Urban and rural planning big data processing method and system

Abstract

The invention provides an urban and rural planning big data processing method and system, and belongs to the technical field of urban and rural planning, and the urban and rural planning big data processing system comprises a data collection module, a data preprocessing module, a data storage management module, a data analysis module, a data visualization module, a decision support module and a management monitoring module. According to the invention, the multi-channel data acquisition module comprises a sensor data acquisition unit, a satellite remote sensing data acquisition unit and a network data acquisition unit, so that more comprehensive and accurate urban and rural planning related data can be collected; the system covers environment, traffic, infrastructure operation state, land utilization, landform, building distribution, network related information and the like, and provides a rich data basis for urban and rural planning.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban and rural planning, and specifically relates to an urban and rural planning big data processing method and system. Background Art

[0002] In the existing technology, urban and rural planning big data processing faces problems such as diverse data sources, uneven data quality, and single data analysis methods. Traditional data processing methods are difficult to meet the requirements of urban and rural planning for data accuracy, comprehensiveness, and real-time performance. In addition, the existing technology also has deficiencies in data visualization and decision support, and cannot provide planners and decision makers with intuitive and effective information support.

[0003] The present invention aims to provide a method and system for processing urban and rural planning big data to solve the problems existing in the prior art. Through a multi-channel data acquisition module, more comprehensive and accurate urban and rural planning related data can be collected. The data preprocessing module can improve data quality and availability, and provide a reliable data basis for subsequent analysis and decision-making. The data analysis module uses a variety of analysis methods and techniques to deeply mine the information and knowledge in the data. The data visualization module displays the analysis results in an intuitive and clear manner, which is convenient for decision makers and planners to understand and apply. The decision support module can evaluate and optimize the planning scheme and provide a scientific basis for decision-making. The management and monitoring module guarantees the operation and safety of the system and ensures the stability and reliability of the system. The method and system for processing urban and rural planning big data of the present invention will improve the scientificity, rationality and effectiveness of urban and rural planning. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for processing urban and rural planning big data, aiming to solve the problem that the data processing methods in the prior art are difficult to meet the requirements of urban and rural planning for data accuracy, comprehensiveness and real-time performance. In addition, the prior art also has deficiencies in data visualization and decision support, and cannot provide planners and decision makers with intuitive and effective information support.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A big data processing system for urban and rural planning includes a data acquisition module, a data preprocessing module, a data storage management module, a data analysis module, a data visualization module, a decision support module and a management and monitoring module. The data acquisition module is responsible for collecting the original data required for urban and rural planning. The data preprocessing module is used to process the collected data to improve data quality and availability. The data storage module management module is used to ensure the safe storage and efficient management of data to achieve rapid access and retrieval of data. The data analysis module is used to analyze the data to provide a decision-making basis for urban and rural planning. The data visualization module is used to intuitively display the data. The decision support module is used to achieve the formulation and optimization of urban and rural planning schemes. The management and monitoring module is used to ensure the operation and security of the system.

[0007] As a preferred solution of the present invention, the data acquisition module includes a sensor data acquisition unit, a satellite remote sensing data acquisition unit and a network data acquisition unit. The sensor data acquisition unit collects different data through multiple sensors. The satellite remote sensing data acquisition unit is used to obtain the distribution of urban and rural land use, depict the terrain undulations and landform features of urban and rural areas, and the location of buildings. The network data acquisition unit is used to collect data information on the network.

[0008] As a preferred solution of the present invention, the data preprocessing module is provided with a data cleaning unit, a data conversion unit and a data integration unit. The data cleaning unit is used to remove impurities in the data, the data conversion unit is used to standardize the data format and value, and the data integration unit is used to fuse multi-source data, establish associations between data, and form a complete data view.

[0009] As a preferred solution of the present invention, the data storage management module is provided with a distributed storage unit, a data index and retrieval unit and a data backup and recovery unit. The distributed storage unit uses the Hadoop distributed file format and the distributed database to store data. The data index and retrieval unit achieves fast and accurate data retrieval by establishing an efficient index. The data backup and recovery unit is used to regularly back up data, formulate a disaster recovery strategy, and ensure the security and availability of data.

[0010] As a preferred solution of the present invention, the data analysis module is provided with a statistical analysis unit, a data mining unit and a spatial analysis unit. The statistical analysis unit summarizes data characteristics and relationships through descriptive statistics, correlation analysis and hypothesis testing. The data mining unit is used to discover potential patterns and trends. The spatial analysis unit is used to study the relationships and layouts in geographic space. The machine learning unit is used to perform complex analysis and prediction.

[0011] As a preferred solution of the present invention, the data visualization module is provided with a two-dimensional visualization unit, a three-dimensional visualization unit and a map visualization unit. The two-dimensional visualization unit presents data comparison and proportional relationship through a line graph, the three-dimensional visualization unit is used to display the three-dimensional model of the city and terrain, and the map visualization unit is used to produce thematic maps and dynamic maps.

[0012] As a preferred solution of the present invention, the line graph is a bar graph, a broken line graph and a pie chart.

[0013] As a preferred solution of the present invention, a planning scheme evaluation unit and an intelligent recommendation unit are provided in the decision support module. The planning scheme evaluation unit is used to simulate and evaluate the effects of different planning schemes, and the intelligent recommendation unit provides optimization strategy suggestions for planning based on data analysis results.

[0014] As a preferred solution of the present invention, the management and monitoring module is provided with a user management unit, a system monitoring unit and a system maintenance unit. The user management unit is used to set user permissions, perform authentication and authorization, the system monitoring unit is used to monitor system performance, and the system maintenance unit is responsible for software updates and hardware maintenance.

[0015] As a preferred solution of the present invention, a method for processing urban and rural planning big data is applied to the urban and rural planning big data processing system according to claim 9, and is characterized in that it comprises the following steps:

[0016] Step S1, data collection: firstly, various data related to urban and rural planning are collected through multiple channels and technical means;

[0017] Step S2, data preprocessing: cleaning, screening and converting the collected raw data to improve data quality and usability;

[0018] Step S3, data storage and management: select appropriate data storage architecture and management strategy to ensure secure, efficient storage and fast retrieval of data;

[0019] Step S4, data analysis: using a variety of analytical methods and techniques to conduct in-depth mining and analysis of the data to extract some information and knowledge;

[0020] Step S5, result visualization: present the analysis results in an intuitive and clear visual form to facilitate decision makers and planners to understand and apply them, using two-dimensional and three-dimensional charts, maps, animations, etc.;

[0021] Step S6: Scheme evaluation and optimization: Based on data analysis and visualization results, evaluate and compare existing urban and rural planning schemes, identify existing problems and deficiencies, and propose optimization and improvement suggestions;

[0022] Step S7, Feedback and Update: Based on the actual application results and new data input, the processing methods and models are continuously adjusted and improved to improve the accuracy and practicality of urban and rural planning big data processing.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. Comprehensive and accurate data: Through multi-channel data collection modules, including sensor data collection units, satellite remote sensing data collection units and network data collection units, more comprehensive and accurate urban and rural planning-related data can be collected, covering the environment, transportation, infrastructure operation status, land use, topography, building distribution and related information on the network, etc., providing a rich data foundation for urban and rural planning.

[0025] 2. Improvement of data quality: The data cleaning unit, data conversion unit and data integration unit in the data preprocessing module can remove impurities in the data, such as duplications, missing values ​​and outliers, standardize the data format and values, integrate multi-source data, establish associations between data, and form a complete data view, thereby improving data quality and availability.

[0026] 3. In-depth data analysis: The statistical analysis unit, data mining unit, spatial analysis unit and machine learning unit set in the data analysis module use a variety of analysis methods and techniques, such as descriptive statistics, correlation analysis, hypothesis testing, cluster analysis, association rule mining, classification and prediction, buffer analysis, overlay analysis, network analysis, deep learning models and reinforcement learning algorithms, etc., which can deeply mine the information and knowledge in the data and provide decision-making basis for urban and rural planning.

[0027] 4. Intuitive data visualization: The two-dimensional visualization unit, three-dimensional visualization unit and map visualization unit in the data visualization module can display the analysis results in an intuitive and clear visualization form through bar charts, line charts, pie charts, three-dimensional models of cities and terrain, thematic maps and dynamic maps, which is easy for decision makers and planners to understand and apply, and provides intuitive information support for planning decisions.

[0028] 5. Optimize planning decisions: The planning scheme evaluation unit and intelligent recommendation unit in the decision support module can simulate and evaluate the effects of different planning schemes, compare the performance of different schemes under various indicators, identify existing problems and deficiencies, and provide optimization strategy suggestions for planning based on data analysis results, thereby improving the formulation and optimization level of urban and rural planning schemes.

[0029] 6. The system is stable and reliable: The user management unit, system monitoring unit and system maintenance unit in the management and monitoring module ensure the operation and security of the system and ensure the stability and reliability of the system by setting user permissions, performing authentication and authorization, monitoring system performance, and being responsible for software updates and hardware maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0031] Figure 1 This is a block diagram of the architecture of the urban and rural planning big data processing system of the present invention;

[0032] Figure 2 It is a system block diagram of the data acquisition module in the present invention;

[0033] Figure 3 It is a system block diagram of the data preprocessing module in the present invention;

[0034] Figure 4 A system block diagram of the data storage management module in the present invention;

[0035] Figure 5 It is a system block diagram of the data analysis module in the present invention;

[0036] Figure 6 It is a system block diagram of the data visualization module in the present invention;

[0037] Figure 7 It is a system block diagram of the decision support module in the present invention;

[0038] Figure 8 It is a system block diagram of the management and monitoring module in the present invention;

[0039] Fig. 9 This is the first Python code algorithm flow chart of the present invention;

[0040] Fig.10 This is the second Python code algorithm flow chart of the present invention;

[0041] Fig.11 This is the third Python code algorithm flow chart of the present invention;

[0042] Fig.12 This is the fourth Python code algorithm flow chart of the present invention. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0044] Example 1

[0045] See also Figure 1 - Fig.12 , the present invention provides the following technical solutions:

[0046] A big data processing system for urban and rural planning includes a data acquisition module, a data preprocessing module, a data storage management module, a data analysis module, a data visualization module, a decision support module and a management and monitoring module. The data acquisition module is responsible for collecting original data required for urban and rural planning. The data preprocessing module is used to process the collected data to improve data quality and availability. The data storage module management module is used to ensure the safe storage and efficient management of data to achieve rapid access and retrieval of data. The data analysis module is used to analyze the data to provide a decision-making basis for urban and rural planning. The data visualization module is used to intuitively display the data. The decision support module is used to achieve the formulation and optimization of urban and rural planning schemes. The management and monitoring module is used to ensure the operation and security of the system.

[0047] In a specific embodiment of the present invention, the data acquisition module is responsible for collecting the original data required for urban and rural planning, providing a basis for subsequent processing and analysis, and comprehensively acquiring data related to urban and rural planning through multiple channels such as sensor data acquisition units, satellite remote sensing data acquisition units, and network data acquisition units, including different data collected by sensors, urban and rural land use distribution, terrain undulations, geomorphic features, and building locations obtained by satellite remote sensing, as well as data information on the network, to provide a rich data basis for subsequent processing and analysis; the data preprocessing module performs preliminary processing on the collected data to improve data quality and availability; the data storage management module can ensure the safe storage and efficient management of data, and facilitate rapid access and retrieval of data; the data analysis module: mines valuable information and knowledge from the data to provide a decision-making basis for urban and rural planning; the data visualization module is used to display the analysis results in an intuitive and easy-to-understand form, which is convenient for planners to understand and make decisions; the decision support module realizes the formulation and optimization of urban and rural planning schemes; the management and monitoring module is used to ensure the operation and safety of the system, and the urban and rural planning big data processing method and system of the present invention can improve the scientificity, rationality and effectiveness of urban and rural planning through the synergistic effect of each module, and provide comprehensive, accurate and intuitive information support for planners and decision makers.

[0048] Specifically, the data acquisition module includes a sensor data acquisition unit, a satellite remote sensing data acquisition unit and a network data acquisition unit. The sensor data acquisition unit collects different data through multiple sensors. The satellite remote sensing data acquisition unit is used to obtain the distribution of urban and rural land use, depict the terrain undulations and landform features of urban and rural areas, and the location of buildings. The network data acquisition unit is used to collect data information on the network.

[0049] In this embodiment: the sensor data acquisition unit can obtain real-time environmental, traffic and infrastructure operation status information, reflecting the actual operation of urban and rural areas, including environmental sensors, traffic flow sensors and infrastructure operation status sensor data. The role of environmental sensor data acquisition is to obtain various indicator data in urban and rural environments in real time, such as air quality, water quality, noise, etc. The method of use is to arrange environmental sensors in different areas of urban and rural areas, and transmit the collected data to the system. Traffic flow sensor data acquisition is used to monitor the traffic volume, speed and other information on the road to understand the traffic conditions. Data acquisition is achieved by installing sensors at key points on the road. Infrastructure operation status sensor data acquisition collects operating parameters of infrastructure such as water and electricity pipe networks and bridges to evaluate their health status. Corresponding sensors are installed on the infrastructure for data collection;

[0050] The satellite remote sensing data collection unit includes topography and geomorphology remote sensing data collection, topography and geomorphology remote sensing data collection, and building distribution remote sensing data collection. The satellite remote sensing data collection unit is used to provide macroscopic and comprehensive spatial information such as urban and rural land use, topography and geomorphology, and building distribution; land use remote sensing data collection can obtain the use distribution of urban and rural land and provide a basis for land use planning. Through the interpretation of satellite remote sensing images, topography and geomorphology remote sensing data collection can depict the terrain undulations and geomorphological features of urban and rural areas, assist in planning layout, and use high-resolution satellite remote sensing image analysis. Building distribution remote sensing data collection can grasp the location and distribution of urban and rural buildings, and identify and extract building information from remote sensing images;

[0051] The network data collection unit includes social media data collection, government public data collection and commercial database data collection. The network data collection unit is used to collect relevant data from social media, government public and commercial databases to enrich data sources and types. Social media data collection can collect public opinions, needs and feedback on urban and rural planning, and use web crawler technology to capture relevant data from social media platforms. Government public data collection is used to obtain government-issued policies and statistical data on urban and rural planning, and access to government departments’ public data platforms to obtain data. Commercial database data collection supplements data related to urban and rural planning provided by other commercial organizations, such as market research data, and obtains access rights to commercial databases through purchase or cooperation;

[0052] Through multiple channels such as sensor data acquisition units, satellite remote sensing data acquisition units and network data acquisition units, comprehensive data related to urban and rural planning are acquired, including different data collected by sensors, urban and rural land use distribution, terrain undulations, landform features and building locations acquired by satellite remote sensing, as well as data information on the Internet, providing a rich data foundation for subsequent processing and analysis.

[0053] Specifically, the data preprocessing module is provided with a data cleaning unit, a data conversion unit and a data integration unit. The data cleaning unit is used to remove impurities in the data, the data conversion unit is used to standardize the data format and value, and the data integration unit is used to fuse multi-source data, establish associations between data, and form a complete data view.

[0054] In this embodiment: the data cleaning unit can remove impurities in the data, such as duplication, missing values ​​and outliers, to make the data accurate and reliable. Removing duplicate data can avoid data redundancy, improve data quality and analysis efficiency, and identify and delete duplicate records through data comparison algorithms; process missing values, and make the data complete and usable by filling or processing the missing parts in the data. According to the characteristics of the data and business needs, select appropriate methods such as mean filling, model prediction, etc.; correct outliers by identifying and correcting data that obviously does not conform to the normal range to ensure data accuracy. Determine outliers through statistical methods or domain knowledge and make corrections;

[0055] The data conversion unit can realize format conversion, value standardization and data discretization, standardize the data format and value, and facilitate unified analysis. Format conversion unifies the data formats from different sources into a format that can be processed by the system, and uses data conversion tools to achieve format conversion. Value standardization can make data of different dimensions comparable, which is convenient for subsequent analysis. Standardized formulas are used to process values. Data discretization divides continuous data into discrete intervals to facilitate the application of certain analysis methods, and discretization is performed according to business rules and analysis requirements;

[0056] The data integration unit can fuse multi-source data, establish associations between data, form a complete data view, integrate data from different channels and types, form a comprehensive data set, achieve fusion through data matching and association technology, establish associations between different data sets, so as to facilitate comprehensive analysis and perform association matching based on common key fields or features;

[0057] The data cleaning unit removes impurities from the data to ensure data accuracy; the data conversion unit standardizes data formats and values ​​to facilitate subsequent analysis and comparison; the data integration unit integrates multi-source data, establishes relationships between data, forms a complete data view, and makes the data more comprehensive and relevant.

[0058] Specifically, the data storage management module is provided with a distributed storage unit, a data index and retrieval unit, and a data backup and recovery unit. The distributed storage unit uses the Hadoop distributed file format and distributed database to store data. The data index and retrieval unit achieves fast and accurate data retrieval by establishing efficient indexes. The data backup and recovery unit is used to regularly back up data, formulate disaster recovery strategies, and ensure data security and availability.

[0059] In this embodiment: the distributed storage unit uses the Hadoop distributed file system and the distributed database to store massive data. The Hadoop distributed file system storage is used to store large-scale massive data with high reliability and scalability. By configuring the Hadoop cluster for data storage, the distributed database storage is suitable for storing structured and semi-structured data, supporting fast query and processing, and building a distributed database system for storage;

[0060] The data indexing and retrieval unit improves the data retrieval speed by establishing efficient indexes, quickly locates the required data, establishes appropriate index structures according to data characteristics and query requirements, realizes fast and accurate data retrieval, quickly obtains relevant data from storage according to user query requests, and uses optimized retrieval algorithms and indexes to realize fast retrieval;

[0061] The data backup and recovery unit can regularly back up data, formulate disaster recovery strategies, ensure data security and availability, regularly back up data to prevent data loss, ensure data security, and perform data backup operations according to the set time cycle. The disaster recovery strategy is to quickly restore data and system operation when the system suffers a disaster or failure, formulate detailed recovery plans and processes, and conduct regular drills;

[0062] The distributed storage unit uses Hadoop distributed file format and distributed database to store data, thereby improving data storage capacity and reliability; the data indexing and retrieval unit accelerates data retrieval and improves data utilization efficiency by establishing efficient indexes; the data backup and recovery unit regularly backs up data, formulates disaster recovery strategies, ensures data security and availability, and prevents data loss or damage.

[0063] Specifically, the data analysis module is equipped with a statistical analysis unit, a data mining unit and a spatial analysis unit. The statistical analysis unit summarizes data characteristics and relationships through descriptive statistics, correlation analysis and hypothesis testing. The data mining unit is used to discover potential patterns and trends. The spatial analysis unit is used to study the relationships and layout in geographic space. The machine learning unit is used to perform complex analysis and prediction.

[0064] In this embodiment: the statistical analysis unit summarizes data characteristics and relationships through descriptive statistics, correlation analysis and hypothesis testing. Descriptive statistical analysis summarizes basic characteristics of data, such as mean, variance, distribution, etc., and uses statistical software or libraries for calculation. Correlation analysis uses methods such as correlation coefficient calculation to find the degree of association between different variables. Hypothesis testing verifies whether certain assumptions about the data are valid and uses statistical testing methods to make judgments.

[0065] The data mining unit uses cluster analysis, association rule mining, classification and prediction to discover potential patterns and trends. Cluster analysis divides data into different categories or clusters to discover potential patterns and use clustering algorithms to group them. Association rule mining finds out the frequently occurring associations in the data and mines rules through association rule mining algorithms. Classification and prediction is achieved by classifying data or predicting future trends using classification algorithms and prediction models.

[0066] Spatial Analysis Unit: Use buffer analysis, overlay analysis and network analysis to study the relationship and layout in geographic space. Buffer analysis determines the influence range of geographic elements and creates and analyzes buffers in geographic information system software. Overlay analysis integrates information from multiple geographic layers and analyzes spatial relationships by overlaying different layers. Network analysis studies network structure and flow, such as optimization of transportation networks, using network analysis tools for analysis.

[0067] The machine learning unit uses deep learning models and reinforcement learning algorithms for more complex analysis and prediction. Deep learning models handle complex data patterns and prediction tasks. Deep learning models are trained and applied to data. Reinforcement learning algorithms optimize decisions by interacting with the environment. Reinforcement learning algorithms are applied in specific scenarios for strategy optimization.

[0068] The statistical analysis unit summarizes data characteristics and relationships through descriptive statistics, correlation analysis, and hypothesis testing to help planners understand the basic situation and interrelationships of the data; the data mining unit discovers potential patterns and trends to provide forward-looking information for planning; the spatial analysis unit studies the relationships and layout in geographic space to help rationally plan urban and rural space; the machine learning unit conducts complex analysis and prediction to provide a scientific basis for decision-making.

[0069] Specifically, the data visualization module is provided with a two-dimensional visualization unit, a three-dimensional visualization unit and a map visualization unit. The two-dimensional visualization unit presents data comparison and proportional relationship through a line graph. The line graphs are bar graphs, line graphs and pie charts. The three-dimensional visualization unit is used to display the three-dimensional models of cities and terrain. The map visualization unit is used to produce thematic maps and dynamic maps.

[0070] In this embodiment: the two-dimensional visualization unit presents simple and intuitive data comparison and proportional relationship through bar graphs, line graphs and pie charts, etc. The bar graph intuitively compares the quantity or size of different categories of data, and draws a bar graph based on the data to show the comparison. The line graph is used to show the trend of data changes over time or other sequential variables, and a line graph is drawn based on time series or sequential data. The pie chart is used to present the proportional relationship of the data, and a pie chart is drawn based on the proportion of the data;

[0071] The 3D visualization unit displays 3D models of cities and landforms, providing a more realistic sense of space. The 3D city model display displays the city's buildings and spatial layout in three dimensions, using 3D modeling software and technology to create and display models. The 3D landform display intuitively presents the terrain undulations of urban and rural areas, and generates a 3D terrain model display based on terrain data.

[0072] The map visualization unit produces thematic maps and dynamic maps to directly reflect spatial distribution and changes. Thematic map production represents data of a specific subject on the map with specific colors, symbols, etc. Thematic maps are produced using geographic information system software. In dynamic map display, the spatial change process of data is dynamically displayed, and dynamic map effects are achieved through programming;

[0073] The two-dimensional visualization unit presents data comparison and proportional relationships through line graphs such as bar graphs, line graphs and pie charts, making the data more intuitive and easy to understand; the three-dimensional visualization unit displays three-dimensional models of cities and terrain, helping planners to understand urban and rural spatial forms more intuitively; the map visualization unit produces thematic maps and dynamic maps to provide more targeted geographic information support for planning.

[0074] Specifically, the decision support module is provided with a planning scheme evaluation unit and an intelligent recommendation unit. The planning scheme evaluation unit is used to simulate and evaluate the effects of different planning schemes, and the intelligent recommendation unit provides optimization strategy suggestions for planning based on data analysis results.

[0075] In this embodiment: the planning scheme evaluation unit is used to simulate and evaluate the effects of different planning schemes, help select the optimal scheme, perform data simulation and effect evaluation on different planning schemes, compare the performance of different planning schemes under various indicators, provide a basis for selecting the optimal scheme, and use simulation tools and data analysis methods to evaluate the schemes;

[0076] The intelligent recommendation unit provides optimization strategy suggestions for planning based on data analysis results, recommends optimized planning strategies based on data analysis results, provides targeted suggestions and strategies for planners based on the conclusions of data mining and analysis, and generates recommended content through algorithms and models;

[0077] The planning scheme evaluation unit simulates and evaluates the effects of different planning schemes to help decision makers choose the optimal scheme; the intelligent recommendation unit provides optimization strategy suggestions for planning based on data analysis results to improve the scientificity and rationality of planning.

[0078] Specifically, the management and monitoring module is provided with a user management unit, a system monitoring unit and a system maintenance unit. The user management unit is used to set user permissions, perform authentication and authorization, the system monitoring unit is used to monitor system performance, and the system maintenance unit is responsible for software updates and hardware maintenance.

[0079] In this embodiment: the user management unit sets user permissions, performs authentication and authorization, protects data security, sets user permissions, controls user access to system functions and data, ensures data security, assigns different permission levels to different users in the system, and performs user authentication and authorization to ensure the legitimacy of the user and the accuracy of the permissions, which is achieved through login authentication and permission verification processes.

[0080] System monitoring unit: monitor system performance, detect faults in time and issue alarms, performance monitoring, real-time monitoring of system performance, such as CPU usage, memory usage, etc., use performance monitoring tools to obtain relevant indicators, fault alarms, issue alarms in time when the system fails or anomalies occur, so as to handle them in time, set alarm thresholds and notification methods;

[0081] System maintenance unit: responsible for software updates and hardware maintenance to ensure the system is stable and reliable. Software updates can timely upgrade system software, fix vulnerabilities and improve functions, and install and update according to the updated version released by the software. Hardware maintenance can regularly check and maintain hardware equipment to ensure the stable operation of the system, including equipment cleaning, component replacement and other operations;

[0082] The user management unit sets user permissions, performs authentication and authorization, and ensures safe use of the system; the system monitoring unit monitors system performance, discovers and solves problems in a timely manner, and ensures stable operation of the system; the system maintenance unit is responsible for software updates and hardware maintenance to keep the system in good operating condition.

[0083] The urban and rural planning big data processing method includes the following steps:

[0084] Step S1, data collection:

[0085] Through various channels and technical means, various data related to urban and rural planning are widely collected. This includes but is not limited to: using sensor networks to obtain real-time dynamic data such as environment and traffic; using satellite remote sensing technology to obtain spatial information such as land use and topography; collecting population, economic, social and other statistical data and text information from government departments, enterprises and the Internet;

[0086] Step S2: Data preprocessing:

[0087] Clean, filter and convert the collected raw data to improve data quality and usability. The specific operations are as follows: remove duplicate and erroneous data records; fill in missing values ​​by using methods such as mean, median or model-based predicted values; identify and process outliers to determine whether they are real anomalies or data errors; convert the data format to meet the requirements of subsequent analysis;

[0088] Step S3: Data storage and management:

[0089] Choose appropriate data storage architecture and management strategies to ensure data security, efficient storage and fast retrieval. For example, use a distributed storage system to handle massive amounts of data; establish data indexes to optimize query performance; perform regular data backup to prevent data loss;

[0090] Step S4: Data analysis:

[0091] Use a variety of analytical methods and techniques to conduct in-depth mining and analysis of data to extract valuable information and knowledge. This may include: using statistical analysis methods to describe the basic characteristics and trends of data; discovering hidden patterns and association rules in data through data mining algorithms; using spatial analysis techniques to study distribution and relationships in geographic space; applying machine learning models for prediction and classification;

[0092] Step S5: Result visualization:

[0093] The analysis results are presented in an intuitive and clear visual form to facilitate the understanding and application of decision makers and planners. A variety of visualization methods such as two-dimensional and three-dimensional charts, maps, animations, etc. can be used to present key information such as population distribution, land use changes, traffic flow, etc. in urban and rural planning;

[0094] Step S6: Solution evaluation and optimization:

[0095] Based on data analysis and visualization results, evaluate and compare existing urban and rural planning schemes, identify existing problems and deficiencies, and put forward suggestions for optimization and improvement;

[0096] Step S7: Feedback and update:

[0097] Based on actual application results and new data input, the processing methods and models are constantly adjusted and improved to improve the accuracy and practicality of urban and rural planning big data processing.

[0098] In the present invention, the urban and rural planning big data processing system uses Python code for data processing. Python is a powerful language for data processing and analysis, and has rich libraries, such as pandas for data manipulation and preprocessing, numpy for numerical calculations, matplotlib and seaborn for data visualization, including data reading and preprocessing, spatial data processing, data analysis and visualization, and data modeling. The specific algorithms are as follows: Figure 9-12 As shown, in the urban and rural planning big data processing system, these functions are called according to specific business needs, and the processing results are used for decision support and planning scheme evaluation.

[0099] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A big data processing system for urban and rural planning, Features: It includes a data acquisition module, a data preprocessing module, a data storage management module, a data analysis module, a data visualization module, a decision support module and a management and monitoring module. The data acquisition module is responsible for collecting the original data required for urban and rural planning. The data preprocessing module is used to process the collected data to improve data quality and availability. The data storage module management module is used to ensure the safe storage and efficient management of data to achieve rapid access and retrieval of data. The data analysis module is used to analyze the data to provide a decision-making basis for urban and rural planning. The data visualization module is used to display the data intuitively. The decision support module is used to formulate and optimize urban and rural planning schemes. The management and monitoring module is used to ensure the operation and security of the system.

2. A system for processing urban and rural planning big data according to claim 1, Features: The data acquisition module includes a sensor data acquisition unit, a satellite remote sensing data acquisition unit and a network data acquisition unit. The sensor data acquisition unit collects different data through multiple sensors. The satellite remote sensing data acquisition unit is used to obtain the distribution of urban and rural land use, describe the terrain undulations and landform features of urban and rural areas, and the location of buildings. The network data acquisition unit is used to collect data information on the network.

3. A system for processing urban and rural planning big data according to claim 2, Features: The data preprocessing module is provided with a data cleaning unit, a data conversion unit and a data integration unit. The data cleaning unit is used to remove impurities in the data, the data conversion unit is used to standardize the data format and value, and the data integration unit is used to fuse multi-source data, establish associations between data, and form a complete data view.

4. A system for processing urban and rural planning big data according to claim 3, Features: The data storage management module is provided with a distributed storage unit, a data index and retrieval unit and a data backup and recovery unit. The distributed storage unit uses the Hadoop distributed file format and distributed database to store data. The data index and retrieval unit achieves fast and accurate data retrieval by establishing an efficient index. The data backup and recovery unit is used to regularly back up data, formulate disaster recovery strategies, and ensure data security and availability.

5. A system for processing urban and rural planning big data according to claim 4, Features: The data analysis module is provided with a statistical analysis unit, a data mining unit and a spatial analysis unit. The statistical analysis unit summarizes data characteristics and relationships through descriptive statistics, correlation analysis and hypothesis testing. The data mining unit is used to discover potential patterns and trends. The spatial analysis unit is used to study the relationships and layouts in geographic space. The machine learning unit is used to perform complex analysis and prediction.

6. A system for processing urban and rural planning big data according to claim 5, Features: The data visualization module is provided with a two-dimensional visualization unit, a three-dimensional visualization unit and a map visualization unit. The two-dimensional visualization unit presents data comparison and proportional relationship through a line graph, the three-dimensional visualization unit is used to display the three-dimensional model of the city and terrain, and the map visualization unit is used to produce thematic maps and dynamic maps.

7. A system for processing urban and rural planning big data according to claim 6, Features: The line graphs are bar graphs, broken line graphs and pie charts.

8. A method and system for processing urban and rural planning big data according to claim 7, Features: The decision support module is provided with a planning scheme evaluation unit and an intelligent recommendation unit. The planning scheme evaluation unit is used to simulate and evaluate the effects of different planning schemes. The intelligent recommendation unit provides optimization strategy suggestions for planning based on data analysis results.

9. A system for processing urban and rural planning big data according to claim 8, Features: The management and monitoring module is provided with a user management unit, a system monitoring unit and a system maintenance unit. The user management unit is used to set user rights, perform authentication and authorization, the system monitoring unit is used to monitor system performance, and the system maintenance unit is responsible for software updates and hardware maintenance.

10. A method for processing urban and rural planning big data, applied to the urban and rural planning big data processing system according to claim 9, It is characterized in that The steps include: Step S1, data collection: firstly, various data related to urban and rural planning are collected through multiple channels and technical means; Step S2, data preprocessing: cleaning, screening and converting the collected raw data to improve data quality and usability; Step S3, data storage and management: select appropriate data storage architecture and management strategy to ensure secure, efficient storage and fast retrieval of data; Step S4, data analysis: using a variety of analytical methods and techniques to conduct in-depth mining and analysis of the data to extract some information and knowledge; Step S5, result visualization: present the analysis results in an intuitive and clear visual form to facilitate decision makers and planners to understand and apply them, using two-dimensional and three-dimensional charts, maps, animations, etc.; Step S6: Scheme evaluation and optimization: Based on data analysis and visualization results, evaluate and compare existing urban and rural planning schemes, identify existing problems and deficiencies, and propose optimization and improvement suggestions; Step S7, Feedback and Update: Based on the actual application results and new data input, the processing methods and models are continuously adjusted and improved to improve the accuracy and practicality of urban and rural planning big data processing.

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