City planning monitoring core database construction method

Abstract

The invention relates to the technical field of data monitoring and database construction, in particular to an urban planning monitoring core database construction method. The method comprises the following steps: acquiring multi-source city data by using a sensor; real-time traffic flow collection is carried out through the unmanned aerial vehicle and the camera, and a real-time traffic flow monitoring video is obtained; performing spectrum separation on the real-time traffic flow monitoring video to generate image spectrum data; performing image enhancement processing on the real-time traffic flow monitoring video according to the image spectrum data to obtain an enhanced image; performing video reconstruction on the enhanced image to generate a real-time traffic flow monitoring enhanced video; video frame feature extraction is carried out on the real-time traffic flow monitoring enhanced video, and a real-time monitoring image matrix is constructed; therefore, through the Internet of Things technology, the data analysis technology, the image processing technology and the prediction analysis technology, the prediction capability for the potential problem of the multi-source city data is improved, and the data retrieval speed of the core database is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of data monitoring and database construction, and in particular to a method for constructing a core database for urban planning monitoring. Background Art

[0002] With the continuous acceleration of urbanization, the complexity and amount of data in urban planning have increased significantly, and traditional urban planning monitoring methods have gradually failed to meet the needs of modern urban management. Early urban planning mainly relied on paper maps and manual statistics. This method not only had slow information processing speed, but also untimely data updates, which severely limited the accuracy and timeliness of planning. With the rise of computer technology, urban planning has gradually introduced electronic data processing methods, and began to use electronic maps and databases to replace traditional paper records. Entering a new stage of development of geographic information system (GIS) technology, digital and intelligent urban planning monitoring methods have begun to appear. Through the integration and visualization of spatial data, urban planners can more intuitively understand the status quo and trends of urban development and support more scientific and reasonable planning decisions. At the same time, the development of database technology has also provided a solid foundation for the management of large-scale, multi-source data, and improved data processing capabilities and storage efficiency. However, the existing technology does not conduct comprehensive analysis of multi-dimensional data, resulting in low prediction capabilities; and when processing large-scale data, the index structure of the existing technology is not sufficient to support fast data retrieval and analysis. Summary of the invention

[0003] Based on this, it is necessary to provide a method for constructing a core database for urban planning monitoring to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for constructing a core database for urban planning monitoring is provided, the method comprising the following steps: Step S1: Use sensors to obtain multi-source city data; use drones and cameras to collect real-time traffic flow and obtain real-time traffic flow monitoring videos; Step S2: performing spectral separation on the real-time traffic flow monitoring video to generate image spectral data; performing image enhancement processing on the real-time traffic flow monitoring video according to the image spectral data to obtain an enhanced image; performing video reconstruction on the enhanced image to generate a real-time traffic flow monitoring enhanced video; performing video frame feature extraction on the real-time traffic flow monitoring enhanced video to construct a real-time monitoring image matrix; Step S3: extracting map features of multi-source city data to obtain a three-dimensional city map; performing a road network topology structure analysis on the three-dimensional city map to generate road network topology structure data; performing road traffic monitoring on the real-time monitoring image matrix through the road network topology structure data to obtain road traffic data; performing green space area identification on the three-dimensional city map to generate green space area data; performing an impact monitoring and analysis on the green space area data according to the road network topology structure data to obtain road green space monitoring data; performing an ecological climate analysis on the road green space monitoring data to obtain green space ecological climate data; Step S4: extracting public facility information from multi-source city data to obtain public facility data; analyzing the usage of the public facility data to obtain public facility usage data; identifying urban construction areas for the multi-source city data based on the public facility usage data to generate urban construction area data; Step S5: extracting environmental characteristics of multi-source urban data to obtain environmental characteristic data; performing environmental quality monitoring on the environmental characteristic data to generate environmental quality data; performing key impact registration on the environmental quality data through urban construction area data to obtain urban construction environmental impact data; Step S6: constructing a city prediction model based on road traffic data, green space ecological climate data and urban construction environmental impact data to obtain a city prediction model; predicting potential problems of multi-source city data based on the city prediction model to obtain city potential problem data; Step S7: Transmit road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data to the central control system for real-time encrypted storage to generate urban monitoring storage data; perform multi-dimensional index structure evaluation on the urban monitoring storage data to obtain multi-dimensional index evaluation data; construct a monitoring core database based on the multi-dimensional index evaluation data to obtain an urban planning monitoring core database to perform urban planning monitoring operations.

[0005] The present invention realizes real-time monitoring of urban traffic flow through multi-source urban data collected by sensors and drone cameras, providing data support for subsequent data analysis; performs spectral separation and image enhancement processing on real-time traffic flow monitoring videos to generate image spectral data and enhanced images, and then constructs a real-time monitoring image matrix to enhance the ability to capture traffic flow details, laying the foundation for in-depth analysis of traffic conditions. By extracting urban map features and constructing a three-dimensional urban map; performing road network topology structure analysis on the map to obtain road network topology structure data; performing road traffic monitoring on the real-time monitoring image matrix through the road network topology structure data to obtain road traffic data, which can provide accurate spatial information for road traffic monitoring; at the same time, the identification and ecological climate analysis of green space areas obtain green space ecological climate data, providing scientific data basis for urban greening and ecological environment protection; by extracting public facility information and analyzing its usage, public facility usage data is obtained to ensure the subsequent reasonable evaluation of the effectiveness of public facilities and planning future urban infrastructure construction. Monitor and analyze environmental characteristic data to obtain environmental quality data; and perform key impact registration on environmental quality data through urban construction area data to obtain urban construction environmental impact data, so as to evaluate the impact of urban construction activities on environmental quality; combine road traffic data, green space ecological climate data and urban construction environmental impact data to build an urban prediction model; and predict potential problems of multi-source urban data to obtain urban potential problem data, so as to provide predictive problem solutions for urban managers; transmit road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data to a central control system for real-time encrypted storage, generate urban monitoring storage data, so that data can be stored safely; perform multi-dimensional index structure evaluation on urban monitoring storage data to obtain multi-dimensional index evaluation data, and ensure rapid retrieval of data; perform monitoring core database construction according to multi-dimensional index evaluation data to obtain urban planning monitoring core database, so as to perform urban planning monitoring operations. Therefore, the present invention improves the prediction ability of multi-source urban data potential problems through Internet of Things technology, data analysis technology, image processing technology and predictive analysis technology, and accelerates the data retrieval speed of the core database.

[0006] Preferably, step S2 comprises the following steps: Step S21: performing image spectrum analysis on the real-time traffic flow monitoring video to generate spectrum component data; Step S22: spectrally sampling the spectral component data to generate a spectral sampling matrix; Step S23: performing visual aberration flattening on the real-time traffic flow monitoring video according to the spectral sampling matrix to generate a spectrally guided flat layer image; Step S24: performing spectral separation on the spectral guided planar image to generate image spectral data; Step S25: performing image enhancement processing on the real-time traffic flow monitoring video according to the image spectrum data to generate an enhanced image; Step S26: reconstruct the enhanced image to generate a real-time traffic flow monitoring enhanced video; extract video frame features from the real-time traffic flow monitoring enhanced video to construct a real-time monitoring image matrix.

[0007] The present invention performs image spectral analysis on real-time traffic flow monitoring video to generate spectral component data, and provides detailed spectral information for subsequent image processing; performs spectral sampling on the spectral component data to generate a spectral sampling matrix; performs visual aberration flattening on the real-time traffic flow monitoring video according to the spectral sampling matrix to generate a spectral guided flat layer image, which can improve image quality and enable subsequent segmentation processing; performs spectral separation on the spectral guided flat layer image to generate image spectral data, which can further refine the spectral information in the image and provide a basis for image enhancement processing; performs image enhancement processing on the real-time traffic flow monitoring video according to the image spectral data to generate an enhanced image, so that the traffic flow information in the image is clearer and more visible; performs video reconstruction on the enhanced image to generate a real-time traffic flow monitoring enhanced video; and performs video frame feature extraction on the real-time traffic flow monitoring enhanced video, thereby constructing a real-time monitoring image matrix, which can provide structured data support for subsequent traffic flow analysis and monitoring.

[0008] Preferably, step S3 comprises the following steps: Step S31: performing road feature recognition on multi-source city data to generate road feature data; performing geographic feature recognition on multi-source city data to generate geographic feature data; Step S32: Integrate the road feature data and the geographic feature data to construct a three-dimensional map to obtain a three-dimensional city map; perform graph analysis on the three-dimensional city map to obtain a network layout diagram; Step S33: extracting the connection attributes and layout mode of the network layout diagram to obtain map network layout data; performing road network topology analysis on the map network layout data to obtain road network topology structure data; Step S34: performing vehicle pattern recognition on the real-time monitoring image matrix through the road network topology data to obtain vehicle pattern data; performing behavior analysis on the vehicle pattern data to obtain vehicle dynamic traffic information; performing real-time trajectory tracking on the vehicle dynamic traffic information to generate vehicle driving trajectory data; Step S35: performing spatiotemporal correlation analysis on the vehicle driving trajectory data to obtain spatiotemporal vehicle traffic data; performing road traffic monitoring on the real-time monitoring image matrix according to the spatiotemporal vehicle traffic data to obtain road traffic data; Step S36: Identify green space areas on the three-dimensional city map and generate green space area data; perform impact monitoring and analysis on the green space area data based on the road network topology data to obtain road green space monitoring data; perform ecological climate analysis on the road green space monitoring data to obtain green space ecological climate data.

[0009] The present invention can provide a data basis for the digital representation of urban roads and geographical environment by identifying roads and geographical features in multi-source urban data, generating road feature data and geographical feature data; integrating road feature data and geographical feature data and constructing a three-dimensional city map; performing graph theory analysis on the three-dimensional city map to obtain a network layout diagram, so as to better understand the structure and function of the urban road network; extracting connection attributes and layout methods from the network layout diagram to generate map network layout data; and performing road network topology analysis on the map network layout data to obtain road network topology structure data, so as to provide an accurate topology structure for urban traffic flow analysis. The road network topology data is used to perform vehicle pattern recognition on the real-time monitoring image matrix to obtain vehicle pattern data; behavioral analysis is performed on the vehicle pattern data to enhance the real-time monitoring and understanding of urban traffic flow, and to grasp the dynamic traffic information of vehicles; real-time trajectory tracking of vehicle dynamic traffic information is performed to generate vehicle driving trajectory data to ensure real-time tracking of vehicle trajectories; real-time trajectory tracking of vehicle dynamic traffic information is performed to generate vehicle driving trajectory data, and vehicle spatiotemporal traffic data is obtained through spatiotemporal correlation analysis to provide data support for in-depth analysis and prediction of traffic flow; green space areas are identified on three-dimensional city maps to generate green space area data; and impact monitoring and analysis are performed in combination with road network topology data to obtain road green space monitoring data; further, green space eco-climate data is obtained through eco-climate analysis, which is helpful to evaluate the contribution of urban green space to the ecological environment.

[0010] Preferably, step S36 includes the following steps: Step S361: performing visual processing on the three-dimensional city map to obtain city map visual data; performing green space area recognition on the city map visual data to generate green space area data; Step S362: Perform multi-parameter environmental sensing on the green space area data to obtain green space environmental status data; perform impact monitoring on the green space environmental status data according to the road network topology data to obtain road green space monitoring data; Step S363: performing time series analysis on the road green space monitoring data to obtain green space change trend data; performing spatial distribution pattern recognition on the green space change trend data to obtain green space spatial distribution feature data; Step S364: Performing ecological effect analysis on the green space spatial distribution characteristic data to obtain green space ecological contribution data; performing urban heat island effect correlation on the green space ecological contribution data to obtain green space cooling effect data; Step S365: Perform urban ecological climate monitoring on the road green space monitoring data according to the green space cooling effect data to obtain green space ecological climate data.

[0011] The present invention performs visual processing on a three-dimensional city map to obtain city map visual data, and then identifies green space area data, providing an accurate information basis for the extraction and analysis of green space features; performs multi-parameter environmental sensing on the green space area data to obtain green space environmental status data; simultaneously, performs impact monitoring on the green space environmental status data in combination with the road network topological structure data to obtain road green space monitoring data, which can reflect the impact of green space on the surrounding environment; performs time series analysis on the road green space monitoring data to obtain green space change trend data, and displays the trend of green space change; performs spatial distribution pattern recognition on the green space change trend data , obtaining green space distribution characteristic data can enhance the understanding of green space change dynamics and spatial pattern; conducting ecological effect analysis on green space spatial distribution characteristic data, obtaining green space ecological contribution data, reflecting the contribution of green space to urban ecology; further, correlating green space ecological contribution data with urban heat island effect, obtaining green space cooling effect data, providing a quantitative assessment of the role of urban green space in alleviating urban heat island effect; conducting urban ecological climate monitoring on road green space monitoring data based on green space cooling effect data, obtaining green space ecological climate data, which helps to optimize urban green space planning and enhance urban ecological climate regulation function.

[0012] Preferably, step S4 comprises the following steps: Step S41: extracting public facility information from multi-source city data to obtain public facility data; performing facility layout identification on the public facility data to obtain public facility layout information; Step S42: simulating the flow distribution of the public facility layout information to obtain a flow pressure map of the public facility; identifying the usage of the flow pressure map of the public facility to obtain the usage data of the public facility; Step S43: Perform frequency time series analysis on the public facility usage data to obtain usage trend peak period data; Step S44: Based on the usage trend peak period data, the multi-source city data is subjected to urban construction area structure identification to generate urban construction area structure data; the urban construction area structure data is subjected to functional area matching to obtain urban construction area function distribution data; Step S45: Integrate the urban construction area structure data and the urban construction area function distribution data to generate urban construction area data.

[0013] The present invention obtains public facility data by extracting public facility information from multi-source urban data; performs facility layout identification on public facility data to obtain public facility layout information, providing a detailed view for understanding the spatial distribution of urban public facilities; performs flow distribution simulation on public facility layout information to obtain a public facility flow pressure map to clarify the flow pressure situation of public facilities; performs usage identification on the public facility flow pressure map to obtain public facility usage data, which can clarify the usage efficiency and demand pressure of public facilities; performs frequency time series analysis on public facility usage data to obtain usage trend peak period data, providing a data basis for predicting the usage pattern of public facilities and optimizing resource allocation; performs urban construction area structure identification on multi-source urban data based on usage trend peak period data to obtain urban construction area structure data, clarifying the structural characteristics of the urban construction area; and performs functional area matching on the urban construction area structure data to obtain urban construction area function distribution data, revealing the intrinsic connection between urban construction structure and function; integrates urban construction area structure data and urban construction area function distribution data to generate urban construction area data; and can provide a comprehensive data view.

[0014] Preferably, step S5 comprises the following steps: Step S51: extracting environmental characteristics of multi-source city data to obtain environmental characteristic data; performing environmental quality monitoring on the environmental characteristic data to generate environmental quality data; performing environmental pressure analysis on the environmental quality data to obtain environmental pressure index data; Step S52: Adaptively divide the environmental pressure index data by thresholds to obtain environmental sensitive area data; perform spatial distribution clustering on the environmental sensitive area data by urban construction area data to obtain environmental risk area data; Step S53: Performing a multi-temporal comparative analysis on the urban construction area data according to the environmental risk area data to obtain environmental change trend data; Step S54: Performing pattern recognition on the environmental change trend data to obtain environmental evolution law data; performing environmental load analysis on the environmental evolution law data through the urban construction area data to obtain urban construction environmental load data; Step S55: Perform key impact registration on the environmental quality data according to the urban construction environmental load data to obtain urban construction environmental impact data.

[0015] The present invention obtains environmental characteristic data by extracting environmental characteristics from multi-source urban data, thereby reflecting the environmental characteristics of the city; performs environmental quality monitoring on the environmental characteristic data to generate environmental quality data; performs environmental pressure analysis on the environmental quality data to obtain environmental pressure index data, thereby identifying potential environmental pressure points in the city; performs adaptive threshold division on the environmental pressure index data to obtain environmental sensitive area data, thereby clarifying the sensitive area information of the environment; performs spatial distribution clustering on environmental sensitive area data through urban construction area data to obtain environmental risk area data, thereby providing accurate risk area positioning for environmental management; performs multi-time relative comparison analysis on urban construction area data according to environmental risk area data to obtain environmental change trend data, thereby better monitoring and understanding the evolution of the urban environment over time; performs pattern recognition on the environmental change trend data to obtain environmental evolution law data, thereby reflecting the evolution law of the urban environment; performs environmental load analysis on the environmental evolution law data through urban construction area data to obtain urban construction environmental load data, thereby providing a quantitative basis for the impact of urban construction activities on the environment; performs key impact registration on environmental quality data according to urban construction environmental load data to obtain urban construction environmental impact data, thereby facilitating the subsequent formulation of targeted environmental improvement measures.

[0016] Preferably, step S6 comprises the following steps: Step S61: constructing a city prediction model based on road traffic data, green space ecological climate data and urban construction environmental impact data to obtain a city prediction pre-model; Step S62: using road traffic data, green space ecological climate data and urban construction environmental impact data to train the city prediction model, and obtain a city prediction training model; Step S63: performing a model cross-validation evaluation on the city prediction training model to obtain city prediction model evaluation data; adjusting model parameters of the city prediction training model using the city prediction model evaluation data to obtain a city prediction model; Step S64: Predict potential problems of multi-source city data according to the city prediction model to obtain city potential problem data.

[0017] The present invention constructs a city prediction pre-model by combining road traffic data, green space ecological climate data and urban construction environmental impact data, thereby providing a preliminary analysis framework for simulating future development trends of cities; the city prediction pre-model is trained using road traffic data, green space ecological climate data and urban construction environmental impact data to obtain a city prediction training model, thereby enhancing the model's learning and adaptability to the behavior of complex urban systems; the city prediction training model is cross-validated and evaluated to obtain city prediction model evaluation data; and accordingly, the parameters of the city prediction training model are adjusted to finally obtain a city prediction model, thereby ensuring the accuracy and reliability of the model; potential problems are predicted for multi-source urban data according to the city prediction model to obtain city potential problem data, thereby providing predictive problem diagnosis and decision support for city managers.

[0018] Preferably, step S64 includes the following steps: Step S641: Performing high-frequency traffic anomaly detection on road traffic data based on the city prediction model to obtain city traffic bottleneck prediction data; Step S642: performing microclimate variation analysis on green space eco-climate data based on the urban prediction model to obtain prediction data on the weakening of green space cooling effect; Step S643: performing building density trend analysis on the urban construction environment impact data based on the urban prediction model to obtain ventilation corridor obstruction prediction data; Step S644: thermal comfort impact assessment is performed on the prediction data of the weakened cooling effect of green space to obtain urban thermal environment risk area data; air circulation simulation is performed on the prediction data of the obstructed ventilation corridor according to the urban thermal environment risk area data to obtain urban air quality degradation prediction data; Step S645: Integrate the city traffic bottleneck prediction data, the city thermal environment risk area data and the city air quality degradation prediction data to obtain the city potential problem data.

[0019] The present invention uses an urban prediction model to detect abnormal flow in high-frequency time periods on road traffic data to obtain urban traffic bottleneck prediction data, which can identify and alleviate traffic congestion problems in advance; based on the urban prediction model, microclimate variation analysis is performed on green space ecological climate data, so as to effectively monitor the ecological service of urban green space, obtain green space cooling effect weakening prediction data, and predict the weakening of green space cooling effect; based on the urban prediction model, building density trend analysis is performed on urban construction environment impact data to obtain ventilation corridor obstruction prediction data, so as to provide measures for improving urban ventilation conditions; thermal comfort impact assessment is performed on green space cooling effect weakening prediction data to obtain urban thermal environment risk area data, and the urban thermal environment risk area is clarified; at the same time, air circulation simulation is performed on ventilation corridor obstruction prediction data according to urban thermal environment risk area data to obtain urban air quality decline prediction data, which provides a scientific basis for the prevention of urban heat island effect and air quality problems; urban traffic bottleneck prediction data, urban thermal environment risk area data and urban air quality decline prediction data are integrated to obtain urban potential problem data, so as to provide comprehensive problem diagnosis and decision support for urban management.

[0020] Preferably, step S7 comprises the following steps: Step S71: using transmission acceleration technology to transmit road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data in parallel, to obtain urban monitoring parallel transmission data; Step S72: transmitting the city monitoring parallel transmission data to the central control system to obtain the city monitoring transmission data; performing sensitivity identification on the city monitoring transmission data to obtain the city monitoring transmission sensitivity data; Step S73: encrypting the city monitoring transmission sensitivity data in real time to generate encrypted metadata; encrypting and storing the city monitoring transmission data through the encrypted metadata to generate city monitoring storage data; Step S74: performing a multi-dimensional index structure evaluation on the city monitoring storage data to obtain multi-dimensional index evaluation data; Step S75: Construct a monitoring core database based on the multi-dimensional index evaluation data to obtain a city planning monitoring core database to perform city planning monitoring operations.

[0021] The present invention utilizes transmission acceleration technology to transmit road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data in parallel, thereby improving the efficiency of data transmission and ensuring that a large amount of urban monitoring data can be quickly processed and analyzed; the urban monitoring parallel transmission data is transmitted to a central control system to realize centralized data management; the sensitivity of urban monitoring transmission data is identified to enhance the awareness of data importance and provide priority guidance for subsequent data processing; the urban monitoring transmission sensitivity data is encrypted in real time to generate encrypted metadata; and the encrypted metadata is used to encrypt and store the urban monitoring transmission data to ensure data security and confidentiality and prevent unauthorized access and data leakage; a multi-dimensional index structure evaluation is performed on the urban monitoring storage data to optimize the data storage structure and improve the efficiency of data retrieval, so that urban planning monitoring operations can be carried out more efficiently; an urban planning monitoring core database is constructed based on the multi-dimensional index evaluation data, providing a unified and structured data management platform for urban planning monitoring operations and supporting urban planning decision-making and monitoring analysis.

[0022] Preferably, step S74 includes the following steps: Step S741: performing high-dimensional space mapping on the urban monitoring storage data to generate high-dimensional mapping storage data; performing multi-dimensional attribute analysis on the high-dimensional mapping storage data to obtain multi-dimensional attribute storage data; Step S742: constructing a hierarchical index for the multi-dimensional attribute storage data to obtain a hierarchical index structure; performing dynamic load balancing processing on the hierarchical index structure to obtain a dynamic load balancing index; Step S743: performing access pattern recognition on the dynamic load balancing index to obtain an access pattern recognition result; performing predictive index pre-construction on the access pattern recognition result to obtain a predictive index structure; Step S744: monitor the performance of the predictive index structure to obtain index performance monitoring data; evaluate the index structure of the city monitoring storage data based on the index performance monitoring data to obtain multi-dimensional index evaluation data.

[0023] The present invention performs high-dimensional spatial mapping on urban monitoring storage data to generate high-dimensional mapping storage data; then obtains multidimensional attribute storage data through multidimensional attribute analysis, enhances the organizational structure of the data, and can cope with complex query requirements; constructs hierarchical indexes on the multidimensional attribute storage data to obtain a hierarchical index structure; optimizes data access efficiency and resource allocation through dynamic load balancing processing; performs access pattern recognition on the dynamic load balancing index to obtain access pattern recognition results; performs predictive index pre-construction on the access pattern recognition results to obtain a predictive index structure, which can be adaptively adjusted according to changes in the access pattern, thereby improving the response speed of data retrieval; performs performance monitoring on the predictive index structure to obtain index performance monitoring data; uses the index performance monitoring data to perform index structure evaluation on urban monitoring storage data to obtain multidimensional index evaluation data, which helps to continuously optimize index performance.

[0024] The present invention uses sensors and drone camera technology to collect multi-source data in the city, realize real-time monitoring of urban traffic flow, and provide basic information for data analysis. Spectral separation and image enhancement technology are implemented on traffic monitoring videos to generate detailed image spectrum data and images with improved quality, and a real-time monitoring image matrix is ​​further constructed, thereby enhancing the ability to capture traffic flow details and providing a solid data foundation for in-depth analysis of traffic conditions. By extracting city map features, a three-dimensional city map is constructed, and the road network topology structure analysis is performed on the three-dimensional city map to obtain road network topology structure data; the road network topology structure data is used to monitor the road traffic status of the real-time monitoring image matrix, providing accurate spatial information for road monitoring; in addition, the urban green space area is identified and the ecological climate analysis is performed to obtain green space ecological climate data, providing a scientific basis for the protection of urban greening and ecological environment. The use of public facility information is analyzed to obtain public facility use data, which provides data support for evaluating the use efficiency of public facilities and future urban planning; at the same time, the environmental characteristic data is monitored and analyzed to obtain environmental quality data; and combined with the urban construction area data, the urban construction environmental impact data is obtained, which can evaluate the impact of urban construction on environmental quality. Further, by integrating road traffic data, green space ecological climate data and urban construction environmental impact data, a city prediction model is constructed; and potential problems of urban data are predicted to obtain urban potential problem data, providing predictive solutions for urban management; road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data are safely transmitted to the central control system, and real-time encrypted storage is performed to obtain urban monitoring storage data, providing data support for building a monitoring core database; multi-dimensional index structure evaluation is performed on urban monitoring storage data to obtain multi-dimensional index evaluation data, which can ensure efficient retrieval of data; based on multi-dimensional index evaluation data, an urban planning monitoring core database is constructed, thereby achieving efficient execution of urban planning monitoring operations. Therefore, the present invention improves the prediction ability of potential problems of multi-source urban data through Internet of Things technology, data analysis technology, image processing technology and predictive analysis technology, and accelerates the data retrieval speed of the core database. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the steps of a method for building a core database for urban planning monitoring; Figure 2 for Figure 1 Detailed implementation steps of step S6 in the flowchart; Figure 3 for Figure 2 Detailed implementation steps of step S64 are shown in the flowchart; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0026] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0028] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0029] To achieve this, please refer to Figures 1 to 3 , a method for constructing a core database for urban planning monitoring, the method comprising the following steps: Step S1: Use sensors to obtain multi-source city data; use drones and cameras to collect real-time traffic flow and obtain real-time traffic flow monitoring videos; Step S2: performing spectral separation on the real-time traffic flow monitoring video to generate image spectral data; performing image enhancement processing on the real-time traffic flow monitoring video according to the image spectral data to obtain an enhanced image; performing video reconstruction on the enhanced image to generate a real-time traffic flow monitoring enhanced video; performing video frame feature extraction on the real-time traffic flow monitoring enhanced video to construct a real-time monitoring image matrix; Step S3: extracting map features of multi-source city data to obtain a three-dimensional city map; performing a road network topology structure analysis on the three-dimensional city map to generate road network topology structure data; performing road traffic monitoring on the real-time monitoring image matrix through the road network topology structure data to obtain road traffic data; performing green space area identification on the three-dimensional city map to generate green space area data; performing an impact monitoring and analysis on the green space area data according to the road network topology structure data to obtain road green space monitoring data; performing an ecological climate analysis on the road green space monitoring data to obtain green space ecological climate data; Step S4: extracting public facility information from multi-source city data to obtain public facility data; analyzing the usage of the public facility data to obtain public facility usage data; identifying urban construction areas for the multi-source city data based on the public facility usage data to generate urban construction area data; Step S5: extracting environmental characteristics of multi-source urban data to obtain environmental characteristic data; performing environmental quality monitoring on the environmental characteristic data to generate environmental quality data; performing key impact registration on the environmental quality data through urban construction area data to obtain urban construction environmental impact data; Step S6: constructing a city prediction model based on the road traffic data, the green space ecological climate data and the urban construction environmental impact data to obtain the city prediction model; predicting potential problems of multi-source city data based on the city prediction model to obtain city potential problem data; Step S7: Transmit road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data to the central control system for real-time encrypted storage to generate urban monitoring storage data; perform multi-dimensional index structure evaluation on the urban monitoring storage data to obtain multi-dimensional index evaluation data; construct a monitoring core database based on the multi-dimensional index evaluation data to obtain an urban planning monitoring core database to perform urban planning monitoring operations.

[0030] The present invention realizes real-time monitoring of urban traffic flow through multi-source urban data collected by sensors and drone cameras, providing data support for subsequent data analysis; performs spectral separation and image enhancement processing on real-time traffic flow monitoring videos to generate image spectral data and enhanced images, and then constructs a real-time monitoring image matrix to enhance the ability to capture traffic flow details, laying the foundation for in-depth analysis of traffic conditions. By extracting urban map features and constructing a three-dimensional urban map; performing road network topology structure analysis on the map to obtain road network topology structure data; performing road traffic monitoring on the real-time monitoring image matrix through the road network topology structure data to obtain road traffic data, which can provide accurate spatial information for road traffic monitoring; at the same time, the identification and ecological climate analysis of green space areas obtain green space ecological climate data, providing scientific data basis for urban greening and ecological environment protection; by extracting public facility information and analyzing its usage, public facility usage data is obtained to ensure the subsequent reasonable evaluation of the effectiveness of public facilities and planning future urban infrastructure construction. Monitor and analyze environmental characteristic data to obtain environmental quality data; and perform key impact registration on environmental quality data through urban construction area data to obtain urban construction environmental impact data, so as to evaluate the impact of urban construction activities on environmental quality; combine road traffic data, green space ecological climate data and urban construction environmental impact data to build an urban prediction model; and predict potential problems of multi-source urban data to obtain urban potential problem data, so as to provide predictive problem solutions for urban managers; transmit road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data to a central control system for real-time encrypted storage, generate urban monitoring storage data, so that data can be stored safely; perform multi-dimensional index structure evaluation on urban monitoring storage data to obtain multi-dimensional index evaluation data, and ensure rapid retrieval of data; perform monitoring core database construction according to multi-dimensional index evaluation data to obtain urban planning monitoring core database, so as to perform urban planning monitoring operations. Therefore, the present invention improves the prediction ability of multi-source urban data potential problems through Internet of Things technology, data analysis technology, image processing technology and predictive analysis technology, and accelerates the data retrieval speed of the core database.

[0031] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a method for constructing a core database for urban planning monitoring of the present invention. In this example, the method for constructing a core database for urban planning monitoring includes the following steps: Step S1: Use sensors to obtain multi-source city data; use drones and cameras to collect real-time traffic flow and obtain real-time traffic flow monitoring videos; In the embodiment of the present invention, a series of sensors are first deployed at key locations in the city, including traffic flow detectors, environmental monitoring sensors, and building sensors, which are responsible for collecting data on the city's operating status in real time, and obtaining multi-source city data. A drone equipped with a high-definition camera is used to cruise over the city according to a preset flight route and schedule to capture real-time images of traffic flow, wherein the camera is equipped with an image acquisition system that can adapt to different lighting conditions and perform high-resolution video recording, and finally obtains a real-time traffic flow monitoring video.

[0032] Step S2: performing spectral separation on the real-time traffic flow monitoring video to generate image spectral data; performing image enhancement processing on the real-time traffic flow monitoring video according to the image spectral data to obtain an enhanced image; performing video reconstruction on the enhanced image to generate a real-time traffic flow monitoring enhanced video; performing video frame feature extraction on the real-time traffic flow monitoring enhanced video to construct a real-time monitoring image matrix; In an embodiment of the present invention, a spectral separation technology is implemented on a real-time traffic flow monitoring video to decompose a video signal into different spectral components, thereby generating image spectral data; the image spectral data is used to enhance the real-time traffic flow monitoring video, and the visibility of vehicles and road signs in the image is improved by adjusting the brightness, contrast and color saturation parameters of the image to generate an enhanced image; video reconstruction is performed on the enhanced image sequence, and continuous image frames are recombined in a time series to form a new, higher-quality real-time traffic flow monitoring enhanced video; feature extraction is performed on each frame of the real-time traffic flow monitoring enhanced video, and an image processing algorithm is used to identify and mark key elements of vehicles, pedestrians and traffic signals, and a real-time monitoring image matrix is ​​constructed, wherein the real-time monitoring image matrix contains the spatial position and time dynamic information of the key elements in the video.

[0033] Step S3: extracting map features of multi-source city data to obtain a three-dimensional city map; performing a road network topology structure analysis on the three-dimensional city map to generate road network topology structure data; performing road traffic monitoring on the real-time monitoring image matrix through the road network topology structure data to obtain road traffic data; performing green space area identification on the three-dimensional city map to generate green space area data; performing an impact monitoring and analysis on the green space area data according to the road network topology structure data to obtain road green space monitoring data; performing an ecological climate analysis on the road green space monitoring data to obtain green space ecological climate data; In the embodiment of the present invention, the multi-source city data is processed by using geographic information system (GIS) technology to extract the characteristic information of the city map, wherein the characteristic information includes roads, buildings and urban construction areas, so as to generate an accurate three-dimensional city map, which shows the spatial layout and terrain characteristics of the city; the road network in the three-dimensional city map is subjected to topological structure analysis, the connection relationship between roads and the hierarchy of the network are identified, and the road network topological structure data is generated. Through image recognition and pattern matching technology, the road network topological structure data is used, and the road traffic conditions, including vehicle flow, speed and congestion conditions, are monitored in combination with the real-time monitoring image matrix, so as to obtain road traffic data; further, the three-dimensional city map is subjected to image processing to identify and distinguish the green space areas of the city, and the green space area data is generated; the green space area data is subjected to impact monitoring and analysis through the road network topological structure data, and the impact of the green space on the surrounding road environment is evaluated, such as the improvement of air quality and microclimate, so as to obtain road green space monitoring data; the road green space monitoring data is subjected to ecological climate analysis, and the principles of ecology and climatology are used to evaluate the contribution of the green space to the urban ecology and climate conditions, so as to obtain green space ecological climate data.

[0034] Step S4: extracting public facility information from multi-source city data to obtain public facility data; analyzing the usage of the public facility data to obtain public facility usage data; identifying urban construction areas for the multi-source city data based on the public facility usage data to generate urban construction area data; In the embodiment of the present invention, public facility information is extracted from multi-source urban data by data mining technology, and this information includes the location and attributes of medical facilities, educational institutions and transportation stations, thereby obtaining public facility data. Then, the use of these public facility data is analyzed by statistical analysis methods, and the use frequency and efficiency of each facility are evaluated to obtain public facility use data; spatial analysis technology is used to identify urban construction areas of multi-source urban data in combination with public facility use data, specifically, the division of urban structure and functional areas is carried out to identify different urban construction and management areas, and obtain urban construction area data.

[0035] Step S5: extracting environmental characteristics of multi-source urban data to obtain environmental characteristic data; performing environmental quality monitoring on the environmental characteristic data to generate environmental quality data; performing key impact registration on the environmental quality data through urban construction area data to obtain urban construction environmental impact data; In an embodiment of the present invention, data extraction technology is used to identify and extract environmental characteristics from a multi-source urban data set, wherein the environmental characteristics cover multiple characteristics such as air quality, water quality and noise level, thereby obtaining environmental characteristic data; the environmental characteristic data is monitored and analyzed in real time through environmental monitoring technology, the current state of the urban environment is evaluated, and environmental quality data is generated; geographic information system (GIS) and spatial analysis technology are used, and key impact alignment is performed on the environmental quality data in combination with urban construction area data. Specifically, key impact alignment is performed on the environmental quality data using the urban construction area data to identify the specific impact of urban construction activities on environmental quality, and urban construction environmental impact data is obtained.

[0036] Step S6: constructing a city prediction model based on the road traffic data, the green space ecological climate data and the urban construction environmental impact data to obtain the city prediction model; predicting potential problems of multi-source city data based on the city prediction model to obtain city potential problem data; In the embodiment of the present invention, data integration technology is used to comprehensively analyze road traffic data, green space ecological climate data, and urban construction environmental impact data, and deep learning technology is used to build an urban prediction model; the urban prediction model is used to conduct in-depth analysis of multi-source urban data, and pattern recognition and prediction algorithms are used to predict potential problems in urban operations, so as to identify potential factors that lead to reduced urban operating efficiency or decreased environmental quality, thereby obtaining urban potential problem data.

[0037] Step S7: Transmit road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data to the central control system for real-time encrypted storage to generate urban monitoring storage data; perform multi-dimensional index structure evaluation on the urban monitoring storage data to obtain multi-dimensional index evaluation data; construct a monitoring core database based on the multi-dimensional index evaluation data to obtain an urban planning monitoring core database to perform urban planning monitoring operations.

[0038] In the embodiment of the present invention, high-speed data transmission technology is used to summarize road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data, and securely transmit them to the central control system; and data encryption technology is implemented in the central control system to perform real-time encryption processing on the transmitted data to generate urban monitoring and storage data; a multi-dimensional index structure evaluation is performed on the urban monitoring and storage data, specifically, an efficient index structure is established for the urban monitoring and storage data using index technology, the data is quickly located and accessed, and multi-dimensional index evaluation data is obtained; based on the multi-dimensional index evaluation data, a monitoring core database is constructed to form a comprehensive urban planning monitoring core database, which provides a centralized and efficient data monitoring management platform for urban planning monitoring operations.

[0039] Preferably, step S2 comprises the following steps: Step S21: performing image spectrum analysis on the real-time traffic flow monitoring video to generate spectrum component data; Step S22: spectrally sampling the spectral component data to generate a spectral sampling matrix; Step S23: performing visual aberration flattening on the real-time traffic flow monitoring video according to the spectral sampling matrix to generate a spectrally guided flat layer image; Step S24: performing spectral separation on the spectral guided planar image to generate image spectral data; Step S25: performing image enhancement processing on the real-time traffic flow monitoring video according to the image spectrum data to generate an enhanced image; Step S26: reconstruct the enhanced image to generate a real-time traffic flow monitoring enhanced video; extract video frame features from the real-time traffic flow monitoring enhanced video to construct a real-time monitoring image matrix.

[0040] In an embodiment of the present invention, image spectral analysis technology is used to process real-time traffic flow monitoring video. Specifically, the spectral information of each pixel in the video frame is captured and analyzed, and the video content is decomposed into different spectral components to generate spectral component data; a spectral sampling technology is performed on the spectral component data to construct a spectral sampling matrix by selecting representative spectral samples; a visual aberration flattening technology is performed on the real-time traffic flow monitoring video using the spectral sampling matrix to eliminate noise and distortion in the image and generate a spectral guided flat layer image; a spectral separation technology is applied to the spectral guided flat layer image to separate different spectral components in the image to obtain image spectral data; image enhancement processing is performed on the real-time traffic flow monitoring video according to the image spectral data, and an enhanced image is generated by adjusting the contrast, brightness and color parameters of the image; a video reconstruction technology is performed on the enhanced image to recombine continuous image frames into a smooth video to obtain a real-time traffic flow monitoring enhanced video; at the same time, a video frame feature extraction technology is performed on the enhanced video to identify and mark key elements in the video to construct a real-time monitoring image matrix.

[0041] Preferably, step S3 comprises the following steps: Step S31: performing road feature recognition on multi-source city data to generate road feature data; performing geographic feature recognition on multi-source city data to generate geographic feature data; Step S32: Integrate the road feature data and the geographic feature data to construct a three-dimensional map to obtain a three-dimensional city map; perform graph analysis on the three-dimensional city map to obtain a network layout diagram; Step S33: extracting the connection attributes and layout mode of the network layout diagram to obtain map network layout data; performing road network topology analysis on the map network layout data to obtain road network topology structure data; Step S34: performing vehicle pattern recognition on the real-time monitoring image matrix through the road network topology data to obtain vehicle pattern data; performing behavior analysis on the vehicle pattern data to obtain vehicle dynamic traffic information; performing real-time trajectory tracking on the vehicle dynamic traffic information to generate vehicle driving trajectory data; Step S35: performing spatiotemporal correlation analysis on the vehicle driving trajectory data to obtain spatiotemporal vehicle traffic data; performing road traffic monitoring on the real-time monitoring image matrix according to the spatiotemporal vehicle traffic data to obtain road traffic data; Step S36: Identify green space areas on the three-dimensional city map and generate green space area data; perform impact monitoring and analysis on the green space area data based on the road network topology data to obtain road green space monitoring data; perform ecological climate analysis on the road green space monitoring data to obtain green space ecological climate data.

[0042] In an embodiment of the present invention, an automated feature extraction technology is used to analyze multi-source urban data to identify road features in the city, including the length, width and direction attributes of the roads, and obtain road feature data; at the same time, a geographic information recognition technology is applied to the multi-source urban data to extract geographic features, such as terrain and landform features, and obtain geographic feature data; the road feature data and the geographic feature data are combined, and a three-dimensional city map is constructed using three-dimensional modeling technology; a graph theory analysis is performed on the three-dimensional city map to identify key nodes and connection paths in the map and generate a network layout diagram; connection attributes and layout methods are extracted from the network layout diagram to form map network layout data; a road network topology analysis is performed on the map network layout data to determine the structure and function of the road network, thereby obtaining road network topology structure data. The real-time monitoring image matrix is ​​analyzed using the road network topology data, and the pattern recognition technology is used to identify the vehicle pattern in the image to obtain vehicle pattern data; the vehicle pattern data is subjected to behavioral analysis to extract the dynamic traffic information of the vehicle, and the vehicle driving trajectory data is generated through real-time trajectory tracking technology; the vehicle driving trajectory data is subjected to spatiotemporal correlation analysis to integrate time and space information to obtain vehicle spatiotemporal traffic data; the real-time monitoring image matrix is ​​analyzed again using the vehicle spatiotemporal traffic data to further monitor the traffic conditions of the road and obtain road traffic data; the green space area is identified on the three-dimensional city map, the green space areas in the city are marked, and the green space area data is generated; the green space area data is subjected to impact monitoring analysis in combination with the road network topology data to obtain road green space monitoring data; the road green space monitoring data is subjected to ecological climate analysis to evaluate the impact of green space on urban ecology and climate to obtain green space ecological climate data.

[0043] Preferably, step S36 includes the following steps: Step S361: performing visual processing on the three-dimensional city map to obtain city map visual data; performing green space area recognition on the city map visual data to generate green space area data; Step S362: Perform multi-parameter environmental sensing on the green space area data to obtain green space environmental status data; perform impact monitoring on the green space environmental status data according to the road network topology data to obtain road green space monitoring data; Step S363: performing time series analysis on the road green space monitoring data to obtain green space change trend data; performing spatial distribution pattern recognition on the green space change trend data to obtain green space spatial distribution feature data; Step S364: Performing ecological effect analysis on the green space spatial distribution characteristic data to obtain green space ecological contribution data; performing urban heat island effect correlation on the green space ecological contribution data to obtain green space cooling effect data; Step S365: Perform urban ecological climate monitoring on the road green space monitoring data according to the green space cooling effect data to obtain green space ecological climate data.

[0044] In an embodiment of the present invention, visual processing technology is implemented on a three-dimensional city map to enhance the visual effect and information readability of the map, thereby obtaining city map visual data; image recognition technology is used to analyze the city map visual data, accurately identify the green space areas in the map, and generate green space area data; multi-parameter environmental sensing technology is applied to the green space area data to monitor the environmental status of the green space area, including temperature, humidity and light parameters, to obtain green space environmental status data; the green space environmental status data is combined with road network topology data to perform impact monitoring on the green space environmental status data, evaluate the interaction between roads and green spaces, and obtain road green space monitoring data. Time series analysis is performed on the road green space monitoring data to track the changes in the green space area over time and obtain green space change trend data; through spatial distribution pattern recognition technology, the green space change trend data is analyzed to specifically reveal the distribution characteristics of green space in urban space and obtain green space spatial distribution characteristic data; ecological effect analysis is performed on the green space spatial distribution characteristic data to evaluate the contribution of green space to the urban ecosystem and obtain green space ecological contribution data; further, the green space ecological contribution data is associated with the urban heat island effect to analyze the role of green space in alleviating the urban heat island effect and obtain green space cooling effect data; based on the green space cooling effect data, urban ecological climate monitoring is performed on the road green space monitoring data to comprehensively evaluate the regulating effect of green space on urban microclimate and obtain green space ecological climate data.

[0045] Preferably, step S4 comprises the following steps: Step S41: extracting public facility information from multi-source city data to obtain public facility data; performing facility layout identification on the public facility data to obtain public facility layout information; Step S42: simulating the flow distribution of the public facility layout information to obtain a flow pressure map of the public facility; identifying the usage of the flow pressure map of the public facility to obtain the usage data of the public facility; Step S43: Perform frequency time series analysis on the public facility usage data to obtain usage trend peak period data; Step S44: Based on the usage trend peak period data, the multi-source city data is subjected to urban construction area structure identification to generate urban construction area structure data; the urban construction area structure data is subjected to functional area matching to obtain urban construction area function distribution data; Step S45: Integrate the urban construction area structure data and the urban construction area function distribution data to generate urban construction area data.

[0046] In the embodiment of the present invention, data extraction technology is used to analyze multi-source urban data to accurately capture relevant information of public facilities, including facility type, location and scale, so as to obtain public facility data; layout recognition technology is used to process public facility data to identify the geographical layout information of each public facility and obtain public facility layout information; simulation technology is used to simulate the flow distribution of public facility layout information to evaluate the pressure of pedestrian and vehicle flow at each facility in different time periods and obtain a public facility flow pressure map; usage recognition is performed on the public facility flow pressure map to identify the frequency of use and peak period of the facility and obtain the public facility usage data. The method is to perform frequency time series analysis on the public facility usage data, analyze the time pattern of facility usage, including daily usage trends and peak traffic in specific time periods, and obtain usage trend peak period data; based on usage trend peak period data, use structure recognition technology to identify multi-source urban data to identify the specific structural characteristics of urban construction areas, and obtain urban construction area structure data; use functional area division and matching technology to correspond urban construction area structure data with urban planning functional areas, and obtain urban construction area functional distribution data; through data integration technology, merge urban construction area structure data and urban construction area functional distribution data to generate comprehensive urban construction area data.

[0047] Preferably, step S5 comprises the following steps: Step S51: extracting environmental characteristics of multi-source city data to obtain environmental characteristic data; performing environmental quality monitoring on the environmental characteristic data to generate environmental quality data; performing environmental pressure analysis on the environmental quality data to obtain environmental pressure index data; Step S52: Adaptively divide the environmental pressure index data by thresholds to obtain environmental sensitive area data; perform spatial distribution clustering on the environmental sensitive area data by urban construction area data to obtain environmental risk area data; Step S53: Performing a multi-temporal comparative analysis on the urban construction area data according to the environmental risk area data to obtain environmental change trend data; Step S54: Performing pattern recognition on the environmental change trend data to obtain environmental evolution law data; performing environmental load analysis on the environmental evolution law data through the urban construction area data to obtain urban construction environmental load data; Step S55: Perform key impact registration on the environmental quality data according to the urban construction environmental load data to obtain urban construction environmental impact data.

[0048] In an embodiment of the present invention, by implementing data extraction technology, environmental characteristics, including air quality index, water quality parameters and noise level, are accurately captured from multi-source urban data to form environmental characteristic data; environmental quality monitoring is performed on the environmental characteristic data to generate environmental quality data; environmental pressure analysis technology is used to evaluate the environmental quality data to determine the environmental pressure level of different regions, thereby obtaining environmental pressure index data; adaptive threshold division technology is used to classify the environmental pressure index data, identify environmental sensitive areas, and form environmental sensitive area data; combined with urban construction area data, and using spatial distribution clustering technology, the environmental sensitive area data is spatially distributed clustered, and the environmental risk area situation is specifically identified to obtain environmental risk area data. Using multi-temporal relative analysis technology, the urban construction area data is compared with the environmental risk area data, the changes in environmental status over time are observed and analyzed, and environmental change trend data are obtained; pattern recognition technology is applied to the environmental change trend data to monitor the regularity of environmental evolution and obtain environmental evolution law data; in combination with the urban construction area data, the environmental load analysis technology is used to evaluate the environmental evolution law data, and the impact of urban construction activities on the environment is quantified to obtain urban construction environmental load data; using key impact registration technology, the environmental quality data is key-aligned according to the urban construction environmental load data, the key impacts of urban construction on the environment are specifically clarified, and urban construction environmental impact data are obtained.

[0049] As an example of the present invention, refer to Figure 2 As shown, in this example, step S6 includes: Step S61: constructing a city prediction model based on road traffic data, green space ecological climate data and urban construction environmental impact data to obtain a city prediction pre-model; Step S62: using road traffic data, green space ecological climate data and urban construction environmental impact data to train the city prediction model to obtain a city prediction training model; Step S63: performing a model cross-validation evaluation on the city prediction training model to obtain city prediction model evaluation data; adjusting model parameters of the city prediction training model using the city prediction model evaluation data to obtain a city prediction model; Step S64: Predict potential problems of multi-source city data according to the city prediction model to obtain city potential problem data.

[0050] In the embodiment of the present invention, deep learning technology is used to construct a city prediction model based on road traffic data, green space ecological climate data and urban construction environmental impact data. Specifically, the data is input into the LSTM network, wherein the LSTM network consists of three hidden layers, each layer contains 50 neurons, and these neurons can learn the long-term dependencies in the data, thereby obtaining a city prediction pre-model; the city prediction pre-model is trained using the road traffic data, green space ecological climate data and urban construction environmental impact data. During the training process, the model adjusts the weights and biases by minimizing the mean square error (MSE) loss function, the learning rate is set to 0.001, the batch size is 32, and the number of iterations is 1 000 times to obtain the urban prediction training model; the urban prediction training model is cross-validated and evaluated. The cross-validation evaluation adopts the 10-fold cross-validation method, that is, the data set is divided into 10 parts, the model is trained on 9 parts, and tested on 1 part. This process is repeated 10 times; then, the accuracy, recall rate and F1 score on each test set are evaluated to obtain the urban prediction model evaluation data; the model parameters of the urban prediction training model are adjusted through the urban prediction model evaluation data, such as increasing the number of neurons in the hidden layer to 100 and adjusting the learning rate to 0.0005; the output layer is set to predict key urban indicators in a certain period of time in the future, such as the traffic congestion index. According to the urban prediction model, multi-source urban data are predicted and analyzed to predict potential problems in the city, including traffic congestion, ecological climate and urban air quality, and obtain urban potential problem data.

[0051] As an example of the present invention, refer to Figure 3 As shown, in this example, step S64 includes: Step S641: Performing high-frequency traffic anomaly detection on road traffic data based on the city prediction model to obtain city traffic bottleneck prediction data; Step S642: performing microclimate variation analysis on green space eco-climate data based on the urban prediction model to obtain prediction data on the weakening of green space cooling effect; Step S643: performing building density trend analysis on the urban construction environment impact data based on the urban prediction model to obtain ventilation corridor obstruction prediction data; Step S644: thermal comfort impact assessment is performed on the prediction data of the weakened cooling effect of green space to obtain urban thermal environment risk area data; air circulation simulation is performed on the prediction data of the obstructed ventilation corridor according to the urban thermal environment risk area data to obtain urban air quality degradation prediction data; Step S645: Integrate the city traffic bottleneck prediction data, the city thermal environment risk area data and the city air quality degradation prediction data to obtain the city potential problem data.

[0052] In an embodiment of the present invention, an urban prediction model is applied to road traffic data to identify traffic anomalies in high-frequency periods. Time series analysis technology is combined to analyze hourly traffic data, and thresholds are set to define normal traffic and abnormal traffic. For example, when the traffic exceeds 120% of the historical average, it is marked as abnormal, thereby obtaining urban traffic bottleneck prediction data, which lists the specific time and location of future traffic congestion; based on the urban prediction model, microclimate variation analysis is performed on green space ecological climate data, such as temperature does not exceed 28 degrees Celsius, humidity is maintained between 40% and 60%, and wind speed is not less than 2 meters per second. When the predicted green space ecological climate data deviates from the set range, green space cooling effect weakening prediction data is generated; based on the urban prediction model, the impact of urban construction environment is analyzed. The building density trend analysis is conducted based on the impact data, and an upper threshold of the building density is set, such as no more than 5,000 building units per square kilometer. When the predicted building density exceeds this threshold, the ventilation corridor obstruction prediction data is generated; the thermal comfort impact assessment is conducted on the green space cooling effect weakening prediction data, including simulating the surface temperature and heat island effect under different green space coverage rates, and using thermal imaging data and surface temperature models to predict the urban thermal environment risk areas; at the same time, the ventilation corridor obstruction prediction data is simulated for air circulation in combination with the urban thermal environment risk area data, the trend of air quality decline is assessed, and the urban air quality decline prediction data is generated; the urban traffic bottleneck prediction data, the urban thermal environment risk area data and the urban air quality decline prediction data are integrated to obtain the city's potential problem data.

[0053] Preferably, step S7 comprises the following steps: Step S71: using transmission acceleration technology to transmit road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data in parallel, to obtain urban monitoring parallel transmission data; Step S72: transmitting the city monitoring parallel transmission data to the central control system to obtain the city monitoring transmission data; performing sensitivity identification on the city monitoring transmission data to obtain the city monitoring transmission sensitivity data; Step S73: encrypting the city monitoring transmission sensitivity data in real time to generate encrypted metadata; encrypting and storing the city monitoring transmission data through the encrypted metadata to generate city monitoring storage data; Step S74: performing a multi-dimensional index structure evaluation on the city monitoring storage data to obtain multi-dimensional index evaluation data; Step S75: Construct a monitoring core database based on the multi-dimensional index evaluation data to obtain a city planning monitoring core database to perform city planning monitoring operations.

[0054] In the embodiment of the present invention, high-speed data transmission technology, such as broadband network and wireless communication protocol, is used to transmit road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data in parallel. During the data transmission process, a specific data compression algorithm is used to reduce the file size and improve the transmission efficiency, so as to obtain urban monitoring parallel transmission data; the urban monitoring parallel transmission data is transmitted to the central control system, and the data classification algorithm is used to identify the sensitivity of the data, and the key information is marked to obtain the urban monitoring transmission sensitivity data. The urban monitoring transmission sensitivity data is encrypted in real time, and standard encryption technology, such as Advanced Encryption Standard (AES) 256-bit encryption, is used to generate encryption metadata; then the urban monitoring transmission data is encrypted and stored according to the encrypted metadata to generate urban monitoring storage data; a multi-dimensional index structure evaluation is performed on the urban monitoring storage data, including analyzing the access mode of the data, determining the most commonly used query type, and designing the index structure accordingly; specifically, a time-based index is created for the road traffic data, and a geographic location-based index is created for the green space ecological climate data, so as to obtain multi-dimensional index evaluation data; a monitoring core database is constructed according to the multi-dimensional index evaluation data, and the database design includes determining the table structure, index, view and storage procedure. For example, create a table to store road traffic data, which contains timestamp, traffic volume and speed fields, as well as corresponding indexes to speed up queries, and finally obtain the urban planning monitoring core database to perform urban planning monitoring tasks.

[0055] Preferably, step S74 includes the following steps: Step S741: performing high-dimensional space mapping on the urban monitoring storage data to generate high-dimensional mapping storage data; performing multi-dimensional attribute analysis on the high-dimensional mapping storage data to obtain multi-dimensional attribute storage data; Step S742: constructing a hierarchical index for the multi-dimensional attribute storage data to obtain a hierarchical index structure; performing dynamic load balancing processing on the hierarchical index structure to obtain a dynamic load balancing index; Step S743: performing access pattern recognition on the dynamic load balancing index to obtain an access pattern recognition result; performing predictive index pre-construction on the access pattern recognition result to obtain a predictive index structure; Step S744: monitor the performance of the predictive index structure to obtain index performance monitoring data; evaluate the index structure of the city monitoring storage data based on the index performance monitoring data to obtain multi-dimensional index evaluation data.

[0056] In an embodiment of the present invention, high-dimensional space mapping processing is performed on urban monitoring storage data, that is, multiple dimensions of data (such as time, space and attributes) are mapped into a high-dimensional space; for example, for road traffic data, timestamps, location coordinates and vehicle flow attributes are mapped into a multidimensional array. Similarly, green space ecological climate data, urban construction environmental impact data and urban potential problem data are also subjected to the same operation; finally, high-dimensional mapping storage data is obtained; multi-dimensional attribute analysis is performed on the high-dimensional mapping storage data, such as principal component analysis (PCA) and t-SNE to extract key features, to obtain multi-dimensional attribute storage data; a hierarchical index structure is constructed for the multi-dimensional attribute storage data, specifically including creating an index based on a B-tree or a hash table; during the index construction process, the hierarchical depth of the index and the maximum number of nodes at each level are defined, for example, the hierarchical depth is set to 3, and each node contains a maximum of 100 data items; in addition, dynamic load balancing processing is implemented on the hierarchical index structure, and the load distribution in the index is dynamically adjusted by monitoring the data access frequency and query response time to obtain a dynamic load balancing index. Perform access pattern recognition on the dynamic load balancing index, identify the most common query types and data access paths by analyzing log files and query records, and obtain access pattern recognition results; perform predictive index pre-construction based on the access pattern recognition results, such as pre-constructing the index of hot data to obtain a predictive index structure. Implement performance monitoring on the predictive index structure, including real-time tracking of the query response time and data throughput performance indicators of the index, and obtain index performance monitoring data; use the index performance monitoring data to evaluate the index structure of the city monitoring storage data, such as analyzing the index hit rate and query optimization potential, and finally obtain multi-dimensional index evaluation data.

[0057] The present invention uses sensors and drone camera technology to collect multi-source data in the city, realize real-time monitoring of urban traffic flow, and provide basic information for data analysis. Spectral separation and image enhancement technology are implemented on traffic monitoring videos to generate detailed image spectrum data and images with improved quality, and a real-time monitoring image matrix is ​​further constructed, thereby enhancing the ability to capture traffic flow details and providing a solid data foundation for in-depth analysis of traffic conditions. By extracting city map features, a three-dimensional city map is constructed, and the road network topology structure analysis is performed on the three-dimensional city map to obtain road network topology structure data; the road network topology structure data is used to monitor the road traffic status of the real-time monitoring image matrix, providing accurate spatial information for road monitoring; in addition, the urban green space area is identified and the ecological climate analysis is performed to obtain green space ecological climate data, providing a scientific basis for the protection of urban greening and ecological environment. The use of public facility information is analyzed to obtain public facility use data, which provides data support for evaluating the use efficiency of public facilities and future urban planning; at the same time, the environmental characteristic data is monitored and analyzed to obtain environmental quality data; and combined with the urban construction area data, the urban construction environmental impact data is obtained, which can evaluate the impact of urban construction on environmental quality. Further, by integrating road traffic data, green space ecological climate data and urban construction environmental impact data, a city prediction model is constructed; and potential problems of urban data are predicted to obtain urban potential problem data, providing predictive solutions for urban management; road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data are safely transmitted to the central control system, and real-time encrypted storage is performed to obtain urban monitoring storage data, providing data support for building a monitoring core database; multi-dimensional index structure evaluation is performed on urban monitoring storage data to obtain multi-dimensional index evaluation data, which can ensure efficient retrieval of data; based on multi-dimensional index evaluation data, an urban planning monitoring core database is constructed, thereby achieving efficient execution of urban planning monitoring operations. Therefore, the present invention improves the prediction ability of potential problems of multi-source urban data through Internet of Things technology, data analysis technology, image processing technology and predictive analysis technology, and accelerates the data retrieval speed of the core database.

[0058] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0059] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing a core database for urban planning monitoring, characterized in that: The following steps are involved: Step S1: Use sensors to obtain multi-source city data; use drones and cameras to collect real-time traffic flow and obtain real-time traffic flow monitoring videos; Step S2: performing spectral separation on the real-time traffic flow monitoring video to generate image spectral data; Perform image enhancement processing on the real-time traffic flow monitoring video according to the image spectrum data to obtain an enhanced image; Perform video reconstruction on the enhanced image to generate enhanced video for real-time traffic flow monitoring; Extract video frame features from real-time traffic flow monitoring enhanced video and construct a real-time monitoring image matrix; Step S3: extracting map features of multi-source city data to obtain a three-dimensional city map; Analyze the road network topology structure of the three-dimensional city map to generate road network topology structure data; use the road network topology structure data to monitor the road traffic of the real-time monitoring image matrix to obtain road traffic data; Identify green space areas on the three-dimensional city map and generate green space area data; conduct impact monitoring and analysis on green space area data based on road network topology data to obtain road green space monitoring data; conduct ecological climate analysis on road green space monitoring data to obtain green space ecological climate data; Step S4: extracting public facility information from multi-source city data to obtain public facility data; analyzing the usage of the public facility data to obtain public facility usage data; identifying urban construction areas for the multi-source city data based on the public facility usage data to generate urban construction area data; Step S5: extracting environmental characteristics of multi-source urban data to obtain environmental characteristic data; performing environmental quality monitoring on the environmental characteristic data to generate environmental quality data; performing key impact registration on the environmental quality data through urban construction area data to obtain urban construction environmental impact data; Step S6: constructing a city prediction model based on the road traffic data, the green space ecological climate data and the urban construction environmental impact data to obtain the city prediction model; predicting potential problems of multi-source city data based on the city prediction model to obtain city potential problem data; Step S7: Transmit road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data to the central control system for real-time encrypted storage to generate urban monitoring storage data; perform multi-dimensional index structure evaluation on the urban monitoring storage data to obtain multi-dimensional index evaluation data; construct a monitoring core database based on the multi-dimensional index evaluation data to obtain an urban planning monitoring core database to perform urban planning monitoring operations.

2. The method for constructing a core database for urban planning monitoring according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing image spectrum analysis on the real-time traffic flow monitoring video to generate spectrum component data; Step S22: spectrally sampling the spectral component data to generate a spectral sampling matrix; Step S23: performing visual aberration flattening on the real-time traffic flow monitoring video according to the spectral sampling matrix to generate a spectrally guided flat layer image; Step S24: performing spectral separation on the spectral guided planar image to generate image spectral data; Step S25: performing image enhancement processing on the real-time traffic flow monitoring video according to the image spectrum data to generate an enhanced image; Step S26: reconstruct the enhanced image to generate a real-time traffic flow monitoring enhanced video; extract video frame features from the real-time traffic flow monitoring enhanced video to construct a real-time monitoring image matrix.

3. The method for constructing a core database for urban planning monitoring according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing road feature recognition on multi-source city data to generate road feature data; performing geographic feature recognition on multi-source city data to generate geographic feature data; Step S32: Integrate the road feature data and the geographic feature data to construct a three-dimensional map to obtain a three-dimensional city map; perform graph analysis on the three-dimensional city map to obtain a network layout diagram; Step S33: extracting the connection attributes and layout mode of the network layout diagram to obtain map network layout data; performing road network topology analysis on the map network layout data to obtain road network topology structure data; Step S34: performing vehicle pattern recognition on the real-time monitoring image matrix through the road network topology data to obtain vehicle pattern data; performing behavior analysis on the vehicle pattern data to obtain vehicle dynamic traffic information; performing real-time trajectory tracking on the vehicle dynamic traffic information to generate vehicle driving trajectory data; Step S35: performing spatiotemporal correlation analysis on the vehicle driving trajectory data to obtain spatiotemporal vehicle traffic data; performing road traffic monitoring on the real-time monitoring image matrix according to the spatiotemporal vehicle traffic data to obtain road traffic data; Step S36: Identify green space areas on the three-dimensional city map and generate green space area data; perform impact monitoring and analysis on the green space area data based on the road network topology data to obtain road green space monitoring data; perform ecological climate analysis on the road green space monitoring data to obtain green space ecological climate data.

4. The method for constructing a core database for urban planning monitoring according to claim 3, characterized in that: Step S36 includes the following steps: Step S361: performing visual processing on the three-dimensional city map to obtain city map visual data; performing green space area recognition on the city map visual data to generate green space area data; Step S362: Perform multi-parameter environmental sensing on the green space area data to obtain green space environmental status data; perform impact monitoring on the green space environmental status data according to the road network topology data to obtain road green space monitoring data; Step S363: performing time series analysis on the road green space monitoring data to obtain green space change trend data; performing spatial distribution pattern recognition on the green space change trend data to obtain green space spatial distribution feature data; Step S364: Performing ecological effect analysis on the green space spatial distribution characteristic data to obtain green space ecological contribution data; performing urban heat island effect correlation on the green space ecological contribution data to obtain green space cooling effect data; Step S365: Perform urban ecological climate monitoring on the road green space monitoring data according to the green space cooling effect data to obtain green space ecological climate data.

5. The method for constructing a core database for urban planning monitoring according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: extracting public facility information from multi-source city data to obtain public facility data; performing facility layout identification on the public facility data to obtain public facility layout information; Step S42: simulating the flow distribution of the public facility layout information to obtain a flow pressure map of the public facility; identifying the usage of the flow pressure map of the public facility to obtain the usage data of the public facility; Step S43: Perform frequency time series analysis on the public facility usage data to obtain usage trend peak period data; Step S44: Based on the usage trend peak period data, the multi-source city data is subjected to urban construction area structure identification to generate urban construction area structure data; the urban construction area structure data is subjected to functional area matching to obtain urban construction area function distribution data; Step S45: Integrate the urban construction area structure data and the urban construction area function distribution data to generate urban construction area data.

6. The method for constructing a core database for urban planning monitoring according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: extracting environmental characteristics of multi-source city data to obtain environmental characteristic data; performing environmental quality monitoring on the environmental characteristic data to generate environmental quality data; performing environmental pressure analysis on the environmental quality data to obtain environmental pressure index data; Step S52: Adaptively divide the environmental pressure index data by thresholds to obtain environmental sensitive area data; perform spatial distribution clustering on the environmental sensitive area data by urban construction area data to obtain environmental risk area data; Step S53: Performing a multi-temporal comparative analysis on the urban construction area data according to the environmental risk area data to obtain environmental change trend data; Step S54: Performing pattern recognition on the environmental change trend data to obtain environmental evolution law data; performing environmental load analysis on the environmental evolution law data through the urban construction area data to obtain urban construction environmental load data; Step S55: Perform key impact registration on the environmental quality data according to the urban construction environmental load data to obtain urban construction environmental impact data.

7. The method for constructing a core database for urban planning monitoring according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: constructing a city prediction model based on road traffic data, green space ecological climate data and urban construction environmental impact data to obtain a city prediction pre-model; Step S62: using road traffic data, green space ecological climate data and urban construction environmental impact data to train the city prediction model to obtain a city prediction training model; Step S63: performing a model cross-validation evaluation on the city prediction training model to obtain city prediction model evaluation data; adjusting model parameters of the city prediction training model using the city prediction model evaluation data to obtain a city prediction model; Step S64: Predict potential problems of multi-source city data according to the city prediction model to obtain city potential problem data.

8. The method for constructing a core database for urban planning monitoring according to claim 7, characterized in that: Step S64 includes the following steps: Step S641: Performing high-frequency traffic anomaly detection on road traffic data based on the city prediction model to obtain city traffic bottleneck prediction data; Step S642: performing microclimate variation analysis on green space eco-climate data based on the urban prediction model to obtain prediction data on the weakening of green space cooling effect; Step S643: performing building density trend analysis on the urban construction environment impact data based on the urban prediction model to obtain ventilation corridor obstruction prediction data; Step S644: Perform a thermal comfort impact assessment on the prediction data of the weakened cooling effect of green space to obtain urban thermal environment risk area data; perform air circulation simulation on the prediction data of the obstructed ventilation corridor according to the urban thermal environment risk area data to obtain urban air quality degradation prediction data; Step S645: Integrate the city traffic bottleneck prediction data, the city thermal environment risk area data and the city air quality degradation prediction data to obtain the city potential problem data.

9. The method for constructing a core database for urban planning monitoring according to claim 1, characterized in that: Step S7 includes the following steps: Step S71: using transmission acceleration technology to transmit road traffic data, green space ecological climate data, urban construction environmental impact data and urban potential problem data in parallel, to obtain urban monitoring parallel transmission data; Step S72: transmitting the city monitoring parallel transmission data to the central control system to obtain the city monitoring transmission data; performing sensitivity identification on the city monitoring transmission data to obtain the city monitoring transmission sensitivity data; Step S73: encrypting the city monitoring transmission sensitivity data in real time to generate encrypted metadata; encrypting and storing the city monitoring transmission data through the encrypted metadata to generate city monitoring storage data; Step S74: performing a multi-dimensional index structure evaluation on the city monitoring storage data to obtain multi-dimensional index evaluation data; Step S75: Construct a monitoring core database based on the multi-dimensional index evaluation data to obtain a city planning monitoring core database to perform city planning monitoring operations.

10. The method for constructing a core database for urban planning monitoring according to claim 9, characterized in that: Step S74 includes the following steps: Step S741: performing high-dimensional space mapping on the urban monitoring storage data to generate high-dimensional mapping storage data; performing multi-dimensional attribute analysis on the high-dimensional mapping storage data to obtain multi-dimensional attribute storage data; Step S742: constructing a hierarchical index for the multi-dimensional attribute storage data to obtain a hierarchical index structure; performing dynamic load balancing processing on the hierarchical index structure to obtain a dynamic load balancing index; Step S743: performing access pattern recognition on the dynamic load balancing index to obtain an access pattern recognition result; performing predictive index pre-construction on the access pattern recognition result to obtain a predictive index structure; Step S744: monitor the performance of the predictive index structure to obtain index performance monitoring data; evaluate the index structure of the city monitoring storage data based on the index performance monitoring data to obtain multi-dimensional index evaluation data.

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