Urban industry space intelligent distribution system based on continuous data learning

Abstract

The invention provides an urban industry space intelligent distribution system based on continuous data learning, and relates to the technical field of planning design, and the system comprises a data collection module, a data processing and analysis module, an intelligent decision module, a user interface module, and a continuous learning module. An industrial space distribution scheme is dynamically adjusted, it is ensured that the system can quickly adapt to changes of traffic flow, industrial requirements and environmental conditions in the urban development process, multi-dimensional targets such as economic benefits, environmental influences and social fairness are comprehensively considered when the system formulates the industrial distribution scheme through a multi-target optimization engine, and the system has a good application prospect. And the comprehensiveness and balance of decision making are ensured.

Description

Technical Field

[0001] The present invention relates to the field of planning and design technology, and in particular to an intelligent allocation system for urban industrial space based on continuous data learning. Background Art

[0002] According to a method and system for industrial resource allocation management based on asset information data disclosed in Chinese patent publication number "CN117252403A", by integrating asset information and industrial subject data into resource allocation decisions, efficient and reliable rational industrial resource allocation is performed, thereby achieving industrial development balance based on efficient allocation of industrial resources. The present invention first performs a resource optimization process based on industrial resource characteristics; secondly, it fully considers the circulation and processing characteristics of resources and introduces industrial resource circulation intensity information to participate in the resource rational allocation decision-making process; and then imports asset structure information and asset scale information of industrial resource allocation targets such as enterprises within the industry, obtains resource processing capacity feedback values ​​of each industrial resource allocation target based on modeled asset evaluation, and performs resource allocation based on comprehensive factors such as industrial resource type information, resource value weight and circulation information, and resource processing capacity feedback values ​​of industrial subjects.

[0003] The existing urban industrial spatial distribution is still processed based on empirical solutions, and intelligent data analysis to obtain industrial allocation solutions is still a blank area. Summary of the Invention

[0004] Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides an intelligent allocation system for urban industrial space based on continuous data learning, which solves the problems of the existing technology.

[0006] Technical Solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent urban industrial space allocation system based on continuous data learning, the allocation system comprising:

[0008] Data collection module, used to collect city-related data from multiple sources in real time, including geographic information system data, traffic flow data, demographic data, industrial development data and environmental monitoring data;

[0009] Data processing and analysis module, which is used to clean, preprocess, and feature engineer the collected data, and apply machine learning models for prediction and classification;

[0010] Intelligent decision-making module, based on optimization algorithms and simulations, is used to generate the optimal industrial space allocation plan;

[0011] A user interface module is used to provide visual display and interactive functions, allowing users to view and adjust the allocation plan in real time;

[0012] The continuous learning module is used for online learning and incremental learning, ensuring that the model can be dynamically updated based on new data.

[0013] Preferably, the data acquisition module further comprises:

[0014] IoT device interfaces for integrating real-time data from various city sensors, including environmental sensors, traffic monitoring cameras, and utility usage sensors;

[0015] Third-party data source interface, used to access APIs of external data providers, meteorological data services, social media data streams and economic indicator databases, to expand the diversity and real-time nature of data.

[0016] Preferably, the data processing and analysis module includes:

[0017] Advanced data cleaning module, which can automatically identify and repair missing values, outliers and noise in data, and improve data quality by using adaptive filtering algorithms and anomaly detection technology;

[0018] The multi-level feature engineering submodule extracts high-dimensional features reflecting the key influencing factors of urban industrial spatial distribution through automatic feature selection, dimensionality reduction technology and feature interaction generation;

[0019] An integrated machine learning framework that supports the integration of multiple machine learning algorithms, including supervised learning, unsupervised learning, and semi-supervised learning models, to enhance the robustness and accuracy of prediction and classification.

[0020] Preferably, the intelligent decision-making module further includes:

[0021] A multi-objective optimization engine, using hybrid optimization algorithms such as linear programming and genetic algorithms, can simultaneously optimize multiple objectives such as economic benefits, environmental impact, social equity, and transportation convenience to find a balance;

[0022] The scenario simulation module constructs different urban development scenarios to simulate the implementation effects of various industrial space allocation plans under different conditions and evaluate their long-term and short-term impacts;

[0023] The constraint management submodule allows users to set and dynamically adjust the constraints of industrial allocation, including land use restrictions, infrastructure carrying capacity, and policy and regulatory requirements, to ensure the feasibility and compliance of the optimization plan.

[0024] Preferably, the user interface module includes:

[0025] Interactive data dashboards display key performance indicators and real-time data analysis results, supporting drag and drop, zooming, and custom view configuration to improve user data exploration and decision-making efficiency;

[0026] The feedback and collaboration function allows users to provide feedback on the allocation plan generated by the system, and supports multi-user collaborative operations to achieve cross-departmental and cross-field decision-making collaboration.

[0027] Preferably, the continuous learning module includes:

[0028] Automatic feature selection mechanism, based on recursive feature elimination (RFE) and tree-based feature importance evaluation, automatically identifies and selects the features that have the greatest impact on industry spatial allocation, improving the adaptability and accuracy of the model;

[0029] Model update strategies, which use incremental learning algorithms such as online gradient descent and elastic net regression to dynamically adjust existing model parameters as new data arrives without retraining the entire model;

[0030] The performance monitoring and evaluation submodule continuously monitors the model's predictive performance and accuracy, and automatically triggers model retraining or adjustment to cope with changes in data distribution and potential concept drift.

[0031] Preferably, the system has anomaly detection and processing functions, including:

[0032] A real-time data monitoring module that continuously monitors collected multi-source data streams and uses statistical methods and machine learning algorithms (such as isolation forests and autoencoders) to identify abnormal data points and abnormal patterns in real time;

[0033] Automatic repair mechanism: When data anomalies are detected, it automatically initiates data repair processes, such as data interpolation, smoothing, or temporary replacement of data sources, to ensure the reliability of subsequent analysis and decision-making;

[0034] The alarm and reporting system sends real-time alarms to system administrators and relevant decision makers when serious anomalies are detected, and generates detailed anomaly reports to support rapid response and problem troubleshooting.

[0035] Preferably, the intelligent decision-making module includes:

[0036] A reinforcement learning-based policy optimization module uses deep reinforcement learning algorithms (such as deep Q-network and policy gradient method) to continuously optimize the industry space allocation strategy through interaction with the environment to maximize long-term benefits;

[0037] Adaptive decision-making mechanism adjusts the weights and priorities of optimization strategies based on real-time feedback and dynamic environmental changes, ensuring a high degree of flexibility and adaptability in the decision-making process;

[0038] The strategy evaluation and selection submodule continuously evaluates the performance of different reinforcement learning strategies and selects the optimal strategy for the generation and implementation of actual allocation plans.

[0039] Beneficial effects

[0040] The present invention provides an intelligent urban industrial space allocation system based on continuous data learning. It has the following beneficial effects:

[0041] 1. Through continuous data learning, the system collects and analyzes multi-source data in real time, dynamically adjusting industrial space allocation plans to ensure rapid adaptation to changes in traffic flow, industrial demand, and environmental conditions during urban development. Through a multi-objective optimization engine, the system comprehensively considers multi-dimensional objectives such as economic benefits, environmental impact, and social equity when formulating industrial allocation plans, ensuring comprehensive and balanced decision-making. Furthermore, the scenario simulation module simulates the effects of different plans under various development scenarios, helping decision-makers assess their feasibility and long-term impact, reducing decision-making risks and improving decision-making quality.

[0042] Through advanced data processing and feature engineering, the system of this invention can accurately extract and analyze the key factors affecting industrial space allocation, ensuring the scientific and rational allocation of resources. When optimizing industrial space allocation, the system comprehensively considers environmental protection and social equity, promotes the development of green industries, optimizes transportation networks, and improves urban environmental quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of the present invention;

[0044] Figure 2 It is a module diagram of the present invention. DETAILED DESCRIPTION

[0045] 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. Specific embodiment one:

[0047] As shown in the figure, the intelligent urban industrial space allocation system based on continuous data learning includes:

[0048] Data collection module, used to collect city-related data from multiple sources in real time, including geographic information system data, traffic flow data, demographic data, industrial development data and environmental monitoring data;

[0049] Data processing and analysis module, which is used to clean, preprocess, and feature engineer the collected data, and apply machine learning models for prediction and classification;

[0050] Intelligent decision-making module, based on optimization algorithms and simulations, is used to generate the optimal industrial space allocation plan;

[0051] A user interface module is used to provide visual display and interactive functions, allowing users to view and adjust the allocation plan in real time;

[0052] The continuous learning module is used for online learning and incremental learning, ensuring that the model can be dynamically updated based on new data.

[0053] The data acquisition module further includes:

[0054] IoT device interfaces for integrating real-time data from various city sensors, including environmental sensors, traffic monitoring cameras, and utility usage sensors;

[0055] Third-party data source interface, used to access APIs of external data providers, meteorological data services, social media data streams and economic indicator databases, to expand the diversity and real-time nature of data.

[0056] Data processing and analysis modules include:

[0057] Advanced data cleaning module, which can automatically identify and repair missing values, outliers and noise in data, and improve data quality by using adaptive filtering algorithms and anomaly detection technology;

[0058] The multi-level feature engineering submodule extracts high-dimensional features reflecting the key influencing factors of urban industrial spatial distribution through automatic feature selection, dimensionality reduction technology and feature interaction generation;

[0059] An integrated machine learning framework that supports the integration of multiple machine learning algorithms, including supervised learning, unsupervised learning, and semi-supervised learning models, to enhance the robustness and accuracy of prediction and classification.

[0060] The intelligent decision-making module further includes:

[0061] A multi-objective optimization engine, using hybrid optimization algorithms such as linear programming and genetic algorithms, can simultaneously optimize multiple objectives such as economic benefits, environmental impact, social equity, and transportation convenience to find a balance;

[0062] The scenario simulation module constructs different urban development scenarios to simulate the implementation effects of various industrial space allocation plans under different conditions and evaluate their long-term and short-term impacts;

[0063] The constraint management submodule allows users to set and dynamically adjust the constraints of industrial allocation, including land use restrictions, infrastructure carrying capacity, and policy and regulatory requirements, to ensure the feasibility and compliance of the optimization plan.

[0064] The user interface module includes:

[0065] Interactive data dashboards display key performance indicators and real-time data analysis results, supporting drag and drop, zooming, and custom view configuration to improve user data exploration and decision-making efficiency;

[0066] The feedback and collaboration function allows users to provide feedback on the allocation plan generated by the system, and supports multi-user collaborative operations to achieve cross-departmental and cross-field decision-making collaboration.

[0067] Continuing Learning modules include:

[0068] Automatic feature selection mechanism, based on recursive feature elimination (RFE) and tree-based feature importance evaluation, automatically identifies and selects the features that have the greatest impact on industry spatial allocation, improving the adaptability and accuracy of the model;

[0069] Model update strategies, which use incremental learning algorithms such as online gradient descent and elastic net regression to dynamically adjust existing model parameters as new data arrives without retraining the entire model;

[0070] The performance monitoring and evaluation submodule continuously monitors the model's predictive performance and accuracy, and automatically triggers model retraining or adjustment to cope with changes in data distribution and potential concept drift.

[0071] The system has anomaly detection and processing capabilities, including:

[0072] A real-time data monitoring module that continuously monitors collected multi-source data streams and uses statistical methods and machine learning algorithms (such as isolation forests and autoencoders) to identify abnormal data points and abnormal patterns in real time;

[0073] Automatic repair mechanism: When data anomalies are detected, it automatically initiates data repair processes, such as data interpolation, smoothing, or temporary replacement of data sources, to ensure the reliability of subsequent analysis and decision-making;

[0074] The alarm and reporting system sends real-time alarms to system administrators and relevant decision makers when serious anomalies are detected, and generates detailed anomaly reports to support rapid response and problem troubleshooting.

[0075] The intelligent decision-making module includes:

[0076] A reinforcement learning-based policy optimization module uses deep reinforcement learning algorithms (such as deep Q-network and policy gradient method) to continuously optimize the industry space allocation strategy through interaction with the environment to maximize long-term benefits;

[0077] Adaptive decision-making mechanism adjusts the weights and priorities of optimization strategies based on real-time feedback and dynamic environmental changes, ensuring a high degree of flexibility and adaptability in the decision-making process;

[0078] The strategy evaluation and selection submodule continuously evaluates the performance of different reinforcement learning strategies and selects the optimal strategy for generating and implementing actual allocation plans. The Urban Industrial Space Intelligent Allocation System based on continuous data learning (hereinafter referred to as the "System") aims to achieve dynamic optimization of urban industrial space allocation by integrating multi-source data and applying advanced data analysis and machine learning technologies. The overall system architecture is divided into the following main layers:

[0079] Data layer: responsible for data collection, storage and management.

[0080] Data processing and analysis layer: including data cleaning, preprocessing, feature engineering, model training and prediction.

[0081] Intelligent decision-making layer: Generate the optimal industrial space allocation plan based on optimization algorithms and simulation.

[0082] User interface layer: provides visual display and interactive functions to support users to view and adjust allocation plans.

[0083] Continuous learning layer: Implements online learning and incremental updates of the model to ensure that the system adapts to new data and changing trends.

[0084] The system's hardware architecture is designed to ensure comprehensive data collection, efficient processing, and system stability and scalability. It mainly includes the following parts:

[0085] Internet of Things (IoT) sensors: Deployed in key areas of the city to collect real-time environmental data, traffic flow data, public facility usage, etc.

[0086] Surveillance cameras: used to collect visual data such as traffic conditions and population flow.

[0087] Mobile terminal devices: such as smartphones and tablets, which collect user behavior data and social media data.

[0088] Data storage server: uses a distributed storage architecture (such as Hadoop HDFS or Apache Spark) to support the storage and fast access of large-scale data.

[0089] Computing server: Equipped with high-performance CPU and GPU for data processing, model training and inference.

[0090] Backup and redundant storage: ensure data security and high system availability.

[0091] High-speed network switches and routers: ensure high-speed data transmission between modules.

[0092] Edge computing nodes: deployed near data collection points to perform preliminary data processing and filtering, reducing the burden on central servers.

[0093] Firewall and Intrusion Detection System (IDS): Ensure the security of the system network.

[0094] Encryption devices: Ensure the confidentiality and integrity of data during transmission and storage.

[0095] The system's software architecture adopts modular design to ensure the independence and scalability of each function. It mainly includes:

[0096] Server side: Based on Linux (such as Ubuntu Server or CentOS), providing a stable operating environment.

[0097] Edge computing devices: Use lightweight operating systems such as embedded Linux or RTOS.

[0098] Relational databases: such as PostgreSQL, used to store structured data.

[0099] NoSQL databases: such as MongoDB or Cassandra, used to store semi-structured and unstructured data.

[0100] Message queue: such as Apache Kafka, used to implement data transmission and communication between modules.

[0101] Data stream processing engines: such as Apache Flink or Spark Streaming, support real-time data processing.

[0102] Data processing and analysis tools: such as Apache Spark, TensorFlow, and PyTorch, for big data processing and machine learning model training.

[0103] Optimization algorithm library: such as COIN-OR and GoogleOR-Tools, used to implement optimization algorithms.

[0104] Visualization tools: such as D3.js and Tableau, for data visualization and user interface presentation.

[0105] Cryptography library: such as OpenSSL, used for data encryption and decryption.

[0106] Identity authentication and access control systems: such as OAuth and LDAP, to ensure system access security.

[0107] The data processing process is the core of the system's intelligent distribution, which mainly includes five steps: data collection, data cleaning, data integration, feature engineering and data storage.

[0108] Data collection collects multi-source data from various data sources (IoT devices, surveillance cameras, third-party APIs, etc.) in real time. The Data Ingestion Module is used to ensure data continuity and real-time performance.

[0109] Data cleaning addresses missing values, outliers, and noise to improve data quality. Automated data cleaning tools are used to repair and filter data using rules and machine learning methods.

[0110] Data integration combines data from different sources and unifies data formats and standards. It uses data fusion techniques such as data matching, data merging, and data transformation to form a unified database.

[0111] Feature engineering extracts and constructs features that are helpful for model training, such as industrial density, transportation convenience, and environmental quality index. Dimensionality reduction techniques (such as principal component analysis (PCA)) and feature selection methods (such as recursive feature elimination (RFE)) are used to optimize the feature set.

[0112] Data storage stores processed data in a distributed database, enabling fast query and access. Data warehouse and data lake technologies are used to meet diverse data access needs.

[0113] Data cleaning aims to improve data quality, and mainly includes steps such as missing value processing, outlier detection and repair, and data standardization.

[0114] Missing value processing interpolation method:

[0115] Mean imputation: For numerical features, the mean of all non-missing values ​​is used to fill missing values.

[0116]

[0117] Outlier detection and correction uses interpolation methods: linear or polynomial interpolation is used to predict missing values ​​based on nearby data points. Machine learning interpolation uses regression models (such as KNN regression and random forest regression) to predict missing values. Statistical methods include boxplots, which define upper and lower bounds and detect out-of-range data points.

[0118] Upper limit=Q3+1.5×IQR

[0119] Lower bound = Q1 - 1.5 × IQR, where Q1 and Q3 are the 25th and 75th percentiles, respectively, and IQR = Q3 - Q1. Machine learning methods: Use isolation forests or support vector machines (SVMs) for outlier detection. Repair methods: Replace outliers with the mean or median of neighboring normal values, or adjust based on contextual information.

[0120] Data normalization uses minimum-maximum normalization:

[0121]

[0122] z-secre normalization:

[0123]

[0124] Where μ is the mean and σ is the standard deviation.

[0125] Feature engineering aims to extract features from raw data that are helpful for model training, including steps such as feature selection, feature construction, and dimensionality reduction.

[0126] Feature selection Recursive feature elimination (RFE): By recursively training the model, the features that have the least impact on the target variable are deleted and the most important features are retained. Select the feature set S = {x1, x2, ..., x k Tree-based feature importance evaluation: Use decision trees or random forest models to calculate feature importance scores and retain features with higher scores.

[0127] Interaction features: Construct interaction terms between features, such as the product term of industrial density and transportation convenience.

[0128] x 交互 =x industry density ×x transportation convenience, polynomial features: construct polynomial combinations of original features, such as square terms, cubic terms, etc.

[0129]

[0130] Dimensionality reduction principal component analysis (PCA): Through linear transformation, high-dimensional features are projected into low-dimensional space, retaining the main variance.

[0131] Z=XW

[0132] Among them, X is the original feature matrix, W is the principal component weight matrix, and Z is the feature matrix after dimensionality reduction.

[0133] Prediction and classification models are used to predict industry development trends and classify different industry types, mainly including regression models, clustering models and deep learning models. Linear regression: establish the target variable y and the feature variable x = [x1, x2, ..., x n ] T The linear relationship between them.

[0134] y=β0+β1x1+β2x2+…+β n x n +∈

[0135] Among them, β i is the regression coefficient, and ∈ is the error term. Ridge Regression: Add L2 regularization term to the loss function of linear regression to prevent overfitting.

[0136]

[0137] The clustering model uses K-Means clustering: the data set is divided into K clusters, and the sum of the squares of the distances from the data points in each cluster to the cluster center is minimized.

[0138]

[0139] Among them, S k is the kth cluster, μ k is the center of the kth cluster, hierarchical clustering: construct a hierarchical structure of data points and achieve clustering through bottom-up merging or top-down segmentation.

[0140] Deep learning models using convolutional neural networks (CNNs) are suitable for processing data with spatial structures, such as geographic information images.

[0141] a (l) =σ(W (l) *a (l-1) +b (l) )

[0142] Among them, W (l) is the convolution kernel of the lth layer, a (l) is the activation value, and σ is the activation function. Recurrent Neural Network (RNN): Suitable for processing time series data, such as time series prediction of traffic flow.

[0143] h t =σ(W h h t-1 +W x x t +b)

[0144] Among them, h t is the hidden state at the current moment, x t is the current input, Wh 、W x is the weight matrix, b is the bias term, and the optimization algorithm is used to generate the optimal industrial space allocation plan under multiple objectives, mainly including linear programming, genetic algorithm and particle swarm optimization. Linear programming (LP) solves optimization problems with linear objective functions and linear constraints. Its standard form is:

[0145] minc T x

[0146] subjecttoAx≤b,x≥0

[0147] Among them, c is the target coefficient vector, x is the decision variable vector, A is the constraint matrix, and b is the constraint vector.

[0148] This optimization algorithm, based on natural selection and genetic mechanisms, is suitable for solving complex nonlinear, multimodal, and high-dimensional optimization problems. Its main steps include: Population initialization: generating a set of initial solutions. Fitness evaluation: calculating the fitness of each individual. Selection: selecting superior individuals based on their fitness. Crossover: generating new individuals through genetic recombination. Mutation: randomly mutating some genes to increase diversity. Iteration: repeating fitness evaluation, selection, crossover, and mutation until a termination condition is met.

[0149] Particle swarm optimization is an optimization algorithm that simulates the swallowing behavior of a flock of birds and is suitable for continuous optimization problems. Its update formula is:

[0150]

[0151] Among them, v i is the particle velocity, x i is the particle position, is the historical optimal position of the particle, g * is the global optimal position, w is the inertia weight, c1 and c2 are acceleration constants, and r1 and r2 are random numbers.

[0152] Reinforcement learning is used to continuously optimize the industrial space allocation strategy through interaction with the environment. It mainly adopts deep reinforcement learning methods. The deep Q network (DQN) approximates the Q function through a neural network. The update rule is:

[0153]

[0154] Among them, s t is the state, a t For action, r t is the reward, γ is the discount factor, θ is the current network parameter, θ - are the target network parameters.

[0155] The policy gradient method directly optimizes the policy function π(a|s;θ), with the goal of maximizing the expected cumulative reward:

[0156]

[0157] Update the parameters using the gradient ascent method:

[0158]

[0159] Among them, α is the learning rate s.

[0160] The system's functional modules are clearly divided to ensure that each part works together to achieve intelligent allocation of industrial space. The data acquisition module collects city-related data from multiple sources in real time. This includes an Internet of Things (IoT) interface, a third-party data source interface, and a data pre-processing interface.

[0161] The data processing and analysis module cleans, preprocesses, and features the collected data, and applies machine learning models for prediction and classification. This includes data cleaning and preprocessing, feature engineering, model training, and prediction.

[0162] The intelligent decision-making module generates the optimal industrial space allocation plan based on optimization algorithms and simulations. It includes a multi-objective optimization engine, scenario simulation module, and constraint management.

[0163] The user interface module provides visual display and interactive functions, allowing users to view and adjust allocation plans. It includes advanced visualization tools (VR / AR), interactive data dashboards, feedback and collaboration functions;

[0164] The continuous learning module implements online learning and incremental updates of the model, ensuring that the system adapts to new data and changing trends. It includes automatic feature selection mechanisms, model update strategies, and performance monitoring and evaluation.

[0165] The security and privacy module ensures data security and system privacy, including data encryption and decryption, access control and authority management, and blockchain data integrity verification;

[0166] The overall process of the system covers the complete process from data collection to decision output, ensuring efficient coordination of all links.

[0167] Data collection collects data from multiple sources in real time through IoT devices, surveillance cameras, third-party APIs, etc. The data is transmitted to the data storage server via the network.

[0168] Data cleaning and preprocessing address missing values ​​and outliers, perform data standardization, and format conversion. Apply data cleaning algorithms to improve data quality.

[0169] Feature engineering extracts key features, performs feature selection and dimensionality reduction, and builds a feature set that helps with model training.

[0170] Model training and prediction: Use historical data to train machine learning models for trend forecasting and classification analysis. Apply prediction results to guide optimization algorithms.

[0171] Optimization and decision-making: Based on the prediction results and multi-objective optimization algorithms, the optimal industrial space allocation plan is generated. The feasibility and effectiveness of the plan are evaluated through scenario simulation.

[0172] Solution display and interaction: The optimization solution is presented to the user through a visual tool. The user can adjust the parameters through the interactive interface and view the adjusted solution in real time.

[0173] The continuous learning and model updating system continuously updates and optimizes the model based on new data and user feedback, ensuring the intelligence and adaptability of the system.

[0174] During the design and implementation of the system, great attention is paid to data security and privacy protection, and multi-level security measures are adopted.

[0175] Transmission encryption: Use TLS / SSL protocol to ensure the confidentiality and integrity of data during transmission.

[0176] Storage encryption: Use advanced encryption standards such as AES-256 to encrypt stored data to prevent data leakage.

[0177] Identity authentication: Use multi-factor authentication (MFA) mechanism to ensure that only authorized users can access the system.

[0178] Permission management: Role-based access control (RBAC) refines user permissions and limits access to sensitive data and functions.

[0179] Data integrity verification: Leveraging the immutability of blockchain, each step of data collection, transmission, and processing is recorded to ensure the authenticity and integrity of the data.

[0180] Smart Contract Management: Automatically execute data sharing agreements and access control policies through smart contracts, clarifying the rights and responsibilities of different stakeholders.

[0181] Deploy intrusion detection systems (IDS) and firewalls to monitor and defend against potential network attacks and security threats in real time.

[0182] Anonymize and desensitize sensitive data to protect user privacy and comply with relevant laws and regulations.

[0183] System performance optimization aims to improve data processing speed, model response time and overall system scalability, ensuring efficient operation of the system in a large-scale data environment.

[0184] Leverage distributed computing frameworks like Hadoop and Spark to achieve parallel data processing and rapid computation. Use distributed storage systems like HDFS or Ceph to ensure efficient storage and access to large amounts of data. Deploy in-memory caches (such as Redis and Memcached) to accelerate the query and processing of frequently accessed data. Use load balancers (such as Nginx and HAProxy) to distribute system requests, prevent single point overloads, and improve system responsiveness and stability.

[0185] Employ techniques such as model pruning and quantization to reduce model parameters and improve model inference speed. Leverage GPU acceleration or dedicated AI accelerators to improve the training and inference efficiency of deep learning models. Parallelize data processing and model training tasks to reduce processing time. Use asynchronous task queues (such as RabbitMQ and Celery) to optimize task scheduling and resource utilization. Deploy in a cloud environment and utilize autoscaling to dynamically adjust computing and storage resources based on load, ensuring high performance even under high load.

[0186] The implementation of the system is divided into five stages: demand analysis, system design, development and testing, deployment and launch, operation and maintenance, to ensure the efficient development and stable operation of the system.

[0187] The requirements analysis defines the core functions that the system must implement, including data collection, data processing, model prediction, optimized decision-making, and visualization. Performance requirements, such as the system's response time, processing power, and data throughput, should be determined. The needs and expectations of various stakeholders, including governments, businesses, and the public, should be gathered to ensure that the system design meets the actual application scenarios.

[0188] The system design is based on the above-mentioned software and hardware architecture. A detailed system architecture diagram is developed, the functions and interfaces of each module are defined, the data storage structure is designed, data tables, indexes, relationships, etc. are defined, and according to system requirements, appropriate algorithm models are selected and model training and optimization plans are developed.

[0189] Development and testing are carried out according to the system design, including the development of data acquisition module, data processing module, intelligent decision-making module, user interface module, etc., to ensure the interface compatibility between modules, conduct system-level integration testing, simulate large-scale data environments, test the system's processing capabilities and response speed, and perform performance optimization.

[0190] Deploy and configure server, network, storage and other hardware resources online, deploy operating systems and middleware, deploy various modules to the server, perform system integration and debugging, conduct trial runs, gradually put into actual use, monitor system operation status, and make necessary adjustments and optimizations.

[0191] Operation and maintenance: continuously monitor system performance and operating status, handle faults and anomalies in a timely manner, regularly update data, retrain and optimize models to ensure the intelligence and accuracy of the system, provide user support, collect user feedback, and continuously improve system functions and user experience.

[0192] 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 variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further restrictions, an element defined by the statement "comprising a reference structure" does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0193] 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. An intelligent urban industrial space allocation system based on continuous data learning, characterized by: The dispensing system comprises: Data collection module, used to collect city-related data from multiple sources in real time, including geographic information system data, traffic flow data, demographic data, industrial development data and environmental monitoring data; Data processing and analysis module, which is used to clean, preprocess, feature engineer the collected data, and apply machine learning models for prediction and classification; Intelligent decision-making module, based on optimization algorithms and simulations, is used to generate the optimal industrial space allocation plan; A user interface module is used to provide visual display and interactive functions, allowing users to view and adjust the allocation plan in real time; The continuous learning module is used for online learning and incremental learning to ensure that the model can be dynamically updated based on new data.

2. The urban industrial space intelligent allocation system based on continuous data learning according to claim 1 is characterized by: The data acquisition module further comprises: IoT device interfaces to integrate real-time data from various sensors in the city, including environmental sensors, traffic monitoring cameras, and public facility usage sensors; Third-party data source interface, used to access APIs of external data providers, meteorological data services, social media data streams and economic indicator databases, to expand the diversity and real-time nature of data.

3. The urban industrial space intelligent allocation system based on continuous data learning according to claim 1 is characterized by: The data processing and analysis module includes: Advanced data cleaning module, which can automatically identify and repair missing values, outliers and noise in data, and improve data quality by using adaptive filtering algorithms and anomaly detection technology; The multi-level feature engineering submodule extracts high-dimensional features reflecting the key influencing factors of urban industrial spatial allocation through automatic feature selection, dimensionality reduction technology and feature interaction generation; An integrated machine learning framework that supports the integration of multiple machine learning algorithms, including supervised learning, unsupervised learning, and semi-supervised learning models, to enhance the robustness and accuracy of prediction and classification.

4. The urban industrial space intelligent allocation system based on continuous data learning according to claim 1 is characterized by: The intelligent decision-making module further includes: Multi-objective optimization engine, using hybrid optimization algorithm; The scenario simulation module constructs different urban development scenarios to simulate the implementation effects of various industrial space allocation plans under different conditions and evaluate their long-term and short-term impacts; The constraint management submodule is used by users to set and dynamically adjust the constraint conditions of industry allocation.

5. The urban industrial space intelligent allocation system based on continuous data learning according to claim 1 is characterized by: The user interface module comprises: Interactive data dashboards display key performance indicators and real-time data analysis results, support drag and drop, zoom and custom view configuration, and improve user data exploration and decision-making efficiency; The feedback and collaboration function allows users to provide feedback on the allocation plan generated by the system, and supports multi-user collaborative operations to achieve cross-departmental and cross-field decision-making collaboration.

6. The urban industrial space intelligent allocation system based on continuous data learning according to claim 1 is characterized by: The continuous learning module includes: Automatic feature selection mechanism, based on recursive feature elimination and tree-based feature importance evaluation, automatically identifies and selects the most influential features for industry space allocation, improving the adaptability and accuracy of the model; Model update strategies, which use incremental learning algorithms such as online gradient descent and elastic net regression, can dynamically adjust existing model parameters when new data arrives without retraining the entire model; The performance monitoring and evaluation submodule continuously monitors the model's predictive performance and accuracy, and automatically triggers model retraining or adjustment to cope with changes in data distribution and potential concept drift.

7. The urban industrial space intelligent allocation system based on continuous data learning according to claim 1 is characterized by: The system has anomaly detection and processing functions, including: Real-time data monitoring module, which continuously monitors the collected multi-source data streams and uses statistical methods and machine learning algorithms to identify abnormal data points and abnormal patterns in real time; Automatic repair mechanism, when data anomalies are detected, automatically starts the data repair process, such as data interpolation, smoothing or temporary replacement of data sources, to ensure the reliability of subsequent analysis and decision-making; The alarm and reporting system sends real-time alarms to system administrators and relevant decision makers when serious anomalies are detected, and generates detailed anomaly reports to support rapid response and troubleshooting.

8. The urban industrial space intelligent allocation system based on continuous data learning according to claim 1 is characterized by: The intelligent decision-making module includes: The strategy optimization module based on reinforcement learning uses deep reinforcement learning algorithms to continuously optimize the industrial space allocation strategy through interaction with the environment to maximize long-term benefits; Adaptive decision-making mechanism, which adjusts the weight and priority of optimization strategies according to real-time feedback and dynamic environmental changes, ensuring that the decision-making process is highly flexible and adaptable; The strategy evaluation and selection submodule continuously evaluates the performance of different reinforcement learning strategies and selects the optimal strategy for the generation and implementation of the actual allocation plan.

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