Intelligent road derivative economic service platform system and method based on big data and GIS technology

Abstract

The invention belongs to the technical field of intelligent traffic and regional economic management, and discloses an intelligent road derivative economic service platform system and method based on big data and a GIS technology. The system comprises a multi-source real-time data fusion and adaptive analysis module, a multi-level GIS intelligent analysis module, a big data intelligent prediction and self-learning analysis module, a personalized intelligent recommendation and feedback learning module and an intelligent visualization and augmented reality interaction module. Through multi-source real-time data acquisition and self-adaptive data processing, the system completes preprocessing such as data cleaning and standardization by utilizing an ETL (Extract-Transform-Load) technology. The GIS module realizes multi-level analysis and visualization of geographic space data, and performs dynamic prediction of traffic flow and economic activities in combination with a machine learning model. And the decision support module generates a multi-objective optimization scheme according to the prediction analysis result, and provides scientific traffic management and economic development suggestions. The system can be widely applied to highways, national and provincial roads, rural highways and other scenes.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent transportation and regional economic management, and in particular, relates to a smart road-derived economic service platform system and method based on big data and GIS technology. Background Art

[0002] With the rapid development of global economic integration and urbanization, road-derived economy, as an important part of transportation economy, has gradually become a key factor in promoting regional economic development. However, the traditional road-derived economic model mainly relies on static economic analysis and fixed transportation planning, and fails to fully consider the dynamic changes of transportation networks and the real-time needs of economic activities, resulting in insufficient response when facing complex traffic flows and uncertain economic environments.

[0003] At present, although some traffic management and economic analysis systems have adopted information technology, they operate independently and lack data integration and effective dynamic analysis functions. The application of big data technology has made the collection, storage and analysis of massive data a reality, while GIS technology provides a powerful tool for the visualization and spatial analysis of geographic spatial information. Combining these two and applying them to the development and management of road-derived economy can effectively improve the synergy efficiency of traffic and economic activities, optimize resource allocation, and achieve sustainable development of regional economy.

[0004] However, the existing technology still lacks a system that can fully integrate big data and GIS technology to support the intelligent and dynamic management of road-derived economy. Therefore, it is urgent to develop a smart road-derived economy service platform system that can integrate multi-source data, conduct real-time dynamic analysis, and provide scientific decision-making support.

[0005] Through the above analysis, the problems and defects of the prior art are as follows:

[0006] The existing technology still lacks a system that can fully integrate big data and GIS technology to support the intelligent and dynamic management of road-derived economy. Summary of the invention

[0007] In response to the problems existing in the prior art, the present invention provides a smart road-derived economic service platform system and method based on big data and GIS technology.

[0008] The present invention is implemented as follows: a smart road-derivative economic service platform system based on big data and GIS technology includes:

[0009] Multi-source real-time data fusion and adaptive analysis module, multi-level GIS intelligent analysis module, big data intelligent prediction and self-learning analysis module, personalized intelligent recommendation and feedback learning module, intelligent visualization and augmented reality interaction module;

[0010] The multi-source real-time data fusion and adaptive analysis module is connected to the multi-level GIS intelligent analysis module to collect data in real time from multi-source data such as traffic cameras, weather stations, smart sensors, social media and user mobile devices, and realize dynamic data fusion and processing; it uses natural language processing technology to parse social media content, identify real-time time information such as sudden traffic accidents, road conditions, weather changes, etc., and standardize and annotate it in combination with data cleaning and preprocessing algorithms; this module uses an adaptive algorithm based on convolutional neural networks to autonomously adjust data processing parameters to improve the system's response speed to emergencies and the adaptability of data fusion;

[0011] The multi-level GIS intelligent analysis module is connected with the big data intelligent prediction and self-learning analysis module. It adopts a multi-level GIS analysis model, including geographic heat map, intelligent path planning, scenario analysis, accessibility calculation, buffer zone analysis, etc., to support multi-dimensional spatiotemporal analysis of data. Through efficient graph theory algorithms and intelligent path identification, it can achieve real-time optimization of complex traffic paths and traffic event simulation. The module also provides adaptive data hierarchy control, allowing users to customize data views and analysis depth, and uses augmented reality (AR) technology to provide users with a real and intuitive visualization experience of traffic and economic resource distribution.

[0012] The big data intelligent prediction and self-learning analysis module is connected with the personalized intelligent recommendation and feedback learning module, and uses deep learning technology and multi-dimensional spatiotemporal models to perform real-time prediction and in-depth analysis of the collected data. The module has intelligent self-learning function and can dynamically adjust parameters according to user feedback and environmental changes to maintain the optimal state of the prediction model and adapt to different environmental changes. It extracts features of multi-dimensional data through convolutional neural networks to make the prediction results more accurate. At the same time, the module supports multiple analysis methods.

[0013] The personalized intelligent recommendation and feedback learning module is connected to the intelligent visualization and augmented reality interaction module, and has a built-in personalized recommendation system. It provides targeted traffic and economic activity suggestions based on the needs of different user roles. This module provides users with customized information and services through collaborative filtering algorithms combined with user preference models, and automatically extracts preference features from user behavior using deep learning models. Users can provide feedback on recommended solutions, and the system optimizes the recommendation algorithm based on feedback information, and gradually enhances the accuracy and adaptability of recommendation results through reinforcement learning, thus realizing truly user-driven intelligent services. The adaptive decision support and optimization simulation module integrates multi-objective optimization algorithms and complex system scenario simulation technologies to provide intelligent optimization solutions in aspects such as traffic management and economic activity layout. This module uses heuristic algorithms to generate the optimal decision path, and introduces scenario prediction and multi-scenario comparison models to enable the system to automatically select the optimal scenario and support the rapid generation of emergency plans.

[0014] The intelligent visualization and augmented reality interaction module is connected with the multi-source real-time data fusion and adaptive analysis module, the multi-level GIS intelligent analysis module, and the personalized intelligent recommendation and feedback learning module. It displays the analysis results through GIS maps, 3D heat maps, dynamic charts and other methods, and supports real-time monitoring and feedback of dynamic data. The module uses augmented reality technology to provide users with intuitive scene views and allows users to customize scene parameters for visual exploration. At the same time, the system supports multi-platform interaction through the Web and mobile platforms to achieve panoramic display of data and immersive experience. The intelligent visualization module has the function of user route recommendation and adjustment. It provides users with the best travel suggestions based on real-time monitoring data and supports multi-level feedback to make the user experience more flexible.

[0015] Furthermore, the multi-source data acquisition and preprocessing module uses ETL technology to extract, convert and load data, and uses data cleaning, outlier detection and processing, data format conversion and data standardization processing technology.

[0016] Furthermore, the GIS module includes using PostGIS or ArcGIS spatial database technology to store geographic information, adopting R-tree or quadtree spatial index technology to improve spatial query efficiency, and supporting dynamic data update of road networks.

[0017] Furthermore, the big data analysis and prediction module includes a time series analysis model, a regression model and a neural network model, and performs distributed data processing through Hadoop / Spark.

[0018] Further, the time series analysis model includes ARIMA and LSTM;

[0019] Regression models include multiple linear regression and gradient boosting regression;

[0020] Neural network models include CNN and RNN.

[0021] Furthermore, the decision support and optimization module uses linear programming, multi-objective optimization, genetic algorithm or particle swarm optimization algorithm to generate the optimal logistics center location, service area layout and transportation infrastructure construction plan.

[0022] Another object of the present invention is to provide a smart road-derivative economic service platform method based on big data and GIS technology, including:

[0023] a) Through the multi-source data collection and preprocessing module, data is collected from traffic, economic activities, environment and meteorology, and social behavior data sources, and the data is cleaned, converted, and standardized;

[0024] b) Use GIS module to perform spatial analysis on the collected data and generate buffer analysis, accessibility analysis, path planning and heat map analysis results;

[0025] c) Use big data analysis and prediction modules to conduct correlation analysis and cluster analysis on traffic flow and economic activity data, and predict future traffic flow and economic activity trends through machine learning models;

[0026] d) Generate optimized traffic management and economic development plans based on analysis results through decision support and optimization modules, supporting multi-objective optimization and scenario simulation;

[0027] e) Display analysis results through visualization and user interaction modules, provide GIS maps, heat maps and dynamic charts, and support real-time monitoring, historical data backtracking and user interaction functions of simulation scenarios;

[0028] In step a), ETL technology is used to extract, transform and load data, and data cleaning, outlier detection, data format conversion and data standardization are applied;

[0029] In step b), PostGIS or ArcGIS spatial database technology is used to store geographic information, and R-tree or quadtree spatial index technology is used to improve spatial query efficiency and support dynamic update of road networks;

[0030] In step c), traffic flow and economic activities are predicted using a time series analysis model, a regression model or a neural network model, and distributed data processing is performed through Hadoop or Spark;

[0031] In step d), linear programming, multi-objective optimization, genetic algorithm or particle swarm optimization algorithm is applied to generate the optimal logistics center location, service area layout and transportation infrastructure construction plan based on the analysis results.

[0032] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the smart road-derived economic service platform method based on big data and GIS technology.

[0033] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the smart road-derived economic service platform method based on big data and GIS technology.

[0034] Another object of the present invention is to provide an information data processing terminal, which is used to implement the smart road-derived economic service platform system based on big data and GIS technology.

[0035] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0036] First, the technical solution of the present invention solves many problems existing in the management of regional transportation and economic activities in the prior art by applying big data and GIS (geographic information system) technology to the road-derived economic service platform. Traditional road-derived economic management usually lacks the ability to collect and integrate multi-source data in real time, making it difficult to accurately analyze and predict dynamic traffic flows and economic activities. In addition, the existing technology is relatively simple in decision support, making it difficult to perform multi-dimensional optimization simulations, and thus difficult to meet the increasingly complex transportation and regional economic development needs.

[0037] The present invention achieves significant technical progress in the following aspects:

[0038] 1. Multi-source data integration and standardized processing: ETL technology is used to uniformly clean and standardize traffic, economic, environmental and other data, laying the foundation for interconnection and collaborative analysis between different data sources. Compared with traditional methods, this significantly improves data quality and the accuracy of analysis results.

[0039] 2. Deepening the application of GIS spatial analysis: Using GIS technology to implement functions such as buffer analysis, accessibility analysis, path optimization, and heat map generation, we can deeply explore the spatial relationship between traffic flow and economic activities. This kind of spatial analysis greatly improves the accurate grasp of regional economic activities and traffic hotspots, making it easier for managers to make scientific decisions in a timely manner.

[0040] 3. Big data analysis and prediction capabilities: The present invention integrates multiple machine learning models such as time series analysis, cluster analysis, and regression analysis, which can predict trends and identify patterns for traffic flow and economic activities, helping managers to conduct forward-looking management. Compared with traditional static models, the prediction model of the present invention is more flexible and adaptable.

[0041] 4. Optimization decision support: Through multi-objective optimization algorithms and scenario simulation, it provides optimization solutions based on actual needs, such as traffic flow diversion and economic layout planning, to help achieve the optimal solution for resource allocation. This multi-dimensional optimization method makes the platform more practical and supports finding the optimal development path in a complex environment.

[0042] 5. Enhanced visualization and user interaction: The analysis results are displayed through a variety of interactive interfaces such as GIS maps, heat maps and dynamic charts, which facilitates users to monitor in real time, trace back historical data and set simulation scenarios, thereby improving the intuitiveness of the system and the scientific nature of decision-making.

[0043] In general, the present invention significantly improves the intelligence, refinement and effectiveness of road-related economic management, is widely applicable to various highway scenarios, and provides strong support for the sustainable development of transportation and economy.

[0044] Second, the technical problems solved by the technical solution of the present invention and the significant progress achieved in industrial applications.

[0045] 1. Technical Problems of Existing Technologies

[0046] 1. Difficulty in integrating multi-source data

[0047] Existing systems find it difficult to integrate data from multiple sources such as traffic cameras, weather stations, smart devices, and social media in real time, resulting in strong data isolation and difficulty in providing dynamic support for complex scenarios.

[0048] 2. Lack of efficient GIS analysis and prediction capabilities

[0049] Traditional GIS analysis models are mostly static analyses, which cannot effectively process dynamic traffic data and lack the ability to respond quickly to real-time events.

[0050] 3. Insufficient personalized services

[0051] Existing systems usually adopt a single recommendation model, which is unable to provide accurate suggestions based on users' dynamic behaviors and preferences, resulting in poor user experience.

[0052] 4. Single visual interaction method

[0053] Data visualization is mostly presented in the form of flat charts, lacking immersive interactive methods supported by technologies such as augmented reality, making it difficult to meet the needs of complex application scenarios.

[0054] 2. Technical solution of the present invention

[0055] 1. Real-time multi-source data fusion and intelligent analysis

[0056] The present invention uses convolutional neural networks and adaptive algorithms to achieve real-time dynamic fusion of multi-source data such as traffic, meteorology, and social media. It can accurately identify emergencies and quickly adjust processing parameters, thereby improving the system's response speed and data adaptability.

[0057] 2. Dynamic GIS analysis and intelligent prediction

[0058] The system introduces multi-level GIS analysis models and deep learning prediction algorithms, which can realize geographic heat maps, intelligent path planning and scenario analysis, support real-time traffic optimization and economic activity prediction, and enhance the decision-making ability of the system.

[0059] 3. Personalized recommendation and feedback learning

[0060] Combining collaborative filtering algorithms and deep learning technology, the system dynamically generates recommendations based on user behavior and preferences, and continuously optimizes the recommendation algorithm through a feedback mechanism, making the service more intelligent and user-driven.

[0061] 4. Augmented reality and multi-platform interaction

[0062] The system uses augmented reality (AR) technology, combined with 3D heat maps and dynamic charts, to provide an intuitive interactive experience; it supports multi-platform operations (such as Web and mobile) to meet multi-scenario application needs.

[0063] III. Significant Technological Advances in Industrial Applications

[0064] 1. Improve data processing and response capabilities

[0065] Through dynamic data fusion and adaptive analysis modules, the system can respond to complex scenarios in real time, such as traffic accidents or changes in economic activities, significantly improving data processing speed and decision-making accuracy.

[0066] 2. Optimize user experience and service efficiency

[0067] The personalized intelligent recommendation module combines the user behavior model to provide tailored service solutions, enabling users to obtain more efficient route planning and economic activity recommendations.

[0068] 3. Enhance visualization and interaction effects

[0069] The intelligent visualization module combines augmented reality technology to provide users with immersive scene analysis and optimization solution display, improving visualization effects and application experience.

[0070] 4. Expand industry applicability

[0071] The system is widely used in various scenarios such as urban traffic optimization, highway service area planning, and logistics route optimization, promoting the efficient coordination of smart transportation and road-derived economy.

[0072] By integrating big data and GIS technology, the present invention has achieved significant technological progress in multi-source data fusion, intelligent analysis and prediction, personalized services, and visual interaction. It has solved problems such as data fragmentation, low prediction accuracy, and poor user experience in traditional systems, and has provided comprehensive support for the intelligent upgrade of smart transportation and road-related economic industries, and has broad industrial application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a structural block diagram of the intelligent road-derived economic service platform based on big data and GIS technology provided by an embodiment of the present invention.

[0074] Figure 2 It is a flow chart of the implementation method of the smart road-derived economic service platform based on big data and GIS technology provided in an embodiment of the present invention.

[0075] Figure 3 It is a detailed structural block diagram of the intelligent road-derived economic service platform system based on big data and GIS technology provided by an embodiment of the present invention.

[0076] Figure 1 In: 1. Multi-source real-time data fusion and adaptive analysis module; 2. Multi-level GIS intelligent analysis module; 3. Big data intelligent prediction and self-learning analysis module; 4. Personalized intelligent recommendation and feedback learning module; 5. Intelligent visualization and augmented reality interaction module. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0078] Application Example 1: Urban Traffic Optimization and Emergency Management

[0079] In urban traffic management, emergencies (such as traffic accidents and severe weather) can have a significant impact on the efficiency of road network operations. Traditional response methods usually cannot provide real-time optimization solutions and it is difficult to alleviate traffic pressure in a timely manner.

[0080] The intelligent road-derived economic service platform of the present invention collects multi-source data such as traffic cameras, weather stations and social media in real time through multi-source real-time data fusion and adaptive analysis modules. When a traffic accident occurs, the system uses the adaptive algorithm of convolutional neural network to analyze the time, location and impact range of the emergency, and generates a geographic heat map through a multi-level GIS intelligent analysis module to dynamically calculate the accessibility and alternative paths of the affected area.

[0081] The system uses intelligent path planning to recommend the best detour route to users and uses augmented reality (AR) technology to intuitively display the path planning effect. At the same time, the traffic management department can use the emergency plan generated by the system to quickly deploy traffic diversion measures and achieve efficient response to emergencies. Tests show that the traffic congestion index in the accident area has been reduced by 30% and the average travel time has been shortened by 15%.

[0082] Application Example 2: Optimization of Economic Activity Layout in Highway Service Areas

[0083] The economic benefits of highway service areas depend on scientific layout and resource allocation, but due to the lack of big data support, traditional planning methods find it difficult to accurately predict user demand and the distribution of economic activities.

[0084] The present invention integrates historical traffic flow, weather data and user behavior data through big data intelligent prediction and self-learning analysis modules to predict passenger flow peaks and economic activity potential in different service areas. The system uses personalized intelligent recommendation and feedback learning modules, combined with collaborative filtering algorithms, to recommend suitable business formats (such as gas stations, restaurants, shopping, etc.) according to user needs and preferences.

[0085] The planning scheme is presented through intelligent visualization and augmented reality interactive modules, including 3D heat maps of the service area and commercial layout optimization schemes. The system uses a scenario comparison model to evaluate the economic benefits of multiple schemes and ultimately selects the optimal layout scheme. The test shows that the revenue of merchants in the optimized service area increased by 20% and user satisfaction increased by 25%.

[0086] like Figure 1 , 3 As shown, an embodiment of the present invention provides a smart road-derivative economic service platform system based on big data and GIS technology, including:

[0087] Multi-source real-time data fusion and adaptive analysis module 1, multi-level GIS intelligent analysis module 2, big data intelligent prediction and self-learning analysis module 3, personalized intelligent recommendation and feedback learning module 4, intelligent visualization and augmented reality interaction module 5;

[0088] The multi-source real-time data fusion and adaptive analysis module 1 is connected to the multi-level GIS intelligent analysis module 2, and is used to collect data from multiple sources such as traffic cameras, weather stations, smart sensors, social media, and user mobile devices in real time, and realize dynamic fusion and processing of data; it uses natural language processing technology to parse social media content, identify real-time time information such as sudden traffic accidents, road conditions, weather changes, etc., and standardizes and annotates data in combination with data cleaning and preprocessing algorithms; this module uses an adaptive algorithm based on convolutional neural networks to autonomously adjust data processing parameters to improve the system's response speed to emergencies and the adaptability of data fusion;

[0089] The multi-level GIS intelligent analysis module 2 is connected to the big data intelligent prediction and self-learning analysis module 3, and adopts a multi-level GIS analysis model, including geographic heat map, intelligent path planning, scenario analysis, accessibility calculation, buffer zone analysis, etc., to support multi-dimensional spatiotemporal analysis of data; through efficient graph theory algorithms and intelligent path identification, it can achieve real-time optimization of complex traffic paths and traffic event simulation; the module also provides adaptive data hierarchy control, allowing users to customize data views and analysis depth, and uses augmented reality (AR) technology to provide users with a real and intuitive visualization experience of traffic and economic resource distribution;

[0090] The big data intelligent prediction and self-learning analysis module 3 is connected to the personalized intelligent recommendation and feedback learning module 4, and uses deep learning technology and multi-dimensional spatiotemporal models to perform real-time prediction and in-depth analysis of the collected data; the module has an intelligent self-learning function and can dynamically adjust parameters according to user feedback and environmental changes to maintain the optimal state of the prediction model and adapt to different environmental changes; the feature extraction of multi-dimensional data is performed through convolutional neural networks to make the prediction results more accurate; at the same time, the module supports multiple analysis methods;

[0091] The personalized intelligent recommendation and feedback learning module 4 is connected to the intelligent visualization and augmented reality interaction module 5, and has a built-in personalized recommendation system. It provides targeted traffic and economic activity suggestions based on the needs of different user roles. This module provides users with customized information and services through a collaborative filtering algorithm combined with a user preference model, and automatically extracts preference features from user behavior using a deep learning model. Users can provide feedback on the recommended solutions, and the system optimizes the recommendation algorithm based on the feedback information, and gradually enhances the accuracy and adaptability of the recommended results through reinforcement learning, thus realizing truly user-driven intelligent services. The adaptive decision support and optimization simulation module integrates multi-objective optimization algorithms and complex system scenario simulation technologies to provide intelligent optimization solutions in aspects such as traffic management and economic activity layout. This module uses a heuristic algorithm to generate the optimal decision path, and introduces scenario prediction and multi-scenario comparison models to enable the system to automatically select the optimal scenario and support the rapid generation of emergency plans.

[0092] The intelligent visualization and augmented reality interaction module 5 is connected with the multi-source real-time data fusion and adaptive analysis module 1, the multi-level GIS intelligent analysis module 2, and the personalized intelligent recommendation and feedback learning module 4, and displays the analysis results in various ways such as GIS maps, 3D heat maps and dynamic charts, and supports real-time monitoring and feedback of dynamic data; this module uses augmented reality technology to provide users with intuitive scene views, and allows users to customize scene parameters for visual exploration; at the same time, the system supports multi-platform interaction through the Web and mobile platforms to achieve panoramic display of data and immersive experience; the intelligent visualization module has the function of user route recommendation and adjustment, and provides users with the best travel suggestions based on real-time monitoring data, and supports multi-level feedback to make the user experience more flexible.

[0093] The multi-source data acquisition and preprocessing module provided in the embodiment of the present invention adopts ETL technology to extract, convert and load data, and adopts data cleaning, outlier detection and processing, data format conversion and data standardization processing technology.

[0094] The GIS module provided by the embodiment of the present invention includes using PostGIS or ArcGIS spatial database technology to store geographic information, adopting R-tree or quadtree spatial index technology to improve spatial query efficiency, and supporting dynamic data update of road networks.

[0095] The big data analysis and prediction module provided in the embodiment of the present invention includes a time series analysis model, a regression model and a neural network model, and performs distributed data processing through Hadoop / Spark.

[0096] The time series analysis models provided by the embodiments of the present invention include ARIMA and LSTM;

[0097] Regression models include multiple linear regression and gradient boosting regression;

[0098] Neural network models include CNN and RNN.

[0099] The decision support and optimization module provided in the embodiment of the present invention adopts linear programming, multi-objective optimization, genetic algorithm or particle swarm optimization algorithm to generate the optimal logistics center location, service area layout and transportation infrastructure construction plan.

[0100] like Figure 2 As shown, an embodiment of the present invention provides a smart road-derivative economic service platform method based on big data and GIS technology, including:

[0101] S101, through the multi-source data collection and preprocessing module, collects data from traffic, economic activities, environment and meteorology, and social behavior data sources, and cleans, converts and standardizes the data;

[0102] S102, using a GIS module to perform spatial analysis on the collected data and generate buffer analysis, accessibility analysis, path planning, and heat map analysis results;

[0103] S103, using the big data analysis and prediction module to conduct correlation analysis and cluster analysis on traffic flow and economic activity data, and predict future traffic flow and economic activity trends through machine learning models;

[0104] S104, through the decision support and optimization module, generates optimized traffic management and economic development plans based on the analysis results, supporting multi-objective optimization and scenario simulation;

[0105] S105 displays the analysis results through visualization and user interaction modules, provides GIS maps, heat maps and dynamic charts, and supports real-time monitoring, historical data backtracking and user interaction functions for simulation scenarios;

[0106] S101 uses ETL technology to extract, transform and load data, and applies data cleaning, outlier detection, data format conversion and data standardization processing;

[0107] S102 uses PostGIS or ArcGIS spatial database technology to store geographic information, and adopts R-tree or quadtree spatial index technology to improve spatial query efficiency and support dynamic update of road networks;

[0108] In S103, a time series analysis model, a regression model or a neural network model is used to predict traffic flow and economic activities, and distributed data processing is performed through Hadoop or Spark;

[0109] In S104, linear programming, multi-objective optimization, genetic algorithm or particle swarm optimization algorithm are applied to generate the optimal logistics center location, service area layout and transportation infrastructure construction plan based on the analysis results.

[0110] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the smart road-derived economic service platform method based on big data and GIS technology.

[0111] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the smart road-derived economic service platform method based on big data and GIS technology.

[0112] Another object of the present invention is to provide an information data processing terminal, which is used to implement the smart road-derived economic service platform system based on big data and GIS technology.

[0113] The present invention is specifically implemented:

[0114] The core of the system provided by the embodiment of the present invention is to realize intelligent and dynamic management of road-derived economy by integrating big data and GIS technology. In order to achieve this goal, the system combines technical modules such as multi-source data collection, advanced data analysis models, real-time spatial analysis and optimization decision support. The specific technical solutions include:

[0115] 1. Multi-source data acquisition and preprocessing module

[0116] In the present invention, the data acquisition module involves various types of data sources, including:

[0117] Traffic data: Real-time information on traffic flow, vehicle speed, congestion, etc. is obtained through road monitoring cameras, intelligent traffic signal systems, vehicle GPS, mobile operator location data, etc.

[0118] Economic activity data: Economic transaction data is obtained through e-commerce platforms, payment systems, logistics management systems, commercial POS machines, etc., including sales, order volume, transaction frequency, etc.

[0119] Environmental and meteorological data: Obtain real-time weather, temperature, humidity, air quality and other environmental data from meteorological satellites, meteorological stations, air quality monitoring stations, etc.

[0120] Social behavior data: Obtain user behavior data through social media, mobile applications, travel platforms, etc., including location check-ins, comments, social interactions, travel plans, etc.

[0121] These data come from a wide range of sources and in different formats, so ETL technology is used in the data collection process to achieve data standardization and consistency through data extraction, conversion, and loading processes.

[0122] In the data preprocessing stage, this system uses the following technologies:

[0123] Data cleaning: including missing value filling (using mean, median or prediction model), noise data filtering (such as using mean filtering, Kalman filter), outlier detection and processing (such as methods based on Z-score or IQR).

[0124] Data conversion: including data format conversion (such as from XML to JSON), timestamp processing, spatial data conversion (such as converting the geographic coordinate system from WGS84 to UTM).

[0125] Data standardization: To ensure data comparability, the numerical data is normalized (Min-Max Scaling or Z-score standardization), and the categorical data is encoded (such as one-hot encoding, label encoding).

[0126] 2. Implementation of GIS technology

[0127] The GIS module is one of the core components of the system, used to manage and process large amounts of geospatial data. This module uses the following technologies:

[0128] Spatial database: Use spatial database technologies such as PostGIS or ArcGIS to store geographic information such as road networks, economic blocks, service area locations, etc. Spatial databases support efficient spatial query and analysis and can handle massive amounts of geographic data.

[0129] Spatial index: To improve the efficiency of spatial query, spatial indexing techniques such as R-tree or quadtree are used to speed up the retrieval of geographic objects, especially when dealing with large-scale spatial data.

[0130] Spatial data update: The system supports dynamic data update, which can reflect the changes in the road network (such as new roads, road closures) and the changes in the spatiotemporal distribution of economic activities in real time.

[0131] The GIS module provides a variety of spatial analysis functions, including:

[0132] Buffer analysis: Calculate the distribution of economic activities within the buffer zone of a geographic object (such as a road or service area) and evaluate its service radius.

[0133] Accessibility analysis: Calculate the shortest path and reachable time from a certain point (such as a logistics center, tourist attraction) to surrounding points to optimize transportation and logistics routes.

[0134] Path planning: Generate optimal path planning based on real-time traffic data, support multi-path analysis and congestion avoidance strategies, and is suitable for scenarios such as logistics, rescue, and travel route planning.

[0135] Heat map generation: Map economic activity data into geographic space, use heat maps to show the density of economic activities, and help identify economic hot spots.

[0136] 3. Big data analysis and prediction technology

[0137] 3.1 Data Mining and Analysis

[0138] The big data analysis module of the present invention adopts a variety of data mining technologies, mainly including:

[0139] Association analysis: Use the Apriori algorithm or FP-Growth algorithm to mine frequent patterns from traffic flow and economic activity data and analyze the association between the two. For example, identify the correlation between certain economic activities and specific traffic conditions.

[0140] Cluster analysis: Use clustering algorithms such as K-means and DBSCAN to cluster traffic and economic data in different regions, identify areas with similar characteristics, and support economic block division and traffic planning.

[0141] Classification and regression: Use classification and regression models such as decision trees, random forests, and support vector machines (SVM) to predict changes in economic activities under certain traffic conditions or predict traffic flow within a certain period of time in the future.

[0142] 3.2 Machine Learning and Prediction Models

[0143] In order to predict future traffic flow and economic activities, the present invention introduces a variety of machine learning algorithms and prediction models:

[0144] Time series analysis: Use time series models such as ARIMA model and LSTM (Long Short-Term Memory Network) to analyze historical traffic data and economic data and predict future traffic flow and economic trends. LSTM is particularly suitable for capturing long-term dependencies.

[0145] Regression analysis: Use multiple linear regression, ridge regression, gradient boosting regression and other models to analyze the impact of various factors (such as weather, holidays, and traffic policies) on economic activities and traffic flow, and make predictions.

[0146] Neural network model: Use deep learning techniques such as convolutional neural network (CNN) combined with GIS spatial features for graphical analysis, or recurrent neural network (RNN) to process time series data to achieve more accurate traffic and economic forecasts.

[0147] Reinforcement learning: Use reinforcement learning algorithms such as Q-learning in the decision support module to simulate the evolution of traffic and economy under different decision-making schemes and select the optimal scheme.

[0148] 3.3 Big Data Processing and Storage Technology

[0149] Since the system needs to process massive amounts of traffic and economic data, distributed data storage and processing technology is used:

[0150] Hadoop / Spark: Distributed computing based on Hadoop or Spark framework to process large-scale data sets, especially suitable for big data analysis and complex model training.

[0151] NoSQL database: Use NoSQL databases such as HBase or MongoDB to store semi-structured and unstructured data, such as social media data and sensor data, to provide high scalability and high availability.

[0152] Stream processing technology: Use real-time stream processing technologies such as Kafka and Flink to process high-speed traffic flow data and achieve real-time data analysis and prediction.

[0153] 4. Decision support and optimization technology

[0154] 4.1 Decision Optimization Model

[0155] The system uses a series of optimization algorithms and models to provide decision support for the development of road-derived economy:

[0156] Linear programming and integer programming: used to optimize resource allocation, such as determining the location of logistics centers, service area layout, transportation infrastructure construction, etc.

[0157] Multi-objective optimization: Consider multiple optimization objectives (such as economic benefits, traffic efficiency, and environmental impact), use algorithms such as genetic algorithms and particle swarm optimization (PSO) to solve multi-objective optimization problems and find the optimal compromise solution.

[0158] Simulation: Based on simulation models (such as traffic micro-simulation and macroeconomic models), the actual effects of different decision-making plans are simulated to help decision makers evaluate the feasibility of the plans.

[0159] 4.2 Scenario Prediction and Simulation

[0160] The system supports multiple scenario prediction and simulation functions:

[0161] Traffic Pressure Simulation: Based on real-time and predicted traffic data, simulate the traffic pressure in a certain period of time in the future and identify potential traffic bottlenecks and congestion points.

[0162] Simulation of economic activity evolution: Combined with big data analysis results, simulate the changes in economic activities under different traffic conditions and predict the development trends of economic hot spots.

[0163] Disaster emergency simulation: simulate the response and evolution of traffic and economic activities under extreme situations (such as natural disasters and emergencies) to help formulate emergency plans.

[0164] 5. Visualization and user interaction technology

[0165] 5.1 Visualization Technology

[0166] The system uses advanced visualization technology to provide users with intuitive decision support:

[0167] GIS map display: Use Web GIS technology (such as Leaflet, Mapbox) to dynamically display information such as traffic flow, economic activities, and service area locations on the map, supporting multi-level and multi-dimensional data overlay display.

[0168] Data heat map: Use D3.js or ECharts to generate data heat maps to show the density of economic activities and traffic flow distribution in different regions, and identify economic and traffic hotspots.

[0169] Dynamic charts: Dynamically display the trend of data changes over time through animations and interactive charts (such as line charts, bar charts, and pie charts), and support real-time data updates.

[0170] 5.2 User Interaction Design

[0171] To improve the usability of the system, the system provides a variety of interactive functions:

[0172] Multi-dimensional screening: Users can customize the analysis results they need by selecting different time periods, geographical areas, traffic types, etc.

[0173] Real-time monitoring: The system provides real-time monitoring functions. Users can view dynamic data such as traffic flow and economic activities in real time on the platform, and set alarm reminders to respond to potential abnormal situations in a timely manner.

[0174] Historical data backtracking: Users can select a specific time period to view changes in historical data and evaluate the impact of policies or events through comparative analysis.

[0175] Simulation scenario settings: Users can customize parameters and set different simulation scenarios (such as increased traffic flow during peak hours and increased economic activities during major events). The system will generate simulation results in real time to provide a reference for decision-making.

[0176] The following are two specific implementation examples of the smart road-based economic service platform based on big data and GIS technology:

[0177] Example 1: Optimization of service area layout along expressways

[0178] The layout of service areas along a certain highway is relatively scattered, with few service facilities, which cannot meet the growing travel demand. We hope to use the smart road-derived economic service platform to optimize the location and layout of service areas and improve economic benefits.

[0179] 1. Data collection and preprocessing: Through the multi-source data collection module, traffic flow, economic activities along the route, environmental meteorological data, and service area passenger flow data are obtained from the traffic management department and service area operators. ETL tools are used for data cleaning, outlier processing and standardization.

[0180] 2. Spatial analysis: Use GIS module to analyze the traffic flow distribution, population density and existing service area distribution along the route. Determine the coverage within the service area radius through buffer analysis, and analyze the accessibility from the highway to the service area through path planning.

[0181] 3. Big data analysis and prediction: In the big data analysis module, cluster analysis methods are used to group economic activities and traffic flows in different areas along the highway, and time series analysis models (such as LSTM) are used based on historical data to predict future traffic growth trends.

[0182] 4. Decision support and optimization: Utilize multi-objective optimization algorithms (such as genetic algorithms) to comprehensively consider passenger flow, geographical distribution, and traffic flow to generate optimized service area layout plans, determine the locations of new service areas, and shorten the path length from passing vehicles to service areas.

[0183] 5. Result visualization and presentation: Generate path accessibility analysis and service area coverage heat map through the visualization module to display the optimal service area layout location and support decision-making departments in making final evaluations.

[0184] Through data analysis and optimization, the platform has improved the rationality of the service area layout, increased the utilization efficiency of the service area, and reduced traffic congestion along the highway.

[0185] Example 2: Traffic flow prediction and diversion plan for urban ring roads

[0186] During peak hours, the traffic volume on a city ring road is large, which can easily cause serious congestion. In order to ease the traffic pressure, it is hoped that the intelligent road-derived economic service platform can be used to predict traffic changes and optimize diversion plans.

[0187] 1. Data collection and preprocessing: Obtain traffic flow data for different time periods from the traffic monitoring system, and obtain external influencing factor data from data sources such as weather forecasts and holiday activities. Use the ETL module to clean the data and standardize the data.

[0188] 2. Spatial analysis: In the GIS module, buffer analysis and heat map generation functions are used to mark areas with high traffic flow; the path planning function analyzes the diversion paths and detour plans of each traffic node.

[0189] 3. Big data analysis and prediction: In the big data analysis module, regression models (such as gradient boosting regression) are used in combination with external factors to analyze the correlation between traffic flow and weather, activities, etc., and time series models are used to predict the peak traffic flow in a specific period of time in the future, and to estimate the traffic distribution in each period.

[0190] 4. Decision support and optimization: Through the decision support module, linear programming and particle swarm optimization algorithms are used to generate the best diversion plan during peak hours, determine the diversion entrances and detours, and reduce traffic flow on the main road.

[0191] 5. Result display and simulation: In the visualization module, a heat map of the urban ring road traffic and a visualization path map of the detour plan are generated. The real-time traffic changes are displayed through dynamic charts, and a variety of diversion scenario simulations are provided to facilitate the traffic management department to select the best solution.

[0192] This embodiment significantly alleviates the traffic pressure on the ring road, optimizes the diversion path, effectively disperses the flow during peak hours, and improves the overall traffic circulation efficiency.

[0193] Example 1: Smart road economic application on highways

[0194] In the highway environment, the system of the present invention can collect real-time highway traffic flow data, service area economic activity data, weather condition data, etc., and transmit these data to the GIS module for spatial analysis. Through the big data analysis module, the system can predict future traffic congestion, service area passenger flow, and potential economic benefit points. For example, during peak holiday seasons, the system can identify traffic bottlenecks in advance and provide specific solutions, such as adding temporary rest areas, optimizing service area resource allocation, adjusting highway diversion strategies, etc., to ensure smooth traffic and maximize the economic benefits of service areas.

[0195] Example 2: Road economic optimization of national and provincial highways

[0196] For national and provincial roads, the system of the present invention can be applied to optimize the economic layout along the road. Through the data acquisition module, the system collects traffic flow, logistics data, road safety data and economic activity data along the road, and performs spatial analysis through the GIS module. The big data analysis module can further identify areas with high incidence of traffic accidents, areas with concentrated logistics demand, and hot spots for economic development. The system can provide optimization suggestions, such as setting up logistics centers on specific sections of roads, planning commercial service areas, or improving road widening and safety facilities to enhance the economic benefits and traffic safety of national and provincial roads.

[0197] Example 3: Applications related to rural roads

[0198] In the application of rural roads, the system of the present invention can collect traffic flow, agricultural production data, rural tourism data and population flow data of rural roads, and the system can use GIS technology to perform spatial analysis on these data to generate a spatial distribution map reflecting rural economic activities. The big data analysis module can identify agricultural and tourism combined areas with development potential and predict the future development trends of these areas. Based on these analyses, the decision support module provides solutions for optimizing the rural road network, improving rural logistics efficiency, and promoting the development of rural tourism, thereby promoting the sustainable development of the rural economy.

[0199] Example 4: Planning and management of tourist roads

[0200] For the planning of tourist roads, the system of the present invention can collect road traffic data, scenic spot passenger flow data, meteorological data, traffic accident data and other information in tourist areas, and combine them with the GIS module to perform spatiotemporal correlation analysis on these data. The system uses the big data analysis module to analyze the changes in tourism popularity and traffic flow, generate the optimal planning scheme for tourist roads, and provide suggestions for traffic diversion and layout of tourist service facilities. The system can also monitor the traffic conditions during the peak tourist season in real time, and dynamically adjust traffic control measures, such as temporarily changing the direction of lanes, setting diversion signs or adding temporary parking lots, to ensure the safety and convenience of tourists' travel.

[0201] Example 5: Coordinated development of logistics channels and industrial parks

[0202] In the planning of logistics channels and industrial parks, the system of the present invention collects information such as traffic flow data, cargo flow data, and land use data of the logistics channels, and combines the GIS module to perform spatial analysis to identify efficient logistics channels and hot spots for industrial development. The big data analysis module further predicts the trend of changes in logistics demand and provides site selection and expansion plans for logistics parks. Through the optimization decisions provided by the system, local governments or enterprises can establish or expand logistics parks in the best locations, enhancing the economic benefits of logistics channels and the coordinated development of the industrial chain.

[0203] Example 6: Economic promotion of urban arterial roads and commercial areas

[0204] The system of the present invention also has important applications in the economic promotion of urban trunk roads and commercial areas. The system can collect traffic flow data of the main urban roads, passenger flow data of commercial areas, consumption data, etc., and perform spatial analysis in the GIS module to identify the correlation between economic activities and traffic flow. Through big data analysis, the system can provide traffic management optimization solutions for commercial areas, such as adjusting public transportation routes, increasing parking lots, optimizing the entrance and exit layout of commercial areas, etc., to further promote the economic benefits of urban trunk roads and the prosperity and development of commercial areas.

[0205] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0206] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A smart road-derived economic service platform system based on big data and GIS technology, characterized in that: include: Multi-source real-time data fusion and adaptive analysis module, multi-level GIS intelligent analysis module, big data intelligent prediction and self-learning analysis module, personalized intelligent recommendation and feedback learning module, intelligent visualization and augmented reality interaction module; The multi-source real-time data fusion and adaptive analysis module is connected with the multi-level GIS intelligent analysis module to collect data from multiple sources such as traffic cameras, weather stations, smart sensors, social media and user mobile devices in real time, and realize dynamic fusion and processing of data; The module uses natural language processing technology to analyze social media content and identify real-time time information, such as sudden traffic accidents, road conditions, weather changes, etc., and standardizes and annotates them in combination with data cleaning and preprocessing algorithms. The module uses an adaptive algorithm based on convolutional neural networks to autonomously adjust data processing parameters to improve the system's response speed to emergencies and the adaptability of data fusion. The multi-level GIS intelligent analysis module is connected with the big data intelligent prediction and self-learning analysis module. It adopts a multi-level GIS analysis model, including geographic heat map, intelligent path planning, scenario analysis, accessibility calculation, buffer zone analysis, etc., to support multi-dimensional spatiotemporal analysis of data. Through efficient graph theory algorithms and intelligent path identification, it can achieve real-time optimization of complex traffic paths and traffic event simulation. The module also provides adaptive data hierarchy control, allowing users to customize data views and analysis depth, and uses augmented reality (AR) technology to provide users with a real and intuitive visualization experience of traffic and economic resource distribution. The big data intelligent prediction and self-learning analysis module is connected with the personalized intelligent recommendation and feedback learning module, and uses deep learning technology and multi-dimensional spatiotemporal models to perform real-time prediction and in-depth analysis of the collected data. The module has intelligent self-learning function and can dynamically adjust parameters according to user feedback and environmental changes to maintain the optimal state of the prediction model and adapt to different environmental changes. It extracts features of multi-dimensional data through convolutional neural networks to make the prediction results more accurate. At the same time, the module supports multiple analysis methods. The personalized intelligent recommendation and feedback learning module is connected with the intelligent visualization and augmented reality interaction module, and has a built-in personalized recommendation system to provide targeted transportation and economic activity suggestions based on the needs of different user roles; This module provides users with customized information and services through collaborative filtering algorithms combined with user preference models, and uses deep learning models to automatically extract preference features from user behavior; Users can provide feedback on recommended solutions. The system optimizes the recommendation algorithm based on the feedback information and gradually enhances the accuracy and adaptability of the recommendation results through reinforcement learning, thus achieving truly user-driven intelligent services. The adaptive decision support and optimization simulation module integrates multi-objective optimization algorithms and complex system scenario simulation technology to provide intelligent optimization solutions in aspects such as traffic management and economic activity layout. This module uses heuristic algorithms to generate the optimal decision path, and introduces scenario prediction and multi-scenario comparison models to enable the system to automatically select the optimal scenario and support the rapid generation of emergency plans. The intelligent visualization and augmented reality interaction module is connected with the multi-source real-time data fusion and adaptive analysis module, the multi-level GIS intelligent analysis module, and the personalized intelligent recommendation and feedback learning module. It displays the analysis results through GIS maps, 3D heat maps, dynamic charts and other methods, and supports real-time monitoring and feedback of dynamic data. The module uses augmented reality technology to provide users with intuitive scene views and allows users to customize scene parameters for visual exploration. At the same time, the system supports multi-platform interaction through the Web and mobile platforms to achieve panoramic display of data and immersive experience. The intelligent visualization module has the function of user route recommendation and adjustment. It provides users with the best travel suggestions based on real-time monitoring data and supports multi-level feedback to make the user experience more flexible.

2. The intelligent road-derived economic service platform system based on big data and GIS technology as claimed in claim 1 is characterized in that: The multi-source data acquisition and preprocessing module adopts ETL technology to extract, convert and load data, and adopts data cleaning, outlier detection and processing, data format conversion and data standardization processing technology.

3. The intelligent road-derived economic service platform system based on big data and GIS technology as claimed in claim 1 is characterized in that: The GIS module includes using PostGIS or ArcGIS spatial database technology to store geographic information, using R-tree or quadtree spatial index technology to improve spatial query efficiency, and supporting dynamic data update of road networks.

4. The intelligent road-derived economic service platform system based on big data and GIS technology as claimed in claim 1 is characterized in that: The big data analysis and prediction module includes a time series analysis model, a regression model and a neural network model, and performs distributed data processing through Hadoop / Spark.

5. The intelligent road-derived economic service platform system based on big data and GIS technology as claimed in claim 4 is characterized in that: The time series analysis model includes ARIMA and LSTM; Regression models include multiple linear regression and gradient boosting regression; Neural network models include CNN and RNN.

6. The intelligent road-derived economic service platform system based on big data and GIS technology as claimed in claim 1 is characterized in that: The decision support and optimization module uses linear programming, multi-objective optimization, genetic algorithm or particle swarm optimization algorithm to generate the optimal logistics center location, service area layout and transportation infrastructure construction plan.

7. A method for implementing the smart road-derived economic service platform system based on big data and GIS technology as claimed in any one of claims 1 to 6, characterized in that: The smart road-derived economic service platform method based on big data and GIS technology includes: a) Through the multi-source data collection and preprocessing module, data is collected from traffic, economic activities, environment and meteorology, and social behavior data sources, and the data is cleaned, converted, and standardized; b) Use GIS module to perform spatial analysis on the collected data and generate buffer analysis, accessibility analysis, path planning and heat map analysis results; c) Use big data analysis and prediction modules to conduct correlation analysis and cluster analysis on traffic flow and economic activity data, and predict future traffic flow and economic activity trends through machine learning models; d) Generate optimized traffic management and economic development plans based on analysis results through decision support and optimization modules, supporting multi-objective optimization and scenario simulation; e) Display analysis results through visualization and user interaction modules, provide GIS maps, heat maps and dynamic charts, and support real-time monitoring, historical data backtracking and user interaction functions of simulation scenarios; In step a), ETL technology is used to extract, transform and load data, and data cleaning, outlier detection, data format conversion and data standardization are applied; In step b), PostGIS or ArcGIS spatial database technology is used to store geographic information, and R-tree or quadtree spatial index technology is used to improve spatial query efficiency and support dynamic update of road networks; In step c), traffic flow and economic activities are predicted using a time series analysis model, a regression model or a neural network model, and distributed data processing is performed through Hadoop or Spark; In step d), linear programming, multi-objective optimization, genetic algorithm or particle swarm optimization algorithm is applied to generate the optimal logistics center location, service area layout and transportation infrastructure construction plan based on the analysis results.

8. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the smart road-derived economic service platform method based on big data and GIS technology as described in claim 7.

9. A computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method of the smart road-derived economic service platform based on big data and GIS technology as described in claim 7.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the intelligent road-derived economic service platform system based on big data and GIS technology as described in any one of claims 1-6.

Images