Urban short-term water supply optimization scheduling method and device based on water-energy balance and medium

Abstract

The invention discloses an urban short-term water supply optimal scheduling method and device based on water-energy balance and a medium, and relates to the technical field of water supply scheduling optimizing.The method comprises the steps that a dynamic topological combination technology is utilized to construct a to-be-scheduled urban water supply system network diagram; extracting a water use sequence sample from the historical water use data of the urban water supply system to be dispatched as an input feature; using the water consumption prediction model and the water consumption prediction error determination model to obtain a water consumption prediction value of the next day; and inputting precipitation forecast information of the current day into the reservoir inflow prediction model to obtain a reservoir inflow prediction value of the next day, determining reservoir water storage change data of the next day, and finishing water consumption scheduling of the to-be-scheduled urban water supply system in combination with the scheduling model. The efficiency and rationality of urban daily water supply scheduling can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of water supply scheduling optimization, and in particular to a method, equipment and medium for optimizing urban short-term water supply scheduling based on water-energy balance. Background Art

[0002] With the acceleration of urbanization, the urban population is growing, and the demand for water resources is also increasing. However, the supply of water resources is limited by natural conditions and infrastructure capacity, and the contradiction between supply and demand is becoming increasingly prominent. Especially during droughts and peak water use periods, how to ensure the reliability and stability of urban water supply has become an important research topic. At the same time, the energy consumption of the water supply system in the process of water transmission and distribution cannot be ignored. Improving the energy efficiency of the water supply system has become the key to optimizing urban water supply management.

[0003] Traditional urban water supply systems mainly rely on experience and historical data for scheduling and management, lack scientific prediction and optimization methods, and are difficult to cope with complex and changing supply and demand situations. In addition, traditional methods are often inefficient when dealing with multi-objective optimization problems and cannot achieve optimal energy efficiency while ensuring water supply security. Summary of the invention

[0004] The purpose of this application is to provide a method, equipment and medium for optimizing the scheduling of urban short-term water supply based on water-energy balance, which can improve the efficiency and rationality of daily urban water supply scheduling.

[0005] To achieve the above objectives, this application provides the following solutions: In the first aspect, the present application provides a method for optimizing the short-term urban water supply based on water-energy balance, including: using dynamic topological combination technology to construct a network diagram of the urban water supply system to be scheduled. The network diagram of the urban water supply system to be scheduled is used to reflect the spatial supply and demand relationship of points, lines and surfaces within the urban water supply system to be scheduled.

[0006] The water consumption sequence samples are extracted from the historical water consumption data of the urban water supply system to be scheduled as input features.

[0007] The input features are input into the water consumption prediction model to obtain an initial prediction value of the water consumption for the next day.

[0008] The input features are input into a water consumption prediction error determination model to obtain a prediction error. The water consumption prediction model and the water consumption prediction error determination model are obtained by jointly training a bidirectional long short-term memory network and an XGBoost model using historical water consumption data. The bidirectional long short-term memory network includes an attention mechanism.

[0009] The prediction error is used to correct the initial prediction value of the next day's water consumption to obtain the next day's water consumption prediction value.

[0010] Get the current daily precipitation forecast information for the city to be dispatched.

[0011] The current day's precipitation forecast information is input into the reservoir inflow prediction model to obtain the next day's reservoir inflow prediction value. The reservoir inflow prediction model is the Xin'anjiang hydrological model.

[0012] Based on the reservoir inflow for the next day and the reservoir water level data for the current period, the reservoir water storage change data for the next day is determined.

[0013] The water consumption scheduling of the urban water supply system to be scheduled is completed using the scheduling model according to the predicted value of water consumption for the next day, the reservoir water storage capacity change data for the next day and the network diagram of the urban water supply system to be scheduled.

[0014] Optionally, extracting water usage sequence samples from historical water usage data of the urban water supply system to be scheduled as input features includes: acquiring historical water usage data of the urban water supply system to be scheduled.

[0015] The historical water use data of the urban water supply system to be scheduled is preprocessed to obtain preprocessed water use data. The preprocessing includes real data processing and abnormal data processing.

[0016] The maximum information coefficient is used to use water series samples from historical water use data as input features.

[0017] Optionally, after extracting water usage sequence samples as input features from the historical water usage data of the urban water supply system to be scheduled, the method further includes: constructing a training set. The training set includes a plurality of data pairs. Any of the data pairs includes the input features of the corresponding historical day and the water usage of the day after the corresponding historical day.

[0018] Optionally, after constructing the training set, the method further includes: inputting multiple input features in the training set into a bidirectional long short-term memory network to obtain an output prediction value corresponding to each input feature.

[0019] The difference between the output prediction and the output corresponding to the same input feature is determined as the actual error.

[0020] Input the historical daily input features into the XGBoost model and get the prediction error.

[0021] The loss function value is determined based on the actual error and the predicted error.

[0022] Based on the loss function value, adjust the parameters of the bidirectional long short-term memory network and the XGBoost model, and return to the step "input multiple input features into the bidirectional long short-term memory network to obtain the output prediction corresponding to each input feature" until the number of iterations reaches the preset number of iterations and the loss function value is less than the loss function value threshold.

[0023] The bidirectional long short-term memory network at the last iteration is determined as the water consumption prediction model.

[0024] The XGBoost model at the last iteration is determined as the water consumption prediction error determination model.

[0025] Optionally, according to the predicted water consumption value for the next day, the water storage capacity change data of the reservoir for the next day and the network diagram of the urban water supply system to be scheduled, the water scheduling of the urban water supply system to be scheduled is completed using the scheduling model, including: determining the product of the early warning factor and the predicted water consumption value for the next day as the reservoir capacity determination amount. The early warning factor is a positive number less than 1.

[0026] Determine whether the next day's water storage reservoir capacity is lower than the water storage reservoir capacity determination amount and obtain a determination result.

[0027] If the judgment result is yes, the emergency dispatch plan is activated.

[0028] If the judgment result is no, the scheduling model is solved using a mixed integer linear programming method based on the current reservoir inflow change data and the network diagram of the urban water supply system to be scheduled, to obtain the water resource allocation optimization result, and to determine the scheduling plan based on the water resource allocation optimization result.

[0029] Control the urban water supply system to be scheduled to execute the scheduling plan.

[0030] Optionally, the scheduling model includes a first objective function, a second objective function, a project water diversion capacity constraint, a reservoir storage capacity limit and a water balance constraint.

[0031] Optionally, the first objective function is: .

[0032] in, is the first objective function, For the i Reservoir j Water Plant No. t The amount of water supplied during the period, For the j The water demand of a water plant in period t.

[0033] The second objective function is: .

[0034] .

[0035] .

[0036] in, is the second objective function, For thei Reservoir j The energy consumption coefficient of water transmission and distribution of each water plant is For the i Reservoir t Time period j The water supply of each water plant, is the number of reservoirs, J is the number of water plants, T is the dispatching period, For the i Reservoir j The water production energy consumption coefficient of a water plant.

[0037] The water diversion capacity constraints of the project are: .

[0038] in, For the i Reservoirs in The amount of water supplied during the period, For the i The maximum water supply capacity of a reservoir.

[0039] The reservoir capacity limit is: .

[0040] in, is the minimum storage capacity of the reservoir, For the i The storage capacity of a reservoir in time period t, The maximum storage capacity of the reservoir.

[0041] The water balance constraint is: .

[0042] in, For the i Reservoirs in t+ Storage capacity for 1 period, For the i Reservoirs t +1 period of inbound traffic, For the i Evaporation and seepage of a reservoir.

[0043] Optionally, the scheduling scheme determination formula is: .

[0044] in, is the actual total water supply of the reservoir, is the total water demand of the water plant, is the total water deficit.

[0045] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned urban short-term water supply optimization scheduling method based on water-energy balance.

[0046] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned urban short-term water supply optimization scheduling method based on water-energy balance.

[0047] According to the specific embodiments provided in this application, this application discloses the following technical effects: The present application provides a method, device and medium for optimizing and scheduling short-term urban water supply based on water-energy balance. Through accurate data collection, feature screening, water demand prediction, water resource optimization configuration, real-time scheduling and early warning emergency mechanism, efficient management and stable operation of urban water supply system are realized. By combining the bidirectional long short-term memory network and extreme gradient boosting (XGBoost) algorithm with attention mechanism, and using particle swarm optimization algorithm for hyperparameter optimization, the accuracy and stability of daily water demand prediction are improved, so as to ensure that the water supply system can accurately respond to changes in urban water demand. Based on the accurate water demand prediction results, the mixed integer linear programming (MILP) model is used for optimizing and scheduling water resources. It ensures that water resources are reasonably and efficiently allocated in different time periods, maximizes resource utilization, minimizes water supply costs and energy consumption, and improves the operating efficiency of urban water supply systems. By monitoring the difference between actual water consumption and predicted values, setting an early warning threshold, triggering an early warning mechanism when the difference exceeds the set range, and providing an emergency scheduling plan, it ensures that the water supply system can still operate stably in emergencies, effectively responds to sudden demands and abnormal conditions, and improves the reliability and safety of urban water supply systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 This is a flow chart of a method for optimizing and scheduling short-term urban water supply based on water-energy balance in one embodiment of the present application.

[0050] Figure 2 This is a schematic diagram of a method for optimizing short-term urban water supply scheduling based on water-energy balance in one embodiment of the present application.

[0051] Figure 3A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0054] In an exemplary embodiment, Figure 1 and Figure 2 As shown, a method for optimizing the short-term urban water supply scheduling based on water-energy balance is provided, including: Step 101: Using dynamic topology combination technology, construct a network diagram of the city water supply system to be scheduled. The network diagram of the city water supply system to be scheduled is used to reflect the spatial supply and demand relationship of points, lines and surfaces in the city water supply system to be scheduled.

[0055] Step 102: Extract water usage sequence samples from the historical water usage data of the urban water supply system to be scheduled as input features.

[0056] Step 103: Input the input features into the water consumption prediction model to obtain an initial prediction value of the water consumption for the next day.

[0057] Step 104: Input the input features into the water consumption prediction error determination model to obtain the prediction error. The water consumption prediction model and the water consumption prediction error determination model are obtained by jointly training the bidirectional long short-term memory network and the XGBoost model using historical water consumption data. The bidirectional long short-term memory network includes an attention mechanism.

[0058] Step 105: Correct the initial prediction value of the next day's water consumption using the prediction error to obtain the next day's water consumption prediction value.

[0059] Step 106: Obtain the current daily precipitation forecast information of the city to be scheduled.

[0060] Step 107: Input the current day's precipitation forecast information into the reservoir inflow prediction model to obtain the next day's reservoir inflow prediction value. The reservoir inflow prediction model is the Xin'anjiang hydrological model.

[0061] The Xin'anjiang hydrological model is used to predict the reservoir inflow. The Xin'anjiang model uses rainfall, evaporation and other hydrological data to make predictions.

[0062]

[0063] is the predicted reservoir inflow for the next day, For the Xin'anjiang model, is the rainfall on day t, is the evaporation on day t, is the hydrological data such as initial soil moisture content.

[0064] Step 108: Based on the reservoir inflow for the next day and the reservoir water level data for the current period, determine the reservoir water storage capacity change data for the next day.

[0065] Step 109: Based on the predicted water consumption for the next day, the reservoir water storage capacity change data for the next day and the network diagram of the urban water supply system to be scheduled, the scheduling model is used to complete the water scheduling of the urban water supply system to be scheduled.

[0066] Before step 102, the method further includes: obtaining historical water use data of the urban water supply system to be scheduled. Preprocessing the historical water use data of the urban water supply system to be scheduled to obtain preprocessed water use data. The preprocessing includes real data processing and abnormal data processing. Using the maximum information coefficient, water sequence samples from the historical water use data are used as input features.

[0067] Collect historical water consumption data of the urban water supply system, mainly including daily water consumption data. Use the maximum information coefficient (MIC) method to select highly relevant input features. Clean the collected data, handle missing values ​​and outliers, and use the sliding window method to convert the data into a supervised learning format.

[0068] Construct a training set. The training set includes multiple data pairs. Any of the data pairs includes input features corresponding to the historical day, and water consumption the day after the corresponding historical day. Input multiple input features in the training set into the bidirectional long short-term memory network to obtain the output prediction amount corresponding to each input feature. Determine the difference between the output prediction amount and the output amount corresponding to the same input feature as the actual error. Input the input features of the historical day into the XGBoost model to obtain the prediction error. Determine the loss function value based on the actual error and the prediction error. Based on the loss function value, adjust the parameters of the bidirectional long short-term memory network and the XGBoost model, and return to the step "input multiple input features into the bidirectional long short-term memory network to obtain the output prediction amount corresponding to each input feature" until the number of iterations reaches the preset number of iterations, and the loss function value is less than the loss function value threshold. Determine that the bidirectional long short-term memory network at the last iteration is a water consumption prediction model. Determine that the XGBoost model at the last iteration is a water consumption prediction error determination model.

[0069] A bidirectional long short-term memory network (Attention-BiLSTM) with an attention mechanism is constructed to capture the bidirectional characteristics of water use data and the impact of important time points. The particle swarm optimization algorithm is used to optimize the model's hyperparameters to improve prediction accuracy.

[0070] The prediction formula is as follows: .

[0071] The predicted water consumption for the next day. is an attention bidirectional long short-term memory network model, is the current input feature, is the historical implicit state, is the optimized hyperparameter, is the residual.

[0072] The historical water use series samples (input features) are used as input, and the extreme gradient boosting (XGBoost) algorithm is used to perform residual correction to improve the prediction accuracy.

[0073]

[0074] Step 109 includes: determining the product of the early warning factor and the next day's water consumption forecast value as the reservoir capacity determination amount. The early warning factor is a positive number less than 1.

[0075] Determine whether the next day's water storage reservoir capacity is lower than the water storage reservoir capacity determination amount and obtain a determination result.

[0076] If the judgment result is yes, the emergency dispatch plan is activated. When the emergency dispatch plan is activated, an emergency dispatch plan warning signal is issued and the backup water source is activated. When the emergency dispatch plan is activated, the priority of domestic water use is higher than that of industrial water use and service water use.

[0077] When the emergency dispatch plan is activated: 1. Issue an early warning reminder: In order to ensure the safety and stability of the urban water supply system, when the system detects that the water supply may face potential risks, the system will immediately issue an early warning reminder. 2. Prioritize domestic water use and moderately reduce industrial and service water use: In the early warning state, prioritize the domestic water use of residents, while maintaining the overall water supply balance by reducing non-emergency industrial and service water demand. 3. Start backup water sources to supplement water supply: Start backup water sources including groundwater sources, emergency reservoirs and other alternative water sources to supplement the water supply gap. Ensure the stable operation of the water supply system.

[0078] If the judgment result is no, the scheduling model is solved using a mixed integer linear programming method based on the current reservoir inflow change data and the network diagram of the urban water supply system to be scheduled, to obtain the water resource allocation optimization result, and to determine the scheduling plan based on the water resource allocation optimization result.

[0079] Control the urban water supply system to be scheduled to execute the scheduling plan.

[0080] Based on the short-term water demand forecast and reservoir inflow forecast results, the mixed integer linear programming (MILP) method is used to allocate water resources to ensure the balance of water resource allocation and water supply energy consumption in different time periods.

[0081] Among them, the scheduling model includes the first objective function, the second objective function, the project water diversion capacity constraint, the reservoir capacity limit and the water balance constraint.

[0082] The first objective function is: .

[0083] in, is the first objective function, For the i Reservoir j Water Plant No. t The amount of water supplied during the period, For the j The water demand of a water plant in period t.

[0084] The second objective function is: .

[0085] .

[0086] .

[0087] in, is the second objective function, For the i Reservoir j The energy consumption coefficient of water transmission and distribution of each water plant is For the i Reservoir t Time period j The water supply of each water plant, is the number of reservoirs, J is the number of water plants, T is the dispatching period, For the i Reservoir j The water production energy consumption coefficient of a water plant.

[0088] The water diversion capacity constraint of the project is: .

[0089] in, For the i Reservoirs in The amount of water supplied during the period, For the i The maximum water supply capacity of a reservoir.

[0090] The reservoir capacity is limited to: .

[0091] in, is the minimum storage capacity of the reservoir, For the i The storage capacity of a reservoir in time period t, The maximum storage capacity of the reservoir.

[0092] The water balance constraint is: .

[0093] in, For the i Reservoirs in t+ Storage capacity for 1 period, For the i The inflow of a reservoir during period t+1 is: For the i Evaporation and seepage of a reservoir.

[0094] The scheduling scheme determination formula is: .

[0095] in, is the actual total water supply of the reservoir, is the total water demand of the water plant, is the total water deficit.

[0096] According to the optimized water resources allocation results, a water supply scheduling plan is formulated. The plan takes into account the real-time monitoring data of the water supply system, historical scheduling records, predicted water demand, and reservoir water inflow forecasts. The water supply scheduling plan is adjusted in real time, and an adaptive dynamic scheduling algorithm is used to dynamically optimize the scheduling plan according to the changes in real-time monitoring data and predicted data. The current reservoir capacity is calculated based on the predicted water inflow. When the predicted reservoir capacity is lower than 80% of the water demand, the early warning mechanism is triggered. Provide emergency scheduling plans to ensure the stable operation of the water system.

[0097] In the process of water demand forecasting, this application uses advanced feature screening and data processing methods to accurately extract the key factors affecting water demand. By performing feature engineering on historical water supply data, the prediction accuracy and stability of the model are improved. A high-precision daily-scale water demand forecasting model is constructed by coupling the Attention mechanism and the bidirectional long short-term memory network (BiLSTM) with the XGBoost algorithm. The Attention mechanism can effectively capture important information in time series data, the BiLSTM network can better handle the front-back dependency of the time series, and the XGBoost algorithm further optimizes the model performance through the gradient boosting method. A water resource allocation method with the goal of minimizing the difference between water supply and demand and minimizing the energy consumption of the water supply system is proposed. The solution is solved by the mixed integer linear programming (MILP) model, which comprehensively considers multiple constraints such as water resource supply and demand balance, energy efficiency optimization, evaporation and leakage loss, and realizes the efficient configuration of the urban water supply system and the balance of water supply energy consumption. In terms of water resource scheduling, the scheme optimizes the scheduling of the reservoir water level process line to ensure the rationality and safety of the reservoir operation under different water inflow conditions. At the same time, an early warning emergency mechanism has been set up. When the predicted water inflow is significantly lower than the water demand, an early warning is triggered and emergency measures are taken in a timely manner to ensure the safety of the city's water supply.

[0098] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing the scheduling of urban short-term water supply based on water-energy balance is implemented.

[0099] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0100] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0101] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0102] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0103] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0104] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing short-term urban water supply based on water-energy balance, characterized in that: include: Use dynamic topology combination technology to construct the network diagram of the urban water supply system to be dispatched; The network diagram of the urban water supply system to be scheduled is used to reflect the spatial supply and demand relationship of points, lines and surfaces within the urban water supply system to be scheduled; Extract water consumption sequence samples from the historical water consumption data of the urban water supply system to be scheduled as input features; Inputting the input features into a water consumption prediction model to obtain an initial prediction value of water consumption for the next day; The input features are input into a water consumption prediction error determination model to obtain a prediction error; the water consumption prediction model and the water consumption prediction error determination model are obtained by jointly training a bidirectional long short-term memory network and an XGBoost model using historical water consumption data; the bidirectional long short-term memory network includes an attention mechanism; Correcting the initial forecast value of the water consumption for the next day by using the forecast error to obtain the forecast value of the water consumption for the next day; Obtain the current daily precipitation forecast information of the city to be dispatched; Input the current day's precipitation forecast information into the reservoir inflow prediction model to obtain the next day's reservoir inflow prediction value; the reservoir inflow prediction model is the Xin'an River hydrological model; Based on the next day's reservoir inflow and the reservoir water level data of the current period, determine the reservoir water storage change data for the next day; The water consumption scheduling of the urban water supply system to be scheduled is completed using the scheduling model according to the predicted value of water consumption for the next day, the reservoir water storage capacity change data for the next day and the network diagram of the urban water supply system to be scheduled.

2. The urban short-term water supply optimization scheduling method based on water-energy balance according to claim 1 is characterized in that: Extract water consumption sequence samples from the historical water consumption data of the urban water supply system to be scheduled as input features, including: Obtain historical water consumption data of the urban water supply system to be dispatched; Preprocessing the historical water use data of the urban water supply system to be dispatched to obtain preprocessed water use data; the preprocessing includes real data processing and abnormal data processing; The maximum information coefficient is used to extract water use sequence samples from historical water use data as input features.

3. The urban short-term water supply optimization scheduling method based on water-energy balance according to claim 2 is characterized in that: After extracting water consumption sequence samples from the historical water consumption data of the urban water supply system to be scheduled as input features, it also includes: Constructing a training set; the training set includes a plurality of data pairs; any of the data pairs includes an input feature corresponding to a historical day, and water consumption on the next day corresponding to the historical day.

4. The urban short-term water supply optimization scheduling method based on water-energy balance according to claim 3 is characterized in that: After building the training set, also include: Input multiple input features in the training set into the bidirectional long short-term memory network to obtain the output prediction corresponding to each input feature; Determine the difference between the output prediction and the output corresponding to the same input feature as the actual error; Input the input features of the historical day into the XGBoost model to get the prediction error; Determine the loss function value based on the actual error and the predicted error; Based on the loss function value, adjust the parameters of the bidirectional long short-term memory network and the XGBoost model, and return to step "input multiple input features into the bidirectional long short-term memory network to obtain the output prediction corresponding to each input feature" until the number of iterations reaches the preset number of iterations and the loss function value is less than the loss function value threshold; Determine that the bidirectional long short-term memory network at the last iteration is the water consumption prediction model; The XGBoost model at the last iteration is determined as the water consumption prediction error determination model.

5. The urban short-term water supply optimization scheduling method based on water-energy balance according to claim 3 is characterized in that: According to the predicted value of water consumption for the next day, the change data of water storage capacity of the reservoir for the next day and the network diagram of the urban water supply system to be scheduled, the water consumption scheduling of the urban water supply system to be scheduled is completed by using the scheduling model, including: Determine the product of the early warning factor and the predicted value of water consumption for the next day as the reservoir capacity determination amount; the early warning factor is a positive number less than 1; Determine whether the next day's water storage reservoir capacity is lower than the water storage reservoir capacity determination amount, and obtain a determination result; If the judgment result is yes, the emergency dispatch plan is activated; If the judgment result is no, then according to the current reservoir inflow change data and the network diagram of the urban water supply system to be scheduled, the mixed integer linear programming method is used to solve the scheduling model to obtain the water resource allocation optimization result, and the scheduling plan is determined based on the water resource allocation optimization result; Control the urban water supply system to be scheduled to execute the scheduling plan.

6. The urban short-term water supply optimization scheduling method based on water-energy balance according to claim 5 is characterized in that: The scheduling model includes a first objective function, a second objective function, a project water diversion capacity constraint, a reservoir storage capacity limit and a water balance constraint.

7. The urban short-term water supply optimization scheduling method based on water-energy balance according to claim 6 is characterized in that: The first objective function is: ； in, is the first objective function, For the i Reservoir j Water Plant No. t The amount of water supplied during the period, For the j The water demand of a water plant in period t; The second objective function is: ； ； ； in, is the second objective function, For the i Reservoir j The energy consumption coefficient of water transmission and distribution of each water plant is For the i Reservoir t Time period j The water supply of each water plant, is the number of reservoirs, J is the number of water plants, T is the dispatching period, For the i Reservoir j The water production energy consumption coefficient of each water plant; The water diversion capacity constraints of the project are: ； in, For the i Reservoirs in The amount of water supplied during the period, For the i The maximum water supply capacity of each reservoir; The reservoir capacity limit is: ； in, is the minimum storage capacity of the reservoir, For the i The storage capacity of a reservoir in time period t, is the maximum storage capacity of the reservoir; The water balance constraint is: ； in, For the i Reservoirs in t+ Storage capacity for 1 period, For the i The inflow of a reservoir in the period t+1 is: For the i Evaporation and seepage of a reservoir.

8. The urban short-term water supply optimization scheduling method based on water-energy balance according to claim 7 is characterized in that: The scheduling scheme determination formula is: ； in, is the actual total water supply of the reservoir, is the total water demand of the water plant, is the total water deficit.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the urban short-term water supply optimization scheduling method based on water-energy balance as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing and scheduling urban short-term water supply based on water-energy balance described in any one of claims 1 to 8 is implemented.

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