Urban building planning method based on machine learning

Abstract

The invention relates to the technical field of urban building planning, and discloses an urban building planning method based on machine learning, comprising the following steps: step 1, data collection and preprocessing: collecting data related to urban building planning, and cleaning, formatting and normalizing the data; step 2, feature extraction and selection: extracting population density, traffic flow, land utilization type and building density features from the preprocessed data, and selecting features having great influence on urban planning through a feature selection technology; and step 3, model selection and training: selecting a machine learning model and training the model by using the training data set. Various urban building planning schemes are evaluated through an intelligent simulation tool, and in this way, urban planning not only considers current requirements, but also can predict and adapt to future changes, so that the overall sustainability of a city is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban building planning, and in particular to an urban building planning method based on machine learning. Background Art

[0002] In the context of modern urban planning, traditional approaches are often limited by their inability to comprehensively consider the environment, the effectiveness of public participation, and the transparency of decision-making. Existing urban planning techniques often lack the ability to integrate multidimensional factors such as environmental protection, transportation accessibility, economic benefits, and social acceptance, resulting in plans that are often unable to fully respond to future development needs and changes. The resulting plans may lead to unintended environmental, social, and economic problems after implementation, such as increased environmental pressure, traffic congestion, and reduced community vitality.

[0003] Furthermore, public participation in traditional planning processes is often limited in form and lacks effective interaction and feedback mechanisms, leading to a lack of public understanding or acceptance of planning proposals. Decision-making transparency is also often insufficient. The closed-door nature of decision-making processes and data makes it difficult for the public to truly participate in planning decisions, hindering widespread support and smooth implementation. Furthermore, data security and privacy protection are significant issues in existing technologies. Traditional data processing and storage methods are prone to information leakage or tampering, further reducing the reliability of planning data and the credibility of decision-making. Summary of the Invention

[0004] In response to the shortcomings of existing technologies, the present invention provides an urban building planning method based on machine learning, which solves the problem that the planning schemes are often unable to fully respond to the needs and changes of future development due to the lack of ability to integrate multi-dimensional factors such as environmental protection, transportation convenience, economic benefits and social acceptance.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for urban building planning based on machine learning, comprising the following steps: Step 1: Data collection and preprocessing: collect data related to urban architectural planning, and clean, format and normalize the data; Step 2: Feature extraction and selection: extract population density, traffic flow, land use type, and building density features from the preprocessed data, and select features that have a greater impact on urban planning through feature selection technology; Step 3: Model selection and training: select a machine learning model and train the model using a training dataset. Step 4: Model evaluation and optimization: Use the validation dataset to evaluate the model and optimize the model based on the evaluation results. Step 5: Planning scheme generation and simulation: Use the trained model to generate a variety of urban building planning schemes, and use simulation tools to evaluate the feasibility and effectiveness of the planning schemes; Step 6: Decision support and visualization: present the simulation results of the planning scheme in the form of charts, maps, and 3D models, and provide them to urban planning decision makers as decision support tools.

[0006] Preferably, the data collection in step one includes integrating satellite remote sensing data, drone data, sensor network data and social media data to improve the richness and real-time nature of the data, and ensure the synchronization and consistency of the data through multi-source data integration technology.

[0007] Preferably, the feature extraction in step 2 adopts a deep learning method to extract building height, shape, material, color, and texture detail features from images and videos, and performs comprehensive feature extraction in combination with the building construction time, temporal changes in traffic flow, temporal changes in environmental data and geographic location information, land use type and distribution, and neighboring facilities and infrastructure.

[0008] Preferably, the machine learning model in step three is selected from at least one of random forest, support vector machine, convolutional neural network and graph convolutional network, and the model architecture design and parameter adjustment are performed according to the application scenario.

[0009] Preferably, the model optimization in step four is achieved through reinforcement learning algorithm and adaptive optimization algorithm, which can dynamically adjust model parameters to adapt to the planning needs of different cities, and improve the robustness and generalization ability of the model through hyperparameter tuning and cross-validation.

[0010] Preferably, the planning scheme generated in step five takes into account factors such as environmental protection, traffic convenience, economic benefits and social acceptance, generates multiple alternative schemes through an adaptive optimization algorithm, and uses a multi-objective optimization method to weigh the pros and cons of different schemes.

[0011] Preferably, the planning scheme in step five is subjected to multi-scenario simulation through intelligent simulation tools, including traffic flow simulation, environmental impact assessment, economic benefit analysis and population migration forecast, so as to comprehensively evaluate the long-term impact and feasibility of different schemes.

[0012] Preferably, step six includes developing an intelligent interactive system that enables urban planners to interact with machine learning models in real time, adjust parameters and instantly view planning effects, while supporting multi-user collaborative work and recording and tracking of decision-making processes.

[0013] Preferably, step six includes using virtual reality technology to provide an immersive urban planning experience, so that decision makers and the public can intuitively feel the actual effects of the planning scheme and provide feedback, thereby improving public participation and decision-making transparency through virtual reality equipment and applications.

[0014] Preferably, this includes using blockchain technology to ensure the security and transparency of urban planning data and ensure that the data cannot be tampered with, while developing data privacy protection algorithms and ensuring compliance with privacy regulations and ethical standards when collecting and using personal data through differential privacy, data encryption and access control technologies.

[0015] The present invention provides a method for urban building planning based on machine learning. It has the following beneficial effects: 1. This invention uses intelligent simulation tools to evaluate various urban architectural planning schemes, taking into account factors such as environmental protection, accessibility, economic benefits, and social acceptance. This not only helps select the optimal planning scheme but also, through simulation, predicts the long-term impacts of the scheme's implementation, such as environmental quality, traffic congestion, and community vitality. In this way, urban planning not only considers current needs but also anticipates and adapts to future changes, thereby improving the overall sustainability of the city.

[0016] 2. This invention utilizes virtual reality and intelligent interactive systems to provide an immersive urban planning experience. Decision makers and the public can intuitively perceive and evaluate different planning options. This interactive approach not only makes the planning process more transparent but also increases public participation. The public can provide immediate feedback and participate in discussions and decision-making, thereby enhancing social acceptance and satisfaction with the planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0018] 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.

[0019] Example: Please see the attached Figure 1 , an embodiment of the present invention provides an urban building planning method based on machine learning, comprising the following steps: Step 1: Data collection and preprocessing: collect data related to urban architectural planning, and clean, format and normalize the data; In the first phase of urban planning, data from multiple sources, including satellite remote sensing data, drone imagery, sensor network data, and social media data, are collected. The integration of this data can provide a comprehensive view of the city's current status, such as population distribution, traffic conditions, and land use.

[0020] Implementation method: Satellite remote sensing and drone data: Obtain high-resolution terrain and building data.

[0021] Sensor data: Real-time data collected from the city’s traffic and environmental monitoring networks.

[0022] Social media data: Analyze public opinions and reactions to urban development.

[0023] Data preprocessing Data cleaning: Remove erroneous and duplicate data entries.

[0024] Formatting: Unify the formats of different data sources for easy processing.

[0025] Normalization: Ensure that the data is of the same magnitude to avoid algorithmic bias.

[0026] Step 2: Feature extraction and selection: extract population density, traffic flow, land use type, and building density features from the preprocessed data, and select features that have a greater impact on urban planning through feature selection technology; Deep learning methods are used to extract key features from images and videos, such as building height, shape, material, color, and texture details. Furthermore, comprehensive feature analysis is performed by considering the time-varying nature of building construction, traffic flow, and environmental data, combined with geographic location information, land use type, and its distribution.

[0027] Feature selection: Use feature selection techniques such as principal component analysis (PCA) or model-based feature selection to identify the features that have the greatest impact on urban planning. This step can significantly improve the efficiency of model training and the accuracy of predictions.

[0028] Step 3: Model selection and training: select a machine learning model and train the model using a training dataset. Select a machine learning model suitable for urban building planning, such as random forest, support vector machine, convolutional neural network, or graph convolutional network, and adjust the model architecture and parameters according to the specific application scenario.

[0029] Model training: Use the segmented training dataset to train the model. Improve the generalization and robustness of the model through cross-validation and parameter tuning.

[0030] Step 4: Model evaluation and optimization: Use the validation dataset to evaluate the model and optimize the model based on the evaluation results. The model's performance was tested using an independent validation dataset. Based on the evaluation results, reinforcement learning and adaptive optimization algorithms were used to adjust the model parameters to suit the planning needs of different cities.

[0031] Step 5: Planning scheme generation and simulation: Use the trained model to generate a variety of urban building planning schemes, and use simulation tools to evaluate the feasibility and effectiveness of the planning schemes; Using trained models, we generate a variety of urban architectural planning schemes, taking into account factors such as environmental protection, transportation convenience, economic benefits, and social acceptance. We then use intelligent simulation tools to conduct multi-scenario simulations and assess the long-term impact and feasibility of each planning scheme.

[0032] Step 6: Decision support and visualization: present the simulation results of the planning scheme in the form of charts, maps, and 3D models, and provide them to urban planning decision makers as decision support tools.

[0033] Develop intelligent interactive systems that enable urban planners to interact with models in real time, adjust parameters, and instantly view planning results. Leverage virtual reality technology to provide immersive urban planning experiences, increasing public participation and decision-making transparency.

[0034] This includes using blockchain technology to ensure the security and transparency of urban planning data and ensure that the data cannot be tampered with. At the same time, it develops data privacy protection algorithms and uses differential privacy, data encryption and access control technologies to ensure compliance with privacy regulations and ethical standards when collecting and using personal data.

[0035] Blockchain technology is used to ensure the integrity and immutability of data, while differential privacy, data encryption and access control technologies are combined to protect personal privacy.

[0036] Application of blockchain technology in urban planning Data storage: All data related to urban planning (such as land use data and traffic flow data) is stored on the blockchain. Each data block must be verified before being added to the blockchain, and once added to the blockchain, it cannot be modified or deleted.

[0037] Smart Contracts: Leverage smart contracts to automatically enforce data processing and access regulations, ensuring only authorized users can access specific data.

[0038] Blockchain is a decentralized distributed ledger technology that uses encryption and consensus algorithms to ensure data immutability and transparency. Each block contains a certain number of transaction records and is linked to the previous block through a cryptographic hash, forming an ever-expanding chain.

[0039] Differential privacy, data encryption, and access control technologies Differential privacy: Before publishing or sharing data, a certain amount of random noise is added to blur the data, thereby preventing the leakage of specific personal information without significantly affecting the purpose of the data.

[0040] Data encryption: Encrypt stored and transmitted data to ensure that even if the data is intercepted, it cannot be interpreted by unauthorized persons.

[0041] Access control: Set strict access permission rules to allow only users with corresponding permissions to access specific data or data sets.

[0042] Differential privacy: By adding noise to the data through an algorithm, the results of data queries will not be significantly different due to the presence or absence of an individual, thereby protecting individual privacy.

[0043] Data encryption: Data is encrypted using a key. Only users with the correct key can decrypt it, enhancing data security.

[0044] Access control: By defining and implementing a set of detailed permission management rules, we ensure that data can only be accessed by authorized users, preventing unauthorized data access and leakage.

[0045] Enhanced security: Blockchain technology ensures the integrity and non-tamperability of urban planning data, greatly enhancing data security.

[0046] Improved transparency: The transparency feature of blockchain ensures that all transaction records are visible to all parties involved, increasing the credibility of the urban planning process.

[0047] Privacy protection: Through the application of differential privacy and data encryption technology, personal privacy is effectively protected while complying with privacy regulations and ethical standards.

[0048] Maintaining data integrity: Access controls ensure that data is accessed only by authorized users, preventing data from being misused or mishandled.

[0049] The integrated application provides a secure and flexible data management framework for urban planning, making urban planning more scientific, efficient and fair, while also safeguarding the interests of the public and the security of personal privacy.

[0050] 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 method for urban building planning based on machine learning, characterized in that: The following steps are involved: Step 1: Data collection and preprocessing: collect data related to urban architectural planning, and clean, format and normalize the data; Step 2: Feature extraction and selection: extract population density, traffic flow, land use type, and building density features from the preprocessed data, and select features that have a greater impact on urban planning through feature selection technology; Step 3: Model selection and training: select a machine learning model and train the model using a training dataset; Step 4: Model evaluation and optimization: Use the validation dataset to evaluate the model and optimize the model based on the evaluation results. Step 5: Planning scheme generation and simulation: Use the trained model to generate a variety of urban building planning schemes, and use simulation tools to evaluate the feasibility and effectiveness of the planning schemes; Step 6: Decision support and visualization: present the simulation results of the planning scheme in the form of charts, maps, and 3D models, and provide them to urban planning decision makers as decision support tools.

2. The urban building planning method based on machine learning according to claim 1, characterized in that: The data collection in step one includes integrating satellite remote sensing data, drone data, sensor network data, and social media data to improve the richness and real-time nature of the data, and ensure the synchronization and consistency of the data through multi-source data integration technology.

3. The urban building planning method based on machine learning according to claim 1, characterized in that: The feature extraction in step 2 adopts a deep learning method to extract building height, shape, material, color, and texture detail features from images and videos, and combines the building construction time, temporal changes in traffic flow, temporal changes in environmental data and geographic location information, land use type and distribution, and neighboring facilities and infrastructure for comprehensive feature extraction.

4. The urban building planning method based on machine learning according to claim 1, characterized in that: The machine learning model in step three is selected from at least one of random forest, support vector machine, convolutional neural network and graph convolutional network, and the model architecture design and parameter adjustment are performed according to the application scenario.

5. The urban building planning method based on machine learning according to claim 1, characterized in that: The model optimization in step 4 is achieved through reinforcement learning algorithms and adaptive optimization algorithms, which can dynamically adjust model parameters to adapt to the planning needs of different cities, and improve the robustness and generalization ability of the model through hyperparameter tuning and cross-validation.

6. The urban building planning method based on machine learning according to claim 1, characterized in that: The planning scheme generated in step five takes into account factors such as environmental protection, traffic convenience, economic benefits and social acceptance. A variety of alternative schemes are generated through an adaptive optimization algorithm, and the pros and cons of different schemes are weighed using a multi-objective optimization method.

7. The urban building planning method based on machine learning according to claim 1, characterized in that: The planning scheme in step five is simulated in multiple scenarios using intelligent simulation tools, including traffic flow simulation, environmental impact assessment, economic benefit analysis and population migration forecast, to comprehensively evaluate the long-term impact and feasibility of different schemes.

8. The urban building planning method based on machine learning according to claim 1, characterized in that: Step six includes developing an intelligent interactive system that enables urban planners to interact with machine learning models in real time, adjust parameters and instantly view planning effects, while supporting multi-user collaboration and recording and tracking of decision-making processes.

9. The urban building planning method based on machine learning according to claim 1, characterized in that: Step six includes using virtual reality technology to provide an immersive urban planning experience, allowing decision makers and the public to intuitively feel the actual effects of the planning scheme and provide feedback, thereby improving public participation and decision-making transparency through virtual reality devices and applications.

10. The urban building planning method based on machine learning according to claim 1, characterized in that: It also includes using blockchain technology to ensure the security and transparency of urban planning data, ensuring that the data cannot be tampered with, and developing data privacy protection algorithms to ensure compliance with privacy regulations and ethical standards when collecting and using personal data through differential privacy, data encryption and access control technologies.

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