Irrigation area water distribution scheduling method and system based on digital twinning

Abstract

The invention discloses a digital twinborn-based irrigation area water distribution scheduling method and system, and relates to the technical field of water distribution scheduling, and the method comprises the steps: collecting data, and constructing a digital twinborn model; dynamically simulating water resources in an irrigation area by using a hydrological model simulation technology based on a digital twinborn model; performing irrigation demand prediction by using a machine learning algorithm according to a simulation result; optimizing a water distribution scheduling model according to a prediction result; performing real-time scheduling control according to the scheduling strategy; performing scheduling effect evaluation according to the scheduling control data; adjusting a scheduling strategy according to an evaluation result; and performing irrigation area water resource management and long-term optimization by using an irrigation area water distribution scheduling system based on digital twinning according to an adjustment result. According to the method, the limitation of static weight distribution of a traditional method is overcome, the accuracy and flexibility of water distribution scheduling are improved, the actual requirements of a complex irrigation area environment are met, the maintenance cost and the operation risk are reduced, and efficient and stable operation of an irrigation area water distribution system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of water distribution scheduling, and specifically provides a method and system for irrigation district water distribution scheduling based on digital twin. Background Technique

[0002] Water distribution scheduling in irrigation districts is a key link in agricultural water resource management. Its goal is to improve water resource utilization efficiency, ensure the healthy growth of crops, and reduce irrigation costs through scientific scheduling. In traditional irrigation district water distribution management, rule-based manual scheduling methods or simple single-objective optimization methods are often used, and these methods are unable to cope with complex and changeable environments. With the development of Internet of Things, big data, artificial intelligence, and digital twin technologies, intelligent irrigation district scheduling systems have gradually become a research hotspot.

[0003] 1. In the prior art, there are limitations in multi-objective optimization algorithms: Existing multi-objective optimization algorithms usually adopt methods based on Pareto optimal solutions to perform static weight allocation for multiple objectives. This method is difficult to adapt to the complex and changeable environmental conditions in irrigation districts, such as meteorological changes, differences in crop growth stages, etc., and cannot achieve dynamic real-time optimization. In addition, traditional optimization methods lack in-depth analysis and feedback mechanisms for the operating status of water distribution scheduling systems, which easily leads to the disconnection between scheduling results and actual needs.

[0004] 2. In the prior art, there are deficiencies in digital twin and predictive maintenance: Existing digital twin technologies mainly rely on historical data or simple time series models in equipment health status assessment and maintenance strategy formulation, and fail to fully combine real-time operating status and environmental variables. This results in insufficient accuracy of predictive maintenance in equipment life prediction and fault warning, and it is difficult to provide reliable equipment status support for irrigation district scheduling. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method and system for irrigation district water distribution scheduling based on digital twin to solve the problems raised in the above background technique.

[0006] To achieve the above object, the present invention provides the following technical solutions: In the first aspect, embodiments of the present invention provide a method and system for irrigation district water distribution scheduling based on digital twin, including the following steps: S1. Collect data and construct a digital twin model; S2. Use hydrological model simulation technology based on the digital twin model to perform dynamic simulation of water resources in the irrigation district to obtain simulation results; S3. Use machine learning algorithms to predict irrigation demand based on the simulation results to obtain prediction results; S4. Optimize the water distribution scheduling model according to the prediction results to obtain a scheduling strategy; S5. Perform real-time scheduling control according to the scheduling strategy to obtain scheduling control data; S6. Evaluate the scheduling effect according to the scheduling control data to obtain an evaluation result; S7. Adjust the scheduling strategy according to the evaluation result to obtain an adjustment result; S8. Use the digital twin-based irrigation district water distribution scheduling system according to the adjustment result for irrigation district water resources management and long-term optimization.

[0007] To further optimize this technical solution, the collecting data and creating a digital twin model in S1 includes: Using Internet of Things sensors and Geographic Information System (GIS) technology, collect data including soil moisture, crop water requirements, meteorological conditions, soil types, and irrigation system status, and comprehensively model the terrain, crop distribution, and water source information of the irrigation district in combination with GIS to create a digital twin model.

[0008] To further optimize this technical solution, the optimization of the water distribution scheduling model in S4 includes: According to the prediction result of irrigation demand, use the reinforcement learning algorithm Q-learning to optimize water resource allocation according to real-time environmental changes, dynamically adjust the irrigation strategy, and achieve the optimal scheduling strategy.

[0009] To further optimize this technical solution, the real-time scheduling control in S5 includes: Using an automated control system and real-time data acquisition technology, monitor the irrigation district data in real time, and automatically adjust the water source allocation according to the preset scheduling strategy and real-time data.

[0010] To further optimize this technical solution, the scheduling effect evaluation in S6 includes: According to the scheduling control data, use a multi-objective optimization algorithm to construct an evaluation model, and evaluate multiple scheduling control results simultaneously to ensure that the scheduling effect meets the expectations.

[0011] To further optimize this technical solution, the multi-objective optimization algorithm includes: Multi-objective optimization formula: ; Where: : The state of the irrigation district at time , including the water resource utilization situation and the crop growth state; : The water resource utilization efficiency objective function, which measures the matching degree between the water distribution volume and the crop demand, specifically including: ; Where, is the actual irrigation volume at time t, To predict the demand quantity; : The objective function of crop growth status, which measures the degree of water requirement sufficiency of the crop in the current state, specifically including: ; Among them, is the actual crop water level at time t, is the ideal crop water level; : The objective function of the operation cost of the irrigation system, specifically including: ; Among them, is the system energy consumption at time t, is the equipment maintenance cost, and are weight coefficients, which are adjusted according to the actual situation; : The weight coefficient, which is adjusted according to the actual situation; : The final optimization objective function, which is the optimal result found by combining all objectives.

[0012] To further optimize this technical solution, the adjustment of the scheduling strategy in S7 includes: Using adaptive control technology, continuously optimize the irrigation strategy by continuously learning historical data and combining real-time feedback of the scheduling strategy, and perform scheduling strategy adjustment.

[0013] To further optimize this technical solution, the water resources management and long-term optimization in S8 include: According to the adjustment results, use digital twin and predictive maintenance technology to conduct long-term prediction and maintenance of the water resources and the status of irrigation facilities in the irrigation area, and help decision-makers formulate reasonable irrigation policies.

[0014] To further optimize this technical solution, the digital twin and predictive maintenance technology includes: The digital twin maps the behavior and status of physical devices, and predictive maintenance analyzes the device operation data, predicts potential faults, and takes measures before problems occur; The formula for device health score: ; Among them: : The device health score, with a range of 0 to 1, and the larger the value, the better the health status; : The operation status of the device at time t, including temperature, vibration frequency, and workload; : Rate of change of device status; : Environmental variables, including external temperature and humidity, wind speed; : Device health status, including historical failure rate, current working life; : Weight coefficient, adjusted according to actual situation; : Function for measuring the stability of device operation status, specifically including: ; Among them, is a small positive number to avoid a zero denominator; : Function for evaluating the impact of environmental conditions on the device, specifically including: ; Among them, is the ideal environmental condition; : Function for calculating the device health degree based on historical health data, specifically including: ; Among them, is the failure rate of the device at time t, is the maximum failure rate of the device; Calculation formula for the remaining service life of the device: ; Among them: : Remaining service life of the device, the lower the remaining service life, the less; : Health critical state of device operation, exceeding the threshold represents potential risks; : Decay factor, determining the impact degree of state deviation on the remaining life, set according to actual situation.

[0015] To further optimize this technical solution, it includes the following modules: Data acquisition and model construction module; Dynamic simulation module; Irrigation demand prediction module; Scheduling strategy optimization module; Water distribution scheduling execution module; Multi-objective optimization module; Learning and feedback module; Digital twin and predictive maintenance module.

[0016] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and among them: when the computer program instructions are executed by the processor, the steps of a water distribution scheduling method and system based on digital twin as described in the first aspect of the present invention are implemented.

[0017] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program instructions are executed by a processor, the steps of a method and system for irrigation district water distribution scheduling based on digital twin as described in the first aspect of the present invention are implemented.

[0018] Compared with the prior art, the present invention provides a method and system for irrigation district water distribution scheduling based on digital twin, having the following beneficial effects: The method and system for irrigation district water distribution scheduling based on digital twin overcome the limitations of static weight allocation in traditional methods and achieve real-time optimization by dynamically adjusting the target weights and constructing an adaptive optimization model in combination with the real-time operation status, meteorological changes, and crop demand changes.

[0019] Through a dynamic feedback mechanism, the combination of the scheduling result and the actual demand is realized, significantly improving the accuracy and flexibility of water distribution scheduling and meeting the actual needs of a complex irrigation district environment.

[0020] Through the deep integration of digital twin and predictive maintenance, a device health status scoring and remaining life prediction model is constructed, which can reflect the device operation status in real time, and through predictive maintenance technology, potential device failures can be identified in advance, the maintenance plan can be optimized, the accuracy of device health assessment is improved, the service life of the device is extended, and at the same time, the maintenance cost and operation risk are reduced, thus ensuring the efficient and stable operation of the irrigation district water distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0022] Figure 1 It is a schematic flowchart of a method for irrigation district water distribution scheduling based on digital twin proposed by the present invention; Figure 2 It is a schematic block diagram of a system for irrigation district water distribution scheduling based on digital twin proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings in the specification.

[0024] In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from this description, and those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0025] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is separate or selectively exclusive of other embodiments.

[0026] Embodiment 1: Referring to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a method for irrigation water distribution scheduling based on digital twin, including the following steps: S1. Collect data and construct a digital twin model.

[0027] In this embodiment, collecting data and creating a digital twin model includes: Using Internet of Things sensors and Geographic Information System (GIS) technology, collect data including soil moisture, crop water requirements, meteorological conditions, soil types, and irrigation system status. Combine GIS to comprehensively model the terrain, crop distribution, and water source information of the irrigation area, and create a digital twin model. This model reflects all physical, chemical, and environmental attributes of the irrigation area, can be updated in real time, and compared with actual system data.

[0028] S2. Use hydrological model simulation technology based on the digital twin model to conduct dynamic simulation of water resources in the irrigation area and obtain simulation results.

[0029] In this embodiment, use hydrological model simulation technology, such as the distributed watershed hydrological model SWAT based on Geographic Information System (which calculates continuously with days as the time unit and simulates various different hydrological physical and chemical processes using spatial information provided by remote sensing and Geographic Information System), to simulate hydrological processes such as water flow, evaporation, and infiltration in the irrigation area. These simulations can consider various factors such as different seasons, precipitation, and crop requirements, and predict the dynamic changes of water resources in each region of the irrigation area through numerical simulation.

[0030] S3. Use machine learning algorithms to predict irrigation demand based on the simulation results and obtain prediction results.

[0031] In this embodiment, according to the simulation results, a machine learning method such as random forest (which classifies or regresses by constructing multiple decision trees and aggregating their prediction results, and each decision tree is trained based on a sample and feature subset randomly selected from the original dataset) is used to accurately predict the crop growth situation and water demand in the irrigation area. This method can combine historical data, meteorological forecasts, and crop types to predict the water demand of different crops in the future for a period of time, thereby providing data support for subsequent irrigation strategies.

[0032] S4. Optimize the water distribution scheduling model according to the prediction results to obtain the scheduling strategy.

[0033] In this embodiment, the optimization of the water distribution scheduling model includes: According to the prediction results of irrigation demand, use the reinforcement learning algorithm Q-learning to optimize the water resource allocation according to the real-time environmental changes, dynamically adjust the irrigation strategy, and achieve the optimal scheduling strategy.

[0034] To adapt to the problem of water distribution scheduling in the irrigation area, we innovate on the basis of the traditional Q-learning model and incorporate factors such as water resource mobility, prediction error of crop water demand, and timeliness of environmental changes. By modeling the water resources in the irrigation area and taking it as a dimension of the state space, at the same time, considering that the environmental changes have a certain delay, we expand the Q-value update formula.

[0035] Construct the following Q-learning update formula: ; Where: : The state of the irrigation area at time t, including soil humidity, crop water demand, meteorological conditions, etc.; : The water distribution action taken at time t, specifically for water volume allocation; : The immediate reward at time t, which not only considers water resource conservation but also the needs of crop growth, specifically including: ; Among them, is the water resource conservation reward, adjusted based on the difference between the water flow and the actual water consumption, is the crop growth state reward, adjusted according to the difference between the water demand of the crop and the actual satisfaction of irrigation, is the environmental fluctuation reward, considering the impact of meteorological factors, soil humidity, etc. on the irrigation strategy, is the weight coefficient, set according to the actual situation; : The learning rate, between 0 and 1, a larger means a faster learning speed, but may lead to overfitting, a smaller means a slower learning speed, but more stable; : Discount factor, which determines the importance of future rewards in the current decision. The value is between 0 and 1. A larger γ means more emphasis on future rewards, while a smaller γ values current rewards more. Set according to the actual situation; : Expected return value, that is, the Q value; : In the next state the maximum Q value that can be obtained among all possible actions taken; Over time, the system continuously adjusts the water distribution strategy according to the actual effect. Through continuous learning and optimization, it gradually finds the optimal irrigation strategy, thereby achieving the balance between water resource conservation and crop growth requirements. This method can minimize water resource waste to the greatest extent while meeting the crop water demand, improving the water resource utilization efficiency of the irrigation area.

[0036] S5. Perform real-time scheduling control according to the scheduling strategy to obtain scheduling control data.

[0037] In this embodiment, the real-time scheduling control includes: Using an automated control system and real-time data acquisition technology, monitor the irrigation area data in real time, and automatically adjust the water source allocation according to the preset scheduling strategy and real-time data. The system can dynamically adjust the water volume allocation of each irrigation system according to the water resource requirements of different regions to ensure irrigation efficiency and reasonable utilization of water resources.

[0038] S6. Evaluate the scheduling effect according to the scheduling control data to obtain an evaluation result.

[0039] In this embodiment, the scheduling effect evaluation includes: According to the scheduling control data, use a multi-objective optimization algorithm to construct an evaluation model, combine multi-dimensional indicators, and evaluate multiple scheduling control results simultaneously to ensure that the scheduling effect meets the expectations, not only considering water resource conservation but also ensuring that the crop growth requirements are met, thereby improving the irrigation effect of the entire irrigation area.

[0040] Furthermore, the multi-objective optimization algorithm includes: Objectives: Maximize water resource utilization efficiency: Reduce water resource waste and meet crop requirements as precisely as possible; Optimize the crop growth state: Ensure that the crop obtains sufficient water at different growth stages to promote its healthy growth; Minimize the operating cost of the irrigation system: reduce the energy consumption and maintenance cost of the system, and optimize the economic benefits of the irrigation process; Comprehensively consider these three objectives and find a balance point.

[0041] Multi-objective optimization formula: ; Where: : The state of the irrigation area at time , including the utilization of water resources and the growth state of crops; : The objective function of water resource utilization efficiency, which measures the matching degree between the water allocation volume and the crop demand, specifically including: ; Where, is the actual irrigation volume at time t, is the predicted demand; this objective function aims to minimize the difference between the irrigation volume and the demand and improve the utilization efficiency of water resources; : The objective function of crop growth state, which measures the degree of water sufficiency required by the crop in the current state, specifically including: ; Where, is the actual crop water level at time t, is the ideal crop water level; this function measures the degree of water satisfaction of the crop at the current moment and aims to maintain the healthy growth state of the crop; : The objective function of the operating cost of the irrigation system, specifically including: ; Where, is the energy consumption of the system at time t, is the equipment maintenance cost, and are the weight coefficients, which are adjusted according to the actual situation; this objective function aims to optimize the economic benefits of the irrigation system; : The weight coefficient, which is adjusted according to the actual situation; : The final optimization objective function, which is the optimal result obtained by combining all objectives.

[0042] The irrigation strategy will be adjusted to make reach the maximum value, so as to achieve the optimal balance among water resources, crop growth and economic benefits.

[0043] S7. Adjust the scheduling strategy according to the evaluation results to obtain the adjustment result.

[0044] In this embodiment, the adjustment of the scheduling strategy includes: Using adaptive control technology, continuously optimize the irrigation strategy by continuously learning historical data and combining with the real-time feedback of the scheduling strategy, adjust the scheduling strategy, and dynamically optimize the water resource allocation. For example, if it is found that the water resource supply in some areas is too much or too little, the system will adjust the water distribution volume and correct it in future scheduling.

[0045] S8. Use the digital twin-based irrigation district water distribution scheduling system for the water resource management and long-term optimization of the irrigation district according to the adjustment results.

[0046] In this embodiment, the water resource management and long-term optimization of the irrigation district include: According to the adjustment results, use digital twin and predictive maintenance technology to conduct long-term prediction and maintenance on the water resources and the status of irrigation facilities in the irrigation area, and help decision-makers formulate reasonable irrigation policies. This model can conduct prediction and optimization on a long-term time scale, help decision-makers formulate reasonable irrigation policies, and adjust the water source configuration. Through the long-term prediction and maintenance of the water resources and the status of irrigation facilities, ensure that the irrigation district can achieve optimal water resource management under different seasons and different water source conditions.

[0047] Furthermore, the digital twin and predictive maintenance technology includes: The digital twin maps the behavior and status of physical devices, and predictive maintenance analyzes the device operation data, predicts potential failures, and takes measures before problems occur; Device health score formula: ; Where: : Device health score, ranging from 0 to 1, the larger the better the health status; : The operation status of the device at time t, including temperature, vibration frequency, and workload; : The change rate of the device status; : Environmental variables, including external temperature and humidity, and wind speed; : Device health status, including historical failure rate and current working life; : Weight coefficient, adjusted according to the actual situation; : The function to measure the stability of the device operation status, specifically including: ; Where, To avoid small positive numbers that make the denominator zero; : A function to evaluate the impact of environmental conditions on the device, specifically including: ; Among them, is the ideal environmental condition; : A function to calculate the device health based on historical health data, specifically including: ; Among them, is the failure rate of the device at time t, is the maximum failure rate of the device; Formula for the remaining useful life of the device: ; Among them: : The remaining useful life of the device, the lower the remaining useful life, the less; : The health critical state of the device operation, exceeding the threshold indicates potential risks; : The attenuation factor, which determines the impact of the state deviation on the remaining life and is set according to the actual situation.

[0048] Health score calculation: Real-time collect the operating state of the device , state change rate , environmental variables , historical health information .

[0049] According to the formula , combined with the device operation stability ( ), environmental condition impact ( ), and historical health data ( ), calculate the current health score of the device; Prediction of the remaining useful life of the device: Use the formula to predict the remaining useful life of the device. By comparing the deviation degree between the current state of the device and the health threshold , dynamically adjust the remaining life estimation.

[0050] If is short, the system will trigger a maintenance reminder and prioritize the maintenance task; Digital twin model update: The calculated and Update it into the digital twin model so that it can reflect the changes in the device status in real time. The digital twin model provides references for operation and maintenance decisions and optimizes the maintenance plan by simulating the effects of different maintenance strategies.

[0051] Embodiment 2: Refer to Figure 2 , which is the second embodiment of the present invention. This embodiment provides an irrigation district water distribution scheduling system based on digital twin, including the following modules: Data acquisition and model construction module, which is used to collect and preprocess multi-source data of the operation of the irrigation district and construct a digital twin model; Dynamic simulation module, which is used to simulate the dynamic changes of water resources in each area of the irrigation district; Irrigation demand prediction module, which is used to predict the crop growth situation and water demand in the irrigation district; Scheduling strategy optimization module, which is used to optimize the scheduling strategy according to the prediction results using a reinforcement learning algorithm; Water distribution scheduling execution module, which is used to perform water distribution scheduling control according to real-time data and scheduling strategies; Multi-objective optimization module, which is used to dynamically balance the water distribution scheduling plan based on multi-objective optimization; Learning and feedback module, which is used to continuously learn historical data and provide real-time feedback to optimize the irrigation strategy; Digital twin and predictive maintenance module, which is used to manage the water resources of the irrigation district and perform long-term optimization.

[0052] Embodiment 3: This embodiment also provides a computer device, which is applicable to a situation of an irrigation district water distribution scheduling method and system based on digital twin, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement an irrigation district water distribution scheduling method and system based on digital twin as proposed in the above embodiment.

[0053] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements an irrigation district water distribution scheduling method and system based on digital twin as proposed in the above embodiment.

[0054] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or may also be a button, a trackball, or a touchpad provided on the housing of the computer device, or may also be an external keyboard, touchpad, or mouse, etc.

[0055] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0056] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0057] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0058] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for water distribution scheduling in irrigation areas based on digital twins, characterized in that: The following steps are involved: S1, collect data and build a digital twin model; S2. Use hydrological model simulation technology based on the digital twin model to simulate the dynamics of water resources in the irrigation area and obtain simulation results; S3, using a machine learning algorithm to predict irrigation demand based on the simulation results to obtain a prediction result; S4. Optimize the water distribution scheduling model according to the prediction results to obtain the scheduling strategy; S5. Perform real-time dispatch control according to the dispatch strategy to obtain dispatch control data; S6. Evaluate the dispatching effect according to the dispatching control data to obtain the evaluation result; S7, adjusting the scheduling strategy according to the evaluation result to obtain an adjustment result; S8. Use the irrigation district water distribution scheduling system based on digital twins to carry out irrigation district water resources management and long-term optimization based on the adjustment results.

2. According to the method of irrigation area water distribution scheduling based on digital twins according to claim 1, it is characterized in that: The data collection and creation of the digital twin model in S1 include: Use IoT sensors and Geographic Information System (GIS) technology to collect data including soil moisture, crop water demand, meteorological conditions, soil type, and irrigation system status. Combined with GIS, comprehensively model the topography, crop distribution, and water source information of the irrigation area to create a digital twin model.

3. The irrigation area water distribution scheduling method based on digital twin according to claim 1 is characterized in that: The optimization of the water distribution scheduling model in S4 includes: Based on the prediction results of irrigation demand, the reinforcement learning algorithm Q-learning is used to optimize water resource allocation according to real-time environmental changes, dynamically adjust irrigation strategies, and achieve the optimal scheduling strategy.

4. The irrigation area water distribution scheduling method based on digital twin according to claim 1 is characterized in that: The real-time scheduling control in S5 includes: Use automated control systems and real-time data acquisition technology to monitor irrigation area data in real time and automatically adjust water allocation based on preset scheduling strategies and real-time data.

5. The irrigation area water distribution scheduling method based on digital twin according to claim 1 is characterized in that: The scheduling effect evaluation in S6 includes: Based on the dispatching and control data, an evaluation model is constructed using a multi-objective optimization algorithm, and multiple dispatching and control results are evaluated simultaneously to ensure that the dispatching effect meets expectations.

6. The irrigation area water distribution scheduling method based on digital twin according to claim 5 is characterized in that: The multi-objective optimization algorithm includes: Multi-objective optimization formula: ； in: :time The status of irrigation areas, including water resource utilization and crop growth status; : Water resource utilization efficiency objective function, which measures the matching degree between water allocation and crop demand, including: ； in, is the actual irrigation amount at time t, To forecast demand; : Crop growth state objective function, which measures the water requirement of crops in the current state, including: ； in, is the actual crop moisture level at time t, for ideal crop moisture levels; : Irrigation system operation cost objective function, including: ； in, is the system energy consumption at time t, The equipment maintenance cost is and is the weight coefficient, which is adjusted according to the actual situation; : Weight coefficient, adjusted according to actual conditions; : The final optimization objective function, combining all objectives to find the optimal result.

7. The irrigation area water distribution scheduling method based on digital twin according to claim 1 is characterized in that: The scheduling strategy adjustment in S7 includes: Adaptive control technology is used to continuously optimize irrigation strategies and adjust scheduling strategies by continuously learning historical data and combining real-time feedback from scheduling strategies.

8. The method for water distribution scheduling in irrigation areas based on digital twin according to claim 1, characterized in that: The irrigation district water resources management and long-term optimization in S8 includes: Based on the adjustment results, digital twin and predictive maintenance technologies are used to conduct long-term prediction and maintenance of water resources and irrigation facility status in irrigation areas, helping decision makers to formulate reasonable irrigation policies.

9. The irrigation area water distribution scheduling method based on digital twin according to claim 8 is characterized in that: The digital twin and predictive maintenance technologies include: Digital twins map the behavior and status of physical equipment, and predictive maintenance analyzes equipment operation data, predicts potential failures, and takes action before problems occur; Equipment health score formula: ； in: : Equipment health score, ranging from 0 to 1, the larger the better the health status; : The operating status of the equipment at time t, including temperature, vibration frequency, and workload; : rate of change of device status; : Environmental variables, including external temperature, humidity, and wind speed; : Equipment health status, including historical failure rate and current working life; : Weight coefficient, adjusted according to actual conditions; : A function that measures the stability of the equipment's operating status, including: ； in, To avoid small positive numbers with zero denominator; : A function that evaluates the impact of environmental conditions on the device, including: ； in, For ideal environmental conditions; : A function that calculates the health of a device based on historical health data, including: ； in, is the failure rate of the equipment at time t, is the maximum failure rate of the equipment; The remaining service life of the equipment is calculated by: ； in: : The remaining service life of the equipment, the lower the remaining service life, the less; : The critical health status of the equipment operation. Exceeding the threshold indicates potential risks; : Attenuation factor, which determines the impact of state deviation on the remaining life and is set according to actual conditions.

10. A digital twin-based irrigation area water distribution scheduling system, used to implement a digital twin-based irrigation area water distribution scheduling method according to any one of claims 1 to 9, characterized in that: Includes the following modules: Data acquisition and model building module; dynamic simulation module; irrigation demand prediction module; scheduling strategy optimization module; water distribution scheduling execution module; multi-objective optimization module; learning and feedback module; digital twin and predictive maintenance module.

Images