Multi-factor integrated planning and design method for green intelligent buildings

Abstract

The present invention relates to the technical field of green intelligent building planning and design, and discloses a multi-factor fusion planning and design method for green intelligent buildings. The method first obtains geographic information data and ecological environment parameters of the building site, collects and integrates multi-dimensional environmental data of the surrounding area, constructs a three-dimensional space topology model, and generates an initial space planning model. Then, climate simulation and resource circulation conditions are loaded to form a comprehensive planning analysis model, and dynamic collaborative evaluation indicators are output. By constructing a multi-objective optimization pre-training framework for iterative learning, a multi-factor coordination model and key coordination parameters are obtained, and then a system collaborative simulation result is generated. A configuration model is then established to optimize facility deployment parameters and obtain the optimal configuration solution. Finally, parameters are dynamically adjusted according to the performance evaluation framework to realize the planning and control of green intelligent buildings. The method integrates multiple factors, realizes the optimization of building planning and design, and improves the sustainability and intelligence level of the building.

Description

Technical Field

[0001] The present invention relates to the technical field of green intelligent building planning and design, and in particular to a multi-factor fusion planning and design method for green intelligent buildings. Background Art

[0002] As global attention to sustainable development and building intelligence continues to increase, green and intelligent buildings have become an important development direction for the construction industry. Traditional architectural planning and design methods have many limitations and are no longer able to meet the diverse architectural needs of modern society.

[0003] When it comes to utilizing geographic information, traditional design often simply references the site's location, failing to fully explore the potential value of geographic information data. For example, sites with complex topography often fail to fully consider the impact of terrain undulations on building layout, lighting, and ventilation, leading to inefficient use of building space and increased energy consumption. For example, some mountainous buildings, due to the lack of scientific design based on the terrain during early planning, not only increase construction costs but also affect the building's user experience and energy efficiency.

[0004] In terms of the ecological environment, previous designs have rarely considered comprehensive ecological parameters. Buildings lack effective integration with surrounding ecosystems, and insufficient attention is paid to protecting plant and animal habitats and water resources. For example, in urban development, some construction projects directly destroy existing wetland ecosystems, resulting in damage to ecological balance, reduced biodiversity, and weakening the wetlands' ecological regulatory functions for cities, such as flood control and air purification.

[0005] Climate factors have also been insufficiently considered in traditional design. Architectural designs have failed to precisely adapt to the climate characteristics of different regions, resulting in high energy consumption. For example, in northern China, some buildings consume significant amounts of energy for heating in winter, but due to poor insulation design, heat loss is severe. In the hotter south, some buildings lack effective shading and natural ventilation, resulting in significant energy consumption for air conditioning and cooling in summer.

[0006] Resource recycling is a particular shortcoming of traditional architectural design. The recycling rate of building materials and energy is low, resulting in a significant amount of recyclable resources being wasted. For example, much of the construction waste generated by building demolition is directly landfilled, which not only consumes a large amount of land resources but also causes environmental pollution. Furthermore, during the operation of buildings, energy recovery and reuse mechanisms are imperfect, and renewable energy sources such as solar and wind power are not effectively collected and utilized.

[0007] Furthermore, traditional building planning and design processes operate independently, lacking systematic integrated analysis and collaborative optimization. Poor communication and collaboration between different specialized design teams can lead to flaws in the overall performance of the building design. For example, when designing the building structure, the structural design team may not fully communicate with the equipment design team. This can result in insufficient space for equipment installation or an inappropriate layout, impacting the building's overall functionality and performance.

[0008] With increasing demands for building quality, environmental friendliness, and intelligent design, it is imperative to develop a green, intelligent building planning and design method that integrates multiple factors, including geographic information, ecological environment, climate, and resource recycling. This method requires the coordinated optimization of these multiple factors to improve the sustainability, functionality, and intelligence of buildings, meet modern society's demand for green, intelligent buildings, and promote the development of the construction industry towards a more environmentally friendly, efficient, and intelligent direction. Summary of the Invention

[0009] The purpose of the present invention is to provide a green intelligent building multi-factor fusion planning and design method to solve the problems raised in the above background technology.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a multi-factor integrated planning and design method for green intelligent buildings, the method comprising:

[0011] Obtain geographic information data and ecological environment parameters of the construction site;

[0012] Collect and integrate multi-dimensional environmental data around the site to establish a three-dimensional spatial topology model. Perform multi-factor coupling analysis on the three-dimensional spatial topology model and combine it with geographic information data to generate an initial spatial planning model.

[0013] Load climate simulation conditions and resource circulation conditions into the initial spatial planning model to form a comprehensive planning analysis model, combine it with ecological and environmental parameters, and output dynamic collaborative evaluation indicators;

[0014] Construct a multi-objective optimization pre-training framework and perform iterative learning through dynamic collaborative evaluation indicators to obtain a multi-factor coordination model and extract key coordination parameters;

[0015] Generate system collaborative simulation results through key coordination parameters, comprehensive planning analysis models and geographic information data;

[0016] Based on the system collaborative simulation results and facility deployment parameters, a configuration model is established and the facility deployment parameters are optimized to obtain the optimal configuration plan for facility deployment;

[0017] Based on the performance evaluation framework, the current stage configuration plan is deduced to obtain the current stage performance evaluation index. Combining the current stage performance evaluation index, the current stage optimal configuration plan and the current stage actual configuration parameters, the facility deployment parameters are dynamically adjusted to achieve the planning and control of green smart buildings.

[0018] Preferably, the dynamic collaborative evaluation indicators include at least resource circulation efficiency, energy distribution gradient and spatial optimization potential set; the key coordination parameters include at least energy coordination nodes, environmental adaptation forms and resource circulation path parameters; the system collaborative simulation results include at least energy transfer trajectory, maximum load distribution and environmental response gradient.

[0019] Preferably, the process of collecting multi-dimensional environmental data around the site and fusing it to establish a three-dimensional spatial topology model, performing multi-factor coupling analysis on the three-dimensional spatial topology model and combining it with geographic information data to generate an initial spatial planning model comprises the following steps:

[0020] Conduct multi-scale environmental monitoring of construction sites to obtain multi-dimensional environmental data sequences;

[0021] Performing fusion processing on the multi-dimensional environmental data sequence to obtain a standardized environmental data set, wherein the fusion processing includes one or more of data cleaning, normalization, feature association, redundancy elimination, data reconstruction and consistency verification;

[0022] Based on the standardized environmental data set, a three-dimensional spatial topology model is constructed using spatial interpolation technology;

[0023] A multi-factor coupling analysis is performed on the three-dimensional spatial topology model to generate an initial spatial planning model, wherein the multi-factor coupling analysis at least includes grid division and parameter mapping, and different functional blocks are divided by grid division, and corresponding geographic information parameters are assigned to each functional block.

[0024] Preferably, the process of loading climate simulation conditions and resource circulation conditions into the initial spatial planning model to form a comprehensive planning analysis model, combining ecological environment parameters, and outputting dynamic collaborative evaluation indicators comprises the following steps:

[0025] Loading climate simulation conditions to the initial spatial planning model to constrain environmental boundaries, and obtaining the first planning analysis model;

[0026] Superimposing resource recycling conditions on the first planning analysis model to form a comprehensive planning analysis model, wherein the resource recycling conditions include energy recovery conditions and material recycling conditions;

[0027] Based on the comprehensive planning analysis model and ecological environment parameters, dynamic collaborative evaluation indicators are calculated. The specific process includes:

[0028] A multi-factor synergistic equation is constructed, which includes at least an energy balance equation, an environmental response equation, and a resource allocation equation. Combined with a comprehensive planning analysis model, the synergistic equation is solved by a numerical discrete method to obtain a set of resource circulation efficiency, energy distribution gradient, and spatial optimization potential.

[0029] Preferably, obtaining the space optimization potential set includes the following steps:

[0030] Based on the energy distribution gradient, calculate the energy load difference of each functional block;

[0031] Identify the functional blocks whose energy load difference is not less than the load threshold in the comprehensive planning analysis model and obtain the set of spatial optimization potentials.

[0032] Preferably, the multi-objective optimization pre-training framework is constructed and iterative learning is performed through dynamic collaborative evaluation indicators to obtain a multi-factor coordination model, and then key coordination parameters are extracted, including the following steps:

[0033] Construct a multi-objective optimization pre-training framework based on feature fusion network;

[0034] The pre-training framework is trained and verified in multiple stages through dynamic collaborative evaluation indicators to obtain a multi-factor coordination model;

[0035] The dynamic collaborative evaluation indicators to be analyzed are input into the multi-factor coordination model, and the predicted spatial optimization potential set is output;

[0036] Based on the predicted spatial optimization potential set, key coordination parameters are extracted, wherein the key coordination parameters at least include energy coordination nodes, environmental adaptation forms and resource circulation path parameters.

[0037] Preferably, extracting key coordination parameters based on the predicted spatial optimization potential set comprises the following steps:

[0038] Determine the geometric center point of the predicted spatial optimization potential set as the energy coordination node;

[0039] Fitting the environmental adaptation morphology according to the distribution characteristics of the predicted spatial optimization potential set to obtain environmental adaptation morphology parameters, wherein the environmental adaptation morphology parameters include morphological curvature and spatial extension dimension;

[0040] The energy load gradient direction of the predicted spatial optimization potential set is analyzed, the weighted average of the gradient direction is calculated, and it is converted into a standard resource cycle path, which is the resource cycle path parameter.

[0041] Preferably, generating system collaborative simulation results by using key coordination parameters, comprehensive planning analysis model and geographic information data includes the following steps:

[0042] The energy coordination node is used as the benchmark anchor point, and the environmental adaptation morphological parameters are mapped into the comprehensive planning analysis model. The resource cycle path parameters are adjusted and the grid of the associated blocks is refined to complete the update of the comprehensive planning analysis model. The co-evolution rules, co-evolution direction and co-evolution step are defined to form a co-evolution planning model.

[0043] Based on the collaborative evolutionary planning model, a dynamic iterative method is used to solve the multi-factor collaborative equation. According to the results of each iterative stage in the solution process, the system collaborative simulation results are generated, including:

[0044] When the co-evolution rules are met, path optimization is performed to obtain the current energy transfer trajectory, co-evolution step length, co-evolution direction, energy load distribution and environmental response gradient, and the co-evolution planning model is updated;

[0045] The multi-factor coordination equation is re-solved based on the updated co-evolutionary planning model until the termination condition is reached, thus ending the co-evolutionary process.

[0046] Calculate the maximum load distribution and environmental response gradient based on the energy load distribution and environmental response gradient at each iteration stage;

[0047] The co-evolution rule is defined as triggering path optimization if the current energy difference accumulation is not less than the difference threshold;

[0048] The co-evolution direction is determined based on historical evolution trends and current energy distribution direction;

[0049] The energy transfer trajectory and the co-evolution step are determined according to climate parameters and resource cycle efficiency, respectively.

[0050] Preferably, the step of establishing a configuration model and optimizing the facility deployment parameters based on the system collaborative simulation results and the facility deployment parameters to obtain the optimal configuration solution for the facility deployment comprises the following steps:

[0051] Establishing a configuration model, wherein the configuration model at least includes a variable definition module, an objective function module, and a constraint condition module. The variable module is defined based on facility deployment parameters. The facility deployment parameters at least include equipment layout density, energy conversion efficiency, monitoring coverage, and data transmission delay. The objective function module is constructed based on a second performance evaluation value and a resource consumption value. The second performance evaluation value is calculated based on system collaborative simulation results. The resource consumption value at least includes a construction cost value, an operation and maintenance cost value, and an energy loss value. The constraint condition module includes a facility deployment constraint module and a performance assurance constraint module. The performance assurance constraint module is defined based on functional integrity, response timeliness, and system stability.

[0052] Genetic algorithm is used to initially solve the configuration model and obtain the initial optimal configuration scheme;

[0053] The configuration model is globally optimized using the particle swarm algorithm, and the particle swarm optimal solution set is obtained as the optimal configuration solution for facility deployment.

[0054] Preferably, the present invention also includes a computer device, which includes a memory, a processor and a communication interface, which are connected via a bus; the memory is used to store a set of computer program instructions and data, and transmit the stored data to the processor, and the processor executes the program instructions stored in the memory to execute the green intelligent building multi-factor fusion planning and design method as described in any one of the above items.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] In terms of the scientific and rational nature of planning and design, by acquiring geographic information data and ecological and environmental parameters of the building site, collecting and integrating multi-dimensional environmental data surrounding the site, establishing a three-dimensional spatial topological model, and conducting multi-factor coupling analysis, the generated initial spatial planning model fully considers the actual site conditions. For example, in sites with complex terrain, the layout and height of buildings can be rationally planned based on geographical information such as the undulations and slopes of the terrain, allowing the buildings to perfectly blend with the terrain. This not only reduces the amount of earthwork and construction costs, but also leverages the terrain's advantages to achieve natural ventilation and lighting, improving the building's energy efficiency. Incorporating ecological and environmental parameters such as vegetation distribution and soil type allows planning to protect and utilize existing ecological resources, avoid damage to the ecosystem, and achieve a harmonious coexistence between architecture and the ecological environment.

[0057] In terms of resource utilization and energy management, climate simulation conditions and resource recycling conditions are incorporated into a comprehensive planning and analysis model. The output dynamic collaborative evaluation indicators include key information such as resource recycling efficiency and energy distribution gradients. This enables precise planning of energy and resource utilization during the building design phase. For example, by analyzing energy distribution gradients, it is possible to determine the differences in energy demand in different areas of the building, rationally allocate energy supply facilities, and reduce energy losses during transmission. Furthermore, the energy recovery and material recycling conditions within the resource recycling conditions promote the collection and utilization of renewable energy during the building's operation, as well as the recycling and reuse of building materials. For example, installing solar panels on the building's roof to collect solar energy for power generation and collecting and treating rainwater for non-potable uses in the building effectively reduces the building's dependence on external energy and resources, improves resource recycling efficiency, and reduces energy consumption and operating costs.

[0058] A multi-objective optimization pre-training framework was constructed and iteratively learned to obtain a multi-factor coordination model. The extracted key coordination parameters further optimized the building design. Key coordination parameters, such as energy coordination nodes, environmental adaptation forms, and resource circulation path parameters, provide important insights into the building's spatial layout and functional design. For example, by fitting the environmental adaptation form to the site's climatic characteristics and surrounding environment, a building form can be designed that is more conducive to natural ventilation and shading, reducing the use of air conditioning and lighting systems and thus lowering energy consumption. Resource circulation path parameters optimize the flow of resources within the building system, improving resource utilization efficiency.

[0059] In terms of facility deployment, configuration models are established based on system collaborative simulation results, and facility deployment parameters are optimized to obtain the optimal configuration solution, ensuring a more rational layout of building equipment. For example, optimizing parameters such as equipment layout density, energy conversion efficiency, monitoring coverage, and data transmission latency can improve the operational efficiency of building intelligent systems. A reasonable equipment layout can reduce interference between devices, improve energy conversion efficiency, and reduce energy loss. Furthermore, optimizing monitoring coverage ensures that all areas of the building are effectively monitored, allowing for timely identification and resolution of issues, ensuring safe and stable operation. Reducing data transmission latency increases the responsiveness of building intelligent systems and enhances the user experience.

[0060] By deriving configuration plans and dynamically adjusting facility deployment parameters based on a performance evaluation framework, dynamic optimization of green smart building planning and control is achieved. Throughout the building's lifecycle, as the external environment and internal needs change, facility deployment parameters can be adjusted promptly to ensure optimal building operation. For example, adjusting the operating parameters of the building's ventilation and air conditioning systems in response to seasonal climate changes can both meet indoor comfort requirements and achieve efficient energy utilization. This dynamic optimization mechanism improves the adaptability and sustainability of buildings and extends their service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a working principle diagram of the multi-factor integrated planning and design method for green intelligent buildings according to the present invention;

[0062] Figure 2 Flowchart for generating initial space planning model;

[0063] Figure 3 A flow chart for forming a comprehensive planning analysis model and outputting indicators;

[0064] Figure 4 Flowchart to obtain key coordination parameters for building the framework;

[0065] Figure 5 Flowchart for extracting key coordination parameters based on potential sets. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0067] See also Figure 1-Figure 5 The present invention provides a green intelligent building multi-factor integration planning and design method, and the specific implementation steps are as follows:

[0068] Obtain geographic information data and ecological and environmental parameters for the construction site. Geographic information data covers the site's location, topography, and geological conditions, and can be obtained through geographic information systems (GIS), satellite remote sensing, and field surveys. Ecological and environmental parameters include the site's climate type, sunshine duration, annual average temperature, humidity, wind speed and direction, as well as soil type, vegetation cover, and surrounding water distribution. These parameters can be obtained from local meteorological departments, environmental protection departments, geological survey reports, and other sources.

[0069] Multi-dimensional environmental data from the site's surroundings is collected and fused. Using a variety of sensors, such as temperature, humidity, light, and noise sensors, the building site is monitored at multiple scales, generating a multi-dimensional environmental data series. These data series may contain missing data, errors, or inconsistent formats, necessitating fusion processing. This process includes data cleaning to remove erroneous and duplicate data; normalization to bring data of varying dimensions into the same numerical range; feature correlation to identify connections between different data features; redundancy elimination to remove redundant information; data reconstruction to reorganize the data according to specific rules; and consistency verification to ensure data accuracy and consistency. After fusion processing, a standardized environmental data set is generated. Based on this standardized environmental data set, a three-dimensional spatial topology model is constructed using spatial interpolation techniques. Multi-factor coupling analysis of the model, combined with geographic information data, generates an initial spatial planning model. This multi-factor coupling analysis includes gridding to delineate functional areas and assigning corresponding geographic information parameters to each functional area.

[0070] The initial spatial planning model is loaded with climate simulation and resource recycling conditions. These conditions constrain environmental boundaries, resulting in a first planning analysis model. Resource recycling conditions, such as energy recovery and material recycling, are then added to form a comprehensive planning analysis model. Combined with ecological and environmental parameters, a multi-factor synergy equation is constructed. This equation is solved using numerical discretization methods, generating a dynamic synergy evaluation indicator. This indicator includes at least resource recycling efficiency, energy distribution gradient, and spatial optimization potential.

[0071] A multi-objective optimization pre-training framework is constructed and iteratively learned using dynamic collaborative evaluation indicators. A multi-objective optimization pre-training framework is constructed based on a feature fusion network. The pre-training framework is trained and validated in multiple stages using dynamic collaborative evaluation indicators to obtain a multi-factor coordination model. The dynamic collaborative evaluation indicators to be analyzed are input into the multi-factor coordination model, which outputs a predicted set of spatial optimization potentials. Based on this output, key coordination parameters are extracted. These key coordination parameters include at least energy coordination nodes, environmental adaptation forms, and resource circulation path parameters.

[0072] System co-simulation results are generated using key coordination parameters, a comprehensive planning analysis model, and geographic information data. Using the energy coordination node as a benchmark anchor, environmental adaptation morphological parameters are mapped to the comprehensive planning analysis model. Resource circulation path parameters are adjusted, and the associated blocks are meshed and refined to update the comprehensive planning analysis model. The co-evolutionary rules, co-evolutionary direction, and co-evolutionary step size are defined to form a co-evolutionary planning model. Based on the co-evolutionary planning model, a dynamic iterative method is used to solve the multi-factor coordination equation. Based on the results of each iterative stage of the solution process, system co-simulation results are generated, including energy transfer trajectories, maximum load distribution, and environmental response gradients.

[0073] Based on the system co-simulation results and facility deployment parameters, a configuration model is established and the facility deployment parameters are optimized. The established configuration model includes at least a variable definition module, an objective function module, and a constraint module. The variable module is defined based on the facility deployment parameters. The facility deployment parameters include at least equipment layout density, energy conversion efficiency, monitoring coverage, and data transmission delay. The objective function module is constructed based on the second performance evaluation value and resource consumption value. The second performance evaluation value is calculated based on the system co-simulation results. The resource consumption value includes at least construction cost value, operation and maintenance cost value, and energy loss value. The constraint module includes a facility deployment constraint module and an efficiency assurance constraint module. The efficiency assurance constraint module is defined based on functional integrity, response timeliness, and system stability. A genetic algorithm is used to initially solve the configuration model to obtain an initial optimized configuration scheme. The configuration model is then globally optimized using a particle swarm algorithm to obtain the particle swarm optimal solution set as the optimal configuration scheme for facility deployment.

[0074] Based on the performance evaluation framework, the current stage configuration plan is deduced to obtain the current stage performance evaluation index. Combining the current stage performance evaluation index, the current stage optimal configuration plan and the current stage actual configuration parameters, the facility deployment parameters are dynamically adjusted to achieve the planning and control of green smart buildings.

[0075] The present invention will be further described below in conjunction with Examples 1 to 5:

[0076] Example 1:

[0077] When planning and designing green smart buildings, multi-dimensional environmental data surrounding the site is collected and integrated to create a three-dimensional spatial topology model and generate an initial spatial planning model. Multi-scale environmental monitoring of the building site is performed, utilizing various sensors and monitoring points at various heights and locations to continuously acquire long-term, multi-dimensional environmental data sequences such as temperature, humidity, light intensity, and air quality. This data may contain outliers, such as erroneous data caused by sensor failure or inconsistent data formats.

[0078] Next, the multi-dimensional environmental data series is fused. Data cleaning begins with careful inspection, removing obvious erroneous data points. For example, temperature values ​​significantly outside the normal range for the local climate are removed. Normalization is then performed to unify the data across different dimensions into a range of 0–1, facilitating subsequent analysis and processing. Feature correlation is then performed to examine the relationships between temperature, humidity, and light intensity. For example, it was found that when light intensity is high, temperature tends to rise while humidity may decrease. Redundancy elimination is also performed to remove duplicate data or redundant information that has little impact on the overall analysis. Data reconstruction is then performed, reorganizing the data according to spatial and temporal order to better reflect environmental changes. Finally, a consistency check is performed to ensure the logical consistency of the data collected by each sensor. After these fusion steps, a standardized environmental data set is obtained.

[0079] Based on a standardized set of environmental data, a three-dimensional spatial topology model is constructed using spatial interpolation technology. Spatial interpolation technology can infer data at unknown locations based on data from known monitoring points, thereby constructing a continuous three-dimensional spatial model. A multi-factor coupling analysis is performed on the three-dimensional spatial topology model, and the model is divided into different functional blocks through gridding, such as residential areas, office areas, and public activity areas. Each functional block is assigned corresponding geographic information parameters. For example, the residential area takes into account lighting and ventilation conditions, and is assigned appropriate orientation and spacing parameters; the office area focuses on transportation convenience and is assigned parameters related to proximity to roads and public transportation stations. Through these steps, an initial spatial planning model is generated.

[0080] Imagine a 50,000-square-meter site on the outskirts of a city where a green, intelligent building complex integrating office, commercial, and residential functions is to be built. The site is relatively flat, bordered by a river, close to a major city road, and near a park.

[0081] Before construction could begin, the surrounding environment needed to be monitored. Various sensors were installed at various locations and heights around the site. Temperature and humidity sensors were installed every 100 meters along the building site boundary to monitor air temperature and humidity in real time. Water quality sensors were installed near the river to monitor indicators such as the pH level and dissolved oxygen in the river water. Light sensors were installed in various areas of the site to measure light intensity, and noise sensors were installed near major roads to collect traffic noise data. These sensors operate continuously, collecting data every 15 minutes, thereby generating a multi-dimensional environmental data series.

[0082] During the data collection process, some sensors may malfunction, resulting in erroneous data. For example, a temperature and humidity sensor may display an abnormal value of 40°C in winter. This type of data needs to be removed during the data cleaning phase. Data collected by different sensors has different dimensions. For example, light intensity is measured in lux, while temperature and humidity are measured in degrees Celsius and percentages, respectively. To facilitate unified analysis, these data must be normalized to the range [0, 1]. Furthermore, research has found a correlation between light intensity and temperature and humidity. Generally, as light intensity increases, temperature rises and humidity decreases. These relationships can be mined through feature correlation. During the data collection process, some duplicate records or redundant data may be present that are not useful for overall analysis. For example, if multiple sensors collect highly similar data within a short period of time, this data can be eliminated through redundancy elimination. To better reflect environmental changes, the data is reorganized according to temporal and spatial order to complete data reconstruction. Finally, a consistency check verifies whether the data collected by different sensors are logically consistent. For example, whether the relationship between temperature, humidity, and light intensity conforms to normal physical laws. If not, further investigation and correction are performed to obtain a standardized environmental data set.

[0083] Based on a standardized set of environmental data, spatial interpolation techniques are used to construct a three-dimensional spatial topology model. Spatial interpolation techniques can infer data from unknown locations based on data from known monitoring points, thereby constructing a continuous three-dimensional spatial model. For example, given temperature and humidity data from a few monitoring points within a site, spatial interpolation techniques can be used to estimate the temperature and humidity in areas without sensors, thereby constructing a three-dimensional temperature and humidity model for the entire site. The same approach is applied to other environmental data such as light intensity and noise, ultimately integrating them to form a three-dimensional spatial topology model that incorporates multiple environmental factors.

[0084] A multi-factor coupling analysis was performed on the constructed three-dimensional spatial topology model to generate an initial spatial planning model. The model was divided into functional zones through meshing. Based on the functional requirements of the buildings, the site was divided into office, commercial, residential, and public green areas. Geographic parameters were assigned to each functional zone. For the office zone, proximity to the city's main roads was considered to ensure accessibility, taking into account employees' daily commutes. To attract more customers, the commercial zone was assigned proximity to parks and the center of the site, enhancing commercial exposure. Residential areas focused on comfort, emphasizing proximity to the river and distance from the main road. Building orientation and spacing were carefully planned, taking into account good lighting and ventilation. The public green area was located in the center of the site to optimize the overall ecological environment and enhance the living and working experience of residents and office workers. This series of operations generated an initial spatial planning model that met multiple considerations and provided a foundational framework for subsequent architectural design.

[0085] Example 2:

[0086] The initial spatial planning model is loaded with climate simulation conditions and resource recycling conditions to form a comprehensive planning analysis model, which outputs dynamic collaborative evaluation indicators. Using professional climate simulation software, the initial spatial planning model is loaded with climate simulation conditions. Local climate data, such as annual average temperature, humidity, wind speed and direction, and sunshine duration, is input, and environmental boundaries are constrained to produce the first planning analysis model. This model considers the impact of local climate on buildings. For example, in hot regions, buildings should be oriented to avoid direct sunlight to reduce energy consumption for air conditioning and cooling.

[0087] Resource recycling conditions are added to the first planning analysis model to form a comprehensive planning analysis model. Resource recycling conditions include energy recovery and material recycling. For energy recovery, consider installing solar panels on building roofs to collect solar energy and convert it into electricity, or installing wind turbines around the building to generate wind energy. Material recycling conditions examine the recycling and reuse of building materials, such as whether discarded bricks and steel can be reprocessed for building repairs or expansion.

[0088] Based on the comprehensive planning analysis model and ecological and environmental parameters, dynamic synergy evaluation indicators are calculated. Multi-factor synergy equations are constructed, which involve aspects such as energy balance, environmental response, and resource allocation. Although no formulas are used, it can be understood as constructing equations from the perspectives of energy generation, consumption, and recovery, the impact of the environment on buildings, and the rational allocation of resources. The synergy equations are solved using numerical discretization methods to obtain resource cycle efficiency, energy distribution gradients, and spatial optimization potential sets. When calculating the spatial optimization potential set, the energy load difference of each functional block is calculated based on the energy distribution gradient. Carefully analyze the energy consumption of each functional block, find the functional blocks whose energy load difference is not less than the load threshold, and form these functional blocks into a spatial optimization potential set. This will clearly identify which areas still have room for improvement in energy utilization, providing a basis for subsequent optimization design.

[0089] Consider a scenario where a green smart hotel is planned for a coastal city. This area experiences high summer temperatures, high humidity, and frequent sea breezes, and there are also strong requirements for resource recycling.

[0090] When loading climate simulation conditions into the initial space planning model, professional climate simulation software was used. First, years of meteorological data for the region were input, including detailed information such as an average summer temperature of approximately 30°C, an average humidity of 75%, a dominant sea breeze direction of southeasterly winds with a wind speed of approximately 3 to 5 meters per second, and an annual sunshine duration of approximately 2,500 hours. Based on this data, the simulation software constrained the environmental boundaries and generated the first planning analysis model. For example, based on the sea breeze direction and speed, the windward and leeward sides of the building were determined in the model, and vents were considered on the windward side to utilize natural sea breezes for indoor ventilation and cooling, reducing the use of air conditioning systems and thus reducing energy consumption. At the same time, the orientation of the building was planned based on the duration and angle of sunshine so that most rooms could receive sufficient sunlight in winter while avoiding excessive exposure in summer.

[0091] Resource recycling conditions are superimposed on the first planning analysis model to form a comprehensive planning analysis model. In terms of energy recovery conditions, solar photovoltaic panels are installed on the hotel's roof and some west-facing walls. Due to the abundant local sunshine, solar photovoltaic panels can convert solar energy into electricity for the hotel's daily use, such as lighting and equipment operation. After calculation, it is estimated that these solar photovoltaic panels can generate approximately 500,000 kWh of electricity per year, which can meet about 30% of the hotel's electricity needs. In addition, small wind turbines are set up in suitable locations around the hotel to generate electricity using sea breezes. Based on the wind speed and frequency of the sea breeze, these wind turbines are expected to generate 100,000 kWh of electricity per year, further supplementing the hotel's energy supply.

[0092] Regarding material recycling, waste materials generated during the hotel's construction process are sorted and recycled. For example, discarded bricks can be crushed and used for road basement construction; discarded steel can be remelted and processed for use in small decorative structures or repair parts within the hotel. Furthermore, throughout the hotel's operations, various types of waste are strictly sorted, with recyclable paper, plastic bottles, and metal products being recycled to achieve resource recycling.

[0093] Based on a comprehensive planning analysis model and local ecological and environmental parameters, dynamic synergy evaluation indicators are calculated. A multi-factor synergy equation is constructed (formulas are not provided here, only conceptual explanations are provided), encompassing energy balance, environmental response, and resource allocation. The equation is constructed from the perspectives of energy generation (such as solar and wind power generation), consumption (electricity consumption of various hotel equipment and air conditioning systems), recycling (the potential energy value generated by recycling waste materials), environmental impacts on the building (the impact of sea breezes and sunlight on the indoor environment and energy consumption), and the rational allocation of resources (the distribution of various resources among different functional areas). The synergy equation is solved using numerical discretization methods to obtain resource cycle efficiency, energy distribution gradients, and a set of spatial optimization potentials.

[0094] When calculating the spatial optimization potential set, the energy load differential for each functional area is calculated in detail based on the energy distribution gradient. Different functional areas of a hotel, such as guest rooms, dining, and fitness areas, have varying energy consumption patterns. The guest rooms primarily consume electricity for lighting, air conditioning, and small appliances; the dining area, in addition to lighting and air conditioning, also utilizes a large amount of kitchen equipment; and the fitness area primarily relies on various fitness equipment. After carefully analyzing the energy consumption of each functional area, a load threshold is set, for example, a monthly energy consumption differential of 5 kilowatt-hours per square meter. Functional areas with energy load differentials no less than this threshold are identified, such as the dining area. Due to the high power consumption of kitchen equipment during peak hours, these energy load differentials are significantly higher than those in other areas. These functional areas are then organized into the spatial optimization potential set. This identifies significant potential for improvement in energy utilization in the dining area, allowing for further energy optimization design efforts, such as replacing more energy-efficient kitchen equipment and optimizing equipment usage hours, to effectively support the goal of achieving green and intelligent buildings.

[0095] Example 3:

[0096] A multi-objective optimization pre-training framework is constructed and iteratively learned using dynamic collaborative evaluation indicators to obtain a multi-factor coordination model and extract key coordination parameters. The multi-objective optimization pre-training framework is constructed based on a feature fusion network. The feature fusion network can integrate different types of data features, such as building geographic information, environmental data, and energy utilization characteristics. The pre-training framework undergoes multi-stage training and verification using dynamic collaborative evaluation indicators. During the training process, the framework parameters are continuously adjusted to better adapt to different situations.

[0097] The dynamic synergy evaluation indicators to be analyzed are input into the multi-factor coordination model, which outputs a predicted set of spatial optimization potentials. Based on the input dynamic synergy evaluation indicators, the multi-factor coordination model analyzes the relationships between various factors and predicts which areas have the greatest spatial optimization potential. Based on the predicted set of spatial optimization potentials, key coordination parameters are extracted. The geometric center point of the predicted set of spatial optimization potentials is determined and designated as the energy coordination node. This center point plays a crucial role in energy allocation and coordination, serving as a benchmark for better planning of energy transmission and utilization.

[0098] Based on the distribution characteristics of the predicted spatial optimization potential set, the environmental adaptation morphology is fitted to obtain the environmental adaptation morphology parameters. The environmental adaptation morphology parameters include morphological curvature and spatial extension dimension. Observe the distribution of the spatial optimization potential set. If the distribution is relatively concentrated, the morphological curvature may be small; if the distribution is relatively dispersed, the morphological curvature may be large. By analyzing the distribution characteristics, a suitable environmental adaptation morphology is fitted, thereby obtaining parameters such as morphological curvature and spatial extension dimension. Analyze the energy load gradient direction of the predicted spatial optimization potential set, calculate the weighted average of the gradient direction, and convert it into a standard resource circulation path. This path is the resource circulation path parameter. In this way, the optimal resource circulation path in the building is determined, improving resource utilization efficiency.

[0099] Suppose you want to build a green and intelligent R&D building in an emerging science and technology park. The area has unique climate and geographical characteristics, and at the same time has high requirements for efficient energy utilization and flexible spatial layout required for scientific and technological research and development.

[0100] When constructing the multi-objective optimization pre-training framework, it was built based on a feature fusion network. The buildings within the park involve various feature data, such as geographic information, including the terrain undulations within the park (although generally flat, there are still slight differences in slope), and the distance to surrounding public facilities (such as bus stops and restaurants). Environmental data covers seasonal variations in light intensity, the average annual temperature, humidity, and prevailing wind direction in the area. Energy utilization features include historical energy consumption data for similar buildings, such as electricity and water consumption by floor and functional area. The feature fusion network integrates these seemingly independent but interrelated data features, providing a comprehensive data foundation for subsequent optimization training.

[0101] The pre-trained framework undergoes multi-stage training and verification using dynamic collaborative evaluation indicators. During training, data such as resource recycling efficiency, energy distribution gradient, and spatial optimization potential sets from the dynamic collaborative evaluation indicators are continuously fed into the framework. For example, if the energy consumption in a certain area of ​​the building (such as the experimental area) is found to be too high and the resource recycling efficiency is low (waste generated by the experiments is not fully recycled), the spatial optimization potential set may indicate that there is room for improvement in the layout adjustment of this area. Based on the feedback from these indicators, the parameters of the framework are adjusted, and the weight distribution of different feature data is adjusted to make the framework pay more attention to areas with high energy consumption and low resource recycling efficiency. After multiple rounds of training and verification, the framework continuously learns and adapts to the actual situation, gradually optimizing its performance, and thus obtaining a multi-factor coordination model.

[0102] The dynamic collaborative evaluation indicators to be analyzed are input into the multi-factor coordination model, which outputs a set of predicted spatial optimization potentials. Suppose the dynamic collaborative evaluation indicators to be analyzed indicate that during high summer temperatures, the energy load in the server room area of ​​the R&D building is excessive, resulting in an abnormal energy distribution gradient across the entire floor and low resource recycling efficiency in this area (waste heat generated by the server room is not effectively utilized). After receiving these indicators, the multi-factor coordination model uses its internal algorithms and logic to analyze the relationships between various factors and predict that the server room area and some adjacent office areas have significant spatial optimization potential.

[0103] Based on the predicted space optimization potential set, key coordination parameters are extracted. First, the geometric center point of the predicted space optimization potential set (the server room area and adjacent office areas) is determined and used as the energy coordination node. Using this center point as a benchmark, energy transmission and distribution can be better planned. For example, consider installing energy storage equipment near this point to temporarily store excess electricity generated by the server room for use in other areas during peak hours.

[0104] Based on the distribution characteristics of the predicted spatial optimization potential set, the environmental adaptation morphology is fitted to obtain the environmental adaptation morphology parameters. Observing the spatial distribution of this area reveals that the server rooms are distributed in a long strip, with adjacent office areas arranged around some of their edges. By analyzing this distribution characteristic, the environmental adaptation morphology is fitted. For example, to better utilize natural ventilation and daylighting, the architectural design could include a curved building shape in this area to increase the light-filled area and ventilation. This results in the environmental adaptation morphology parameters. The morphological curvature reflects the degree of curvature of the building's shape, while the spatial extension dimension identifies the spatial expansion possibilities of the area in different directions, such as determining whether floor heights can be appropriately increased in a certain direction to optimize space utilization.

[0105] The energy load gradients of the predicted spatial optimization potential set are analyzed, and a weighted average of these gradients is calculated. This is then converted into a standard resource circulation path, which serves as the resource circulation path parameter. For example, in a server room, the energy load is primarily concentrated in the heat generated by equipment operation and electricity consumption. The energy load gradients point toward the exterior of the room, where heat dissipation and power output are used. A weighted average of these gradients is calculated, and each gradient is assigned a different weight based on the energy needs and importance of different areas. For example, office areas, with their relatively stable electricity demand, receive a higher weight, while auxiliary areas such as corridors receive a lower weight. After calculating the weighted average, it is converted into a standard resource circulation path. For example, waste heat from the server room can be collected and transported through a specific duct system to the fresh air system in adjacent office areas to preheat the fresh air in winter, thus recycling the waste heat. This waste heat transfer path serves as the resource circulation path parameter, thereby improving resource utilization efficiency across the entire area.

[0106] Example 4:

[0107] System co-simulation results are generated using key coordination parameters, the integrated planning analysis model, and geographic information data. The energy coordination node serves as a benchmark anchor point, and the environmental adaptation morphological parameters are mapped to the integrated planning analysis model. The energy coordination node acts as a core hub around which the building layout and energy distribution are adjusted. The environmental adaptation morphological parameters are used to optimize the building's form and spatial layout to better adapt it to the surrounding environment. Resource circulation path parameters are adjusted and the mesh of associated blocks is refined to complete the update of the integrated planning analysis model. Based on the needs and efficiency of resource circulation, resource circulation path parameters are adjusted, and associated functional blocks are meshed more finely for more accurate analysis and simulation.

[0108] The co-evolution rules, co-evolution direction, and co-evolution step size are defined to form a co-evolution planning model. The co-evolution rule is defined as triggering path optimization if the current cumulative energy difference is not less than the difference threshold. Energy usage is monitored in real time, and when the energy difference accumulates to a certain level, the energy transmission path is optimized. The co-evolution direction is determined based on historical evolution trends and the current energy distribution direction. The energy evolution direction is determined by referring to previous energy usage and current energy distribution. The energy transmission trajectory and co-evolution step size are determined based on climate parameters and resource cycle efficiency, respectively. Climate conditions affect energy transmission, while resource cycle efficiency determines the step size of each evolution.

[0109] Based on the co-evolutionary planning model, a dynamic iterative method is used to solve the multi-factor synergy equation. During the solution process, when the co-evolutionary rules are met, path optimization is performed to obtain the current energy transfer trajectory, co-evolutionary step size, co-evolutionary direction, energy load distribution, and environmental response gradient, and the co-evolutionary planning model is updated. The multi-factor synergy equation is then resolved based on the updated co-evolutionary planning model until the termination condition is met, ending the co-evolutionary process. Based on the energy load distribution and environmental response gradient at each iterative stage, the maximum load distribution and environmental response gradient are calculated to generate the system co-simulation results.

[0110] Suppose a green smart resort hotel is to be built in an ecotourism resort area that has rich natural landscapes such as mountains and lakes and has strict requirements for efficient energy utilization.

[0111] Using the energy coordination node as a benchmark anchor, the planning and design of this resort hotel identified the hotel's core energy consumption areas through preliminary analysis. For example, the hotel's large kitchen and hot spring bathing area are areas of high energy consumption. The geometric center point between these two areas was identified as the energy coordination node. The environmental adaptation morphological parameters were mapped into the comprehensive planning analysis model. Based on the resort's topography and landscape distribution, the hotel's architectural form was designed as a curved shape that echoes the surrounding mountain ranges. This was used to determine environmental adaptation morphological parameters such as morphological curvature and spatial extension. The resource circulation path parameters were then adjusted to take into account the hotel's hot spring water resource utilization. The route from the hot spring water collection point to the bathing area and then to the recycling and treatment point was optimized to reduce energy loss during transportation. Simultaneously, the associated blocks were meshed to accurately determine parameters such as the area and human activity density of each area, completing the update of the comprehensive planning analysis model.

[0112] Define the co-evolution rules, co-evolution direction and co-evolution step size to form a co-evolution planning model. The co-evolution rule is defined as triggering path optimization if the current energy difference accumulation is not less than the difference threshold. Assuming that the difference threshold is set to 500kW·h (kW·h is kilowatt-hour, which is the unit of energy and represents the electrical energy consumed by an electrical device with a power of 1 kilowatt in 1 hour), the energy difference accumulation is calculated by the formula

[0113] calculate,

[0114] in Indicates the cumulative amount of energy difference, Indicates the number of statistical time periods. Indicates the The total energy supply in a period of time, Indicates the The total energy consumption within a time period is determined by the co-evolution direction based on historical evolution trends and current energy distribution. By analyzing the hotel's energy consumption data for different time periods over the past week and combining it with the current energy usage in each area, the co-evolution direction is determined to be the direction in which energy flows primarily from energy supply areas to high-energy-consuming areas. The energy transfer trajectory and co-evolution step size are determined based on climate parameters and resource recycling efficiency, respectively. For example, in summer, due to high temperatures, the energy transfer trajectory tends to direct excess heat to the cooling system for reuse. When resource recycling efficiency is high, the co-evolution step size is appropriately increased to accelerate the optimization process.

[0115] Based on the co-evolutionary planning model, a dynamic iterative approach is used to solve the multi-factor synergy equation. During the solution process, path optimization is performed when the co-evolutionary rules are met. For example, after a period of operational monitoring, the cumulative energy difference reaches 550 kW·h, exceeding the difference threshold of 500 kW·h, triggering path optimization. By adjusting the layout of the energy transmission pipeline and valve control, the current energy transmission trajectory, co-evolution step size, co-evolution direction, energy load distribution, and environmental response gradient are determined, and the co-evolutionary planning model is updated. The multi-factor synergy equation is then re-solved based on the updated co-evolutionary planning model. This process is repeated until the termination condition is met. For example, the termination condition is set to be that the cumulative energy difference calculated for five consecutive times is less than 100 kW·h, ending the co-evolution process. Based on the energy load distribution and environmental response gradient at each iterative stage, the maximum load distribution and environmental response gradient are calculated, generating system co-simulation results. These results provide a clear understanding of the hotel's energy utilization and environmental impact at different operational stages, providing a useful basis for subsequent optimization and adjustment.

[0116] Example 5:

[0117] Based on the system collaborative simulation results and facility deployment parameters, a configuration model is established and the facility deployment parameters are optimized to obtain the optimal configuration plan for facility deployment. A configuration model is established, which at least includes a variable definition module, an objective function module, and a constraint condition module. In the variable definition module, the facility deployment parameters are defined based on the facility deployment parameters. The facility deployment parameters at least include equipment layout density, energy conversion efficiency, monitoring coverage, and data transmission delay. The layout density of the equipment should be reasonably planned. It should not be too dense to cause mutual interference between devices, nor too sparse to affect utilization efficiency; improve energy conversion efficiency and reduce energy loss during the conversion process; expand monitoring coverage to ensure comprehensive monitoring of the building environment; reduce data transmission delay to ensure timely transmission of information.

[0118] The objective function module is constructed based on the second performance evaluation value and resource consumption value. The second performance evaluation value is calculated based on the results of system collaborative simulation, taking into account factors such as the building's energy efficiency, environmental adaptability, and space efficiency. The resource consumption value includes at least the construction cost value, the operation and maintenance cost value, and the energy loss value. These costs and losses are minimized while meeting the functional requirements of the building. The constraint condition module includes the facility deployment constraint module and the performance assurance constraint module. The performance assurance constraint module is defined based on functional integrity, response timeliness, and system stability. Ensure that all functions of the building can operate normally, respond to environmental changes and user needs in a timely manner, and that the entire system is stable and reliable.

[0119] A genetic algorithm is used to initially solve the configuration model and obtain an initial optimized configuration. The genetic algorithm simulates the biological evolution process, searching for optimal solutions among numerous possible configurations through operations such as selection, crossover, and mutation. The configuration model is then globally optimized using a particle swarm algorithm. This algorithm leverages information sharing and collaboration between particles to search the solution space and obtain the optimal particle swarm solution set, which serves as the optimal configuration for facility deployment. This approach identifies the most suitable facility deployment solution for green smart buildings, achieving efficient and sustainable building operation.

[0120] Suppose a green smart community is to be built in a newly developed area of ​​a city. The community plan includes multiple residential buildings, supporting commercial facilities, and public activity areas, aiming to achieve efficient energy utilization, a comfortable living environment, and intelligent management.

[0121] A configuration model was established, consisting of a variable definition module, an objective function module, and a constraint module. Within the variable definition module, facility deployment parameters were defined. Regarding equipment density, for example, in community parking lot planning, the density of charging stations was appropriately determined, taking into account residents' vehicle ownership and convenient parking needs. After preliminary research and data analysis, it was determined that one fast-charging station and three slow-charging stations would be installed for every 20 parking spaces. This approach not only meets residents' daily charging needs but also avoids resource waste and space congestion caused by overcrowding. To enhance energy conversion efficiency, new solar panels and a ground-source heat pump system were selected. The solar panels achieved a photovoltaic conversion efficiency of 22%, while the ground-source heat pump system achieved a heating / cooling coefficient of performance (COP) of 4.5 and 5.0, respectively, significantly improving energy conversion efficiency compared to traditional equipment. To ensure monitoring coverage, various sensors, such as environmental monitoring sensors and security cameras, were installed throughout the community. Environmental monitoring sensors are evenly distributed throughout the community, providing real-time monitoring of air quality (including PM2.5, PM10, sulfur dioxide, and other indicators), noise levels, and other environmental parameters, ensuring coverage of over 98% of the community. Utilizing a 5G network and high-performance IoT devices, data transmission from sensors to the community management center is minimized to under 50 milliseconds, ensuring timely and accurate information delivery.

[0122] The objective function module is constructed based on the second performance evaluation value and resource consumption value. The second performance evaluation value is calculated based on the results of system co-simulation, comprehensively considering factors such as the community's energy efficiency, environmental comfort, and space efficiency. For example, by simulating the community's energy consumption and environmental changes in different seasons and time periods, it was determined that during the peak summer air conditioning usage period, the community's energy self-sufficiency rate reached 30% (the proportion of solar and geothermal energy supply to total energy consumption), the indoor temperature remained in the comfortable range of 24-26°C, and the outdoor noise level was below 50 decibels. These data, combined, constitute part of the second performance evaluation value. The resource consumption value includes at least construction cost, operation and maintenance cost, and energy loss. Construction costs include land acquisition, building materials, equipment procurement, and installation costs. It is estimated that the entire community cost approximately 200 million yuan. Operation and maintenance costs, including equipment maintenance, staff salaries, utilities, etc., are expected to cost 5 million yuan annually. The energy loss value is obtained by calculating the energy loss of various energy equipment in the community during operation, such as line loss during power transmission, efficiency loss of energy conversion equipment, etc. The annual energy loss is equivalent to about 800,000 yuan.

[0123] The constraint condition module includes the facility deployment constraint module and the performance assurance constraint module. The performance assurance constraint module is defined based on functional integrity, response timeliness and system stability. In terms of functional integrity, it ensures that various facilities in the community can meet the daily life needs of residents, such as commercial facilities providing basic shopping and dining services, and public activity areas equipped with fitness equipment, leisure seats and other facilities. Response timeliness requires that the community management system can respond to residents' needs and equipment failures in a timely manner. For example, if a resident reports an equipment failure through a mobile phone APP, the management system must respond within 1 hour and arrange for maintenance personnel to handle it. In terms of system stability, it ensures that the community's electricity, water supply, network and other systems can operate stably, with power outages not exceeding 24 hours throughout the year and network interruptions not exceeding 2 hours per month.

[0124] A genetic algorithm is used to initially solve the configuration model and obtain an initial optimized configuration. This algorithm simulates the process of biological evolution, treating various possible community facility deployment scenarios as individual organisms. Through operations such as selection, crossover, and mutation, it searches for the optimal solution among the numerous possible configuration scenarios. For example, 100 initial scenarios are randomly generated, each including specific parameters such as charging station layout, equipment selection, and sensor location. These scenarios are evaluated based on the objective function, and those with high fitness (i.e., low resource consumption and high efficiency evaluation values) are selected for crossover and mutation operations. After multiple rounds of iteration, a relatively optimal initial optimized configuration is obtained, such as determining the optimal installation location and number of solar panels within the community and the appropriate distribution of ground-source heat pump systems.

[0125] The configuration model is then globally optimized using a particle swarm algorithm. This algorithm leverages information sharing and collaboration among particles to search within the solution space. The initial optimized configuration is used as the initial position of the particle swarm, with each particle representing a facility deployment solution. During the search, particles continuously adjust their flight direction and speed based on their own flight experience and the position of the optimal particle in the swarm. For example, during the iteration process, particles continuously experiment with adjusting the layout density of charging piles and the parameters of energy conversion equipment. By comparing the objective function values ​​of different solutions, the particle swarm's optimal solution set is found, serving as the optimal configuration for facility deployment. Ultimately, the optimal configuration for each type of facility within the community is determined, achieving a balanced and optimized green smart community in terms of resource utilization, quality of life for residents, and system stability.

[0126] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements that are inherent to such process, method, article, or apparatus.

[0127] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-factor integrated planning and design method for green intelligent buildings, characterized by: The following steps are involved: Obtain geographic information data and ecological environment parameters of the construction site; Collect and integrate multi-dimensional environmental data around the site to establish a three-dimensional spatial topology model. Perform multi-factor coupling analysis on the three-dimensional spatial topology model and combine it with geographic information data to generate an initial spatial planning model. Load climate simulation conditions and resource circulation conditions into the initial spatial planning model to form a comprehensive planning analysis model, combine it with ecological and environmental parameters, and output dynamic collaborative evaluation indicators; Construct a multi-objective optimization pre-training framework and perform iterative learning through dynamic collaborative evaluation indicators to obtain a multi-factor coordination model and extract key coordination parameters; Generate system collaborative simulation results through key coordination parameters, comprehensive planning analysis models and geographic information data; Based on the system collaborative simulation results and facility deployment parameters, a configuration model is established and the facility deployment parameters are optimized to obtain the optimal configuration plan for facility deployment; Based on the performance evaluation framework, the current configuration plan is deduced to obtain the current performance evaluation index. Combining the current performance evaluation index, the current optimal configuration plan and the current actual configuration parameters, the facility deployment parameters are dynamically adjusted to achieve the planning and control of green smart buildings. The multi-objective optimization pre-training framework is constructed and iterative learning is performed through dynamic collaborative evaluation indicators to obtain a multi-factor coordination model, thereby extracting key coordination parameters, including the following steps: Construct a multi-objective optimization pre-training framework based on feature fusion network; The pre-training framework is trained and verified in multiple stages through dynamic collaborative evaluation indicators to obtain a multi-factor coordination model; The dynamic collaborative evaluation indicators to be analyzed are input into the multi-factor coordination model, and the predicted spatial optimization potential set is output; Extracting key coordination parameters based on the predicted spatial optimization potential set, wherein the key coordination parameters at least include energy coordination nodes, environmental adaptation forms, and resource circulation path parameters; The method of extracting key coordination parameters based on the predicted spatial optimization potential set includes the following steps: Determine the geometric center point of the predicted spatial optimization potential set as the energy coordination node; Fitting the environmental adaptation morphology according to the distribution characteristics of the predicted spatial optimization potential set to obtain environmental adaptation morphology parameters, wherein the environmental adaptation morphology parameters include morphological curvature and spatial extension dimension; The energy load gradient direction of the predicted spatial optimization potential set is analyzed, the weighted average of the gradient direction is calculated, and it is converted into a standard resource cycle path, which is the resource cycle path parameter.

2. The multi-factor integrated planning and design method for green intelligent buildings according to claim 1 is characterized in that: The dynamic collaborative evaluation indicators include at least resource circulation efficiency, energy distribution gradient and spatial optimization potential set; the key coordination parameters include at least energy coordination nodes, environmental adaptation form and resource circulation path parameters; the system collaborative simulation results include at least energy transfer trajectory, maximum load distribution and environmental response gradient.

3. The multi-factor integrated planning and design method for green intelligent buildings according to claim 1 is characterized in that: The method of collecting multi-dimensional environmental data around the site and fusing it to establish a three-dimensional spatial topology model, performing multi-factor coupling analysis on the three-dimensional spatial topology model and combining it with geographic information data to generate an initial spatial planning model includes the following steps: Conduct multi-scale environmental monitoring of construction sites to obtain multi-dimensional environmental data sequences; Performing fusion processing on the multi-dimensional environmental data sequence to obtain a standardized environmental data set, wherein the fusion processing includes one or more of data cleaning, normalization, feature association, redundancy elimination, data reconstruction and consistency verification; Based on the standardized environmental data set, a three-dimensional spatial topology model is constructed using spatial interpolation technology; A multi-factor coupling analysis is performed on the three-dimensional spatial topology model to generate an initial spatial planning model, wherein the multi-factor coupling analysis at least includes grid division and parameter mapping, and different functional blocks are divided by grid division, and corresponding geographic information parameters are assigned to each functional block.

4. The multi-factor integrated planning and design method for green intelligent buildings according to claim 1 is characterized in that: The process of loading climate simulation conditions and resource circulation conditions into the initial spatial planning model to form a comprehensive planning analysis model, combining ecological and environmental parameters, and outputting dynamic collaborative evaluation indicators includes the following steps: Loading climate simulation conditions to the initial spatial planning model to constrain environmental boundaries, and obtaining the first planning analysis model; Superimposing resource recycling conditions on the first planning analysis model to form a comprehensive planning analysis model, wherein the resource recycling conditions include energy recovery conditions and material recycling conditions; Based on the comprehensive planning analysis model and ecological environment parameters, dynamic collaborative evaluation indicators are calculated. The specific process includes: A multi-factor synergistic equation is constructed, which includes at least an energy balance equation, an environmental response equation, and a resource allocation equation. Combined with a comprehensive planning analysis model, the synergistic equation is solved by a numerical discrete method to obtain a set of resource circulation efficiency, energy distribution gradient, and spatial optimization potential.

5. The multi-factor integrated planning and design method for green intelligent buildings according to claim 4 is characterized in that: Obtaining the space optimization potential set includes the following steps: Based on the energy distribution gradient, calculate the energy load difference of each functional block; Identify the functional blocks whose energy load difference is not less than the load threshold in the comprehensive planning analysis model and obtain the set of spatial optimization potentials.

6. The multi-factor integrated planning and design method for green intelligent buildings according to claim 1 is characterized in that: The method of generating system collaborative simulation results by using key coordination parameters, comprehensive planning analysis models and geographic information data includes the following steps: The energy coordination node is used as the benchmark anchor point, and the environmental adaptation morphological parameters are mapped into the comprehensive planning analysis model. The resource cycle path parameters are adjusted and the grid of the associated blocks is refined to complete the update of the comprehensive planning analysis model. The co-evolution rules, co-evolution direction and co-evolution step are defined to form a co-evolution planning model. Based on the collaborative evolutionary planning model, a dynamic iterative method is used to solve the multi-factor collaborative equation. According to the results of each iterative stage in the solution process, the system collaborative simulation results are generated, including: When the co-evolution rules are met, path optimization is performed to obtain the current energy transfer trajectory, co-evolution step length, co-evolution direction, energy load distribution and environmental response gradient, and the co-evolution planning model is updated; The multi-factor coordination equation is re-solved based on the updated co-evolutionary planning model until the termination condition is reached, thus ending the co-evolutionary process. Calculate the maximum load distribution and environmental response gradient based on the energy load distribution and environmental response gradient at each iteration stage; The co-evolution rule is defined as triggering path optimization if the current energy difference accumulation is not less than the difference threshold; The co-evolution direction is determined based on historical evolution trends and current energy distribution direction; The energy transfer trajectory and the co-evolution step are determined according to climate parameters and resource cycle efficiency, respectively.

7. The multi-factor integrated planning and design method for green intelligent buildings according to claim 1 is characterized in that: The method of establishing a configuration model based on the system collaborative simulation results and the facility deployment parameters and optimizing the facility deployment parameters to obtain the optimal configuration solution for the facility deployment includes the following steps: Establishing a configuration model, wherein the configuration model at least includes a variable definition module, an objective function module, and a constraint condition module. The variable module is defined based on facility deployment parameters. The facility deployment parameters at least include equipment layout density, energy conversion efficiency, monitoring coverage, and data transmission delay. The objective function module is constructed based on a second performance evaluation value and a resource consumption value. The second performance evaluation value is calculated based on system collaborative simulation results. The resource consumption value at least includes a construction cost value, an operation and maintenance cost value, and an energy loss value. The constraint condition module includes a facility deployment constraint module and a performance assurance constraint module. The performance assurance constraint module is defined based on functional integrity, response timeliness, and system stability. Genetic algorithm is used to initially solve the configuration model and obtain the initial optimal configuration scheme; The configuration model is globally optimized using the particle swarm algorithm, and the particle swarm optimal solution set is obtained as the optimal configuration solution for facility deployment.

8. A computer device, characterized in that: The computer device includes a memory, a processor and a communication interface, which are connected by a bus; the memory is used to store a set of computer program instructions and data, and transmit the stored data to the processor, and the processor executes the program instructions stored in the memory to execute the green intelligent building multi-factor fusion planning and design method as described in any one of claims 1 to 6.

Images