Eco-friendly urban environment design implementation method

Abstract

The invention relates to the technical field of environment management, and discloses an eco-friendly urban environment design implementation method, which comprises the following steps: S1, constructing a distributed clean energy system: realizing local energy production, storage and allocation through micro-grid and regional collaboration; s2, establishing an environment monitoring and response system: acquiring data through a sensor network, and implementing hierarchical response; s3, resource demand prediction and scheduling are realized: resource demands are predicted by using a dynamic model, and multi-level scheduling optimization is carried out; s4, establishing an ecological building and community treatment system: intelligently regulating and controlling the temperature and humidity of the building, and constructing a community water resource circulation system; and S5, designing a public participation incentive platform: providing an incentive mechanism to encourage environmental protection behaviors. Through the distributed clean energy management system, localized production, storage and flexible allocation of energy are realized, and meanwhile, the energy is intelligently scheduled by adopting an optimization algorithm, so that the supply and demand balance of the energy is realized, and the utilization rate of the clean energy is improved.

Description

Technical Field

[0001] The invention relates to the technical field of environmental management, in particular to an eco-friendly urban environmental design implementation method. Background Art

[0002] With the acceleration of global urbanization, the rapid growth of urban population has brought huge energy consumption and environmental pressure. Traditional urban management methods have great deficiencies in dealing with the growth of energy demand, the intensification of environmental pollution and the balance of resource supply and demand, and it is difficult to meet the requirements of sustainable development of modern cities. Therefore, how to achieve clean energy, intelligent environmental monitoring, efficient resource scheduling, energy-saving buildings and the improvement of public environmental awareness has become an important research direction for eco-friendly urban environment design.

[0003] At present, traditional urban energy management systems mostly rely on centralized fossil fuel energy supply and lack flexible scheduling and optimization methods. This approach not only has large transmission losses and high operating costs, but also has huge carbon emissions, causing serious air pollution and greenhouse effects. Although distributed clean energy such as solar energy and wind energy has gradually been introduced into the urban energy system, due to the lack of scheduling and management technology, the utilization efficiency of distributed energy is not high, and it is impossible to achieve dynamic balance and coordinated deployment with the centralized energy system.

[0004] On the other hand, existing urban environmental monitoring systems usually use fixed-point, decentralized monitoring methods, with a single data collection method and limited coverage, making it difficult to achieve real-time monitoring and dynamic response across the city. In addition, these systems lack data fusion technology and are easily interfered by external noise, resulting in poor accuracy and reliability of environmental data. When environmental pollution incidents occur, traditional monitoring systems have a slow response speed and lack a hierarchical emergency response mechanism, making it difficult to solve pollution problems in a timely and effective manner.

[0005] In terms of resource management, traditional urban resource scheduling relies on historical statistical data and lacks accurate prediction of future demand. Due to insufficient demand prediction, it is difficult to balance the supply and demand of resources such as water, electricity, and gas, and resource waste and supply shortages often occur. Especially during peak periods, insufficient resource supply often leads to management crises.

[0006] Building energy consumption is an important part of the total energy consumption of the city, but traditional buildings lack intelligent temperature and humidity control systems, have high energy consumption and poor comfort. At the same time, the problem of water shortage is becoming increasingly prominent in many cities. Rainwater resources are often directly discharged into the sewer instead of being recycled, resulting in a waste of water resources.

[0007] In addition, the public's environmental awareness and participation are also crucial to urban environmental management. In the traditional urban management model, public participation is often low, and there is a lack of incentives for environmental protection behavior, making it difficult to form sustained public support. The lack of transparency and fairness in the recording and management of citizens' environmental protection behavior makes it difficult to increase the public's enthusiasm for environmental protection. Summary of the invention

[0008] In view of the shortcomings of the existing technology, the present invention provides an eco-friendly urban environment design implementation method, which solves the problems of low efficiency, lack of intelligent optimization and collaborative management in traditional urban environment design in energy management, environmental monitoring, resource scheduling, building energy conservation and public environmental protection participation.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: an eco-friendly urban environment design implementation method, comprising the following steps:

[0010] S1. Build a distributed clean energy management system: localize energy production, storage and deployment through the microgrid layer, urban grid layer and regional coordination layer;

[0011] S2. Establish an integrated environmental monitoring and multi-layer response system: deploy a multi-type sensor network to collect environmental data, generate heat maps and perform hierarchical responses through data fusion and pollution spatial distribution estimation;

[0012] S3. Realize high-precision resource demand forecasting and multi-scale scheduling system: Based on historical and real-time data, use dynamic forecasting models to forecast water, electricity, and gas resource demand, and perform multi-scale resource scheduling to optimize resource allocation;

[0013] S4. Establish an ecological building and smart community governance system: realize temperature and humidity control through the smart building management system, and build a water resource recycling system in the community;

[0014] S5. Design a public participation feedback incentive platform: record and manage citizens’ environmental protection behavior points through blockchain technology, and provide a points-based incentive mechanism to encourage environmental protection behavior.

[0015] Preferably, in step S1, the microgrid layer includes a solar photovoltaic system, a wind power generation system and a small geothermal power generation unit, and a mixed integer nonlinear programming model is used to optimize energy scheduling;

[0016] The energy scheduling specifically includes comprehensive energy cost, carbon emission and energy storage cost, and is optimized by the following formula:

[0017]

[0018] Among them, C iis the unit cost of energy type i, E i is the supply, α is the carbon emission weight;

[0019] The formula includes energy demand balance constraints and energy storage capacity constraints, specifically:

[0020] E stored,t =E stored,t-1 +η charge E excess -η discharge E required

[0021] where η charge and η discharge For charging and discharging efficiency.

[0022] Preferably, in step S2, the environmental data includes but is not limited to air quality, water quality, noise and soil; the data fusion is performed using Kalman filtering, and the corresponding formula is:

[0023]

[0024] in is the estimated value after filtering, K k is the Kalman gain, z k is the observed value, and H is the measurement matrix.

[0025] Preferably, in step S2, the environmental monitoring and multi-layer response system further includes a spatial distribution estimation and multi-layer response mechanism, which estimates the spatial distribution of pollution events based on a Markov random field model, and triggers community-level, municipal-level and regional coordinated response mechanisms according to the pollution level;

[0026] The Markov random field model estimation formula is:

[0027]

[0028] Among them, X is the state variable, Z is the normalization constant, ψ c (X c ) is the potential function.

[0029] Preferably, in the step S3, the dynamic prediction model is constructed based on a variational autoencoder and an attention mechanism, and the variational autoencoder formula is:

[0030] Encoder: Encodes input data X into latent variables z

[0031] z~q(z|X)=N(z;μ(X),σ(X))

[0032] Decoder: Generates prediction output

[0033]

[0034] The attention mechanism assigns weights to important factors affecting resource requirements through the attention layer.

[0035] Preferably, the S3 step further includes a multi-scale hierarchical scheduling model, which optimizes resource allocation through a deep Q network.

[0036] Preferably, in step S4, the temperature and humidity are adjusted by a fuzzy controller, and the corresponding control formula of the fuzzy controller is:

[0037]

[0038] Among them, u is the control quantity, α i is the weight of the control rule, μ i (x) is the membership function.

[0039] Preferably, in the step S4, the water resource recycling system collects water by laying rainwater collection devices on the roofs, balconies and the ground in public areas of the buildings, specifically including rainwater gutters and pipe systems;

[0040] The specific amount of rainwater collection can be calculated using the following formula:

[0041] V 回收 =η 集水 ·A·R

[0042] Where A is the area of ​​the building or community, R is the precipitation, η 集水 is the water collection efficiency, V 回收 The amount of rainwater recycled;

[0043] The rainwater ditch and pipe system is used to quickly collect rainwater and flow it to the storage area, and it also needs to pass through a filtration system before collection;

[0044] The filtration system comprises:

[0045] Coarse filter, used to filter larger impurities;

[0046] Sedimentation tanks, used to further reduce the solid impurities content in rainwater;

[0047] The adsorption zone is used to further adsorb fine impurities, organic pollutants and odors through activated carbon;

[0048] In the disinfection area, ultraviolet disinfection equipment is used to eliminate microorganisms and germs in rainwater;

[0049] Each section of the filtration system corresponds to a storage area for using water quality after different filtration steps according to different water uses.

[0050] Preferably, in step S5, the environmental protection behavior score calculation formula is:

[0051]

[0052] Where M is the number of types of environmental protection behaviors, w j is the behavior weight;

[0053] The points are recorded using a distributed ledger based on blockchain.

[0054] Preferably, in step S5, the incentive mechanism for points includes but is not limited to shopping discounts, scenic spot discounts, environmental protection home selection and public transportation discounts.

[0055] The present invention provides an eco-friendly urban environment design implementation method, which has the following beneficial effects:

[0056] 1. The present invention uses distributed clean energy management, adopts a multi-layer structure of microgrids, urban power grids and regional coordination, combines solar photovoltaic, wind power generation and small geothermal power generation units, realizes localized production, storage and flexible allocation of energy, and uses optimization algorithms to intelligently dispatch energy, which can effectively reduce dependence on traditional fossil energy, reduce carbon emissions, reduce energy costs, and achieve energy supply and demand balance, and improve the utilization rate of clean energy, which makes urban energy management more sustainable and environmentally friendly.

[0057] 2. The present invention uses an integrated environmental monitoring and multi-layer response system to deploy various types of sensor networks to monitor environmental data such as air, water, noise, and soil in real time, and generates accurate heat maps through data fusion and pollution distribution estimation to timely identify pollution sources and high-risk areas. The system triggers a multi-layer response mechanism at the community, municipal, and regional levels according to the pollution level, which can respond to environmental pollution incidents quickly and effectively, reduce the impact on residents' health and the ecological environment, and improve the accuracy and efficiency of environmental management.

[0058] 3. The present invention uses a high-precision resource demand prediction and multi-scale scheduling system to build a dynamic prediction model based on historical and real-time data, realize the accurate prediction and multi-scale scheduling of resource demands such as water, electricity, and gas, and adopts a deep learning optimization algorithm to realize the tiered supply of resources. It can flexibly adjust resource allocation to cope with demand fluctuations, reduce resource waste, and ensure the continuity and stability of resource supply. The system significantly improves the utilization efficiency of urban resources and meets the requirements of sustainable urban development.

[0059] 4. The present invention improves the comfort level in the building and effectively reduces the energy consumption of equipment such as air conditioners through intelligent building management systems and fuzzy controllers to intelligently adjust the temperature and humidity of the building, thereby achieving energy saving. At the same time, a rainwater collection and filtration system is designed so that rainwater can be recycled and reused for green irrigation and cleaning, reducing the use of tap water. This design helps to ease the pressure on urban water resources and achieve building energy conservation and sustainable use of water resources.

[0060] 5. The blockchain technology of the present invention records the environmental protection behavior points of citizens, ensures the transparency and non-tamperability of the point records, establishes a fair incentive mechanism, and improves the enthusiasm and sustainability of citizens' participation in environmental protection through various forms of rewards, making environmental protection behavior a conscious public activity, promoting the improvement of the overall ecological environment of the city, and forming a good public environmental awareness. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 The figure is a flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Embodiment 1:

[0064] Please see attached Figure 1 The embodiment of the present invention provides an eco-friendly urban environment design implementation method, comprising the following steps:

[0065] S1. Build a distributed clean energy management system: localize energy production, storage and deployment through the microgrid layer, urban grid layer and regional coordination layer;

[0066] S2. Establish an integrated environmental monitoring and multi-layer response system: deploy a multi-type sensor network to collect environmental data, generate heat maps and perform hierarchical responses through data fusion and pollution spatial distribution estimation;

[0067] S3. Realize high-precision resource demand forecasting and multi-scale scheduling system: Based on historical and real-time data, use dynamic forecasting models to forecast water, electricity, and gas resource demand, and perform multi-scale resource scheduling to optimize resource allocation;

[0068] S4. Establish an ecological building and smart community governance system: realize temperature and humidity control through the smart building management system, and build a water resource recycling system in the community;

[0069] S5. Design a public participation feedback incentive platform: record and manage citizens’ environmental protection behavior points through blockchain technology, and provide a points-based incentive mechanism to encourage environmental protection behavior;

[0070] In step S1, the microgrid layer includes solar photovoltaic systems, wind power systems, and small geothermal power generation units, and a mixed integer nonlinear programming model is used to optimize energy scheduling;

[0071] Energy scheduling specifically includes comprehensive energy cost, carbon emissions and energy storage cost, and is optimized through the following formula:

[0072]

[0073] Among them, C i is the unit cost of energy type i, E i is the supply, α is the carbon emission weight;

[0074] The formula includes energy demand balance constraints and energy storage capacity constraints, specifically:

[0075] E stored,t =E stored,t-1 +η charge E excess -η discharge E required

[0076] where η charge and η discharge for charging and discharging efficiency;

[0077] In step S2, environmental data include but are not limited to air quality, water quality, noise and soil; data fusion is performed using Kalman filtering, and the corresponding formula is:

[0078]

[0079] in is the estimated value after filtering, K k is the Kalman gain, z k is the observed value, H is the measurement matrix;

[0080] In step S2, the environmental monitoring and multi-layer response system further includes a spatial distribution estimation and a multi-layer response mechanism, which estimates the spatial distribution of pollution events based on the Markov random field model and triggers community-level, municipal-level, and regional coordinated response mechanisms according to the pollution level;

[0081] The Markov random field model estimation formula is:

[0082]

[0083] Among them, X is the state variable, Z is the normalization constant, ψ c (X c ) is the potential function;

[0084] In step S3, the dynamic prediction model is constructed based on the variational autoencoder and the attention mechanism. The variational autoencoder formula is:

[0085] Encoder: Encodes input data X into latent variables z

[0086] z~q(z|X)=N(z;μ(X),σ(X))

[0087] Decoder: Generates predicted output X

[0088]

[0089] The attention mechanism assigns weights to important factors affecting resource requirements through the attention layer;

[0090] In step S3, a multi-scale hierarchical scheduling model is further included, which optimizes resource allocation through a deep Q network;

[0091] In step S4, the temperature and humidity are adjusted by the fuzzy controller, and the corresponding control formula of the fuzzy controller is:

[0092]

[0093] Among them, u is the control quantity, α i is the weight of the control rule, μ i (x) is the membership function;

[0094] In step S4, the water resource recycling system collects water by laying rainwater collection devices on the roofs, balconies and the ground in public areas of the building, including rainwater gutters and pipe systems;

[0095] The specific amount of rainwater collection can be calculated using the following formula:

[0096] V 回收 =η 集水 ·A·R

[0097] Where A is the area of ​​the building or community, R is the precipitation, η 集水 is the water collection efficiency, V 回收 The amount of rainwater recycled;

[0098] Stormwater gutters and pipe systems are used to quickly collect rainwater and move it to storage areas, and it also needs to pass through a filtration system before collection;

[0099] The filtration system includes:

[0100] Coarse filter, used to filter larger impurities;

[0101] Sedimentation tanks, used to further reduce the solid impurities content in rainwater;

[0102] The adsorption zone is used to further adsorb fine impurities, organic pollutants and odors through activated carbon;

[0103] In the disinfection area, ultraviolet disinfection equipment is used to eliminate microorganisms and germs in rainwater;

[0104] Each section of the filtration system has a corresponding storage area, which is used to adjust the water quality after different filtration steps according to different water use;

[0105] In step S5, the environmental protection behavior score calculation formula is:

[0106]

[0107] Where M is the number of types of environmental protection behaviors, w j is the behavior weight;

[0108] Points are recorded in a distributed ledger based on blockchain;

[0109] In step S5, the incentive mechanism for points includes but is not limited to shopping discounts, scenic spot discounts, environmental protection home selection and public transportation discounts.

[0110] Specifically, in step S1, it realizes the localized production, storage and deployment of urban energy through the microgrid layer, the urban power grid layer and the regional coordination layer. The microgrid layer includes solar photovoltaic systems, wind power generation systems and small geothermal power generation units. These distributed energy devices can independently produce energy in communities or buildings, reducing dependence on centralized power supply and improving the utilization rate of clean energy. The microgrid units are connected together through the urban power grid layer to achieve energy complementarity and balanced deployment among microgrids, thereby ensuring the continuity of power supply. The regional coordination layer further enhances the flexibility and scalability of the system. When energy demand surges or power supply is tight, energy can be shared through coordination with surrounding regional power grids to form a larger-scale energy network. This design makes energy supply and demand more flexible, enhances the resilience of urban power systems, reduces dependence on fossil energy, and brings clean and stable energy supply.

[0111] In step S2, a multi-type sensor network covering the city is deployed to collect environmental data including air quality, water quality, noise and soil. By fusing and processing multi-type data, environmental quality can be monitored in real time, providing a basis for early warning and response to pollution incidents. Combined with Kalman filtering technology, the system can fuse the collected environmental data, reduce noise interference, and make the monitoring data more accurate. In addition, based on the monitoring data, the system generates a heat map of the spatial distribution of environmental pollution and performs hierarchical response processing. By responding to pollution incidents in a timely manner, it effectively protects residents' health and optimizes environmental quality.

[0112] In step S3, it uses a dynamic prediction model to predict the demand for resources such as water, electricity, and gas based on historical and real-time data. The variational autoencoder can extract the key features of resource demand data, and the attention mechanism gives higher weights to important features to improve prediction accuracy. Based on demand forecasting, resource allocation is optimized through a multi-scale hierarchical scheduling model to ensure balanced resource supply at different levels of cities, regions, and communities and reduce resource waste.

[0113] In step S4, the intelligent building management system is used to achieve intelligent control of the building's temperature and humidity, thereby optimizing the building's energy consumption and comfort. In addition, a water resource recycling system is built in the community to reduce the use of urban tap water and alleviate water resource pressure through rainwater collection and reuse.

[0114] In step S5, it uses blockchain technology to record citizens' environmental protection behavior points and provides a variety of incentive mechanisms based on points, including shopping discounts, scenic spot discounts, environmental protection home selection and public transportation discounts. This platform ensures fair and just records of environmental protection behaviors through transparent and tamper-proof point records, and encourages citizens to actively participate in environmental protection activities through a variety of reward forms.

[0115] Example 2: Urban environment design method with centralized energy management and fixed scheduling

[0116] Implementation steps:

[0117] Centralized energy management system: Cities rely on centralized energy production and power supply systems, which mainly use fossil fuels and a small amount of clean energy as energy sources. They are uniformly controlled and managed by central power stations and lack local distributed energy production units.

[0118] Basic environmental monitoring system: Air quality and water quality monitoring equipment are installed at fixed locations, but it does not have the ability to monitor noise and soil; data collection relies on manual periodic reading and lacks real-time monitoring and dynamic data fusion functions.

[0119] Fixed resource supply scheduling: There is no resource demand forecasting model. The supply of resources such as water, electricity, and gas is fixed based on average historical data, which makes it difficult to cope with demand fluctuations.

[0120] Traditional building management system: There is no intelligent building management system, the temperature and humidity adjustment in the building depends entirely on manual operation, and the building has no rainwater recycling system, resulting in waste of water resources.

[0121] No public participation platform: The city has no public participation platform, and citizens lack an incentive mechanism for environmental protection participation, making it difficult to form positive feedback on environmental protection behavior.

[0122] Summary of Example 2:

[0123] The centralized energy management model of Example 2 leads to low efficiency in energy dispatching, energy sources mostly rely on fossil fuels, and resource supply cannot be adjusted dynamically, making it difficult to cope with fluctuations in demand. Environmental monitoring is not comprehensive enough, public participation is low, and it is difficult to achieve eco-friendly goals.

[0124] Example 3: Regional collaborative energy management system and artificial response environment design method

[0125] Implementation steps:

[0126] Regional collaborative energy management system: There are solar and wind power facilities in the city, but it mainly relies on the collaborative supply of electricity from surrounding areas. It lacks microgrids and energy storage systems, the utilization rate of clean energy is low, and energy allocation relies on manual scheduling.

[0127] Regional environmental monitoring system: Air quality and water quality monitoring points are only set up in major urban areas. Pollution monitoring relies on regular manual inspections. There is a lack of multi-sensor data fusion of Kalman filtering, and real-time estimation of pollution distribution cannot be achieved.

[0128] Basic resource demand analysis system: formulates monthly resource supply plans based on seasonal supply and demand fluctuations, but there is no dynamic time series forecasting model, and resource supply is not flexible enough to cope with sudden changes in demand.

[0129] Energy-saving building system: The building uses timer switches to control temperature and humidity, and does not have an intelligent control system; a basic rainwater collection device is set up, but there is no multi-layer filtration and disinfection, and rainwater can only be used in scenarios with low water quality requirements.

[0130] Simple environmental protection publicity platform: The municipal government sets up environmental protection publicity slogans and encourages citizens to participate in environmental protection, but does not provide point incentives and blockchain records, making it difficult for citizens' environmental protection behaviors to continue.

[0131] Summary of Example 3:

[0132] Embodiment 3 relies on regional coordinated supply and manual energy scheduling, lacks autonomy and real-time performance. Environmental monitoring and response efficiency is low, resource supply cannot be flexibly adjusted, and the incentive mechanism of the public participation platform is insufficient, making it difficult to maintain long-term environmental protection effects.

[0133] Example 4: Urban environment design method for distributed energy and regular monitoring

[0134] Implementation steps:

[0135] Distributed energy management system: There are solar and wind microgrids in the city, but the system does not have an automated scheduling model and is only deployed through a fixed schedule. It lacks a mixed integer nonlinear programming (MINLP) optimization model.

[0136] Regular environmental monitoring: Air quality and water quality data are collected at set time points every month. Data collection and analysis rely on manual processing. The lack of Kalman filtering and Markov random field (MRF) models makes it impossible to achieve real-time data fusion and pollution distribution estimation.

[0137] Monthly resource scheduling: Resource supply analysis is conducted monthly. There is no dynamic forecasting model and hierarchical scheduling system, which makes it difficult to cope with daily fluctuations and sudden demands.

[0138] Ecological building management system: The temperature and humidity adjustment in the building relies on manual settings, and there is no intelligent control system; the rainwater collection device lacks an automatic cleaning and filtration system, the water quality is difficult to guarantee, and the scope of application is limited.

[0139] Basic environmental protection activities: Citizens’ environmental protection behaviors are recorded on handwritten record cards, and the municipal government organizes quarterly environmental protection publicity activities. There is no continuous incentive and data management.

[0140] Summary of Example 4:

[0141] Embodiment 4 has a distributed energy system, but lacks automated scheduling and optimization functions; environmental monitoring relies on manual regular processing, which is inefficient and lacks flexibility in resource scheduling. Ecological building management and environmental protection incentive mechanisms are insufficient, and comprehensive eco-friendly environmental design cannot be achieved.

[0142] Example 5: Urban environmental approach to smart energy systems and community-level environmental design

[0143] Implementation steps:

[0144] Smart energy system: uses solar and wind power for electricity generation and has some automated scheduling capabilities, but the system does not support regional coordination and dynamic allocation, and the matching degree between energy supply and demand is low.

[0145] Community-level environmental monitoring: Air and water quality monitors are set up within the community, but do not cover the entire city. Data fusion uses a simple averaging method without the support of Kalman filtering and MRF models.

[0146] Short-term resource demand scheduling: Short-term resource demand forecasting is performed based on the demand data of the past two weeks, but the variational autoencoder (VAE) and attention mechanism are not used, and the prediction accuracy is not high.

[0147] Community ecological building system: A manually adjusted temperature and humidity system is set up in the community building. Rainwater is collected only for primary filtration and ultraviolet disinfection is not possible, making it difficult to ensure water quality.

[0148] Community environmental protection activities: Environmental protection publicity and cleaning activities are organized by the community. Due to the lack of a points incentive platform recorded by blockchain, citizens’ environmental protection behaviors cannot be continuously tracked.

[0149] Summary of Example 5:

[0150] Example 5 has an intelligent energy system, but has not achieved dynamic scheduling and regional coordination. The environmental monitoring coverage is limited and lacks advanced data fusion technology. Resource scheduling is not accurate enough. The environmental incentive system does not use data management technologies such as blockchain, which affects the enthusiasm and sustainability of citizen participation.

[0151] Summary of the embodiments:

[0152] Example 1 demonstrates the best practices of eco-friendly urban environmental design. Through the combination of distributed energy, intelligent monitoring and dynamic scheduling, it achieves the maximization of resource utilization, the precision of environmental monitoring and the incentive of citizen participation. In contrast, the other four examples have obvious deficiencies in energy management, environmental monitoring, resource scheduling, ecological building management and public incentives, and cannot achieve true eco-friendly urban design. Therefore, Example 1 is the best implementation plan, which fully reflects the innovation and practical value of the present invention.

[0153] Test experiment:

[0154] Experimental design overview:

[0155] Target:

[0156] The performance of the five embodiments was tested on the following five core indicators to compare the advantages and disadvantages of the first embodiment with the other four embodiments:

[0157] Energy management efficiency;

[0158] Environmental monitoring accuracy;

[0159] Resource scheduling response;

[0160] Building energy saving effect;

[0161] Public participation in environmental protection;

[0162] Test environment:

[0163] Urban environment: simulates a medium-sized urban environment, including office buildings, residential buildings, public facilities and green areas.

[0164] Test cycle: The test cycle is set to 30 days, and the data collection frequency is daily.

[0165] Test equipment: sensor networks, energy dispatch systems, IoT devices, rainwater collection and management systems, public participation platforms, etc.

[0166] Experiment 1: Energy management efficiency test

[0167] Purpose of the test:

[0168] The performance of each embodiment in terms of energy management efficiency is compared to verify the advantage of the distributed clean energy management system of the first embodiment in terms of energy utilization.

[0169] Test steps:

[0170] Energy production and consumption data collection: In the five embodiments, the energy production and consumption data of each energy management system are collected respectively.

[0171] Energy supply and demand balance test: record the difference between daily energy supply and actual demand.

[0172] Energy efficiency calculation: Calculate the energy efficiency of each embodiment, that is, the ratio of actual energy consumption to production.

[0173] Energy storage loss test: Record the energy loss of the energy storage device every day to evaluate the energy storage efficiency.

[0174] Data and results:

[0175]

[0176] Result analysis:

[0177] The energy utilization efficiency of Example 1 is the highest, the supply-demand balance difference is the lowest, and the energy storage loss is the smallest, which shows that its distributed clean energy management system has obvious advantages in energy management.

[0178] Experiment 2: Environmental monitoring accuracy test

[0179] Purpose of the test:

[0180] The environmental monitoring accuracy of the five embodiments is compared, especially in terms of air quality and water quality monitoring, to verify the advantages of the first embodiment in terms of environmental data fusion and response speed.

[0181] Test steps:

[0182] Monitoring equipment placement: Air quality and water quality sensors are placed in the test area of ​​each embodiment to collect data.

[0183] Real-time data collection: record air quality (PM2.5 concentration) and water quality (pH value) data every hour.

[0184] Data fusion accuracy test: measure the error between the actual collected data and the standard detection value.

[0185] Pollution incident response test: simulate pollution incidents and observe the system's response speed and processing efficiency.

[0186] Data and results:

[0187]

[0188] Result analysis:

[0189] Example 1 has the highest accuracy in PM2.5 and pH monitoring and the shortest response time, indicating that its data fusion technology and multi-layer response mechanism have significant advantages.

[0190] Experiment 3: Resource Scheduling Response Test

[0191] Purpose of the test:

[0192] The scheduling response speed and accuracy of the five embodiments when resource demand fluctuates are tested to evaluate the superiority of the resource demand prediction and multi-scale scheduling system of Embodiment 1.

[0193] Test steps:

[0194] Demand Fluctuation Simulation: During the test period, the demand for water, electricity, and gas is randomly increased or decreased to simulate peak and trough demand.

[0195] Demand forecast accuracy test: Compare the differences between the resource demand predicted by the system and the actual demand.

[0196] Scheduling response speed: records the system’s response time from demand changes to completion of resource scheduling.

[0197] Data and results:

[0198] Example Resource demand forecast error (%) Scheduling response time (seconds) Embodiment 1 2％ 10 Embodiment 2 10％ 60 Embodiment 3 8％ 50 Embodiment 4 6％ 40 Embodiment 5 4％ 30

[0199] Result analysis:

[0200] The resource demand prediction error of Example 1 is the smallest and the response speed is the fastest, indicating that its dynamic time series prediction model and multi-scale scheduling optimization perform outstandingly in resource scheduling.

[0201] Experiment 4: Building energy saving effect test

[0202] Purpose of the test:

[0203] The building energy-saving effects of the five embodiments are compared to evaluate the advantages of the intelligent building management system in embodiment 1 in terms of temperature and humidity control and energy saving.

[0204] Test steps:

[0205] Temperature and humidity control data collection: The temperature, humidity and energy consumption data of the air conditioning system of each building were recorded during the test.

[0206] Energy consumption comparison: record daily energy consumption and compare the energy saving effect of each embodiment in terms of temperature and humidity control.

[0207] Energy saving effect calculation: Calculate the energy saving rate of each embodiment compared to the baseline value without energy saving control.

[0208] Data and results:

[0209]

[0210] Result analysis:

[0211] The temperature and humidity control of Example 1 has the highest accuracy, the lowest energy consumption, and the highest energy saving rate, indicating that its intelligent building management system has significant effects in energy saving and temperature and humidity control.

[0212] Experiment 5: Public environmental protection participation test

[0213] Purpose of the test:

[0214] By comparing citizens' participation in environmental protection behaviors, the incentive effect of the public participation feedback incentive platform in Example 1 is evaluated.

[0215] Test steps:

[0216] Environmental protection behavior record: record the number of environmental protection behaviors of citizens during the test period (such as garbage sorting, reducing water consumption, etc.).

[0217] Points redemption: Count the number of citizens who use points to redeem rewards in each embodiment.

[0218] Calculation of public participation rate: Calculate the percentage of citizens participating in environmental protection behaviors to the total number of citizens.

[0219] Data and results:

[0220]

[0221]

[0222] Result analysis:

[0223] The average daily number of environmental protection behaviors, number of points redeemed, and public participation rate of Example 1 are all the highest, indicating that its blockchain-based environmental protection behavior points system has significantly improved the public's environmental protection participation.

[0224] Summary of the embodiments:

[0225] Energy management efficiency: Example 1 has the best performance in terms of energy utilization and energy storage loss, while other examples have problems such as supply-demand imbalance or low energy storage efficiency.

[0226] Environmental monitoring accuracy: The environmental monitoring accuracy and response speed of Example 1 are significantly higher than those of other examples, indicating that its data fusion and response mechanism are more advanced.

[0227] Resource scheduling response: The resource prediction and scheduling response speed of embodiment 1 performed best, while the other embodiments were poor in prediction accuracy and response speed.

[0228] Building energy-saving effect: The building energy-saving effect of embodiment 1 is the most significant, and the intelligently controlled temperature and humidity system reduces energy consumption. The energy-saving effect of other embodiments is not as good as that of embodiment 1.

[0229] Public participation in environmental protection: Example 1 has the highest public participation in environmental protection, and its point incentive mechanism effectively enhances citizens' enthusiasm for environmental protection. The incentive mechanisms of other examples are less effective.

[0230] In general, the first embodiment is superior to other embodiments in all indicators, demonstrating its significant advantages in eco-friendly urban environment design. The test results of other embodiments reflect their shortcomings in energy management, environmental monitoring, resource scheduling, building management and public incentives, further highlighting the technical innovation and application value of the first embodiment.

[0231] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An eco-friendly urban environment design implementation method, characterized in that: The following steps are involved: S1. Build a distributed clean energy management system: localize energy production, storage and deployment through the microgrid layer, urban grid layer and regional coordination layer; S2. Establish an integrated environmental monitoring and multi-layer response system: deploy a multi-type sensor network to collect environmental data, generate heat maps and perform hierarchical responses through data fusion and pollution spatial distribution estimation; S3. Realize high-precision resource demand forecasting and multi-scale scheduling system: Based on historical and real-time data, use dynamic forecasting models to forecast water, electricity, and gas resource demand, and perform multi-scale resource scheduling to optimize resource allocation; S4. Establish an ecological building and smart community governance system: realize temperature and humidity control through the smart building management system, and build a water resource recycling system in the community; S5. Design a public participation feedback incentive platform: record and manage citizens’ environmental protection behavior points through blockchain technology, and provide a points-based incentive mechanism to encourage environmental protection behavior.

2. The eco-friendly urban environment design implementation method according to claim 1, characterized in that: In the step S1, the microgrid layer includes a solar photovoltaic system, a wind power generation system and a small geothermal power generation unit, and a mixed integer nonlinear programming model is used to optimize energy scheduling; The energy scheduling specifically includes comprehensive energy cost, carbon emission and energy storage cost, and is optimized by the following formula: Among them, C i is the unit cost of energy type i, E i is the supply, α is the carbon emission weight; The formula includes energy demand balance constraints and energy storage capacity constraints, specifically: AND stored,t =And stored,t-1 +η charge AND excess -η discharge AND required where η charge and η discharge For charging and discharging efficiency.

3. The eco-friendly urban environment design implementation method according to claim 1, characterized in that: In step S2, environmental data include but are not limited to air quality, water quality, noise and soil; the data fusion is performed using Kalman filtering, and the corresponding formula is: in is the estimated value after filtering, K k is the Kalman gain, z k is the observed value, and H is the measurement matrix.

4. The eco-friendly urban environment design implementation method according to claim 1, characterized in that: In the step S2, the environmental monitoring and multi-layer response system further includes a spatial distribution estimation and a multi-layer response mechanism, which estimates the spatial distribution of pollution events based on a Markov random field model and triggers community-level, municipal-level and regional coordinated response mechanisms according to the pollution level; The Markov random field model estimation formula is: Among them, X is the state variable, Z is the normalization constant, ψ c (X c ) is the potential function.

5. The eco-friendly urban environment design implementation method according to claim 1, characterized in that: In the step S3, the dynamic prediction model is constructed based on the variational autoencoder and the attention mechanism. The variational autoencoder formula is: Encoder: Encodes input data X into latent variables z z~q(z|X)=N(z;μ(X),σ(X)) Decoder: Generates prediction output The attention mechanism assigns weights to important factors affecting resource requirements through the attention layer.

6. The eco-friendly urban environment design implementation method according to claim 1, characterized in that: The S3 step further includes a multi-scale hierarchical scheduling model, which optimizes resource allocation through a deep Q network.

7. The eco-friendly urban environment design implementation method according to claim 1, characterized in that: In step S4, the temperature and humidity are adjusted by a fuzzy controller, and the corresponding control formula of the fuzzy controller is: Among them, u is the control quantity, α i is the weight of the control rule, μ i (x) is the membership function.

8. The eco-friendly urban environment design implementation method according to claim 1, characterized in that: In the step S4, the water resource recycling system collects water by laying rainwater collection devices on the roofs, balconies and the ground in public areas of the building, specifically including rainwater gutters and pipe systems; The specific amount of rainwater collection can be calculated using the following formula: V 回收 =the 集水 ·A·R Where A is the area of ​​the building or community, R is the precipitation, η 集水 is the water collection efficiency, V 回收 The amount of rainwater recycled; The rainwater ditch and pipe system is used to quickly collect rainwater and flow it to the storage area, and it also needs to pass through a filtration system before collection; The filtration system comprises: Coarse filter, used to filter larger impurities; Sedimentation tanks, used to further reduce the solid impurities content in rainwater; The adsorption zone is used to further adsorb fine impurities, organic pollutants and odors through activated carbon; In the disinfection area, ultraviolet disinfection equipment is used to eliminate microorganisms and germs in rainwater; Each section of the filtration system corresponds to a storage area for using water quality after different filtration steps according to different water uses.

9. The eco-friendly urban environment design implementation method according to claim 1, characterized in that: In step S5, the environmental protection behavior score calculation formula is: Where M is the number of types of environmental protection behaviors, w j is the behavior weight; The points are recorded using a distributed ledger based on blockchain.

10. The eco-friendly urban environment design implementation method according to claim 1, characterized in that: In the step S5, the incentive mechanism of points includes but is not limited to shopping discounts, scenic spot discounts, environmental protection home selection and public transportation discounts.

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