Intelligent resource scheduling management system and method for urban river systems based on dynamic data collaboration

Abstract

This invention relates to the field of water resource scheduling technology, and proposes an intelligent resource scheduling management system and method for urban river systems based on dynamic data collaboration. The system includes the following steps: S1, real-time collection of river hydrological data, water quality indicators, urban water demand, and water diversion facility status data, followed by spatiotemporal alignment and outlier filtering; S2, establishment of a multi-source hydrodynamic-water quality coupling model, combined with constraints such as ecological baseflow and engineering upper limits, to establish a multi-objective optimization model. The multiple water sources include, but are not limited to, river water, groundwater, reservoir water, reclaimed water, rainwater, and desalinated seawater; S3, global dynamic optimization of the water diversion ratio and water diversion facility parameters using model algorithms to minimize water diversion costs and ecological impacts. By quantifying the maximum flow upper limit and ecological baseflow threshold of each water source, the water diversion ratio and facility parameters are optimized to improve water resource utilization efficiency and ensure a balance between water diversion costs and ecological impacts during the scheduling of urban river water.

Description

Technical Field

[0001] This invention relates to the field of water resource scheduling technology, and in particular to an intelligent resource scheduling management system and method for urban inland river systems based on dynamic data collaboration. Background Technology

[0002] With the acceleration of urbanization, urban river systems are facing problems such as intensified water supply and demand contradictions, insufficient ecological base flow guarantee, and poor coordination of multi-source water scheduling. Traditional scheduling methods rely on manual experience and lack the ability to respond in real time to dynamic hydrological data, water quality indicators, and constraints from multiple water sources. This leads to groundwater over-extraction, a disconnect between reservoir scheduling and water demand, low utilization rates of non-traditional water sources such as reclaimed water, and difficulty in effectively addressing ecological risks such as water quality deterioration and eutrophication.

[0003] Meanwhile, the single water source scheduling model cannot meet the multi-objective needs of complex urban water systems, and lacks the ability to integrate multi-source data, perform dynamic modeling, and optimize in real time, resulting in delayed scheduling decisions, extensive facility control, and an inability to achieve coordinated management of water quantity, water quality, and ecology. Summary of the Invention

[0004] In view of the problems existing in the prior art, the purpose of this invention is to provide an intelligent resource scheduling and management system and method for urban inland river systems based on dynamic data collaboration, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this invention provides an intelligent resource scheduling and management method for urban inland river systems based on dynamic data collaboration, comprising the following steps:

[0006] S1. Real-time collection of river hydrological data, water quality indicators, urban water demand, and water diversion facility status data, and spatiotemporal alignment and outlier filtering;

[0007] S2. Establish a hydrodynamic-water quality coupling model with multiple water sources. Combine the constraints of ecological base flow and engineering upper limit to establish a multi-objective optimization model. The multiple water sources include, but are not limited to, river water, groundwater, reservoir water, reclaimed water, rainwater and desalinated seawater.

[0008] S3. Employ model algorithms to perform global dynamic optimization of water diversion ratio and water diversion facility parameters to minimize water diversion costs and ecological impacts;

[0009] S4. Based on the optimization results, automatically adjust the equipment in the water transfer facility to complete the real-time dynamic adjustment and scheduling of multiple water sources, and dynamically allocate the water transfer volume through zone metering.

[0010] S5. Compare the actual water transfer data with the model predictions, correct the model parameters based on deviation analysis, optimize the ecological baseflow threshold by combining ecological response data, and update and optimize the water transfer plan in real time.

[0011] Preferably, in step S1, dynamic data acquisition and multi-source data fusion include the following steps:

[0012] S11. Collect urban water demand data, establish a water use forecasting model, and predict the total water consumption and water consumption during specific time periods for the next day. The formula is as follows: In the formula, For the predicted total daily water consumption, For the predicted hourly water consumption, In order to keep pace with time The relevant cumulative water consumption ratio, For smoothing coefficients, This represents the predicted water demand for the previous period. This represents the actual water consumption in the previous period.

[0013] S12. Real-time collection of river flow data to predict the average daily river flow, using the following formula: In the formula, To predict the average daily flow of the river, The number of data points used in the average calculation. For the River flow data collected at specific times. The current moment;

[0014] S13. Monitor river water index data, establish a water quality model, and predict changes in river water quality during the water transfer process. The formula is: In the formula, and These are the water quality concentration values ​​for the next and current moments, respectively. and These are the time step and the spatial step, respectively. and In spatial location and flux at the point, This refers to the generation or consumption of water-related substances.

[0015] S14. Remove outlier data and update the state estimate using the following formula: In the formula, and These are the state estimates for the current time and the previous time, respectively. , and These are the state transition matrix, control matrix, and observation matrix, respectively. For Kalman gain, For the observed values, For the updated state estimate, and These are particle weights and particle states, respectively. for The control matrix that controls the input at all times. The total number of particles, This is the starting count for the summation operation;

[0016] S15. Store the processed data on the blockchain and ensure its immutability through a hash function.

[0017] Preferably, in step S2, the construction of the multi-water source collaborative scheduling model includes the following steps:

[0018] S21. Based on the diffusion wave equation to describe river flow, construct a hydrodynamic model, the formula of which is: In the formula, For river flow, For a unit of time, The cross-sectional average velocity is... The longitudinal coordinates are along the direction of river flow. The side inflow flow rate, The cross-sectional area of ​​the water passage. The sign for partial derivatives;

[0019] S22. Define multiple objective functions and calculate the weights using the entropy weight method, as shown in the formula: In the formula, Let be the minimum value of the objective function. For the The entropy value of each target. For the Sub-objective functions;

[0020] S23. Define the constraints for ecological baseflow and engineering upper limit constraints, and use hierarchical clustering to divide the effective region into ineffective regions, as shown in the formula: In the formula, For reservoir and reservoir European distance, and Reservoirs and reservoir The characteristic parameters;

[0021] S24. Construct a hybrid scheduling model, employing particle swarm optimization scheduling, with the following formula: In the formula, and These are the particle velocities at the current moment and the previous moment, respectively. For inertial weights, and These are self-learning factors and social learning factors, and They are random numbers between 0 and 1. and The first The optimal position experienced by an individual particle and the optimal position experienced by the entire particle swarm. For the The particle in the first The position of the era.

[0022] Preferably, in step S3, the global optimization of water regulation parameters includes the following steps:

[0023] S31. Based on the water transfer ratio vector and water transfer facility parameters, the water transfer cost and ecological impact are extrapolated using the following formula: In the formula, To cover water diversion costs, For the Unit water transfer cost of similar water sources For the number of water source types, For the The proportion of water diversion from similar water sources This represents the total water demand. For the number of water diversion facilities, For the Unit energy cost of facilities For the Facility operating parameters The numerical values ​​represent the negative impacts of water diversion on the ecological environment. and As ecological weight, and , and These are the river's remaining flow after water diversion and the river's ecological base flow, respectively. and These are the measured water quality concentration and the standard water quality concentration, respectively.

[0024] S32. Add weights to water transfer costs and ecological impacts, and establish an objective function that minimizes both costs and impacts. The formula is as follows: In the formula, To minimize the projected values ​​of cost and impact, and For the corresponding weights, This is the deviation term;

[0025] S33. The roulette wheel selection operation calculates the selection probability based on chromosome fitness values ​​and seeks the optimal combination among various objective functions that minimize cost and impact. The formula is as follows: In the formula, Chromosomes The probability of choosing, This is the fitness value;

[0026] S34. By employing single-point crossover and Gaussian mutation operations to recombine genes and randomly perturb chromosomes, population diversity is increased, allowing the exploration of optimal solutions for decision variables within a larger search space. The formula is as follows: In the formula, These are the new individuals obtained after crossover and mutation operations. and These are the two parent individuals participating in the crossover operation. For the crossover operator, The standard deviation of variation It follows a standard normal distribution.

[0027] Preferably, in step S4, the dynamic allocation of water supply includes pump station power, sluice gate opening degree, and valve flow control. The pump station power control utilizes frequency conversion technology to adjust the motor speed, thereby achieving dynamic flow adjustment. The formula is: In the formula, For the pump station flow rate, The efficiency coefficient of the water pump. For the operating power of the pumping station, The power-flow index is used. The sluice gate opening control is based on an optimized sluice gate opening, and the flow rate is calculated using the broad-crested weir formula to drive the gate actuator. The formula is: In the formula, The flow rate of the sluice gate. For flow coefficient, For the gate width, It is the acceleration due to gravity. For the water head above the weir, This is the proportionality coefficient. For the sluice gate opening, The valve flow control, using the intercept coefficient, adjusts the pipeline pressure based on the optimized valve opening using the flow coefficient formula to ensure the water volume in each zone. The formula is: In the formula, For valve flow rate, For flow coefficient, The pressure before and after the valve. For fluid density, This is a coefficient representing the inherent characteristics of the valve. To describe the flow coefficient With the opening of the sluice gate An exponent of the non-linear relationship between them.

[0028] Preferably, in step S12, the real-time collection of river flow data also includes a comprehensive water quality index, predicted pollutant concentration, and assessment of eutrophication. The comprehensive water quality index is calculated by weighted averaging of various water quality indicators, comprehensively considering different indicators and their importance, and is derived using the following formula: In the formula, The comprehensive water quality index, As the indicator weight, , as well as The first The measured values, upper limit, and lower limit of the standard values ​​for the water quality indicators in the tank are used. The predicted pollutant concentration is determined by establishing a regression model based on the historical relationship between relevant influencing factors and pollutant concentration. The formula is as follows: In the formula, for The predicted concentration of pollutants at any given time. This is the constant term in the regression model. For regression coefficients, As a lagged variable, the assessment of eutrophication is based on chlorophyll concentration, and the Carlson index is calculated using a specific formula: In the formula, For the Carlson index, This represents the chlorophyll concentration.

[0029] Preferably, in step S23, the constraint setting includes the following steps:

[0030] S231. Determine the minimum ecological flow and calculate the ecological base flow threshold using the following formula: In the formula, To minimize ecological flow, This is the proportionality coefficient. Average flow rate;

[0031] S232. Based on the maximum opening of the sluice gate and the power of the pumping station, calculate the maximum water diversion volume and determine the water conveyance capacity of the project. The formula is as follows: In the formula, To the maximum water diversion volume, For flow coefficient, This represents the maximum opening of the sluice gate. For the sluice gate opening, It is the acceleration due to gravity;

[0032] S233. Quantify the substitution relationships of various water sources and establish a water source complementarity matrix. The formula is as follows: In the formula, They are complementary matrices. The substitution coefficient, and .

[0033] Preferably, in step S13, monitoring river water index data includes the following steps:

[0034] S131. Obtain the cross-sectional velocity distribution using an acoustic Doppler current meter and measure the river flow velocity distribution.

[0035] S132. Calculate the longitudinal diffusion coefficient of the river water, and perform spatial difference analysis on the measured concentration data to solve for the concentration gradient. The formula is as follows: In the formula, The longitudinal diffusion coefficient is... To adjust the coefficient, For substance concentration, For spatial coordinates, and The first The and the first The measured substance concentration at each monitoring point The distance between two adjacent monitoring points. As a variable indicator of river flow velocity, For gradient concentrations;

[0036] S133. Set the diffusion flux threshold corresponding to the environmental capacity, calculate the diffusion flux based on the longitudinal diffusion coefficient and gradient concentration calculation results, and assess the diffusion impact. The formula is: In the formula, For the actual diffusion flux, when the actual diffusion flux is... When the threshold is exceeded, meaning the spread exceeds the environment's carrying capacity, emergency dispatch needs to be triggered.

[0037] Preferably, in step S5, the formula for correcting the model parameters is: In the formula, and The first Subsequent Parameter values ​​at the next iteration For learning rate, Let be the loss function, and , This represents the sample size of the actual water transfer data. and These are actual water transfer data and model predictions, respectively.

[0038] The intelligent resource scheduling and management system for urban inland river systems based on dynamic data collaboration, applied to any one of the above-mentioned intelligent resource scheduling and management methods for urban inland river systems, includes:

[0039] The data acquisition and processing module is used to collect various data of the river in real time and process the data to provide an accurate data foundation for subsequent analysis.

[0040] The model building module is used to establish a multi-source water scheduling model, providing model support for the formulation of water transfer schemes;

[0041] The optimization calculation module is used to establish a global dynamic optimization model to minimize water transfer costs and ecological impacts;

[0042] The scheduling and execution module is used to intelligently control water diversion facilities and equipment based on optimization results to ensure the rational allocation of water resources.

[0043] The feedback optimization module is used to compare actual water transfer data, correct model parameters based on deviation analysis, and ensure that the water transfer plan is updated in real time and continuously optimized.

[0044] The intelligent resource scheduling and management system and method for urban inland river systems based on dynamic data collaboration provided by this invention have the following beneficial effects:

[0045] 1. By collecting real-time hydrological, water quality, water demand, and facility status data, and combining algorithms such as Kalman filtering and particle swarm optimization, a hydrodynamic-water quality coupling model and a multi-objective optimization model are constructed to achieve coordinated scheduling of multiple water sources such as river water, groundwater, and reclaimed water. At the same time, the maximum flow limit and ecological base flow threshold of each water source are quantified to optimize the water transfer ratio and facility parameters, improve water resource utilization efficiency, and ensure that the water transfer cost and ecological impact are balanced during the scheduling of urban river water.

[0046] 2. By optimizing the results, the pump station power, sluice gate opening and valve flow are automatically adjusted to achieve dynamic and precise flow control. Through the closed loop of "monitoring-modeling-optimization-execution-feedback", the gradient descent algorithm is used to correct the model parameters, and the ecological baseflow threshold is dynamically updated in combination with ecological response data. At the same time, the comprehensive water quality index, pollutant diffusion risk and eutrophication level are evaluated in real time, triggering the emergency dispatch mechanism, strengthening ecological security and system resilience, and improving the intelligence and reliability of urban river water resource management. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 A schematic diagram of the method steps for the intelligent resource scheduling and management system and method for urban inland river systems based on dynamic data collaboration provided in this application;

[0049] Figure 2A schematic diagram of the system modules of the intelligent resource scheduling and management system and method for urban inland river systems based on dynamic data collaboration provided in this application. Detailed Implementation

[0050] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0051] like Figure 1-Figure 2 As shown, this embodiment proposes an intelligent resource scheduling and management method for urban inland river systems based on dynamic data collaboration, including the following steps:

[0052] S1. Real-time collection of river hydrological data, water quality indicators, urban water demand, and water diversion facility status data, and spatiotemporal alignment and outlier filtering;

[0053] S2. Establish a hydrodynamic-water quality coupling model with multiple water sources. Combine the constraints of ecological base flow and engineering upper limit to establish a multi-objective optimization model. Multiple water sources include, but are not limited to, river water, groundwater, reservoir water, reclaimed water, rainwater and desalinated seawater.

[0054] S3. Employ model algorithms to perform global dynamic optimization of water diversion ratio and water diversion facility parameters to minimize water diversion costs and ecological impacts;

[0055] S4. Based on the optimization results, automatically adjust the equipment in the water transfer facility to complete the real-time dynamic adjustment and scheduling of multiple water sources, and dynamically allocate the water transfer volume through zone metering.

[0056] S5. Compare the actual water transfer data with the model predictions, correct the model parameters based on deviation analysis, optimize the ecological baseflow threshold by combining ecological response data, and update and optimize the water transfer plan in real time.

[0057] In this embodiment, step S1, dynamic data acquisition and multi-source data fusion, includes the following steps:

[0058] S11. Collect urban water demand data, establish a water use forecasting model, and predict the total water consumption and water consumption during specific time periods for the next day. The formula is as follows: In the formula, For the predicted total daily water consumption, For the predicted hourly water consumption, In order to keep pace with time The relevant cumulative water consumption ratio, For smoothing coefficients, This represents the predicted water demand for the previous period. This represents the actual water consumption in the previous period.

[0059] S12. Real-time collection of river flow data to predict the average daily river flow, using the following formula: In the formula, To predict the average daily flow of the river, The number of data points used in the average calculation. For the River flow data collected at specific times. The current moment;

[0060] S13. Monitor river water index data, establish a water quality model, and predict changes in river water quality during the water transfer process. The formula is: In the formula, and These are the water quality concentration values ​​for the next and current moments, respectively. and These are the time step and the spatial step, respectively. and In spatial location and flux at the point, This refers to the generation or consumption of water-related substances.

[0061] S14. Remove outlier data and update the state estimate using the following formula: In the formula, and These are the state estimates for the current time and the previous time, respectively. , and These are the state transition matrix, control matrix, and observation matrix, respectively. For Kalman gain, For the observed values, For the updated state estimate, and These are particle weights and particle states, respectively. for The control matrix that controls the input at all times. The total number of particles, This is the starting count for the summation operation;

[0062] S15. Store the processed data on the blockchain and ensure its immutability through a hash function.

[0063] In this embodiment, step S12, the real-time acquisition of river flow data also includes a comprehensive water quality index, predicted pollutant concentration, and assessment of eutrophication levels. The comprehensive water quality index is calculated by weighted averaging of various water quality indicators, comprehensively considering different indicators and their importance, to arrive at the comprehensive water quality index, as shown in the formula: In the formula, The comprehensive water quality index, As the indicator weight, , as well as The first The measured values, upper and lower limits of standard values ​​of water quality indicators in the tank are used to predict pollutant concentrations. A regression model is established by exploring the relationship between historical influencing factors and pollutant concentrations, and the formula is as follows: In the formula, for The predicted concentration of pollutants at any given time. This is the constant term in the regression model. For regression coefficients, As a lagged variable, the assessment of eutrophication is based on chlorophyll concentration, and the Carlson index is calculated using a specific formula: In the formula, For the Carlson index, This represents the chlorophyll concentration.

[0064] In this embodiment, step S13, monitoring river water index data includes the following steps:

[0065] S131. Obtain the cross-sectional velocity distribution using an acoustic Doppler current meter and measure the river flow velocity distribution.

[0066] S132. Calculate the longitudinal diffusion coefficient of the river water, and perform spatial difference analysis on the measured concentration data to solve for the concentration gradient. The formula is as follows: In the formula, The longitudinal diffusion coefficient is... To adjust the coefficient, For substance concentration, For spatial coordinates, and The first The and the first The measured substance concentration at each monitoring point The distance between two adjacent monitoring points. As a variable indicator of river flow velocity, For gradient concentrations;

[0067] S133. Set the diffusion flux threshold corresponding to the environmental capacity, calculate the diffusion flux based on the longitudinal diffusion coefficient and gradient concentration calculation results, and assess the diffusion impact. The formula is: In the formula, For the actual diffusion flux, when the actual diffusion flux is... When the threshold is exceeded, meaning the spread exceeds the environment's carrying capacity, emergency dispatch needs to be triggered.

[0068] In this embodiment, step S2, the construction of the multi-water source collaborative scheduling model includes the following steps:

[0069] S21. Based on the diffusion wave equation to describe river flow, construct a hydrodynamic model, the formula of which is: In the formula, For river flow, For a unit of time, The cross-sectional average velocity is... The longitudinal coordinates are along the direction of river flow. The side inflow flow rate, The cross-sectional area of ​​the water passage. The sign for partial derivatives;

[0070] S22. Define multiple objective functions and calculate the weights using the entropy weight method, as shown in the formula: In the formula, Let be the minimum value of the objective function. For the The entropy value of each target. For the Sub-objective functions;

[0071] S23. Define the constraints for ecological baseflow and engineering upper limit constraints, and use hierarchical clustering to divide the effective region into ineffective regions, as shown in the formula: In the formula, For reservoir and reservoir European distance, and Reservoirs and reservoir The characteristic parameters;

[0072] S24. Construct a hybrid scheduling model, employing particle swarm optimization scheduling, with the following formula: In the formula, and These are the particle velocities at the current moment and the previous moment, respectively. For inertial weights, and These are self-learning factors and social learning factors, and They are random numbers between 0 and 1. and The first The optimal position experienced by an individual particle and the optimal position experienced by the entire particle swarm. For the The particle in the first The position of the era.

[0073] In this embodiment, step S23, setting the constraint conditions includes the following steps:

[0074] S231. Determine the minimum ecological flow and calculate the ecological base flow threshold using the following formula: In the formula, To minimize ecological flow, This is the proportionality coefficient. Average flow rate;

[0075] S232. Based on the maximum opening of the sluice gate and the power of the pumping station, calculate the maximum water diversion volume and determine the water conveyance capacity of the project. The formula is as follows: In the formula, To the maximum water diversion volume, For flow coefficient, This represents the maximum opening of the sluice gate. For the sluice gate opening, It is the acceleration due to gravity;

[0076] S233. Quantify the substitution relationships of various water sources and establish a water source complementarity matrix. The formula is as follows: In the formula, They are complementary matrices. The substitution coefficient, and .

[0077] Specifically, by collecting real-time hydrological, water quality, water demand, and facility status data, and combining algorithms such as Kalman filtering and particle swarm optimization, a hydrodynamic-water quality coupling model and a multi-objective optimization model are constructed to achieve coordinated scheduling of multiple water sources such as river water, groundwater, and reclaimed water. At the same time, the maximum flow limit and ecological base flow threshold of each water source are quantified to optimize the water transfer ratio and facility parameters, improve water resource utilization efficiency, and ensure that the scheduling of urban river water balances water transfer costs and ecological impacts.

[0078] In this embodiment, step S3, the global optimization of water adjustment parameters includes the following steps:

[0079] S31. Based on the water transfer ratio vector and water transfer facility parameters, the water transfer cost and ecological impact are extrapolated using the following formula: In the formula, To cover water diversion costs, For the Unit water transfer cost of similar water sources For the number of water source types, For the The proportion of water diversion from similar water sources This represents the total water demand. For the number of water diversion facilities, For the Unit energy cost of facilities For the Facility operating parameters The numerical values ​​represent the negative impacts of water diversion on the ecological environment. and As ecological weight, and , and These are the river's remaining flow after water diversion and the river's ecological base flow, respectively. and These are the measured water quality concentration and the standard water quality concentration, respectively.

[0080] S32. Add weights to water transfer costs and ecological impacts, and establish an objective function that minimizes both costs and impacts. The formula is as follows: In the formula, To minimize the projected values ​​of cost and impact, and For the corresponding weights, This is the deviation term;

[0081] S33. The roulette wheel selection operation calculates the selection probability based on chromosome fitness values ​​and seeks the optimal combination among various objective functions that minimize cost and impact. The formula is as follows: In the formula, Chromosomes The probability of choosing, This is the fitness value;

[0082] S34. By employing single-point crossover and Gaussian mutation operations to recombine genes and randomly perturb chromosomes, population diversity is increased, allowing the exploration of optimal solutions for decision variables within a larger search space. The formula is as follows: In the formula, These are the new individuals obtained after crossover and mutation operations. and These are the two parent individuals participating in the crossover operation. For the crossover operator, The standard deviation of variation It follows a standard normal distribution.

[0083] In this embodiment, step S4, dynamically allocating water supply includes pump station power, sluice gate opening, and valve flow control. Pump station power control utilizes frequency conversion technology to adjust motor speed, thereby achieving dynamic flow adjustment. The formula is: In the formula, For the pump station flow rate, The efficiency coefficient of the water pump. For the operating power of the pumping station, The power-flow index is used. The sluice gate opening control is based on an optimized sluice gate opening, employing the broad-crested weir formula to calculate the flow rate and drive the gate actuator. The formula is: In the formula, The flow rate of the sluice gate. For flow coefficient, For the gate width, It is the acceleration due to gravity. For the water head above the weir, This is the proportionality coefficient. For the sluice gate opening, The intercept coefficient is used in valve flow control. Based on the optimized valve opening, the flow coefficient formula is used to adjust the pipeline pressure to ensure the water volume in each zone. The formula is: In the formula, For valve flow rate, For flow coefficient, The pressure before and after the valve. For fluid density, This is a coefficient representing the inherent characteristics of the valve. To describe the flow coefficient With the opening of the sluice gate An exponent of the non-linear relationship between them.

[0084] In this embodiment, the formula for correcting the model parameters in step S5 is: In the formula, and The first Subsequent Parameter values ​​at the next iteration For learning rate, Let be the loss function, and , This represents the sample size of the actual water transfer data. and These are actual water transfer data and model predictions, respectively.

[0085] The intelligent resource scheduling and management system for urban inland waterways based on dynamic data collaboration, and the intelligent resource scheduling and management method for urban inland waterways based on dynamic data collaboration applied to any of the above, include:

[0086] The data acquisition and processing module is used to collect various data of the river in real time and process the data to provide an accurate data foundation for subsequent analysis.

[0087] The model building module is used to establish a multi-source water scheduling model, providing model support for the formulation of water transfer schemes;

[0088] The optimization calculation module is used to establish a global dynamic optimization model to minimize water transfer costs and ecological impacts;

[0089] The scheduling and execution module is used to intelligently control water diversion facilities and equipment based on optimization results to ensure the rational allocation of water resources.

[0090] The feedback optimization module is used to compare actual water transfer data, correct model parameters based on deviation analysis, and ensure that the water transfer plan is updated in real time and continuously optimized.

[0091] Specifically, by optimizing the results, the power of pumping stations, the opening of sluice gates, and the flow of valves are automatically adjusted to achieve dynamic and precise flow control. Through a closed loop of "monitoring-modeling-optimization-execution-feedback", the gradient descent algorithm is used to correct model parameters, and the ecological baseflow threshold is dynamically updated in combination with ecological response data. At the same time, the comprehensive water quality index, pollutant diffusion risk, and eutrophication level are assessed in real time, triggering an emergency dispatch mechanism to strengthen ecological security and system resilience, and improve the intelligence and reliability of urban river water resource management.

[0092] The above embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Although the invention has been described in detail with reference to the embodiments, those skilled in the art should understand that various combinations, modifications, or equivalent substitutions of the technical solutions of the invention do not depart from the spirit and scope of the invention and should be covered within the scope of the claims of the invention.

Claims

1. A method for intelligent resource scheduling and management of urban inland river systems based on dynamic data collaboration, characterized in that: Includes the following steps: S1. Real-time collection of river hydrological data, water quality indicators, urban water demand, and water diversion facility status data, and spatiotemporal alignment and outlier filtering; S2. Establish a hydrodynamic-water quality coupling model with multiple water sources. Combine the constraints of ecological base flow and engineering upper limit to establish a multi-objective optimization model. The multiple water sources include, but are not limited to, river water, groundwater, reservoir water, reclaimed water, rainwater and desalinated seawater. S3. Employ model algorithms to perform global dynamic optimization of water diversion ratio and water diversion facility parameters to minimize water diversion costs and ecological impacts; S4. Based on the optimization results, automatically adjust the equipment in the water transfer facility to complete the real-time dynamic adjustment and scheduling of multiple water sources, and dynamically allocate the water transfer volume through zone metering. S5. Compare the actual water transfer data with the model predictions, correct the model parameters based on deviation analysis, optimize the ecological baseflow threshold by combining ecological response data, and update and optimize the water transfer plan in real time.

2. The intelligent resource scheduling and management method for urban inland river systems based on dynamic data collaboration as described in claim 1, characterized in that, Step S2 includes the following steps: S21. Based on the diffusion wave equation to describe river flow, construct a hydrodynamic model, the formula of which is: In the formula, For river flow, For a unit of time, The cross-sectional average velocity is... The longitudinal coordinates are along the direction of river flow. The side inflow flow rate, The cross-sectional area of ​​the water passage. The sign for partial derivatives; S22. Define multiple objective functions and calculate the weights using the entropy weight method, as shown in the formula: In the formula, Let be the minimum value of the objective function. For the The entropy value of each target. For the Sub-objective functions; S23. Define the constraints for ecological baseflow and engineering upper limit constraints, and use hierarchical clustering to divide the effective and ineffective regions, as shown in the formula: In the formula, For reservoir and reservoir European distance, and Reservoirs and reservoir The characteristic parameters; S24. Construct a hybrid scheduling model, employing particle swarm optimization scheduling, with the following formula: In the formula, and These are the particle velocities at the current moment and the previous moment, respectively. For inertial weights, and These are self-learning factors and social learning factors, and They are random numbers between 0 and 1. and The first The optimal position experienced by an individual particle and the optimal position experienced by the entire particle swarm. For the The particle in the first The position of the era.

3. The intelligent resource scheduling and management method for urban inland river systems based on dynamic data collaboration as described in claim 2, characterized in that, Step S3 includes the following steps: S31. Based on the water transfer ratio vector and water transfer facility parameters, the water transfer cost and ecological impact are extrapolated using the following formula: In the formula, To cover water diversion costs, For the Unit water transfer cost of similar water sources For the number of water source types, For the The proportion of water diversion from similar water sources This represents the total water demand. For the number of water diversion facilities, For the Unit energy cost of facilities For the Facility operating parameters The numerical values ​​represent the negative impacts of water diversion on the ecological environment. and As ecological weight, and , and These are the river's remaining flow after water diversion and the river's ecological base flow, respectively. and These are the measured water quality concentration and the standard water quality concentration, respectively. S32. Add weights to water transfer costs and ecological impacts, and establish an objective function that minimizes both costs and impacts. The formula is as follows: In the formula, To minimize the projected values ​​of cost and impact, and For the corresponding weights, This is the deviation term; S33. The roulette wheel selection operation calculates the selection probability based on chromosome fitness values ​​and seeks the optimal combination among various objective functions that minimize cost and impact. The formula is as follows: In the formula, Chromosomes The probability of choosing, This is the fitness value; S34. By employing single-point crossover and Gaussian mutation operations to recombine genes and randomly perturb chromosomes, population diversity is increased, allowing the exploration of optimal solutions for decision variables within a larger search space. The formula is as follows: In the formula, These are the new individuals obtained after crossover and mutation operations. and These are the two parent individuals participating in the crossover operation. For the crossover operator, The standard deviation of variation It follows a standard normal distribution.

4. The intelligent resource scheduling and management method for urban inland river systems based on dynamic data collaboration as described in claim 2, characterized in that, In step S4, the dynamic allocation of water supply includes pump station power, sluice gate opening degree, and valve flow control. The pump station power control utilizes frequency conversion technology to adjust the motor speed, thereby achieving dynamic flow adjustment. The formula is as follows: In the formula, For the pump station flow rate, The efficiency coefficient of the water pump. For the operating power of the pumping station, The power-flow index is used. The sluice gate opening control is based on an optimized sluice gate opening, and the flow rate is calculated using the broad-crested weir formula to drive the gate actuator. The formula is: In the formula, The flow rate of the sluice gate. For flow coefficient, For the gate width, It is the acceleration due to gravity. For the water head above the weir, This is the proportionality coefficient. For the sluice gate opening, The valve flow control, using the intercept coefficient, adjusts the pipeline pressure based on the optimized valve opening using the flow coefficient formula to ensure the water volume in each zone. The formula is: In the formula, For valve flow rate, For flow coefficient, The pressure before and after the valve. For fluid density, This is a coefficient representing the inherent characteristics of the valve. To describe the flow coefficient With the opening of the sluice gate An exponent of the non-linear relationship between them.

5. The intelligent resource scheduling and management method for urban inland river systems based on dynamic data collaboration according to claim 4, characterized in that, In step S23, setting the constraint conditions includes the following steps: S231. Determine the minimum ecological flow and calculate the ecological base flow threshold using the following formula: In the formula, To minimize ecological flow, This is the proportionality coefficient. Average flow rate; S232. Based on the maximum opening of the sluice gate and the power of the pumping station, calculate the maximum water diversion volume and determine the water conveyance capacity of the project. The formula is as follows: In the formula, To the maximum water diversion volume, For flow coefficient, This represents the maximum opening of the sluice gate. For the sluice gate opening, It is the acceleration due to gravity; S233. Quantify the substitution relationships of various water sources and establish a water source complementarity matrix. The formula is as follows: In the formula, They are complementary matrices. The substitution coefficient, and .

6. The intelligent resource scheduling and management method for urban inland river systems based on dynamic data collaboration according to claim 1, characterized in that, In step S5, the formula for correcting the model parameters is: In the formula, and The first Subsequent Parameter values ​​at the next iteration For learning rate, Let be the loss function, and , This represents the sample size of the actual water transfer data. and These are actual water transfer data and model predictions, respectively.

7. A smart resource scheduling and management system for urban inland river systems based on dynamic data collaboration, applied to the smart resource scheduling and management method for urban inland river systems based on dynamic data collaboration as described in any one of claims 1-6, characterized in that, include: The data acquisition and processing module is used to collect various data of the river in real time and process the data to provide an accurate data foundation for subsequent analysis. The model building module is used to establish a multi-source water scheduling model, providing model support for the formulation of water transfer schemes; The optimization calculation module is used to establish a global dynamic optimization model to minimize water transfer costs and ecological impacts; The scheduling and execution module is used to intelligently control water diversion facilities and equipment based on optimization results to ensure the rational allocation of water resources. The feedback optimization module is used to compare actual water transfer data with model predictions, and correct model parameters based on deviation analysis to ensure that the water transfer plan is updated in real time and continuously optimized.

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