial calculation point is less than the preset threshold. Finally, from all the safety assessment results, the candidate value of the total allocated water volume that meets the safety conditions is selected as the total allocated water volume.

[0029] In one implementation, the real-time hydraulic load is calculated as the ratio of the current flow rate to the maximum safe flow capacity of the branch canal at the current water level.

[0030] In one implementation, the calculation process for the expected net inflow increment involves using the real-time rainfall data of the target branch canal scheduling area in the operational data as the calculation benchmark, and sequentially connecting and fusing it with the predicted rainfall data for the future period in the short-term weather forecast. Specifically, firstly, based on the real-time rainfall data, the current soil infiltration and surface retention status are calibrated to form a dynamic rainfall-runoff conversion coefficient; then, this coefficient is applied to the predicted rainfall for each future period provided by the short-term weather forecast to calculate the effective runoff that the predicted rainfall may generate in each period; finally, the effective runoff of these future periods is accumulated in chronological order, and the sum is the expected net inflow increment during the scheduling period.

[0031] In one implementation, the calculation process for the maximum adjustable capacity is as follows: First, real-time status information of the gate terminal is collected, including communication connection signals, drive motor current, limit switch trigger status, and current opening feedback value. The gate's availability is determined based on normal communication, motor current within the no-load to rated range, and the absence of non-emergency fault codes. For available gates, the opening margin from the fully open and fully closed positions is calculated based on their current opening feedback value. Simultaneously, historical data from the gate's most recent complete opening and closing action is retrieved, and the correlation between its opening change and the upstream and downstream water level difference and flow rate changes during the same period is analyzed. The instantaneous opening-flow influence coefficient under the current hydraulic conditions is fitted in real time, and this coefficient is multiplied by the opening margin to obtain the gate's maximum flow increase and decrease capacity at the current moment. The flow increase and decrease capacities of all key available gates within the scheduling area are summed to constitute the maximum adjustable capacity of the area.

[0032] In one implementation, the process of constructing the feasible interval of the total allocated water volume is as follows: First, the real-time hydraulic load is analyzed. If the value is greater than the preset hydraulic load threshold, it is determined that the channel safety margin is insufficient. At this time, the current total flow is directly determined as the upper limit of the feasible interval. If the value is less than or equal to the preset hydraulic load threshold, the current total flow is added to the flow increase capacity in the maximum adjustable capacity, and the sum is used as the upper limit of the feasible interval. Subsequently, the system adds the expected net inflow increment to the downstream non-reducible basic water demand, and the sum is used as the lower limit of the feasible interval. Finally, a closed interval defined by the lower and upper limits is output. This interval fully represents the set of all possible values ​​of the total allocated water volume in the next scheduling period that meet both the requirements for water reception and basic water supply, and strictly follow the current safe carrying capacity and instant adjustment potential of the channel.

[0033] In one implementation, the initial calculation point is selected by multiplying the total length of the feasible interval by the golden ratio based on the lower limit of the feasible interval, and then adding this product to the lower limit. The resulting value is the initial calculation point. This initial calculation point is the first candidate value of the total water allocation used for evaluation in the golden ratio search process.

[0034] In one implementation, the process of generating the safety assessment result involves using the candidate value of the total allocated water volume as the input condition for the target branch canal, and combining it with the fixed water use patterns of other branch canals to simulate the distribution and propagation of water volume along the topology of the canal network, generating a set of predicted values ​​for the hydraulic state of the entire network. Subsequently, starting from the upper limit of the safe water level of key nodes and the upper limit of the safe flow of channels, the maximum allowable input water volume of the target branch canal is calculated in reverse to ensure that all nodes and channels do not exceed the limits. Finally, the predicted values ​​of the entire network state obtained by forward deduction are compared with the limit constraints obtained by reverse calculation. At the same time, the input candidate value of the total allocated water volume is compared with the maximum allowable input water volume calculated in reverse. If all the forward prediction values ​​meet the constraints and the input candidate values ​​do not exceed the maximum allowable input water volume, the output safety assessment result is safe; if any of the above conditions are not met, the output safety assessment result is exceeded.

[0035] In one implementation, the iterative update process is based on the rules of the golden section search method, combined with the safety assessment result of the current calculation point, to update the search interval. If the safety assessment result is safe, the current calculation point is retained as the lower limit of the new interval, and the upper limit of the original interval is used as the new initial calculation point for the next round of assessment. If the safety assessment result is out of bounds, the current calculation point is retained as the upper limit of the new interval, and a new initial calculation point is determined between the lower limit of the original interval and the current calculation point according to the golden ratio. This iterative update is repeated, with each iteration generating an updated calculation point and using it as the initial calculation point for the next round of iteration, and the safety assessment and interval update are repeated.

[0036] In one embodiment, the edge computing node performs predictive collaborative control of all gates within the target branch canal scheduling area, determining the opening and closing sequence and optimal opening degree sequence for each gate, including: Real-time monitoring of communication latency and gate response latency between edge computing nodes and target gate terminals within the target branch canal scheduling area; prediction of the total latency required for the next control command to take effect from generation to the target gate terminal; the target gate is any one of all gates. Based on the total allocated water volume, real-time water level and flow rate, calculate the amount of water that the target gate needs to regulate in the next scheduling cycle; The preset scheduling cycle of the target gate is divided into control sub-windows of equal length according to the total delay time; Query the preset opening degree water volume mapping table, and obtain the initial opening degree value based on the adjusted water volume and the target gate opening value; In the first control sub-window, the initial opening value is used as the opening value. Based on the remaining regulating water volume and the total delay time, the opening of each subsequent control sub-window is calculated using a preset dynamic programming algorithm to obtain the optimal opening sequence of the target gate. Statistically analyze all optimal planned opening sequences to determine the coordinated opening and closing sequence of all gates within the target branch canal scheduling area and the optimal opening sequence corresponding to each gate.

[0037] In one implementation, the specific process of generating the regulated water volume is as follows: First, the total allocated water volume is converted into the average flow rate that the target branch canal needs to achieve within the scheduling cycle. Then, combining the real-time water level and real-time flow rate at the cross-section where the target gate is located, the water volume surplus or deficit value that will be formed at the end of the scheduling cycle under the current state, if the real-time flow rate remains unchanged, is calculated through integral calculation. Next, the cumulative excess flow rate that needs to be increased or decreased at the cross-section where the target gate is located to adjust the average flow rate of the branch canal to the target value is calculated. Finally, the above water volume surplus or deficit value and the cumulative excess flow rate required for flow adjustment are superimposed, and the algebraic sum is the total regulated water volume that the target gate needs to complete in the future scheduling cycle to achieve the total allocated water volume target.

[0038] In one implementation, the optimal opening sequence is generated by starting with the ideal initial opening value in the first control sub-window. Based on the current trend of water demand changes in front of the gate and the remaining water demand to be regulated, and combined with the total delay time, multiple equal-length control sub-windows are divided. The optimal opening sequence is then calculated recursively using a preset dynamic programming algorithm. Based on the initial opening state of each sub-window, the water demand and delay constraints of subsequent time windows are comprehensively considered. The optimal sub-opening of each control sub-window is solved sequentially, thereby gradually constructing an optimal opening sequence covering the entire scheduling cycle. Finally, the opening value corresponding to each control sub-window in the sequence is determined as the real-time planned opening of the gate.

[0039] In one embodiment, obtaining the optimal opening sequence by independently adjusting the opening of the target interruption gate includes: The target interrupt gate last received the total regulating water volume and the real-time water level measurement value of the local water level sensor at the target interrupt gate terminal before the communication interruption. The actual water storage volume is obtained by converting the real-time water level measurement value through a preset water level and water storage volume mapping table; The difference between the actual water storage and the target total regulating water volume is used to obtain the water volume deviation value. The ratio of the target total regulating water volume to the actual water storage volume is used as the regulating intensity coefficient; The optimal opening value is obtained by calculating the opening degree of the target interruption gate based on the water volume deviation value, the target total regulating water volume, and the regulating intensity coefficient.

[0040] In one implementation, the gate terminal pre-stores a data table representing the relationship between the water level and the corresponding static water storage of the channel section controlled by the gate. This mapping table is pre-prepared by calculating the cross-sectional geometry and length integral of the channel, or trained and generated using water level data collected during the gate's historical normal operation and water storage data calculated by back-calculation through flow accumulation. In each control cycle, the gate terminal processor reads the real-time water level measurement value collected by the local water level sensor, uses it as a query key, and searches the mapping table using linear interpolation or nearest neighbor matching algorithms to directly output the corresponding actual water storage value. To ensure reliability under extreme water level values, the coverage of the mapping table should be slightly larger than the normal operating water level range of the gate, and a preset boundary water storage value should be returned for queries outside the table.

[0041] In one embodiment, the optimal opening value is obtained by calculating the opening of the target interruption gate based on the water volume deviation value, the target total regulating water volume, and the regulating intensity coefficient, including: pass The opening value is calculated; where, This is the opening value. This represents the maximum permissible opening of the gate. This represents the water volume deviation value. The target total regulating water volume, To adjust the intensity coefficient, H is the real-time water level value. This is the upper limit of the water level.

[0042] In one implementation, sgn(·) is a sign function used to determine the direction of opening adjustment; positive indicates the opening direction, and negative indicates the closing direction.

[0043] In one implementation, the maximum permissible opening is determined based on the physical travel limit of the gate actuator or the maximum safe opening set based on hydraulic safety constraints. This parameter is an inherent mechanical characteristic parameter of the gate body or a protection threshold set according to safety operation procedures. This parameter is fixed and stored in the terminal controller after travel calibration or safety setting during the gate installation and commissioning phase. It is generally a fixed value during the equipment's life cycle and does not change with the control process. The upper limit of the water level is determined based on the design safe water level or the maximum permissible operating water level of the channel section or pool controlled by the gate. This value is a fixed parameter pre-set during the engineering survey and design phase based on the channel structure strength, flood control standards, and safety procedures, and is stored in the non-volatile memory of the gate terminal as a factory configuration or on-site commissioning parameter. In special cases, if the system has online safety verification capabilities, this value can also be dynamically issued and updated by the upper-level control system according to real-time operating conditions during normal communication periods.

[0044] The foregoing has described one embodiment of the present invention in detail, but this content is merely a preferred embodiment and should not be considered as limiting the scope of the present invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the scope of the claims of this invention.

Claims

1. A method for intelligent control of irrigation diversion gates, characterized in that, The method includes: Obtain operational data for all gates within the target branch canal's scheduling area; the operational data includes water level, flow rate, rainfall, and gate status information; Based on the operational data, the total allocated water volume for the target branch canal scheduling area is obtained through global optimization calculation of long-term cross-regional water allocation via the cloud hub; the cloud hub is a computing platform for allocating water resources in all branch canal scheduling areas. The total allocated water volume is sent to the edge computing node of the target branch canal scheduling area; the edge computing node is a control unit that controls the opening and closing sequence and opening degree of multiple gates on the target branch canal scheduling area; Determine the communication status between the gate terminal and the cloud hub or edge computing node; If the communication status of all gate terminals is normal, the edge computing node performs predictive collaborative control on all gates within the target branch canal scheduling area to determine the opening and closing sequence and optimal opening degree sequence of each gate. If the communication status of a gate terminal is interrupted, the optimal opening value is obtained by independently adjusting the opening of the target interrupted gate; the target interrupted gate is any one of the gates among all communication interrupted gates.

2. The intelligent control method for irrigation diversion gates according to claim 1, characterized in that, Based on the operational data, the total allocated water volume for the target branch canal scheduling area is obtained through global optimization calculation of cross-regional long-term water volume allocation via the cloud hub, including: Step 1: Calculate the real-time hydraulic load of the target branch canal scheduling area at the current moment based on the water level and flow rate in the operational data; Step 2: Based on the rainfall data in the operational data and combined with short-term weather forecasts, calculate the expected net inflow increase of the target branch canal scheduling area during the future scheduling period; Step 3: Based on the gate status information in the operational data, determine the availability and current opening of the key control gates within the target branch canal scheduling area to obtain the maximum adjustable capacity; Step 4: Construct a feasible range for the total water allocation based on the real-time hydraulic load, the expected net inflow, and the maximum adjustable capacity; Step 5: Select initial calculation points for the feasible interval using the golden section search method; Step 6: Input the candidate value of the total allocated water volume corresponding to the initial calculation point into the preset evaluation model, and determine whether the candidate value of the total allocated water volume causes any key node water level to exceed the limit or the channel flow to exceed the limit to obtain the safety evaluation result; Step 7: Update the initial calculation point according to the golden section search method and the safety assessment result to obtain an updated calculation point. Use the updated calculation point as the new initial calculation point and repeat step 6 until the interval length between the updated calculation point and the initial calculation point is less than a preset threshold. Finally, select the candidate total water allocation value with the largest value from all safety assessment results that meet the safety conditions as the total water allocation value.

3. The intelligent control method for irrigation diversion gates according to claim 1, characterized in that, The edge computing node performs predictive collaborative control of all gates within the target branch canal scheduling area, determining the opening and closing sequence and optimal opening degree sequence for each gate, including: The system monitors in real time the communication delay and gate response delay between the edge computing node and the target gate terminal within the target branch canal scheduling area, and predicts the total delay time required for the next control command to take effect from generation to the target gate terminal; the target gate is any one of all gates. Based on the total allocated water volume, real-time water level, and flow rate, calculate the amount of water that the target gate needs to regulate in the next scheduling cycle; The preset scheduling cycle of the target gate is divided into control sub-windows of equal length according to the total delay time; Query the preset opening degree water volume mapping table, and obtain the initial opening degree value based on the adjusted water volume and the opening degree value of the target gate; In the first control sub-window, with the initial opening value as the opening value, the optimal opening sequence of the target gate is obtained by calculating the opening of each subsequent control sub-window based on the remaining regulating water volume and the total delay time using a preset dynamic programming algorithm. By statistically analyzing all optimal planned opening sequences, the coordinated opening and closing sequence of all gates within the target branch canal scheduling area and the optimal opening sequence corresponding to each gate are determined.

4. The intelligent control method for irrigation diversion gates according to claim 1, characterized in that, The optimal opening sequence for independently adjusting the target interruption gate includes: The target interrupt gate last received the total regulating water volume and the real-time water level measurement value of the local water level sensor at the target interrupt gate terminal before the communication interruption. The actual water storage volume is obtained by converting the real-time water level measurement value using a preset water level and water storage volume mapping table. The difference between the actual water storage volume and the target total regulating water volume is calculated to obtain the water volume deviation value; The ratio of the target total regulating water volume to the actual water storage volume is used as the regulating intensity coefficient; The optimal opening value is obtained by calculating the opening degree of the target interruption gate based on the water volume deviation value, the target total regulating water volume, and the regulating intensity coefficient.

5. The intelligent control method for irrigation diversion gates according to claim 4, characterized in that, The optimal opening value of the target interruption gate is obtained by calculating the opening based on the water volume deviation value, the target total regulating water volume, and the regulating intensity coefficient, including: pass The opening value is calculated; where, This is the opening value. This represents the maximum permissible opening of the gate. This represents the water volume deviation value. The target total regulating water volume, To adjust the intensity coefficient, H is the real-time water level value. This is the upper limit of the water level.

6. An intelligent control system for irrigation diversion gates, characterized in that, The system includes: The data acquisition module is used to acquire the operational data of all gates within the target branch canal scheduling area; the operational data includes water level, flow rate, rainfall, and gate status information. The total water volume calculation module is used to calculate the total allocated water volume of the target branch canal scheduling area by performing global optimization calculations on long-term cross-regional water volume allocation through the cloud hub based on the operation data; the cloud hub is a calculation platform for allocating water resources in all branch canal scheduling areas. The sending module is used to send the total allocated water volume to the edge computing node of the target branch canal scheduling area; the edge computing node is a control unit that controls the opening and closing sequence and opening degree of multiple gates on the target branch canal scheduling area; The judgment module is used to determine the communication status between the gate terminal and the cloud hub or edge computing node; The first condition module is used to determine the opening and closing sequence and optimal opening degree sequence of each gate if the communication status of all gate terminals is normal. The second condition module is used to independently adjust the opening of the target interrupted gate to obtain the optimal opening value if the communication status of a gate terminal is interrupted; the target interrupted gate is any one of the gates among all communication interrupted gates.

7. The intelligent control system for an irrigation diversion gate according to claim 6, characterized in that, The total water volume calculation module includes: The load calculation module is used to calculate the real-time hydraulic load of the target branch canal scheduling area at the current moment based on the water level and flow rate in the operation data. The incremental calculation module is used to calculate the expected net inflow increment of the target branch canal scheduling area during the future scheduling period based on the rainfall data in the operation data and combined with short-term weather forecasts. The capacity calculation module is used to determine the availability and current opening of key control gates within the target branch canal scheduling area based on the gate status information in the operation data to obtain the maximum adjustable capacity. The interval construction module is used to construct a feasible interval for the total water allocation based on the real-time hydraulic load, the expected net inflow, and the maximum adjustable capacity. The calculation point selection module is used to select initial calculation points from the feasible interval using the golden section search method. The result generation module is used to input the candidate value of the total allocated water volume corresponding to the initial calculation point into the preset evaluation model, and determine whether the candidate value of the total allocated water volume causes any key node water level to exceed the limit or channel flow to exceed the limit to obtain the safety evaluation result. The loop module is used to update the initial calculation point according to the golden section search method and the safety assessment result to obtain an updated calculation point. The updated calculation point is used as the new initial calculation point. The result generation module is repeatedly executed until the interval length between the updated calculation point and the initial calculation point is less than a preset threshold. Finally, the candidate value of the total allocated water volume that meets the safety conditions from all safety assessment results is selected as the total allocated water volume.

8. The intelligent control system for an irrigation diversion gate according to claim 6, characterized in that, The first condition module includes: The delay prediction module is used to monitor in real time the communication delay and gate response delay between the edge computing node and the target gate terminal within the target branch canal scheduling area, and to predict the total delay time required for the next control command to take effect from generation to the target gate terminal; the target gate is any one of all gates. The water volume adjustment calculation module is used to calculate the amount of water volume that the target gate needs to adjust in the next scheduling cycle based on the total allocated water volume, real-time water level and flow rate. The partitioning module is used to divide the preset scheduling cycle of the target gate into control sub-windows of equal length according to the total delay time; The opening value acquisition module is used to query a preset opening water volume mapping table and obtain the initial opening value based on the adjusted water volume and the opening value of the target gate. The first opening value calculation module is used in the first control sub-window to calculate the opening of each subsequent control sub-window based on the initial opening value, the remaining regulating water volume and the total delay time, and to obtain the optimal opening sequence of the target gate by using a preset dynamic programming algorithm. The statistics module is used to count all optimal planned opening sequences and determine the coordinated opening and closing sequence of all gates within the target branch canal scheduling area and the optimal opening sequence corresponding to each gate.

9. The intelligent control system for irrigation diversion gates according to claim 6, characterized in that, The second condition module includes: The parameter acquisition module is used to acquire the total regulating water volume of the target gate last received before the communication interruption and the real-time water level measurement value of the local water level sensor at the terminal of the target interruption gate. The water storage acquisition module is used to convert the real-time water level measurement value into the actual water storage value through a preset water level and water storage mapping table; The difference calculation module is used to calculate the difference between the actual water storage volume and the target total regulating water volume to obtain the water volume deviation value; The coefficient calculation module is used to use the ratio of the target total regulating water volume to the actual water storage volume as the regulating intensity coefficient. The second opening value calculation module is used to calculate the optimal opening value of the target interruption gate based on the water volume deviation value, the target total regulating water volume, and the regulating intensity coefficient.

10. The intelligent control system for an irrigation diversion gate according to claim 9, characterized in that, The second opening value calculation module includes: pass The opening value is calculated; where, This is the opening value. This represents the maximum permissible opening of the gate. This represents the water volume deviation value. The target total regulating water volume, To adjust the intensity coefficient, H is the real-time water level value. This is the upper limit of the water level.

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