A Smart City Planning Simulation Dynamic Simulation System and Method

Abstract

This application relates to the field of smart city planning and management, and discloses a smart city planning simulation dynamic simulation system and method, including: a traffic flow model module for describing the spatio-temporal evolution process of urban traffic flow; an energy consumption model module for describing the energy consumption of each region of the city and its changes over time, and combining with an energy supply control strategy to achieve the optimal scheduling of urban energy; a pollutant diffusion model module for describing the spatio-temporal diffusion behavior of pollutants in the city, and combining with a pollutant control strategy to achieve the optimization of pollutant emissions; an objective function calculation module for calculating the multi-objective optimization function of the smart city. By using a partial differential equation model to describe the spatio-temporal evolution process of traffic flow, energy consumption and pollutant diffusion, the technical effect of accurately simulating the dynamic changes of various urban resources is achieved. It can more comprehensively predict the impact of urban resource scheduling.

Description

Technical Field

[0001] The present invention relates to the field of smart city planning and management, and specifically to a smart city planning simulation dynamic simulation system and method. Background Art

[0002] With the continuous advancement of smart city construction, problems such as urban traffic flow, energy consumption, and environmental pollution have become increasingly prominent, and there is an urgent need for an efficient resource scheduling and optimization solution. However, there are some fundamental deficiencies in the technical solutions applied in the field of urban resource scheduling. Most of the existing technologies focus on the optimization of a specific field, such as separately solving traffic flow problems, reducing energy consumption, or reducing pollution emissions. This single optimization method cannot cope with the mutual influence of multi-dimensional complex problems in the city, so its optimization effect is often not ideal, and it is difficult to provide a comprehensive solution.

[0003] Many existing smart city planning simulation schemes use models that only focus on a single factor, such as the optimization of traffic flow, the reduction of energy consumption, or the control of pollutant emissions. Although these methods have achieved certain results in their respective fields, they usually ignore the interaction between different fields in the city. For example, simply optimizing traffic flow may lead to an increase in energy consumption, or the improvement of traffic flow may exacerbate pollutant emissions in certain areas. This one-sided optimization method lacks comprehensive consideration of urban resource scheduling and cannot effectively balance various resources and environmental impacts. Moreover, existing technologies usually adopt single-objective optimization methods, separating multiple objectives of urban management and optimizing them separately. Although in some cases, this method can achieve short-term optimization effects, it cannot find a reasonable balance between multiple objectives. For example, excessive optimization of traffic flow may lead to a sharp increase in energy consumption, and measures to reduce pollution emissions may have an adverse impact on energy consumption. This limitation makes existing technologies often unable to maximize the overall benefit when facing complex urban planning tasks. Therefore, technical personnel in this field have proposed a smart city planning simulation dynamic simulation system and method to solve the above problems. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides a smart city planning simulation dynamic simulation system and method, aiming to solve problems such as one-sided optimization, lagging response, insufficient resource scheduling, and poor control adaptability in the existing technology.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A smart city planning simulation dynamic simulation system, comprising:

[0006] A traffic flow model module for describing the spatio-temporal evolution process of urban traffic flow;

[0007] An energy consumption model module, which is used to describe the energy consumption of each region in the city and its changes over time, and combines with an energy supply control strategy to achieve the optimal scheduling of urban energy;

[0008] A pollutant diffusion model module, which is used to describe the spatio-temporal diffusion behavior of pollutants in the city and combines with a pollutant control strategy to optimize pollutant emissions;

[0009] A target function calculation module, which is used to calculate the multi-objective optimization function of the smart city;

[0010] An optimization control module, which is used to adjust the scheduling strategies of traffic flow, energy, and pollutants in real time based on the optimal control theory and combining optimization methods for multi-objectives and multi-constraints;

[0011] A feedback control module, which is used to obtain the state data of each system in the city through a real-time data monitoring system and provide feedback;

[0012] A numerical solution module, which is used to discretize partial differential equations and solve the optimal control strategy through numerical optimization algorithms.

[0013] Preferably, the traffic flow model module is based on the Lighthill-Whitham-Richards model and uses the relationship between traffic density and flow velocity to simulate the evolution process of traffic flow. The state equation of the traffic flow is as follows:

[0014]

[0015] where: ρ(x,t) represents the traffic density at time t and spatial position x; v(x,t) is the flow velocity; x is the spatial position, representing different positions on the road; and t is the time.

[0016] Preferably, the energy consumption model module is based on a dynamic model of energy demand. The state equation of the energy consumption is as follows:

[0017]

[0018] where: E(x,t) represents the energy demand at time t and spatial position x; α is the energy decay coefficient; β is the energy supply coefficient; and u(x,t) is the energy scheduling control variable.

[0019] Preferably, the pollutant diffusion model module is based on a diffusion equation to describe the spatial diffusion of pollutants and the control strategy. The state equation of the pollutant diffusion is as follows:

[0020]

[0021] Where: c(x,t) represents the pollutant concentration at time t and spatial position x; D is the diffusion coefficient; δ is the pollutant decay coefficient; γ is the pollution control coefficient; u(x,t) is the pollutant control strategy.

[0022] Preferably, the objective function calculation module calculates based on the weighted sum of objective functions such as traffic flow, energy consumption, and pollutant emissions. The form of the objective function is:

[0023] J = ∫0 T (α1f1(x(t),u(t)) + α2f2(x(t),u(t)) + α3f3(x(t),u(t)))dt

[0024] Where: f1(x(t),u(t)) is the cost function of traffic flow, describing the performance of traffic flow under given conditions; f2(x(t),u(t)) is the cost function of energy consumption, representing the energy consumption at a certain time and spatial position, reflecting the diffusion of pollutants in the city; f3(x(t),u(t)) is the cost function of pollutants; α1, α2, α3 are the weight coefficients of each objective; x(t) represents the state variables of the urban system, covering factors such as traffic flow, energy consumption, and pollutant concentration; u(t) is the control variable, representing the control decision of resource scheduling in the city.

[0025] Preferably, the optimal control module uses the optimal control theory to optimize the system through the Hamiltonian and the Lagrange multiplier method. The Hamiltonian is:

[0026] H = L(x(t),u(t)) + λ1(t)g1(x(t),u(t)) + λ2(t)g2(x(t),u(t))

[0027] + λ3(t)g3(x(t),u(t))

[0028] Where: L(x(t),u(t)) is the objective function, representing the total cost of urban resource scheduling; g1(x(t),u(t)), g2(x(t),u(t)), g3(x(t),u(t)) are the constraint conditions, representing the maximum capacity limit of traffic flow, the upper limit of energy consumption, and the maximum limit of pollutant concentration respectively; λ1(t), λ2(t), λ3(t) are the Lagrange multipliers, used to describe the influence of the constraint conditions.

[0029] Preferably, the feedback control module obtains the state data such as urban traffic flow, energy consumption, and pollutant concentration through a real-time data monitoring system and feeds it back to the optimal control module to achieve dynamic adjustment and optimization of the system.

[0030] Preferably, the numerical solution module discretizes the partial differential equation using the finite difference method and the finite element method, and solves it through optimization algorithms such as the interior point method and the genetic algorithm.

[0031] Preferably, the numerical solution module further includes online adjustment of the optimal control strategy of the system using real-time monitoring data to respond to changes in urban resource scheduling.

[0032] A dynamic simulation method for smart city planning and simulation includes the following steps:

[0033] Obtain state data such as traffic flow, energy consumption, and pollutant concentration of the city;

[0034] Based on the partial differential equation model, describe the spatio-temporal evolution process of traffic flow, energy consumption, and pollutant diffusion;

[0035] Calculate the objective function, including multi-objective optimization of traffic flow, energy consumption, and pollutant emissions;

[0036] Based on the optimal control theory, optimize the resource scheduling strategy through the Hamiltonian and the Lagrange multiplier method;

[0037] Through the numerical solution module, discretize and optimize the optimal control strategy, and adjust the urban resource scheduling plan in real time;

[0038] Use real-time data for feedback control to dynamically adjust traffic, energy, and pollution control strategies.

[0039] The present invention provides a dynamic simulation system and method for smart city planning and simulation. It has the following beneficial effects:

[0040] 1. The present invention uses a partial differential equation model to describe the spatio-temporal evolution process of traffic flow, energy consumption, and pollutant diffusion, achieving the technical effect of accurately simulating the dynamic changes of various urban resources. Compared with the single traffic or energy model in the prior art, this solution comprehensively considers multiple factors, can more comprehensively predict the impact of urban resource scheduling, and avoids the problem of insufficient consideration of the multi-variable coupling relationship in traditional methods.

[0041] 2. The present invention optimizes the resource scheduling strategy through the optimal control theory combined with the Hamiltonian and the Lagrange multiplier method, achieving a more efficient dynamic adjustment effect. Compared with the existing static scheduling scheme, the present invention can respond to changes in urban resource status in real time, reduce resource waste and energy consumption, and solve the problem of lack of flexibility in traditional control strategies.

[0042] 3. The present invention utilizes a real-time feedback control mechanism to dynamically adjust traffic, energy, and pollution control strategies, enhancing the urban emergency response ability. Compared with existing preset schemes, this feedback mechanism based on real-time data can quickly respond when emergencies occur, thereby reducing the negative impacts on the environment and traffic and addressing the drawback of slow response in traditional methods.

[0043] 4. The present invention adopts a multi-objective optimization method to balance the optimization of traffic flow, energy consumption, and pollution emissions, achieving a more reasonable resource allocation effect. Compared with existing single-objective optimization technologies, this scheme can flexibly adjust priorities according to different needs, avoiding the deficiency of over-optimizing one aspect while neglecting others, and making the overall urban planning more coordinated and sustainable. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0045] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0047] Please refer to the attached Figure 1 , the embodiments of the present invention provide a smart city planning simulation dynamic simulation system, including:

[0048] A traffic flow model module for describing the spatio-temporal evolution process of urban traffic flow;

[0049] Specifically, the traffic flow model module is one of the key components. This module simulates the changes in various traffic flows in the city through mathematical modeling, helps analyze the distribution of traffic flows at different times and spaces, and thus provides support for traffic management decisions. Specifically, the traffic flow model module uses the Lighthill-Whitham-Richards (LWR) model to describe the spatio-temporal changes of traffic flow and performs dynamic calculations through relevant partial differential equations. The LWR model is a classic traffic flow model. By modeling the relationship between traffic density and flow velocity, it can efficiently predict the distribution state of traffic flow on different road sections.

[0050] The traffic flow model module is based on the LWR model and is described as follows. This module models the changes in traffic flow through partial differential equations (PDEs). The formula is as follows:

[0051]

[0052] where: ρ(x,t) represents the traffic density at time t and spatial location x; v(x,t) is the flow velocity; x is the spatial location, representing different positions on the road; t is the time.

[0053] There is a certain relationship between the density and flow velocity of traffic flow. When the vehicle density on the road increases, the driving speed of vehicles will slow down. Therefore, there is a certain dependence relationship between v(x,t) and ρ(x,t). To further accurately simulate this relationship, the following formula is used to describe the relationship between the flow velocity and traffic density:

[0054]

[0055] where: V max is the maximum flow velocity; ρ max is the maximum traffic density. This formula indicates that when the traffic density reaches the maximum value ρ max , the flow velocity will approach zero, and when the traffic density is zero, the flow velocity is the maximum value V max .

[0056] [[ID=。 The traffic flow model module can also dynamically predict the traffic flow of different road sections. In this implementation method, the model not only considers the traffic density and flow velocity, but also combines external influencing factors (such as weather, road conditions, emergencies, etc.), which further improves the accuracy of the model.

[0057] The traffic flow model module realizes the dynamic simulation of traffic flow by solving partial differential equations. In the calculation process, numerical methods such as the finite difference method (FDM) or the finite element method (FEM) are used to discretize the time and space variables, thereby transforming the partial differential equations into algebraic equations that can be calculated. Through these numerical methods, the evolution process of traffic flow over time and space can be accurately simulated.

[0058] To further improve the calculation efficiency, the traffic flow model can be simplified. Specifically, a more simplified traffic flow model can be adopted, such as a model based on flow density or a network model based on road section connections. These simplification methods can reduce the calculation amount and are applicable to large-scale urban traffic simulation.

[0059] The traffic flow model module can also work in collaboration with other modules (such as the energy consumption module, pollutant dispersion module, etc.), and through the joint optimization of resources in multiple aspects such as transportation, energy, and environment, the multi-objective optimization of the smart city can be achieved. Through this joint simulation method, optimal urban planning decisions can be made considering traffic flow, energy utilization efficiency, and pollution emissions comprehensively.

[0060] When conducting joint optimization, the traffic flow model will be part of the constraint conditions and objective function, affecting the final scheduling strategy of the entire smart city simulation system. For example, when the traffic flow density reaches a certain threshold, it may be necessary to adjust the energy scheduling strategy or introduce pollutant emission control measures. In this way, the traffic flow model not only provides information for urban traffic management but also provides basic data for the optimization of energy and environment.

[0061] The traffic flow model module can also be dynamically adjusted according to real-time traffic data. By accessing the city's real-time monitoring system, such as traffic cameras, road sensors, etc., the traffic flow model can obtain traffic density and flow velocity data in real time and update the prediction results. This real-time feedback mechanism can ensure that the traffic flow simulation system can make a quick response and optimize traffic scheduling in a timely manner when facing traffic emergencies (such as traffic accidents, weather changes, etc.).

[0062] The energy consumption model module is used to describe the energy consumption in each urban area and its changes over time, and combined with the energy supply control strategy, to achieve the optimal scheduling of urban energy;

[0063] Specifically, the energy consumption model module is an important part of the smart city simulation system. This module ensures the efficient scheduling and distribution of energy by simulating and optimizing the energy demand and supply within the urban area. Combined with the traffic flow model module, the energy consumption module can predict the real-time energy demand and manage the energy supply, thereby reducing energy waste and achieving sustainable development. Especially in a complex urban environment, energy demand and consumption are dynamically changing, so it is necessary to model and optimize energy consumption through an accurate mathematical model.

[0064] The energy consumption model module describes the energy demand of the city and its changes in the following way. First, assume that the energy consumption E(x,t) in each urban area changes over time and is affected by the control variable u(x,t). The dynamic equation of this model is:

[0065]

[0066] where: E(x,t) represents the energy demand at time t and spatial position x; α is the energy decay coefficient; β is the energy supply coefficient; u(x,t) is the energy scheduling control variable.

[0067] The energy consumption shows a decaying trend over time. Specifically, when the energy demand in a certain area is large, the energy supply will increase to ensure that the demand is met. Through the decay coefficient α in the model, the reduction of energy consumption over time can be simulated. Meanwhile, the control variable u(x,t) is used to adjust the energy supply in different regions.

[0068] The energy consumption model can also dynamically adjust the energy supply to cope with emergencies or extreme weather conditions. For example, during peak hours or under special climate conditions, the energy demand may suddenly increase. At this time, the change of the control variable u(x,t) can be reflected in the model calculation in a timely manner, adjusting the energy supply strategy to avoid energy shortage or waste.

[0069] The energy consumption model also takes into account the influence of external factors on energy demand. These external factors can include weather conditions, seasonal variations, residents' activity patterns, etc. For example, in summer, the increase in air conditioner use may lead to a significant increase in energy demand, or in cold weather, the heating demand will also increase significantly. Therefore, the change of energy consumption in the model is not only determined by the decay coefficient and the control variable, but also needs to adjust the parameters according to the actual external environment.

[0070] The energy consumption model module can also be linked with the traffic flow model module. When the traffic flow is high, it may lead to an increase in the urban electricity demand, especially in traffic-intensive areas. Therefore, in the process of optimizing urban energy dispatching, the energy consumption model module will conduct data interaction and optimization adjustment with the traffic flow model to ensure the coordination of energy supply and traffic demand. Specifically, when the traffic density in a certain area increases, the energy consumption in the corresponding area will also increase, and the model will automatically adjust the energy dispatching plan according to these changes.

[0071] The energy consumption model module can cooperate with the city's real-time energy management system, using the real-time data provided by sensors and monitoring systems to dynamically adjust the energy supply. By comparing with the data collected by sensors, the system can continuously optimize the energy supply decision to ensure that the energy consumption is always within a reasonable range.

[0072] The model solves the partial differential equation through discretization methods (such as the finite difference method or the finite element method). To improve the computational efficiency of the model, a more concise energy consumption model can be adopted, especially in large-scale urban simulations. This simplified model can be based on macro demand forecasting, ignoring some minor details and focusing on the prediction and dispatching of large-scale energy consumption. This method can significantly reduce the computational burden and is suitable for quickly responding to changes in urban energy demand.

[0073] The energy consumption model module can also incorporate machine learning algorithms to predict future energy demand trends by learning historical data. Specifically, using historical energy consumption data and environmental factors, the model can automatically adjust parameters to improve the accuracy of energy consumption prediction. In this way, the energy consumption model can not only rely on mathematical formulas for prediction but also be further optimized through intelligent data-driven methods.

[0074] The pollutant dispersion model module is used to describe the spatio-temporal dispersion behavior of pollutants in the city and, in combination with pollutant control strategies, optimize pollutant emissions.

[0075] Specifically, the pollutant dispersion model module plays a key role. The dispersion and accumulation of pollutants in the city directly affect the health of residents and environmental quality. Therefore, an accurate pollutant emission and dispersion model is crucial for smart city management. This module simulates the dispersion of pollutants in the urban space and, in combination with emission source data in different regions, dynamically optimizes pollutant control measures. Acting together with the traffic flow and energy consumption modules, the pollutant dispersion model can provide comprehensive environmental optimization strategies for the smart city to ensure the achievement of environmental quality and urban sustainable development goals.

[0076] The pollutant dispersion model module models the spatio-temporal changes in pollutant concentration through classical diffusion equations. Specifically, the pollutant dispersion process can be described by the following equation:

[0077]

[0078] where: c(x,t) represents the pollutant concentration at time t and spatial position x; D is the diffusion coefficient; δ is the pollutant decay coefficient; γ is the pollution control coefficient; and u(x,t) is the pollutant control strategy.

[0079] Pollutant dispersion follows the natural dispersion law, that is, pollutants disperse from high-concentration areas to low-concentration areas. The above equation can accurately simulate the concentration changes of pollutants at different times and in different spaces. The diffusion coefficient D determines the speed of pollutant dispersion, while the decay coefficient δ simulates the speed at which pollutants dissipate in the air due to chemical reactions or natural processes. The control coefficient γ is related to the pollutant control strategy u(x,t) and represents the impact of pollution control measures (such as vegetation cover, emission restrictions, etc.) on pollutant concentration.

[0080] The pollutant dispersion model module can also be dynamically updated in combination with the real-time monitoring data of the city. For example, by integrating the real-time data of air quality sensors, traffic flow data, and industrial emission monitoring equipment in the city, the model can adjust the prediction and control strategies for pollutant dispersion in real time. When the pollutant concentration in a certain area reaches a certain level, the pollutant control strategy u(x,t) will be immediately adjusted to mitigate the further spread of pollution. Specifically, when the traffic density increases, the pollutant emissions will also increase accordingly. At this time, by adjusting the pollutant control strategy, the diffusion rate of pollutants can be reduced.

[0081] The pollutant dispersion model can also incorporate more complex terrain and climate factors. Topographical features (such as mountains, rivers, etc.) and climate factors (such as wind speed, temperature, humidity, etc.) have a significant impact on the dispersion of pollutants. For example, in the case of high wind speed, the dispersion of pollutants will accelerate, while in a closed low-pressure area, pollutants may accumulate locally. To more accurately simulate these influencing factors, the pollutant dispersion model in this embodiment may incorporate meteorological data and use a higher-order dispersion model for prediction.

[0082] When performing numerical calculations of pollutant dispersion, the model uses the finite difference method (FDM) or the finite element method (FEM) for spatio-temporal discretization, transforming the partial differential equation into a difference equation that is easy to calculate. Through these numerical solutions, the pollutant dispersion process in the city can be efficiently and accurately simulated. In some embodiments, to improve the calculation efficiency, the model may be simplified to reduce the computational complexity. For example, for a large-scale urban area, a lower-precision model can be selected for calculation in some areas, while for key monitoring areas, a high-precision model can be used for detailed simulation.

[0083] The pollutant dispersion model module can also be combined with the energy consumption and traffic flow model modules to achieve more comprehensive environmental scheduling and control. For example, when the traffic flow is large, the pollutant emissions also increase accordingly. At this time, the pollutant emissions can be reduced by adjusting the energy supply strategy, or other environmental protection measures can be taken to slow down the pollution spread.

[0084] The pollutant dispersion model module can also be connected to the environmental monitoring platform of the city government in real time to regularly adjust the control strategy. Through the real-time feedback monitoring data, the control strategy can be adjusted when the pollutant concentration is too high, thus ensuring that the urban environmental quality is not affected by excessive pollution.

[0085] The pollutant diffusion model module, through its interaction with other modules, can not only optimize environmental quality but also provide important data support and decision-making basis for the overall operation of a smart city. Based on real-time monitoring and prediction of pollutant diffusion, this module can take corresponding control measures in a timely manner, contributing to the sustainable development of the city.

[0086] The objective function calculation module is used to calculate the multi-objective optimization function of a smart city;

[0087] Specifically, the objective function calculation module is one of the core components. This module is responsible for comprehensively considering factors such as traffic flow, energy consumption, and pollutant emissions, and constructing an objective function based on these factors to achieve multi-objective optimization. The objective function calculation module determines a comprehensive evaluation index through the weighted sum of each objective. In this way, the system can optimize resource scheduling under different constraint conditions and achieve coordinated management of traffic, energy, and the environment. Therefore, the design and calculation of the objective function are crucial as it provides a clear optimization direction and goal for the subsequent optimization control module.

[0088] The objective function calculation module uses the method of weighted sum to combine the three objectives of traffic flow, energy consumption, and pollutant emissions to form a comprehensive objective function. The expression of this objective function is:

[0089] J=∫0 T (α1f1(x(t),u(t))+α2f2(x(t),u(t))+α3f3(x(t),u(t)))dt

[0090] Where: f1(x(t),u(t)) is the cost function of traffic flow, describing the performance of traffic flow under given conditions; f2(x(t),u(t)) is the cost function of energy consumption, representing the energy consumption at a certain time and spatial location and reflecting the diffusion of pollutants in the city; f3(x(t),u(t)) is the cost function of pollutants; α1, α2, α3 are the weight coefficients of each objective; x(t) represents the state variables of the urban system, covering factors such as traffic flow, energy consumption, and pollutant concentration; u(t) is the control variable, representing the control decision of resource scheduling in the city.

[0091] Each sub-objective (traffic flow, energy consumption, pollutant emission) in the objective function has different importance. Therefore, in order to ensure the balance of each objective, the weighted coefficients α1, α2, α3 are used to weight the objectives. The selection of the weighted coefficients can be adjusted according to the requirements in specific application scenarios. For example, if traffic congestion has a greater impact on urban operations, the weight of α1 can be appropriately increased to make the optimization of traffic flow more important; if the energy consumption is too high, the weight of α2 can be appropriately increased.

[0092] The objective function calculation module can also adopt other forms of objective functions, such as polynomial form or exponential form, to meet different optimization requirements. Specifically, in some cases, the non-linear relationship between objective functions may need to be considered. At this time, the comprehensive evaluation of each objective can be carried out by means of non-linear weighting, so as to improve the accuracy and adaptability of the optimization results.

[0093] The objective function calculation module can also dynamically adjust the weight coefficients of the objective functions. Specifically, when the urban environment changes, such as sudden traffic accidents or the increase of pollution sources, the module can adjust the weight coefficients according to real-time data, so as to re-evaluate the importance of each objective. This dynamic adjustment mechanism can more flexibly cope with complex urban management problems and optimize resource allocation.

[0094] The calculation of the objective function is not just a simple weighted summation. In the actual calculation process, each sub-objective of the objective function needs to be evaluated in real time in combination with the current urban state. For example, the calculation of traffic flow may need to be evaluated according to the current congestion situation of the road section, while the energy consumption needs to be predicted in combination with the electricity load of each region. In addition, the calculation of pollutant emissions also needs to consider external factors such as weather and climate, and dynamically adjust the relevant parameters in the prediction model. Therefore, the calculation of the objective function not only depends on the model parameters, but also needs to obtain the data of sensors and monitoring systems in real time to ensure the accuracy of the calculation results.

[0095] The objective function calculation module can also combine historical data for predictive analysis. In this way, the module can use machine learning algorithms to extract rules from historical data and predict the trends of traffic flow, energy demand and pollutant emissions in the next period of time. Through this prediction mechanism, the system can take corresponding resource scheduling measures in advance to reduce resource waste and pollution emissions in the urban system.

[0096] The objective function calculation module may also combine the long-term development plan of the city for multi-stage optimization. For example, considering the long-term development goals of the city, a time discount factor can be introduced into the objective function to convert future costs and benefits into the current moment, so as to realize the anticipation and adjustment of future resource allocation. This method can ensure the forward-looking of urban planning and management and provide support for the sustainable development of the city.

[0097] An optimization control module, which is used to adjust the scheduling strategies of traffic flow, energy and pollutants in real time based on the optimal control theory and combining multi-objective and multi-constraint optimization methods;

[0098] Specifically, the optimization control module is the core part for achieving the optimal resource scheduling. Based on the optimal control theory, this module is responsible for calculating the optimal resource scheduling strategy according to the objective function and constraint conditions of the system, so as to ensure the efficient utilization of urban resources and the coordinated optimization among various subsystems (such as transportation, energy, environment, etc.). By adjusting the control strategy, the optimization control module ensures that the city can always achieve optimal operation in a complex and dynamic environment. Collaborating with modules such as the previously discussed objective function calculation module and pollutant diffusion module, the optimization control module can make efficient and flexible decisions based on the calculated objective function and real-time data.

[0099] The optimization control module solves the resource scheduling problem through the optimal control theory. Specifically, the module optimizes the entire system based on the Hamiltonian and the Lagrange multiplier method. The definition of the Hamiltonian H is as follows:

[0100] H = L(x(t), u(t)) + λ1(t)g1(x(t), u(t)) + λ2(t)g2(x(t), u(t))

[0101] + λ3(t)g3(x(t), u(t))

[0102] Where: L(x(t), u(t)) is the objective function, representing the total cost of urban resource scheduling; g1(x(t), u(t)), g2(x(t), u(t)), g3(x(t), u(t)) are the constraint conditions, representing the maximum capacity limit of traffic flow, the upper limit of energy consumption, and the maximum limit of pollutant concentration respectively; λ1(t), λ2(t), λ3(t) are the Lagrange multipliers, used to describe the influence of the constraint conditions.

[0103] The optimization control module first needs to evaluate the objective function and solve the optimal control strategy under the constraint conditions. The control strategy u(t) corresponds to the specific scheduling behaviors of resources in the city, such as traffic flow scheduling, energy supply adjustment, and pollutant emission control. The goal of the optimal control problem is to maximize or minimize the objective function and find the optimal control strategy under multiple constraint conditions.

[0104] The optimization control module may use different optimization methods to solve the optimal control problem. Specifically, in some embodiments, the optimization algorithm can select numerical solutions such as the interior point method and the gradient descent method. These methods can effectively handle optimization problems with constraint conditions and quickly converge to the optimal solution. The optimization control module may combine global optimization algorithms such as genetic algorithms or simulated annealing algorithms to further improve the accuracy and efficiency of the solution.

[0105] During the optimization process, the adjustment of the control strategy u(t) needs to consider not only the optimization of a single objective but also the balance among multiple objectives. For example, the optimization of traffic flow may increase the road capacity, but this may lead to an increase in energy consumption or a worsening of pollutant emissions. Therefore, the key to the optimization control module is to reasonably balance the conflicts among various objectives so that each objective can be appropriately optimized under the overall resource constraints.

[0106] The optimization control module can also be adjusted in combination with real-time monitoring data. For example, when there are drastic fluctuations in traffic flow density, the optimization control module can adjust traffic signals or traffic control strategies in real time to reduce traffic congestion and optimize energy consumption. Similarly, when the pollutant concentration is too high, the optimization control module can timely adjust the energy supply strategy or take pollution control measures to reduce the pollutant emissions.

[0107] The optimization control module can also be integrated with the city's real-time data acquisition system to obtain relevant data such as traffic flow, energy consumption, and pollutant concentration through sensors and monitoring systems, and adjust the control strategy in real time. In this way, the optimization control module can cope with various emergencies in the city, such as traffic accidents, weather changes, or energy supply fluctuations, to ensure that the system can always maintain the optimal state in a dynamically changing environment.

[0108] The optimization control module can calculate the optimal resource scheduling scheme based on various input data, such as objective functions, constraints, and real-time feedback. Through optimization control, the module can achieve the following functions:

[0109] Traffic flow optimization: According to data such as traffic density and flow velocity, optimize traffic signal timing or section allocation to reduce congestion and improve road traffic efficiency.

[0110] Energy consumption optimization: According to the energy demand and supply situations in different regions of the city, optimize energy scheduling to ensure the balance between supply and demand and reduce energy waste.

[0111] Pollutant emission control: According to the pollutant concentration and emission source data in the city, optimize pollution control strategies to reduce the spread of pollutants and their negative impacts on the environment.

[0112] The optimization control module can simulate different scenarios and scheduling strategies and perform multiple iterations to find the most suitable resource scheduling scheme. Through this method, the optimization control module can adapt to different urban demands and development stages and provide flexible and efficient resource scheduling schemes.

[0113] A feedback control module is used to obtain the status data of each system in the city through a real-time data monitoring system and provide feedback;

[0114] Specifically, the feedback control module plays an important role. Closely connected with the optimization control module, the feedback control module adjusts control strategies in a timely manner by obtaining the state information of the urban system in real time (such as traffic flow, energy consumption, pollutant concentration, etc.), so as to respond to dynamic changes. This module ensures that urban resource management can adapt to complex and uncertain environmental conditions in real time and can quickly respond to emergencies. At all levels of urban operation, the feedback control module guarantees the flexibility and adaptability of the system, thus optimizing traffic flow, energy consumption, and environmental quality.

[0115] The feedback control module mainly consists of the following functional parts: data collection, data processing, feedback mechanism, and real-time control strategy adjustment. The feedback control module collects data on various resources and the environment in the city in real time by connecting with sensors and monitoring devices (such as traffic monitoring cameras, air quality sensors, power metering devices, etc.). These data are transmitted to the control system through a real-time feedback system, thereby adjusting the resource scheduling strategy.

[0116] In practical applications, the data collection part first obtains real-time data from sensors in various regions of the city. These data cover factors such as traffic flow, road capacity, energy demand, pollutant concentration, temperature, and humidity. For example, traffic flow data can reflect the traffic density and vehicle speed on a specific road section; energy demand data provides information on energy consumption in various urban areas; pollutant concentration data helps to monitor urban air quality in real time. All these data are collected at regular time intervals and uploaded to the central processing system.

[0117] The data processing part conducts preliminary cleaning, analysis, and classification on the acquired data to ensure the accuracy and integrity of the data. By processing the data, noise data or data that does not meet expectations can be eliminated, thus ensuring the effectiveness of subsequent analysis and decision-making. The data processing part also normalizes various types of data according to preset rules for subsequent calculations and adjustments of feedback control strategies. For example, traffic flow data may need to be adjusted according to time periods (such as morning rush hour or evening rush hour) and seasons, and energy demand data may need to be preprocessed according to weather and regional characteristics.

[0118] The feedback control module can dynamically adjust the control strategy based on the deviation between the current state and the reference strategy provided by the optimization control module. For example, when the traffic density on a certain road exceeds the preset threshold, the feedback control module will instruct the traffic lights to be adjusted to allocate more traffic resources; when the energy demand in a certain area exceeds the supply capacity, the module will automatically adjust the energy supply strategy or guide users to reduce energy consumption; when the pollutant concentration exceeds the safety limit, the module will initiate pollutant control measures, adjust industrial emissions, and adjust energy scheduling, etc.

[0119] The core function of the feedback control module is to adjust the control strategy in real time. By interacting with other modules in the system (such as the optimal control module, the objective function calculation module, the pollutant diffusion model module, etc.), the feedback control module can flexibly respond to changes in urban resource management. For example, when the system detects a significant increase in traffic flow at a certain moment, which may lead to congestion, the feedback control module will immediately cooperate with the traffic flow model to adjust the traffic signal cycle and relieve traffic pressure. When it is found that the air quality deteriorates and the pollutant concentration rises, the feedback control module can activate the pollutant control strategy, such as increasing greenery and restricting the activities of certain high-pollution emission sources.

[0120] The response time of the feedback control module is very short, which is particularly important for real-time urban resource management. In a complex urban environment, the control module needs to be able to make effective adjustments in a short time to cope with uncertain external environmental changes. Therefore, the feedback control module can generate a new resource scheduling plan based on real-time data and control strategies and quickly feedback it to the optimal control module to achieve dynamic optimization.

[0121] The feedback control module can also be further optimized by combining machine learning algorithms. For example, based on past urban operation data, the module can predict possible traffic, energy, or pollution problems and adjust the control strategy in advance. This prediction-based control method can take actions before problems occur, thus improving the response efficiency and predictability of the system.

[0122] The feedback control module provides a flexible and efficient control method for the dynamic simulation system of smart cities through real-time feedback and adjustment mechanisms. In terms of traffic, energy, and pollution, etc., the feedback control module can not only ensure the real-time scheduling of various resources but also effectively avoid resource waste and environmental pollution, providing guarantee for the sustainable development of the city.

[0123] The feedback control module can also be combined with the urban emergency management system. By simulating and predicting emergencies in the city (such as traffic accidents, large-scale energy demand fluctuations, or air pollution events, etc.), this module can quickly activate the emergency response mechanism, coordinate resource scheduling, and prevent the situation from deteriorating further.

[0124] The numerical solution module is used to discretize partial differential equations and solve the optimal control strategy through numerical optimization algorithms.

[0125] Specifically, the numerical solution module is a key component for implementing the simulation and optimization of various dynamic systems. The main task of this module is to discretize partial differential equations through numerical methods, thereby transforming complex continuous models into discrete mathematical problems, and then solving the optimal solutions for processes such as urban resource scheduling and pollutant diffusion. The numerical solution module collaborates closely with other modules in the system (such as the optimization control module, the objective function calculation module, the pollutant diffusion model module, etc.) to ensure that the entire system can operate efficiently in a dynamically changing urban environment.

[0126] The numerical solution module adopts a variety of numerical solution methods to efficiently and accurately solve various partial differential equations, especially those used to describe urban traffic flow, energy consumption, and pollutant diffusion. Specifically, the numerical solution module discretizes continuous variables in space and time through discretization methods such as the finite difference method (FDM) and the finite element method (FEM), and transforms partial differential equations into algebraic equations. Through these methods, the numerical solution module can calculate the state variables of the system within a given time and space region.

[0127] The finite difference method (FDM) is used to handle spatial problems with regular grids and is very effective in solving problems such as traffic flow and energy demand. Specifically, FDM discretizes continuous variables in time and space into finite grid points, and then approximates partial derivatives through difference formulas, thereby transforming partial differential equations into difference equations for solution. In the calculation of traffic flow, the numerical solution module discretizes the distribution of traffic density and flow velocity and calculates the changes in traffic flow at different time steps.

[0128] The finite element method (FEM) has good adaptability when dealing with complex geometries or irregular regions. In some actual urban traffic and pollutant diffusion models, due to the complex shape or network structure of the region, the finite element method can provide a more flexible and accurate discretization method. The finite element method divides the calculation region into multiple small units (elements) and uses appropriate shape functions to approximate each small unit, thereby transforming partial differential equations into linear equation systems.

[0129] The numerical solution module can also combine other numerical solution methods, such as spectral methods and mixed finite element methods, to further improve the calculation accuracy and efficiency. These methods are applicable to different application scenarios. For example, when high-precision problems need to be solved or complex boundary conditions need to be handled, these advanced numerical solution methods can be selected to meet the accuracy requirements.

[0130] The numerical solution module first discretizes partial differential equations such as traffic flow, energy consumption, and pollutant diffusion through discretization methods to obtain the state update equations for each time step. Then, appropriate solution algorithms (such as iterative methods, direct methods, etc.) are used to solve the discretized equations to obtain the system state under specific spatio-temporal conditions. In some embodiments, to improve the solution efficiency, the numerical solution module may use parallel computing methods to distribute the computing tasks to multiple processing units, thereby greatly increasing the solution speed.

[0131] The numerical solution module can also be dynamically updated in combination with real-time data. For example, based on real-time traffic flow data or pollutant concentration data, the module can dynamically adjust the parameters of the traffic flow model or pollutant diffusion model to make the simulation results more accurate and real-time. This dynamic update mechanism can ensure that the numerical solution module can quickly respond and provide accurate predictions when facing urban environmental changes.

[0132] Through the interaction with the optimization control module, the numerical solution module can not only calculate the state of the system but also provide feedback on the optimal solution. For example, when the optimization control module calculates the optimal resource scheduling strategy, the numerical solution module can verify the feasibility of these strategies in real time and adjust the strategies through the simulation results to ensure that the entire system always remains in the optimal state.

[0133] The linkage between the numerical solution module and other modules is very crucial. When the optimization control module proposes a new scheduling plan, the numerical solution module quickly evaluates the effects of these plans by simulating processes such as traffic flow, energy demand, and pollutant diffusion, and feeds back to the control system for adjustment. In this way, the numerical solution module can achieve fast real-time feedback and optimization during the simulation process.

[0134] In addition, to further improve the computing efficiency, the numerical solution module can adopt some acceleration techniques, such as multigrid methods, fast Fourier transform (FFT), etc. These methods can provide efficient solutions when dealing with complex large-scale problems.

[0135] A dynamic simulation method for smart city planning described below can be correspondingly referred to with a dynamic simulation system for smart city planning described above.

[0136] Please refer to the appendix Figure 2 , a dynamic simulation method for smart city planning, includes the following steps:

[0137] Obtain state data such as traffic flow, energy consumption, and pollutant concentration of the city;

[0138] Describe the spatio-temporal evolution process of traffic flow, energy consumption, and pollutant diffusion based on the partial differential equation model;

[0139] Calculate the objective function, including multi-objective optimization of traffic flow, energy consumption, and pollutant emissions;

[0140] Based on the optimal control theory, optimize the resource scheduling strategy through the Hamiltonian and Lagrange multiplier methods;

[0141] Through the numerical solution module, discretize and optimize the optimal control strategy, and adjust the urban resource scheduling plan in real time;

[0142] Use real-time data for feedback control to dynamically adjust traffic, energy, and pollution control strategies.

[0143] Specifically, data acquisition and integration: In addition to traffic flow, energy consumption, and pollutant concentration, data such as weather conditions, road conditions, and public transportation operation conditions can also be integrated to achieve more accurate urban resource scheduling and environmental control. Use Internet of Things (IoT) devices and sensor networks to monitor various types of data in real time to ensure the timeliness and accuracy of the data.

[0144] Modeling and simulation of the spatio-temporal evolution process: When using partial differential equation (PDE) models to describe the spatio-temporal evolution processes of traffic flow, energy consumption, and pollutant diffusion, multi-dimensional simulations (such as air flow, vehicle speed, and spatial differences in energy production and consumption efficiency) can be considered, combined with deep learning or machine learning algorithms to model and predict dynamic and non-linear changes.

[0145] Multi-objective optimization and trade-off: During the optimization process, the trade-off of multiple objectives should be considered, such as traffic flow efficiency, energy use efficiency, environmental pollution control, and economic costs. On this basis, the concept of Pareto optimal solutions can be introduced to provide different optimization schemes for decision-makers to choose the most suitable scheme according to actual needs.

[0146] Application of the optimal control theory: When optimizing the resource scheduling strategy using the Hamiltonian and Lagrange multiplier methods, in addition to traditional control algorithms, intelligent algorithms such as reinforcement learning can also be combined to achieve adaptive control. By setting up a feedback mechanism, the system can gradually adjust the control strategy to improve resource utilization and reduce pollutant emissions.

[0147] Real-time feedback control mechanism: Through the real-time feedback of data streams, not only can traffic, energy, and pollution control strategies be adjusted, but also rapid response strategies can be implemented. For example, in the event of an emergency (such as a traffic accident or an energy supply interruption), the system can quickly make adjustments to avoid large-scale resource waste and environmental impacts.

[0148] System Dynamic Evaluation and Adjustment: To ensure the effectiveness of the planning method, it is necessary to monitor the operation effect of the system in real time and conduct periodic evaluation and adjustment. A feedback loop can be established between the simulation results and the actual urban data to continuously optimize the simulation model and improve the accuracy and reliability of the decision support system.

[0149] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart city planning simulation dynamic simulation system, characterized in that, Including: A traffic flow model module for describing the spatio-temporal evolution process of urban traffic flow; The traffic flow model module is based on the Lighthill-Whitham-Richards model and uses the relationship between traffic density and flow velocity to simulate the evolution process of traffic flow. The state equation of the traffic flow is: Wherein: represents the traffic density at the time and the spatial position is the flow velocity, is the spatial position, representing different positions of the road, is the time; An energy consumption model module for describing the energy consumption in each region of the city and its change over time, and combining with the energy supply control strategy to achieve the optimal scheduling of urban energy; The energy consumption model module is based on the dynamic model of energy demand. The state equation of the energy consumption is: Wherein: represents the energy demand at the time and spatial position is the energy attenuation coefficient, is the energy supply coefficient, is the energy scheduling control variable; A pollutant diffusion model module for describing the spatio-temporal diffusion behavior of pollutants in the city and combining with the pollutant control strategy to achieve the optimization of pollutant emissions; The pollutant diffusion model module is based on the diffusion equation to describe the spatial diffusion of pollutants and the control strategy. The state equation of the pollutant diffusion is: Wherein: represents the pollutant concentration at time and spatial position is the diffusion coefficient, is the pollutant decay coefficient, is the pollution control coefficient, is the pollutant control strategy; A target function calculation module for calculating the multi-objective optimization function of the smart city; The target function calculation module calculates based on the weighted sum of objective functions such as traffic flow, energy consumption, and pollutant emissions. The form of the target function is: Wherein: is the cost function of traffic flow, which describes the performance of traffic flow under given conditions, is the cost function of energy consumption, which represents the energy consumption at a certain time and spatial position and reflects the diffusion of pollutants in the city, is the cost function of pollutants, , , are the weight coefficients of each objective, represents the state variables of the urban system, covering factors such as traffic flow, energy consumption and pollutant concentration, is the control variable, representing the control decision of resource scheduling in the city; An optimization control module for, based on the optimal control theory, combining the optimization methods of multi-objectives and multi-constraints, and adjusting the scheduling strategies of traffic flow, energy, and pollutants in real time; The optimization control module adopts the optimal control theory and optimizes the system through the Hamiltonian and the Lagrange multiplier method. The Hamiltonian is: Wherein: is the objective function, representing the overall cost of urban resource scheduling, , , are the constraint conditions, representing the maximum capacity limit of traffic flow, the upper limit of energy consumption, and the maximum limit of pollutant concentration respectively, , , are the Lagrange multipliers, used to describe the influence of the constraint conditions; A feedback control module for, through the real-time data monitoring system, obtaining the state data of each system in the city and giving feedback; A numerical solution module for discretizing the partial differential equation and solving the optimal control strategy through a numerical optimization algorithm.

2. The smart city planning simulation dynamic simulation system according to claim 1, characterized in that, The feedback control module obtains the state data such as urban traffic flow, energy consumption, and pollutant concentration through the real-time data monitoring system and feeds it back to the optimization control module to achieve the dynamic adjustment and optimization of the system.

3. The intelligent city planning simulation dynamic simulation system according to claim 1, wherein, The numerical solution module uses the finite difference method and the finite element method to discretize the partial differential equation and solves it through optimization algorithms such as the interior point method and the genetic algorithm.

4. A smart city planning simulation dynamic simulation system according to claim 1, characterized in that, The numerical solution module further includes using the real-time monitoring data to perform online adjustment of the optimal control strategy of the system to respond to the changes in urban resource scheduling.

5. A method for dynamic simulation of smart city planning and simulation, applied to a smart city planning and simulation dynamic simulation system according to any one of claims 1-4, characterized in that Including the following steps: Obtaining the state data such as urban traffic flow, energy consumption, and pollutant concentration; Based on the partial differential equation model, describing the spatio-temporal evolution process of traffic flow, energy consumption, and pollutant diffusion; Calculating the target function, including the multi-objective optimization of traffic flow, energy consumption, and pollutant emissions; Based on the optimal control theory, optimizing the resource scheduling strategy through the Hamiltonian and the Lagrange multiplier method; Performing the discretization and optimization of the optimal control strategy through the numerical solution module and adjusting the urban resource scheduling plan in real time; Using the real-time data for feedback control to dynamically adjust the traffic, energy, and pollution control strategies.

Images