Water conservancy project scheduling system based on artificial intelligence

Abstract

The invention discloses a water conservancy project scheduling system based on artificial intelligence. The system comprises a water conservancy data monitoring module, a water level prediction module, a water conservancy scheduling module and a water conservancy power generation module. The water conservancy data monitoring module is used for monitoring and acquiring water conservancy data in real time and acquiring meteorological data in real time; the water level prediction module is used for acquiring historical water conservancy data and historical meteorological data, establishing a BP neural network model and acquiring predicted water level data; the water conservancy scheduling module is used for obtaining a first water conservancy scheduling strategy by using a particle swarm algorithm according to the predicted water level data, establishing a fluid simulation model according to the water conservancy scheduling strategy, and obtaining a second water conservancy scheduling strategy; and the hydroelectric power generation module is used for determining a hydroelectric power generation strategy by utilizing edge calculation, realizing intelligent hydraulic engineering scheduling, ensuring the feasibility of the hydraulic engineering scheduling strategy and the reliability of actual scheduling, supplying power to power supply users according to the hydroelectric power generation strategy, and being beneficial to fully utilizing water energy to generate power and improving the energy utilization rate.

Description

Technical Field

[0001] The technical field involved in the present invention, in particular, relates to a water conservancy project scheduling system based on artificial intelligence. Background Art

[0002] In recent years, as water conservancy project scheduling is a key link to ensure the efficient utilization of water resources, traditional scheduling methods are difficult to cope with complex and changeable hydrological conditions and multi-objective requirements. With the development of artificial intelligence technology, applying it to water conservancy project scheduling has become an important way to improve scheduling efficiency.

[0003] Currently, a Chinese invention with the publication number of CN119151238A discloses a real-time scheduling method for a water conservancy project system based on neural network technology. Although it uses a deep learning model to predict the future water level of the reservoir and calculates the vulnerability coefficient according to flood control data; calculates the flood control coefficient based on the rainfall coefficient, rain cloud influence coefficient, and vulnerability coefficient, and constructs a flood control regulation model, which effectively improves the flexibility of the real-time scheduling of the water conservancy project system, but it does not conduct detailed regulation on the future water level of the reservoir, and does not effectively utilize the water energy generated after water conservancy regulation, which is not conducive to fully carrying out new energy power generation. Summary of the Invention

[0004] The technical problem solved by the present invention is that the prior art does not conduct detailed regulation on the future water level of the reservoir, and does not effectively utilize the water energy generated after water conservancy regulation, resulting in energy waste and being not conducive to fully carrying out new energy power generation.

[0005] To solve the above technical problem, the present invention provides a water conservancy project scheduling system based on artificial intelligence, which is characterized by including a water conservancy data monitoring module, a water level prediction module, a water conservancy scheduling module, and a water conservancy power generation module; The water conservancy data monitoring module is used to use sensors to monitor and obtain water conservancy data in real time, and obtain meteorological data from the meteorological monitoring platform in real time; The water level prediction module is used to obtain historical water conservancy data and historical meteorological data, establish a BP neural network model according to the historical water conservancy data and historical meteorological data, and input the water conservancy data and the meteorological data into the BP neural network model to obtain predicted water level data; The water conservancy scheduling module is used to obtain a first water conservancy scheduling strategy according to the predicted water level data by using the particle swarm algorithm, establish a fluid simulation model according to the water conservancy scheduling strategy and obtain a simulation result, and adjust the first water conservancy scheduling strategy according to the simulation result to obtain a second water conservancy scheduling strategy; The hydroelectric power generation module is used to obtain user demand information, obtain power supply users by using edge computing, determine a hydroelectric power generation strategy according to the user demand information and the second water conservancy scheduling strategy, and perform hydroelectric power supply for the power supply users through the hydroelectric power generation strategy; As a preferred embodiment of the water conservancy project scheduling system based on artificial intelligence according to the present invention, wherein: The water conservancy data monitoring module includes a water conservancy monitoring unit and a meteorological monitoring module; The water conservancy monitoring unit is used to obtain water conservancy data in real time by using sensors, and the water conservancy data includes water level data, water flow data, evaporation data, water quality data and gate data; The meteorological monitoring module is used to obtain meteorological data within a future time period in real time from a meteorological monitoring platform, and the meteorological data includes temperature, humidity, air pressure, wind speed, wind direction, rainfall time, rainfall intensity and rainfall speed; As a preferred embodiment of the water conservancy project scheduling system based on artificial intelligence according to the present invention, wherein: The water level prediction module includes a prediction model unit and a water level prediction unit; The prediction model unit is used to obtain historical water conservancy data and historical meteorological data, input the historical water conservancy data and the historical meteorological data into a BP neural network for training, use the historical water conservancy data and the historical meteorological data as inputs, and historical water level data as outputs, to establish a BP neural network model; The water level prediction unit is used to input the water conservancy data and the meteorological data of each water station into the BP neural network model, obtain the predicted water level data of each station, retrieve the predicted water station water level in the water level data, define the water station with the predicted water station water level greater than or equal to the first expected threshold as a flood warning water station, and define the water station with the predicted water station water level less than or equal to the second expected threshold as a drought warning water station; The historical water conservancy data includes historical water level data, historical water flow data, historical evaporation data, historical water quality data and historical gate data; The historical meteorological data includes historical temperature, historical humidity, historical air pressure, historical wind speed, historical wind direction, historical rainfall time, historical rainfall intensity and historical rainfall speed; As a preferred embodiment of the water conservancy project scheduling system based on artificial intelligence according to the present invention, wherein: The water conservancy scheduling module includes a scheduling strategy unit and a simulation and simulation unit; The scheduling policy unit is used to obtain flood particle information based on flood warning water stations. The flood particle information includes the water station types, water station location data, water station water level data, and water station sluice data of other water stations except the flood warning water stations. Obtain the flood water station location data of the flood warning water station, obtain the flood water station scheduling distance based on the water station location data and the flood water station location data, determine the flood resistance particle values of each water station according to the flood water station scheduling distance and the water station type, obtain the water station with the largest flood resistance particle value, and define it as the flood discharge water station of the flood warning water station; Obtain drought particle information based on drought warning water stations. The drought particle information includes the water station types, water station location data, water station water level data, and water station sluice data of other water stations except the drought warning water stations. Obtain the drought water station location data of the drought warning water station, obtain the drought water station scheduling distance based on the water station location data and the drought water station location data, determine the drought resistance particle values of each water station according to the drought water station scheduling distance and the water station type, obtain the water station with the largest drought resistance particle value, and define it as the drought resistance water station of the drought warning water station. Repeat the above steps to obtain the first water conservancy scheduling policy; The simulation unit is used to establish a fluid simulation model based on water conservancy data, determine the first fluid increment data according to the predicted water level data, and update the fluid simulation model according to the first fluid increment data. Obtain the second fluid adjustment data through the first water conservancy scheduling policy, input the second fluid adjustment data into the fluid simulation model to obtain fluid simulation data, retrieve the simulation flow rate of the fluid simulation data, define the flow rate greater than or equal to the first expected threshold as the first abnormal flow rate, define the flow rate less than or equal to the second expected threshold of the fluid simulation model as the second abnormal flow rate, define the first water conservancy scheduling policy corresponding to the first abnormal flow rate or the second abnormal flow rate as the abnormal fluid policy, and adjust the abnormal fluid policy according to the first abnormal flow rate and the second abnormal flow rate to obtain the second water conservancy scheduling policy; Determining the drought resistance particle values of each water station according to the drought water station scheduling distance and the water station type includes: Obtain the scheduling distance coefficient according to the drought water station scheduling distance, obtain the scheduling water station coefficient according to the water station type. The scheduling water station coefficient of the flood warning water station is 1, the scheduling water station coefficient of the normally operating water station is 0, and the scheduling water station coefficient of the drought warning water station is -1. Perform weighted calculation on the scheduling distance coefficient and the scheduling water station coefficient to obtain the drought resistance particle value; Determining the flood resistance particle values of each water station according to the flood water station scheduling distance and the water station type includes: Obtain the scheduling distance coefficient according to the scheduling distance of the flood control water station, and obtain the scheduling water station coefficient according to the water station type. The scheduling water station coefficient of the flood warning water station is -1, the scheduling water station coefficient of the normally operating water station is 0, and the scheduling water station coefficient of the drought warning water station is 1. Perform weighted calculation on the scheduling distance coefficient and the scheduling water station coefficient to obtain the post-flood fighting particle value; The water station types include flood warning water stations, drought warning water stations and normally operating water stations; The fluid simulation data includes simulation water level, simulation flow rate and simulation flow velocity; As a preferred scheme of the water conservancy project scheduling system based on artificial intelligence according to the present invention, wherein: The hydropower generation module includes a power generation information unit and a power generation strategy unit; The power generation information unit is used to obtain the user demand information of the water station, determine the user power supply distance according to the water station location and the user location, and use edge computing to determine the power supply users according to the user power supply distance; The power generation strategy unit is used to obtain the scheduling water station pair information according to the second water conservancy scheduling strategy, retrieve the gate data of the scheduling water station pair information, obtain the water head through the gate data, and obtain the single-gate power supply amount, double-gate power supply amount, triple-gate power supply amount... of the water station through the water head using the hydropower generation calculation formula. Obtain the electricity consumption peak within a unit time according to the user demand information of the power supply users, and determine the hydropower generation strategy according to the electricity consumption peak. When the electricity consumption peak is less than or equal to the single-gate power supply amount, only the single gate is opened. When the electricity consumption peak is greater than the single-gate power supply amount and less than or equal to the double-gate power supply amount, the double gate is opened. When the electricity consumption peak is greater than or equal to the double-gate power supply amount and less than or equal to the triple-gate power supply amount, the triple gate is opened. Repeat the above steps to obtain the hydropower generation strategy, and schedule the water station gate according to the hydropower generation strategy; As a preferred scheme of the water conservancy project scheduling system based on artificial intelligence according to the present invention, wherein: The establishment of the BP neural network model specifically includes: Input the historical water conservancy data and the historical meteorological data into the input layer of the BP neural network, obtain prediction factors according to the historical water conservancy data and the historical meteorological data, randomly obtain the first weight value and the first bias value of the prediction factors, and use the first activation function to obtain the first prediction value according to the first weight value and the first bias value. Take the first prediction value as the input of the hidden layer, and use the loss function to obtain the loss value according to the first prediction value and the true value. Adjust the first weight value and the first bias value according to the loss value to obtain the second weight value and the second bias value. Repeat the above steps n times until the loss value is less than or equal to the loss expected threshold, then obtain the final weight value and the final bias value, and use the second activation function to obtain the predicted water level data according to the final weight value and the final bias value as the output of the output layer; As a preferred scheme of the water conservancy project scheduling system based on artificial intelligence according to the present invention, wherein: The water level data includes the average water level per unit time and the water level distribution per unit time; The water flow data includes the flow rate per unit time, the flow velocity per unit time and the runoff per unit time; The evaporation data includes the average evaporation per unit time; The water quality data includes water flow temperature, turbidity, dissolved oxygen content, pH value, chemical oxygen demand, total phosphorus content and total nitrogen content; The gate data includes the water level before the gate, the water level after the gate, the gate opening, the gate working condition and the flow rate through the gate; As a preferred scheme of the water conservancy project scheduling system based on artificial intelligence according to the present invention, wherein: The water power generation calculation formula is as follows: ; Wherein, Q is the water power generation, q is the flow rate per unit time, H is the head value, g is the acceleration of gravity, and t is the power generation time; As a preferred scheme of the water conservancy project scheduling system based on artificial intelligence according to the present invention, wherein: Using edge computing to determine power supply users according to the user power supply distance includes: Arrange the user power supply distances in ascending order, screen out the first m users, and determine them as power supply users; As a preferred scheme of the water conservancy project scheduling system based on artificial intelligence according to the present invention, wherein: The scheduling water station pair information includes the first water station data, the second water station data and the gate data; The user demand information includes the power consumption area number, the power consumption demand and the power consumption time.

[0006] Advantages of the present invention: Based on historical water conservancy data and historical meteorological data, the present invention uses a BP neural network model to predict the water level of a water station, obtains flood warning water stations and drought warning water stations according to the prediction results, and uses a particle swarm algorithm to obtain a first water conservancy dispatching strategy based on the flood warning water stations and drought warning water stations, diverting flood water from the flood warning water stations to the drought warning water stations, solving flood problems and drought problems simultaneously, making full use of water resources, and realizing intelligent water conservancy project dispatching.

[0007] Using a fluid simulation model to simulate the dispatching process according to the first water conservancy dispatching strategy, obtaining the dispatching result, and obtaining a second water conservancy dispatching strategy according to the dispatching result to improve the deficiencies of the first water conservancy dispatching strategy and ensure the feasibility of the water conservancy dispatching strategy and the safety and reliability of the actual dispatching.

[0008] Using edge computing to obtain power supply users, determining a water conservancy power generation strategy according to the user demand information and the second water conservancy dispatching strategy, generating electricity according to the water conservancy power generation strategy and supplying power to the power supply users, which is conducive to making full use of water energy for power generation and improving energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a schematic diagram of the basic process of a water conservancy project dispatching system based on artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0010] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention.

[0011] Embodiment, referring to Figure 1 This is an embodiment of the present invention, which provides a water conservancy project dispatching system based on artificial intelligence, including a water conservancy data monitoring module, a water level prediction module, a water conservancy dispatching module, and a water conservancy power generation module; The water conservancy data monitoring module is used to use sensors to monitor and obtain water conservancy data in real time, and obtain meteorological data from a meteorological monitoring platform in real time; The water level prediction module is used to obtain historical water conservancy data and historical meteorological data, establish a BP neural network model according to the historical water conservancy data and historical meteorological data, and input the water conservancy data and the meteorological data into the BP neural network model to obtain predicted water level data; The water conservancy dispatching module is used to obtain a first water conservancy dispatching strategy according to the predicted water level data using a particle swarm algorithm, establish a fluid simulation model according to the water conservancy dispatching strategy and obtain a simulation result, and adjust the first water conservancy dispatching strategy according to the simulation result to obtain a second water conservancy dispatching strategy; The hydropower generation module is used to obtain user demand information, use edge computing to obtain power supply users, determine a hydropower generation strategy according to the user demand information and the second water conservancy scheduling strategy, and perform water conservancy power supply for the power supply users through the hydropower generation strategy.

[0012] In this embodiment, the BP neural network model is a multi-layer feedforward neural network trained according to the error backpropagation algorithm; In this embodiment, the particle swarm optimization algorithm is a computer algorithm that, based on the observation of the collective activities of animals, uses the sharing of information among individuals in the group to cause the movement of the entire group to evolve from disorder to order in the problem-solving space, thereby obtaining the optimal solution; In this embodiment, the fluid simulation model is a simulation model that uses computer technology and numerical calculations to simulate the actual fluid flow; In this embodiment, edge computing provides cloud services and IT environment services for application developers and service providers on the edge side of the network, with the goal of providing computing, storage, and network bandwidth close to the data input or the user; In this embodiment, the water level of the water station is predicted using the BP neural network model based on historical water conservancy data and historical meteorological data, and flood warning water stations and drought warning water stations are obtained according to the prediction results. The particle swarm optimization algorithm is used to obtain the first water conservancy scheduling strategy based on the flood warning water stations and drought warning water stations, divert flood water from the flood warning water stations to the drought warning water stations, solve both flood and drought problems simultaneously, make full use of water resources, and achieve intelligent water conservancy project scheduling.

[0013] The fluid simulation model is used to simulate the scheduling process according to the first water conservancy scheduling strategy, obtain the scheduling result, and obtain the second water conservancy scheduling strategy according to the scheduling result to improve the deficiencies of the first water conservancy scheduling strategy and ensure the feasibility of the water conservancy scheduling strategy and the safety and reliability of the actual scheduling.

[0014] Edge computing is used to obtain power supply users, and a hydropower generation strategy is determined according to the user demand information and the second water conservancy scheduling strategy. Power generation is carried out according to the hydropower generation strategy and power supply is provided for the power supply users, which helps to make full use of hydropower generation and improve energy utilization efficiency.

[0015] The water conservancy data monitoring module includes a water conservancy monitoring unit and a meteorological monitoring module; The water conservancy monitoring unit is used to obtain water conservancy data in real time using sensors, and the water conservancy data includes water level data, water flow data, evaporation data, water quality data, and gate data; The meteorological monitoring module is used to obtain meteorological data within a future time period in real time from a meteorological monitoring platform, and the meteorological data includes temperature, humidity, air pressure, wind speed, wind direction, rainfall time, rainfall intensity, and rainfall speed.

[0016] In this embodiment, a water level sensor is used to obtain water level data, a flow velocity sensor is used to obtain water flow data, an evaporation sensor is used to obtain evaporation data, and a water quality sensor is used to obtain water quality data; In this embodiment, by real-time monitoring, water conservancy data and meteorological data are obtained, providing detailed and reliable data support for establishing a BP neural network model to predict the water level of the water station, and facilitating real-time understanding of the water level, water quality, and meteorological conditions of each water station.

[0017] The water level prediction module includes a prediction model unit and a water level prediction unit; The prediction model unit is used to obtain historical water conservancy data and historical meteorological data, input the historical water conservancy data and the historical meteorological data into a BP neural network for training, use the historical water conservancy data and the historical meteorological data as inputs, and historical water level data as outputs to establish a BP neural network model; The water level prediction unit is used to input the water conservancy data and the meteorological data of each water station into the BP neural network model, obtain the predicted water level data of each station, retrieve the predicted water level of the water station in the water level data, define the water station with the predicted water level greater than or equal to the first expected threshold as a flood warning water station, and define the water station with the predicted water level less than or equal to the second expected threshold as a drought warning water station; The historical water conservancy data includes historical water level data, historical water flow data, historical evaporation data, historical water quality data, and historical gate data; The historical meteorological data includes historical temperature, historical humidity, historical air pressure, historical wind speed, historical wind direction, historical rainfall time, historical rainfall intensity, and historical rainfall speed; In this embodiment, a BP neural network is used to establish a BP neural network model based on historical water conservancy data and historical meteorological data, and the BP neural network model is used to obtain the predicted water level data of each water station, providing a specific and accurate scheduling basis for water conservancy scheduling according to the predicted water level data and obtaining a water conservancy scheduling strategy.

[0018] The water conservancy scheduling module includes a scheduling strategy unit and a simulation unit; The scheduling strategy unit is used to obtain flood particle information according to the flood warning water station. The flood particle information includes the water station type, water station location data, water station water level data, and water station sluice data of other water stations except the flood warning water station, obtain the flood water station location data of the flood warning water station, obtain the flood water station scheduling distance according to the water station location data and the flood water station location data, determine the flood resistance particle value of each water station according to the flood water station scheduling distance and the water station type, obtain the water station with the largest flood resistance particle value, and define it as the flood discharge water station of the flood warning water station; Obtain drought particle information based on the drought warning water station. The drought particle information includes the types of other water stations except the drought warning water station, water station location data, water station water level data, and water station sluice data. Obtain the drought water station location data of the drought warning water station. Obtain the drought water station scheduling distance based on the water station location data and the drought water station location data. Determine the drought resistance particle values of each water station according to the drought water station scheduling distance and the water station type. Obtain the water station with the largest drought resistance particle value and define it as the drought resistance water station of the drought warning water station. Repeat the above steps to obtain the first water conservancy scheduling strategy; The simulation unit is used to establish a fluid simulation model based on water conservancy data, determine the first fluid increment data according to the predicted water level data, update the fluid simulation model according to the first fluid increment data, obtain the second fluid adjustment data through the first water conservancy scheduling strategy, input the second fluid adjustment data into the fluid simulation model, obtain fluid simulation data, retrieve the simulation flow rate of the fluid simulation data, define the flow rate greater than or equal to the first expected threshold as the first abnormal flow rate, define the flow rate less than or equal to the second expected threshold of the fluid simulation model as the second abnormal flow rate, define the first water conservancy scheduling strategy corresponding to the first abnormal flow rate or the second abnormal flow rate as the abnormal fluid strategy, and adjust the abnormal fluid strategy according to the first abnormal flow rate and the second abnormal flow rate to obtain the second water conservancy scheduling strategy; Determining the drought resistance particle values of each water station according to the drought water station scheduling distance and the water station type includes: Obtain the scheduling distance coefficient according to the drought water station scheduling distance, obtain the scheduling water station coefficient according to the water station type. The scheduling water station coefficient of the flood warning water station is 1, the scheduling water station coefficient of the normally operating water station is 0, and the scheduling water station coefficient of the drought warning water station is -1. Perform weighted calculation on the scheduling distance coefficient and the scheduling water station coefficient to obtain the drought resistance particle value.

[0019] Determining the flood resistance particle values of each water station according to the flood water station scheduling distance and the water station type includes: Obtain the scheduling distance coefficient according to the flood water station scheduling distance, obtain the scheduling water station coefficient according to the water station type. The scheduling water station coefficient of the flood warning water station is -1, the scheduling water station coefficient of the normally operating water station is 0, and the scheduling water station coefficient of the drought warning water station is 1. Perform weighted calculation on the scheduling distance coefficient and the scheduling water station coefficient to obtain the flood resistance particle value; The water station types include flood warning water stations, drought warning water stations, and normally operating water stations; The fluid simulation data includes simulation water level, simulation flow rate, and simulation flow velocity.

[0020] In this embodiment, the particle swarm optimization algorithm is used to obtain the first water conservancy dispatching strategy based on the flood warning water stations and drought warning water stations, divert floods from the flood warning water stations to the drought warning water stations, solve the flood problem and drought problem simultaneously, make full use of water resources, and realize the intelligent dispatching of water conservancy projects.

[0021] The fluid simulation model is used to simulate the dispatching process according to the first water conservancy dispatching strategy, obtain the dispatching result, and obtain the second water conservancy dispatching strategy according to the dispatching result to improve the deficiencies of the first water conservancy dispatching strategy and ensure the feasibility of the water conservancy dispatching strategy and the safety and reliability of the actual dispatching.

[0022] The water power generation module includes a power generation information unit and a power generation strategy unit; The power generation information unit is used to obtain the user demand information of the water station, determine the user power supply distance according to the water station location and user location, and use edge computing to determine the power supply users according to the user power supply distance; The power generation strategy unit is used to obtain the dispatching water station pair information according to the second water conservancy dispatching strategy, retrieve the gate data of the dispatching water station pair information, obtain the water head through the gate data, and obtain the single-gate power supply amount, double-gate power supply amount, triple-gate power supply amount... of the water station through the water head using the water power generation calculation formula. Obtain the power consumption peak within a unit time according to the user demand information of the power supply users, and determine the water power generation strategy according to the power consumption peak. When the power consumption peak is less than or equal to the single-gate power supply amount, only the single gate is opened. When the power consumption peak is greater than the single-gate power supply amount and less than or equal to the double-gate power supply amount, the double gate is opened. When the power consumption peak is greater than or equal to the double-gate power supply amount and less than or equal to the triple-gate power supply amount, the triple gate is opened. Repeat the above steps to obtain the water power generation strategy, and dispatch the water station gates according to the water power generation strategy.

[0023] In this embodiment, edge computing is used to obtain the power supply users, determine the water power generation strategy according to the user demand information and the second water conservancy dispatching strategy, generate electricity according to the water power generation strategy and supply power to the power supply users, which is beneficial to making full use of water energy for power generation and improving energy utilization efficiency.

[0024] The specific steps of establishing the BP neural network model include: Input the historical water conservancy data and the historical meteorological data into the input layer of the BP neural network, obtain prediction factors based on the historical water conservancy data and the historical meteorological data, randomly obtain the first weight value and the first bias value of the prediction factors, and use the first activation function to obtain the first prediction value according to the first weight value and the first bias value. Take the first prediction value as the input of the hidden layer, and use the loss function to obtain the loss value according to the first prediction value and the true value. Adjust the first weight value and the first bias value according to the loss value to obtain the second weight value and the second bias value. Repeat the above steps n times until the loss value is less than or equal to the loss expected threshold, then obtain the final weight value and the final bias value, and use the second activation function to obtain the predicted water level data according to the final weight value and the final bias value as the output of the output layer.

[0025] In this embodiment, by establishing a BP neural network model and continuously adjusting the weight value and the bias value, it provides a reliable model support for obtaining the predicted water level data, and ensures the accuracy and reliability of the predicted value.

[0026] The water level data includes the average water level per unit time and the water level distribution per unit time; The water flow data includes the flow rate per unit time, the flow velocity per unit time, and the runoff per unit time; The evaporation data includes the average evaporation amount per unit time; The water quality data includes the water flow temperature, turbidity, dissolved oxygen content, pH value, chemical oxygen demand, total phosphorus content, and total nitrogen content; The gate data includes the water level before the gate, the water level after the gate, the gate opening, the gate working condition, and the flow rate through the gate.

[0027] In this embodiment, by obtaining the water conservancy data and the meteorological data through real-time monitoring, it provides detailed and reliable data support for establishing a BP neural network model to predict the water level of the water station, and is conducive to understanding the water level conditions, water quality conditions, and meteorological conditions of each water station in real time.

[0028] The water power generation calculation formula is as follows: ; Where Q is the water power generation, q is the flow rate per unit time, H is the head value, g is the acceleration due to gravity, and t is the power generation time.

[0029] In this embodiment, the water power generation is obtained by using the water power generation calculation formula, which provides an effective data basis for adjusting the water power generation strategy according to the water power generation.

[0030] Using edge computing to determine the power supply users according to the user's power supply distance includes: Arrange the user power supply distances in ascending order, select the top m users, and determine them as the power supply users.

[0031] In this embodiment, edge computing is used to determine the power supply users according to the user power supply distances, which improves the end response speed and reliability of the water conservancy project scheduling system, reduces the requirements of the system for the computing power and network bandwidth of the cloud server, and realizes the design requirements of intuitive and efficient analysis, scientific judgment, and intelligent scheduling of urban water conservancy scheduling in scenarios such as flood control, waterlogging control, and water activation.

[0032] The information of the dispatching water station includes the data of the first water station, the data of the second water station, and the gate data; The user demand information includes the power consumption area number, the power consumption demand, and the power consumption time.

[0033] The present invention uses a BP neural network model to predict the water level of the water station according to historical water conservancy data and historical meteorological data, obtains flood warning water stations and drought warning water stations according to the prediction results, uses the particle swarm algorithm to obtain the first water conservancy scheduling strategy according to the flood warning water stations and drought warning water stations, diverts the flood of the flood warning water stations to the drought warning water stations, solves the flood problem and the drought problem at the same time, makes full use of water resources, and realizes intelligent water conservancy project scheduling.

[0034] Use a fluid simulation model to simulate the scheduling process according to the first water conservancy scheduling strategy, obtain the scheduling result, and obtain the second water conservancy scheduling strategy according to the scheduling result to improve the deficiencies of the first water conservancy scheduling strategy and ensure the feasibility of the water conservancy scheduling strategy and the safety and reliability of the actual scheduling.

[0035] Use edge computing to obtain the power supply users, determine the water conservancy power generation strategy according to the user demand information and the second water conservancy scheduling strategy, generate electricity according to the water conservancy power generation strategy and supply power to the power supply users, which is conducive to making full use of water energy for power generation and improving energy utilization efficiency.

[0036] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the process Figure 1 in one process or multiple processes and / or blocks Figure 1 the functions specified in one block or multiple blocks.

[0037] 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 water conservancy project dispatching system based on artificial intelligence, characterized in that: It includes water conservancy data monitoring module, water level prediction module, water conservancy scheduling module and water conservancy power generation module; The water conservancy data monitoring module is used to monitor and obtain water conservancy data in real time using sensors, and the meteorological monitoring platform is used to obtain meteorological data in real time; The water level prediction module is used to obtain historical water conservancy data and historical meteorological data, and establish a BP neural network model based on the historical water conservancy data and historical meteorological data, and input the water conservancy data and the meteorological data into the BP neural network model to obtain predicted water level data; The water conservancy scheduling module is used to obtain a first water conservancy scheduling strategy using a particle swarm algorithm according to the predicted water level data, establish a fluid simulation model according to the water conservancy scheduling strategy and obtain simulation results, and adjust the first water conservancy scheduling strategy according to the simulation results to obtain a second water conservancy scheduling strategy; The hydropower generation module is used to obtain user demand information, use edge computing to obtain power supply users, and determine a hydropower generation strategy based on the user demand information and a second hydropower scheduling strategy, and use the hydropower generation strategy to provide hydropower supply to the power supply users.

2. The artificial intelligence-based water conservancy project dispatching system according to claim 1, characterized in that: The water conservancy data monitoring module includes a water conservancy monitoring unit and a meteorological monitoring module; The water conservancy monitoring unit is used to obtain water conservancy data in real time using sensors, and the water conservancy data includes water level data, water flow data, evaporation data, water quality data and gate data; The meteorological monitoring module is used to obtain meteorological data within a future time period from the meteorological monitoring platform in real time, and the meteorological data includes temperature, humidity, air pressure, wind speed, wind direction, rainfall time, rainfall intensity and rainfall speed.

3. The artificial intelligence-based water conservancy project dispatching system according to claim 1, characterized in that: The water level prediction module includes a prediction model unit and a water level prediction unit; The prediction model unit is used to obtain historical water conservancy data and historical meteorological data, input the historical water conservancy data and the historical meteorological data into the BP neural network for training, and use the historical water conservancy data and the historical meteorological data as input and the historical water level data as output to establish a BP neural network model; The water level prediction unit is used to input the water conservancy data and the meteorological data of each water station into the BP neural network model, obtain the predicted water level data of each station, retrieve the predicted water station water level in the water level data, define the water station whose predicted water station water level is greater than or equal to the first expected threshold as a flood warning water station, and define the water station whose predicted water station water level is less than or equal to the second expected threshold as a drought warning water station; The historical water conservancy data includes historical water level data, historical water flow data, historical evaporation data, historical water quality data and historical gate data; The historical meteorological data include historical temperature, historical humidity, historical air pressure, historical wind speed, historical wind direction, historical rainfall time, historical rainfall intensity and historical rainfall speed.

4. The artificial intelligence-based water conservancy project dispatching system according to claim 1, characterized in that: The water conservancy scheduling module includes a scheduling strategy unit and a simulation unit; The scheduling strategy unit is used to obtain flood particle information according to the flood warning water station, the flood particle information includes the water station type, water station location data, water station water level data and water station sluice data of other water stations except the flood warning water station, obtain the flood water station location data of the flood warning water station, obtain the flood water station scheduling distance according to the water station location data and the flood water station location data, determine the flood resistance particle value of each water station according to the flood water station scheduling distance and the water station type, obtain the water station with the largest flood resistance particle value, and define it as the flood discharge station of the flood warning water station; Acquire drought particle information according to the drought warning water station, the drought particle information includes other water station types, water station location data, water station water level data and water station sluice data except the drought warning water station, acquire drought water station location data of the drought warning water station, acquire drought water station dispatching distance according to the water station location data and the drought water station location data, determine drought resistance particle values ​​of each water station according to the drought water station dispatching distance and the water station type, acquire the water station with the largest drought resistance particle value, define it as the drought resistance water station of the drought warning water station, repeat the above steps to acquire the first water conservancy dispatching strategy; The simulation unit is used to establish a fluid simulation model according to the water conservancy data, determine the first fluid increment data according to the predicted water level data, and update the fluid simulation model according to the first fluid increment data, obtain the second fluid adjustment data through the first water conservancy scheduling strategy, and input the second fluid adjustment data into the fluid simulation model to obtain the fluid simulation data, retrieve the simulated flow of the fluid simulation data, define the flow of the simulated flow greater than or equal to the first expected threshold as the first abnormal flow, define the flow of the fluid simulation model less than or equal to the second expected threshold as the second abnormal flow, define the first water conservancy scheduling strategy corresponding to the first abnormal flow or the second abnormal flow as the abnormal fluid strategy, and adjust the abnormal fluid strategy according to the first abnormal flow and the second abnormal flow to obtain the second water conservancy scheduling strategy; Determining the drought-resistant particle value of each water station according to the drought water station dispatching distance and the water station type includes: Obtain a dispatching distance coefficient according to the dispatching distance of the drought water station, obtain a dispatching water station coefficient according to the type of the water station, the dispatching water station coefficient of the flood warning water station is 1, the dispatching water station coefficient of the normal operating water station is 0, and the dispatching water station coefficient of the drought warning water station is -1, and the dispatching distance coefficient and the dispatching water station coefficient are weightedly calculated to obtain a drought-resistant particle value; Determining the flood-resistant particle value of each water station according to the flood water station dispatching distance and the water station type includes: Obtain a dispatching distance coefficient according to the dispatching distance of the flood water station, obtain a dispatching water station coefficient according to the type of the water station, the dispatching water station coefficient of the flood warning water station is -1, the dispatching water station coefficient of the normal operating water station is 0, and the dispatching water station coefficient of the drought warning water station is 1, and the dispatching distance coefficient and the dispatching water station coefficient are weightedly calculated to obtain the post-flood resistance particle value; The types of water stations include flood warning water stations, drought warning water stations and normal operating water stations; The fluid simulation data includes simulated water level, simulated flow rate and simulated flow velocity.

5. The artificial intelligence-based water conservancy project dispatching system according to claim 1, characterized in that: The hydropower generation module includes a power generation information unit and a power generation strategy unit; The power generation information unit is used to obtain user demand information of the water station, and determine the user power supply distance according to the water station location and the user location, and use edge computing to determine the power supply user according to the user power supply distance; The power generation strategy unit is used to obtain the scheduling water station pair information according to the second water conservancy scheduling strategy, call the gate data of the scheduling water station pair information, and obtain the water head through the gate data, and use the water conservancy power generation calculation formula to obtain the single-gate power supply, double-gate power supply, triple-gate power supply of the water station through the water head... The peak power consumption per unit time is obtained through the user demand information of the power supply user, and the water conservancy power generation strategy is determined according to the peak power consumption. When the peak power consumption is less than or equal to the single-gate power supply, only the single gate is opened. When the peak power consumption is greater than the single-gate power supply and less than or equal to the double-gate power supply, the double gates are opened. When the peak power consumption is greater than or equal to the double-gate power supply and less than or equal to the three-gate power supply, the three gates are opened. Repeat the above steps to obtain the water conservancy power generation strategy, and schedule the water station gates according to the water conservancy power generation strategy.

6. The artificial intelligence-based water conservancy project dispatching system according to claim 3, characterized in that: The establishment of the BP neural network model specifically includes: Input the historical water conservancy data and the historical meteorological data into the input layer of the BP neural network, obtain a prediction factor based on the historical water conservancy data and the historical meteorological data, randomly obtain a first weight value and a first bias value of the prediction factor, and use a first activation function to obtain a first prediction value based on the first weight value and the first bias value, use the first prediction value as the input of the hidden layer, and use a loss function to obtain a loss value based on the first prediction value and the true value, adjust the first weight value and the first bias value based on the loss value, obtain a second weight value and a second bias value, repeat the above steps n times until the loss value is less than or equal to the loss expectation threshold, then obtain a final weight value and a final bias value, and use a second activation function based on the final weight value and the final bias value to obtain predicted water level data as the output of the output layer.

7. The artificial intelligence-based water conservancy project dispatching system according to claim 2, characterized in that: The water level data includes the average water level per unit time and the water level distribution per unit time; The water flow data includes flow rate per unit time, flow velocity per unit time and runoff per unit time; The evaporation data include average evaporation per unit time; The water quality data include water temperature, turbidity, dissolved oxygen content, pH value, chemical oxygen demand, total phosphorus and total nitrogen; The gate data includes the water level before the gate, the water level after the gate, the gate opening, the gate operating conditions and the flow rate passing through the gate.

8. The artificial intelligence-based water conservancy project dispatching system according to claim 5, characterized in that: The hydropower generation calculation formula is as follows: ； Among them, Q is the hydropower generation, q is the flow rate per unit time, H is the head value, g is the gravitational acceleration, and t is the power generation time.

9. The artificial intelligence-based water conservancy project dispatching system according to claim 5, characterized in that: Determining the power supply user according to the power supply distance of the user by using edge computing includes: The power supply distances of the users are arranged in ascending order, and the first m users are selected and determined as power supply users.

10. The artificial intelligence-based water conservancy project dispatching system according to claim 5, characterized in that: The dispatching water station pair information includes the first water station data, the second water station data and the gate data; The user demand information includes the power consumption area number, power demand and power consumption time.

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