Intelligent city planning system, method and equipment based on big data analysis and medium

Abstract

The invention particularly relates to an intelligent city planning system, method, equipment and medium based on big data analysis, and the system comprises a city data integration platform, a data analysis engine and a city planning visual display platform, and storing the preprocessed multi-source city data into a big data lake of a distributed file system. And the data analysis engine is used for carrying out data mining on all the multi-source city data in the big data lake by using a deep learning algorithm, so that potential laws and models of the multi-source city data can be deeply mined, and the accurate city potential laws and development trends are beneficial to improving the accuracy of city planning. And the urban planning visual display platform is used for visually displaying the key urban indexes according to the urban index display mode and the urban development trend according to the development trend display mode.

Description

Technical Field

[0001] The present application relates to the technical field of smart city planning, and in particular to smart city planning systems, methods, devices and media based on big data analysis. Background Art

[0002] With the rapid development of the global economy and the continuous increase in population density, cities are facing more and more challenges, such as traffic congestion, environmental pollution, resource shortages, and inefficient urban management. In order to meet these challenges, building smart cities has become an important strategic choice for governments and urban planners. Smart cities provide new methods and tools for urban planning and management that are intelligent, visualized, networked, and interactive by utilizing innovative technologies such as advanced information technology, communication technology, sensing technology, and big data technology.

[0003] In smart city planning, big data analysis technology plays a core role. However, there are some deficiencies in the smart city planning methods in related technologies. For example, in terms of data fusion, they often only focus on the integration of a single data source or data type, ignoring the correlation and complementarity between multi-source data. At the same time, in terms of data mining, they mainly rely on traditional statistical analysis methods, which makes it difficult to deeply explore the potential laws and patterns in urban data. The above deficiencies in related technologies limit the application scope and effect of mining information in urban planning practice, which in turn affects the accuracy of smart city planning.

[0004] Therefore, how to solve the above-mentioned technical defects is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0005] The purpose of this application is to provide a smart city planning system, method, device and medium based on big data analysis to solve at least one of the above technical problems.

[0006] The above invention objectives of the present application are achieved through the following technical solutions: In the first aspect, the present application provides an intelligent city planning system based on big data analysis, which adopts the following technical solutions: An intelligent city planning system based on big data analysis, comprising: The city data integration platform is used to obtain multi-source city data, pre-process the multi-source city data using a distributed computing framework to obtain pre-processed multi-source city data, and store the pre-processed multi-source city data in a big data lake of a distributed file system, wherein the multi-source city data includes: city dynamic data collected by IoT sensors, city static data accessed by public databases, and city additional data accessed by third-party API interfaces; A data analysis engine, used to perform data mining on all multi-source city data in the big data lake using a deep learning algorithm to determine key city indicators and city development trends, wherein the key city indicators are used to reflect the city's operating status and development level, and the city development trends are used to reflect the prediction of the city's future development trends; The urban planning visualization display platform is used to obtain the urban indicator display mode corresponding to the key urban indicators and the development trend display mode corresponding to the urban development trend, and visualize the key urban indicators according to the urban indicator display mode and the urban development trend according to the development trend display mode, so as to assist urban planners in making urban planning decisions.

[0007] By adopting the above technical solution, the intelligent city planning system includes an urban data integration platform, a data analysis engine and an urban planning visualization display platform, wherein: the urban data integration platform is used to obtain multi-source urban data, pre-process the multi-source urban data using a distributed computing framework, obtain the pre-processed multi-source urban data, and store the pre-processed multi-source urban data in the big data lake of the distributed file system. The urban data integration platform integrates urban data from different sources and realizes the effective integration of multi-source urban data, so as to utilize the correlation and complementarity between urban data from different sources. The data analysis engine is used to use a deep learning algorithm to perform data mining on all multi-source urban data in the big data lake, determine key urban indicators and urban development trends, and use a deep learning algorithm to perform data mining, so as to deeply mine the potential laws and models of multi-source urban data. Accurate urban potential laws and development trends help improve the accuracy of urban planning. The urban planning visualization display platform is used to visualize key urban indicators according to the urban indicator display mode and urban development trends according to the development trend display mode, so as to assist urban planners in making urban planning decisions.

[0008] In a preferred example, the present application may be further configured as follows: the data analysis engine is further used to: Obtain urban planning objectives, perform automated planning based on the key urban indicators, the urban development trends and the urban planning objectives, and determine the urban planning to be reviewed.

[0009] In a preferred example, the present application can be further configured as follows: The intelligent consulting service platform is used to receive the urban planning consulting text input by the citizens, perform intent recognition based on the urban planning consulting text, and determine the urban planning consulting intention; Obtaining an established urban planning database, performing knowledge retrieval based on the urban planning consultation intention and the established urban planning database, determining the urban planning to be responded to, and performing planning interpretation based on the urban planning to be responded to, to obtain consultation response information, wherein the planning interpretation is used to interpret the urban planning to be responded to into an answer that is easy for citizens to understand; When a suggestion instruction carrying a citizen participatory planning suggestion is detected, planning requirements are extracted based on the citizen participatory planning suggestion to determine the citizen-oriented urban planning.

[0010] In a preferred example, the present application can be further configured as follows: An urban planning simulation platform for obtaining a first simulation model corresponding to the urban planning to be reviewed and a second simulation model corresponding to the citizen-oriented urban planning; Performing automated planning simulation based on the first simulation model and the city planning to be reviewed to obtain first simulation data; Performing a participatory planning simulation based on the second simulation model and the citizen-oriented urban planning to obtain second simulation data; Based on the first simulation data and the second simulation data, the planning effectiveness of the urban planning to be reviewed and the citizen-oriented urban planning are evaluated to determine the effective urban planning.

[0011] In a preferred example, the present application may be further configured as follows: when the city data integration platform performs preprocessing on the multi-source city data using a distributed computing framework to obtain the preprocessed multi-source city data, it is used to: Based on the data sources and data volumes of the multi-source city data, the multi-source city data is distributed to a plurality of computing nodes in a distributed computing cluster, wherein each computing node is used to process a fixed amount of target city data from the same data source; For each of the computing nodes, multi-dimensional data processing is performed based on the target city data to obtain multi-dimensional processed city data, wherein the multi-dimensional data processing includes: data cleaning and data format conversion; Data fusion is performed based on each of the multi-dimensionally processed urban data to obtain pre-processed multi-source urban data.

[0012] In a preferred example, the present application can be further configured as follows: The three-dimensional model display platform is used to obtain the three-dimensional model of the smart city, and to display the visual model based on the multi-source city data, the key city indicators and the city development trend to obtain a visual display model, wherein the multi-source city data, the key city indicators and the city development trend are respectively in different display layers, so that city planners can overlay different display layers based on application requirements.

[0013] In the second aspect, the present application provides a smart city planning method based on big data analysis, which adopts the following technical solutions: Acquire multi-source city data, pre-process the multi-source city data using a distributed computing framework to obtain pre-processed multi-source city data, and store the pre-processed multi-source city data in a big data lake of a distributed file system, wherein the multi-source city data includes: city dynamic data collected by IoT sensors, city static data accessed by a public database, and city additional data accessed by a third-party API interface; Using deep learning algorithms to perform data mining on all multi-source city data in the big data lake to determine key city indicators and city development trends, wherein the key city indicators are used to reflect the city's operating status and development level, and the city development trends are used to reflect the prediction of the city's future development trends; Obtain the city indicator display mode corresponding to the key city indicators and the development trend display mode corresponding to the city development trend, and visualize the key city indicators according to the city indicator display mode and the city development trend according to the development trend display mode, so as to assist city planners in making urban planning decisions.

[0014] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned smart city planning method based on big data analysis.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, which, when executed in a computer, enables the computer to execute the above-mentioned smart city planning method based on big data analysis.

[0016] In summary, the present application includes at least one of the following beneficial technical effects: The intelligent city planning system includes an urban data integration platform, a data analysis engine, and an urban planning visualization display platform, wherein: the urban data integration platform is used to obtain multi-source urban data, pre-process the multi-source urban data using a distributed computing framework, obtain the pre-processed multi-source urban data, and store the pre-processed multi-source urban data in a big data lake of a distributed file system. The urban data integration platform integrates urban data from different sources and realizes the effective integration of multi-source urban data, so as to utilize the correlation and complementarity between urban data from different sources. The data analysis engine is used to use a deep learning algorithm to perform data mining on all multi-source urban data in the big data lake, determine key urban indicators and urban development trends, and use a deep learning algorithm to perform data mining, so as to deeply mine the potential laws and models of multi-source urban data. Accurate urban potential laws and development trends help improve the accuracy of urban planning. The urban planning visualization display platform is used to visualize key urban indicators according to the urban indicator display mode and urban development trends according to the development trend display mode, so as to assist urban planners in making urban planning decisions.

[0017] The intelligent consulting service platform receives the urban planning consultation text input by citizens, and performs intent recognition based on the urban planning consultation text to determine the urban planning consultation intention. Then, based on the urban planning consultation intention and the established urban planning database, knowledge retrieval is performed to determine the urban planning to be responded to, and planning interpretation is performed based on the urban planning to be responded to obtain the consultation response information. When a suggestion instruction carrying citizen participatory planning suggestions is detected, planning requirements are extracted based on the citizen participatory planning suggestions to determine the citizen-oriented urban planning. The intelligent consulting service platform provides citizens with an urban planning query function, which helps ordinary citizens to easily obtain planning interpretations about urban planning. At the same time, it also provides citizens with a channel for feedback, which helps urban planning departments to quickly collect citizen feedback, enhance public participation, and promote transparent governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flowchart of a smart city planning system based on big data analysis according to one embodiment of the present application; Figure 2 This is a structural diagram of a smart city planning method based on big data analysis in one embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device according to one embodiment of the present application. DETAILED DESCRIPTION

[0019] The following combination Figures 1 to 3 This application is described in further detail.

[0020] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, a person skilled in the art may make non-creative modifications to the present embodiment as needed, but such modifications are protected by the patent law as long as they are within the scope of the present application.

[0021] In order to make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application. It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the data related to the object is involved in the embodiment of the present application, it needs to be obtained through the authorization and consent of the object, the authorization and consent of the relevant departments, and in accordance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiment, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained, and the embodiment also needs to be implemented with the authorization and consent of the object.

[0022] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0023] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0024] The embodiment of the present application provides an intelligent city planning system based on big data analysis, including: a city data integration platform 101, a data analysis engine 102 and a city planning visualization display platform 103, wherein: the city data integration platform 101 is used to obtain multi-source city data, pre-process the multi-source city data using a distributed computing framework, obtain the pre-processed multi-source city data, and store the pre-processed multi-source city data in a big data lake of a distributed file system. The city data integration platform 101 integrates city data from different sources, realizes the effective integration of multi-source city data, so as to utilize the correlation and complementarity between city data from different sources. The data analysis engine 102 is used to use a deep learning algorithm to perform data mining on all multi-source city data in the big data lake to determine key city indicators and city development trends. Compared with traditional statistical analysis methods, deep learning algorithms can more deeply explore the potential laws and patterns in city data, thereby determining key city indicators that reflect the city's operating status and development level, as well as city development trends that characterize the city's future development. The urban planning visualization display platform 103 is used to visualize key urban indicators according to the urban indicator display mode and urban development trends according to the development trend display mode, so as to assist urban planners in making urban planning decisions.

[0025] An intelligent city planning system based on big data analysis, such as Figure 1 As shown, the system includes: a city data integration platform 101, a data analysis engine 102 and a city planning visualization display platform 103, wherein: The city data integration platform 101 is used to obtain multi-source city data, pre-process the multi-source city data using a distributed computing framework to obtain pre-processed multi-source city data, and store the pre-processed multi-source city data in a big data lake of a distributed file system, wherein the multi-source city data includes: city dynamic data collected by IoT sensors, city static data accessed by public databases, and city additional data accessed by third-party API interfaces.

[0026] For the embodiments of the present application, in the process of obtaining urban data, a single data source can often only reflect information on a specific aspect or level of the city. Urban data from different sources can complement each other. By integrating urban data from different sources, more complete and accurate urban information can be obtained. Therefore, multi-source urban data is obtained, and the multi-source urban data includes: urban dynamic data collected by IoT sensors (for example, urban air quality data, road traffic flow data, urban electricity consumption data, urban water source quality, etc.), urban static data accessed by public databases (for example, census data, traffic violation data, cultural level data, facility placement data, etc.), and urban additional data accessed by third-party API interfaces, wherein the urban additional data is social media data and commercial data obtained by calling third-party API interfaces, and the urban additional data has auxiliary value for understanding the socio-economic conditions and public behavior patterns of the city.

[0027] Due to the huge amount of data in multi-source urban data, traditional data processing operations are difficult to cope with such large-scale data processing needs, and processing large amounts of data usually takes a long time. Therefore, the distributed computing framework is used to split the multi-source urban data into multiple subtasks, and each subtask is controlled to be executed in parallel on the corresponding computing node, so as to significantly improve the processing efficiency of large-scale data and meet the needs of large-scale data processing. At the same time, in the distributed computing framework, since multiple nodes are used to execute tasks in parallel, even if some nodes fail, other nodes can continue to execute tasks. This fault-tolerant mechanism ensures the smooth completion of data preprocessing tasks and improves the stability and reliability of data processing. Therefore, the distributed computing framework is used to preprocess the multi-source urban data to obtain preprocessed multi-source urban data, where the preprocessing includes but is not limited to: data cleaning, data format conversion, data transformation and data fusion. There are many specific implementation methods for preprocessing, which are no longer limited in the embodiments of the present application. In one feasible method, based on the data source and data volume of the multi-source city data, the multi-source city data is distributed to multiple computing nodes in a distributed computing cluster, wherein each computing node is used to process a fixed amount of target city data from the same data source; for each computing node, multi-dimensional data processing is performed based on the target city data to obtain multi-dimensional processed city data, wherein the multi-dimensional data processing includes: data cleaning, data format conversion and data transformation; data fusion is performed based on each multi-dimensional processed city data to obtain pre-processed multi-source city data.

[0028] Then, the pre-processed multi-source urban data is stored in the big data lake of the distributed file system, which is a centralized repository for storing a large amount of raw data, including structured, semi-structured and unstructured data. In order to improve the efficiency of subsequent data retrieval and query, the multi-source urban data is partitioned and stored based on the business logic or timestamp information of the pre-processed multi-source urban data. At the same time, a data permission management mechanism can be established to ensure that only authorized users can access and modify the data. The urban data integration platform 101 integrates urban data from different sources and realizes the effective integration of multi-source urban data, so as to utilize the relevance and complementarity between urban data from different sources.

[0029] The data analysis engine 102 is used to use deep learning algorithms to perform data mining on all multi-source urban data in the big data lake to determine key urban indicators and urban development trends, where key urban indicators are used to reflect the operating status and development level of the city, and urban development trends are used to reflect the prediction of the future development trend of the city.

[0030] For the embodiments of the present application, since different types of urban data are adapted to different deep learning models, a suitable deep learning model is selected based on the type of multi-source urban data, that is, a variety of deep learning models about multi-source urban data are pre-stored in the intelligent city planning system, so that the deep learning model can extract and predict information that can characterize the city's conditions from the multi-source urban data. The deep learning model is obtained by continuously training the network model based on a large number of training sets. The relationship between the multi-source urban data type and the deep learning model type is as follows: for image-type urban data, a convolutional neural network can be used to perform image classification and object recognition tasks; for text-type urban data, a recurrent neural network can be used to perform natural language text mining tasks; for urban data with time series distribution, a long short-term memory network can be used to perform time series prediction tasks; of course, there can also be other types of urban data and deep learning model types. For this, the embodiments of the present application are no longer limited, and only the deep learning model type can accurately and efficiently complete the mining tasks of this type of urban data.

[0031] Then, multi-source city data is input into the corresponding deep learning model for data mining to determine key city indicators and city development trends, where key city indicators are used to reflect the city's operating status and development level, and city development trends are used to reflect the prediction of the city's future development trends. Key city indicators include, but are not limited to: economic indicators (e.g., GDP growth rate, unemployment rate, etc.), social welfare indicators (e.g., crime rate, education level, medical facilities, etc.), environmental indicators (e.g., air quality, water quality, etc.), population indicators (e.g., population size, population growth rate, age structure, etc.), infrastructure indicators (e.g., transportation network, public facilities and housing conditions, etc.); city development trends include, but are not limited to: economic trends, population trends, infrastructure development trends, environmental trends and social welfare trends.

[0032] Compared with traditional statistical analysis methods, deep learning algorithms can more deeply explore the potential laws and patterns in multi-source urban data, thereby determining key urban indicators that reflect the operating status and development level of the city, as well as urban development trends that characterize the future development of the city. In the real-time example of this application, the urban data integration platform 101 acquires multi-source urban data and performs multi-source data fusion, which helps to utilize the correlation and complementarity between urban data from different sources, and then uses deep learning algorithms to perform data mining, so as to deeply explore the potential laws and models of multi-source urban data. Accurate urban potential laws and development trends help improve the accuracy of urban planning.

[0033] The urban planning visualization display platform 103 is used to obtain the urban indicator display mode corresponding to the key urban indicators and the development trend display mode corresponding to the urban development trend, and visualize the key urban indicators according to the urban indicator display mode and the urban development trend according to the development trend display mode, so as to assist urban planners in making urban planning decisions.

[0034] For the embodiment of the present application, the urban planning visualization display platform 103 is intended to display key urban indicators and urban development trends to urban planners in an intuitive and easy-to-understand manner to assist them in making scientific and reasonable urban planning decisions. The corresponding relationship between key urban indicators, urban development trends and display modes is pre-stored in the electronic device so that the corresponding display mode can be quickly and accurately determined based on the corresponding relationship. For example, for population indicators and infrastructure indicators, a map display mode can be selected to intuitively and clearly display the population distribution and facility distribution in different regions; for economic indicators, social welfare indicators, environmental indicators, etc., a chart (for example, a bar chart, a line chart, a pie chart, etc.) display mode can be selected to intuitively and clearly display the economic change trend, the social welfare distribution and the environmental condition change trend. At the same time, for urban development trends, a chart display mode is usually selected to display the future development trend of the city.

[0035] It can be seen that in the embodiment of the present application, the intelligent city planning system includes an urban data integration platform 101, a data analysis engine 102 and an urban planning visualization display platform 103, wherein: the urban data integration platform 101 is used to obtain multi-source urban data, pre-process the multi-source urban data using a distributed computing framework, obtain pre-processed multi-source urban data, and store the pre-processed multi-source urban data in a big data lake of a distributed file system. The urban data integration platform 101 integrates urban data from different sources, realizes the effective integration of multi-source urban data, so as to utilize the relevance and complementarity between urban data from different sources. The data analysis engine 102 is used to use a deep learning algorithm to perform data mining on all multi-source urban data in the big data lake, determine key urban indicators and urban development trends, and use a deep learning algorithm to perform data mining, so as to deeply mine the potential laws and models of multi-source urban data. Accurate urban potential laws and development trends help improve the accuracy of urban planning. The urban planning visualization display platform 103 is used to visualize key urban indicators according to the urban indicator display mode and urban development trends according to the development trend display mode, so as to assist urban planners in making urban planning decisions.

[0036] Furthermore, in order to reduce the cost of urban planning, increase efficiency, and help smart cities better meet the needs of urban residents, in the embodiment of the present application, the data analysis engine 102 is also used to: Obtain urban planning objectives, conduct automated planning based on key urban indicators, urban development trends and urban planning objectives, and determine urban planning for review.

[0037] For the embodiment of the present application, the urban planning goal is obtained, and the urban planning goal represents the development direction of the city in the future, and stipulates the requirements that the smart city needs to meet in history, culture, economy, society and other aspects, for example, to create a beautiful living environment, an efficient and safe transportation system, etc. The specific content of the urban planning goal is no longer limited in the embodiment of the present application. Further, based on each requirement in the urban planning goal, the corresponding information representing the current situation of the city is screened out from the key urban indicators and urban development trends, and the information representing the current situation of the city is matched with the planning requirements in the urban planning goal based on the information representing the current situation of the city. If the current and future urban conditions meet the planning requirements, the urban development representing the dimension meets the requirements, and no other operations are performed; if the current or future urban conditions do not meet the planning requirements, the urban conditions representing the dimension do not meet the requirements, therefore, based on the key urban indicators, urban development trends and urban planning goals of the dimension, automatic planning is performed to determine the urban planning to be reviewed, wherein the urban planning to be reviewed is used to improve the urban development status so that the future development status of the city meets the urban planning goals. Since the urban planning to be reviewed is automatically planned by the data analysis engine 102 and has not been reviewed and determined, in order to ensure the scientificity, rationality and feasibility of the planning, the urban planning to be reviewed needs to be reviewed and revised through multiple rounds of expert review and public participation. The automated planning performed by the data analysis engine 102 helps the urban planning department to have a more comprehensive understanding of the city's resources and resources, thereby formulating a more optimized and refined urban planning, which helps to reduce the cost of urban planning, increase efficiency, and help smart cities better meet the needs of urban residents and enhance the competitiveness of cities.

[0038] The specific implementation process of automated planning is as follows: obtain the first city status assessment standard corresponding to the key city indicators and the second city status assessment standard corresponding to the city development trend. The first city status assessment standard records the city status corresponding to each indicator range of different city indicators, and the second city status assessment standard records the city status corresponding to different city development trends. Therefore, based on the first city status assessment standard, the second city status assessment standard, the key city indicators and the city development trend, the city status analysis is carried out to determine the multi-dimensional status of the city, that is, the key city indicators are matched with the first city status assessment standard to determine the results reflecting the current status of the city, and the city development trend is matched with the second city status assessment standard to determine the results reflecting the future status of the city. For example, based on the air quality in the key urban indicators (environmental indicators) and the first urban status assessment standard, the urban multi-dimensional status is determined to include: any one of the following: safe air quality status, relatively safe air quality status, and air quality warning status; based on the population trend in the urban development trend and the second urban status assessment standard, the development trend is matched to determine the multi-dimensional status of the city, including: the total population is facing downward pressure, the fertility level remains low, the total population is on an upward trend, the fertility level remains stable, and the population is concentrated in large provinces and cities within the province. Finally, based on the relationship between the multi-dimensional status of the city and the urban planning goals, the corresponding optimization measures are selected and recorded as the urban planning to be reviewed.

[0039] It can be seen that in the embodiment of the present application, the city planning objectives are obtained, and the city planning to be reviewed is determined by automatic planning based on key city indicators, city development trends and city planning objectives. The data analysis engine 102 performs automatic planning to help the city planning department to have a more comprehensive understanding of the city's resources and resources, thereby formulating a more optimized and refined city planning, which helps to reduce the cost of city planning, increase efficiency, and help smart cities better meet the needs of urban residents and enhance the competitiveness of cities.

[0040] Furthermore, in order to enable ordinary citizens to easily obtain planning interpretations on urban planning, enhance public participation, and promote transparent governance, in the embodiment of the present application, it also includes: The intelligent consultation service platform is used to receive urban planning consultation texts input by citizens, identify the intentions based on the urban planning consultation texts, and determine the urban planning consultation intentions; Obtaining an established urban planning database, performing knowledge retrieval based on the urban planning consultation intention and the established urban planning database, determining the urban planning to be responded to, and interpreting the planning based on the urban planning to be responded to, and obtaining consultation response information, wherein the planning interpretation is used to interpret the urban planning to be responded to into an answer that is easy for citizens to understand; When a suggestion instruction carrying a citizen participatory planning suggestion is detected, planning requirements are extracted based on the citizen participatory planning suggestion to determine the citizen-oriented urban planning.

[0041] For the embodiment of the present application, an intelligent consulting service platform is also set up in the intelligent city planning system. The intelligent consulting service platform provides citizens with an urban planning query function, which helps ordinary citizens to easily obtain planning interpretations about urban planning. At the same time, it also provides citizens with a channel for feedback, which helps urban planning departments to quickly collect feedback from citizens, enhance public participation, and promote transparent governance.

[0042] Specifically, the intelligent consulting service platform has a user interface to realize data communication with citizens. Therefore, citizens input urban planning consultation texts in the user interface. The urban planning consultation texts are text contents input by citizens to query or understand specific urban planning information. These texts usually have clear query purposes and specific urban planning concerns, which can reflect the user's interest, needs or questions about a certain aspect of urban planning. Therefore, after the citizens complete the text input in the user interface, the intelligent consulting service platform will receive the urban planning consultation texts input by the citizens. Then, the intention recognition is performed based on the urban planning consultation text to determine the urban planning consultation intention, that is, the urban planning consultation text is preprocessed, and the preprocessing includes but is not limited to: text cleaning, word segmentation and part-of-speech tagging, and removal of stop words; then, the intention keyword extraction is performed based on the preprocessed urban planning consultation text to determine the urban planning consultation intention, wherein the urban planning consultation intention includes but is not limited to: consultation topic, consultation planning area, consultation time range, consultation planning project, consultation policy and standard. For consultation topics, it reflects the specific areas or aspects of urban planning that citizens want to know about. For example, citizens may be concerned about different topics such as traffic planning, public facilities layout, green space system planning, etc.; for consultation planning areas, citizens may want to know about the urban planning of a specific area, such as the planning of a block, community, urban new district or the entire city. This dimension provides spatial scope information for the query; for consultation time range, since urban planning is a long-term process, citizens may be concerned about the planning of a certain period of time, such as current planning, future planning, historical planning, etc. The time range dimension helps to determine the time background of the consultation; for consultation planning projects, citizens may be interested in a specific urban planning project, such as the planning of a commercial complex, transportation hub, park green space and other projects. This dimension provides specific target information for the consultation; for consultation policies and standards, citizens may want to understand policies and standards related to urban planning, such as land policies, construction standards, environmental protection requirements, etc. These policies and standards have an important impact on the implementation and effectiveness of urban planning.

[0043] Then, obtain a certain urban planning database, in which the urban planning data is obtained from the urban planning department, government agencies or related databases. Then, based on the keywords in the urban planning consultation intention, perform keyword search in the certain urban planning database to screen out the urban planning information related to the user's needs. At the same time, if multiple relevant urban planning information is to be retrieved, the information can be prioritized according to factors such as importance and timeliness, and the urban planning information with the highest priority can be selected as the urban planning to be replied. Then, conduct a detailed analysis of the content of the urban planning to be replied, and convert the complex urban planning information into easy-to-understand language to ensure that citizens can understand it. Of course, visual aids such as charts and maps can also be used to help citizens understand the urban planning information more intuitively, and finally obtain the consultation reply information.

[0044] At the same time, the intelligent consulting service platform also provides an effective communication channel, which allows citizens to feedback their needs and expectations upward through this communication channel, which helps to introduce new ideas, viewpoints and methods, and promote the innovation and development of urban planning. Therefore, when a suggestion instruction carrying citizen participatory planning suggestions is detected, planning needs are extracted based on citizen participatory planning suggestions, and citizen-oriented urban planning is determined, where citizen participatory planning suggestions are the needs and expectations for urban planning input by citizens through the user interface. The specific implementation process for extracting planning needs is as follows: sort and classify the collected citizen suggestions, summarize them according to different themes and fields, remove duplicate, invalid or unreasonable suggestions, and retain representative and valuable opinions; then, conduct in-depth analysis of the sorted citizen suggestions, extract the main needs and expectations of citizens, and obtain citizen-oriented urban planning.

[0045] It can be seen that in the embodiment of the present application, the intelligent consulting service platform receives the urban planning consultation text input by the citizens, and performs intent recognition based on the urban planning consultation text to determine the urban planning consultation intention. Then, knowledge retrieval is performed based on the urban planning consultation intention and the established urban planning database to determine the urban planning to be replied, and planning interpretation is performed based on the urban planning to be replied to obtain the consultation reply information. When a suggestion instruction carrying citizen participatory planning suggestions is detected, planning requirements are extracted based on the citizen participatory planning suggestions to determine the citizen-oriented urban planning. The intelligent consulting service platform provides citizens with an urban planning query function, which helps ordinary citizens to easily obtain planning interpretations about urban planning. At the same time, it also provides citizens with a channel for feedback, which helps urban planning departments to quickly collect citizens' feedback, enhance public participation, and promote transparent governance.

[0046] Furthermore, in order to provide urban planners with a more comprehensive and effective basis for decision-making and reduce the risks and uncertainties in the decision-making process, in the embodiment of the present application, it also includes: An urban planning simulation platform for obtaining a first simulation model corresponding to the urban planning to be reviewed and a second simulation model corresponding to the citizen-oriented urban planning; Performing automated planning simulation based on the first simulation model and the city planning to be reviewed to obtain first simulation data; Perform participatory planning simulation based on the second simulation model and citizen-oriented urban planning to obtain second simulation data; Based on the first simulation data and the second simulation data, the planning effectiveness of the urban planning to be reviewed and the citizen-oriented urban planning are evaluated to determine the effective urban planning.

[0047] For the embodiments of the present application, since the urban planning to be reviewed and the citizen-oriented urban planning have not been reviewed and determined, and the rationality and effectiveness of the urban planning in subsequent practical applications cannot be known, therefore, in order to provide urban planners with a more comprehensive and effective decision-making basis and reduce the risks and uncertainties in the decision-making process, simulations are performed based on the urban planning to be reviewed and the citizen-oriented urban planning respectively to obtain the urban development status after the implementation of the urban planning, so as to facilitate urban planners to intuitively recognize the effect of the planning implementation, which helps to ensure the smooth implementation and effective implementation of the urban planning.

[0048] Specifically, based on the urban planning types corresponding to the urban planning to be reviewed and the citizen-oriented urban planning, a simulation model consistent with the urban planning type is selected. For example, if the urban planning type is urban traffic planning, the selected simulation model should be a virtual model that can reflect the current traffic conditions of the city; if the urban planning type is urban environmental planning, the selected simulation model should be a virtual model that reflects the current environmental conditions of the city. Then, based on the first simulation model and the urban planning to be reviewed, an automated planning simulation is performed to obtain the first simulation data; based on the second simulation model and the citizen-oriented urban planning, a participatory planning simulation is performed to obtain the second simulation data. The specific process of using simulation software for planning simulation is as follows: the urban planning to be reviewed or the citizen-oriented urban planning is converted into the planning parameters corresponding to the simulation model, so that the simulation model can perform planning simulation according to the planning parameters, so as to model the development status of the smart city under different planning schemes. For example, when the urban planning type is urban traffic planning, the simulation data is the traffic flow of urban roads under urban planning; when the urban planning type is urban environmental planning, the simulation data is the change of environmental quality under urban planning. Furthermore, the first simulation data and the second simulation data are matched with the urban planning objectives to determine whether the city can meet the requirements of the urban planning objectives after implementing the urban planning. The urban planning that can meet the requirements of the urban planning objectives will be recorded as a valid urban planning; otherwise, it will be recorded as an invalid urban planning.

[0049] It can be seen that in the embodiment of the present application, in order to provide urban planners with a more comprehensive and effective basis for decision-making and reduce the risks and uncertainties in the decision-making process, the urban planning simulation platform is used to perform automated planning simulation based on the first simulation model and the urban planning to be reviewed to obtain the first simulation data, and to perform participatory planning simulation based on the second simulation model and the citizen-oriented urban planning to obtain the second simulation data. Furthermore, based on the first simulation data and the second simulation data, the planning effectiveness of the urban planning to be reviewed and the citizen-oriented urban planning is evaluated to determine the effective urban planning.

[0050] Furthermore, in order to significantly improve the processing efficiency of large-scale data and improve the stability and reliability of data processing, in the embodiment of the present application, when the city data integration platform 101 performs preprocessing of multi-source city data using a distributed computing framework to obtain preprocessed multi-source city data, it is used to: Based on the data sources and data volumes of the multi-source city data, the multi-source city data are distributed to multiple computing nodes in a distributed computing cluster, wherein each computing node is used to process a fixed amount of target city data from the same data source; For each computing node, multi-dimensional data processing is performed based on the target city data to obtain multi-dimensional processed city data, wherein the multi-dimensional data processing includes: data cleaning and data format conversion; Data fusion is performed based on each multi-dimensional processed urban data to obtain pre-processed multi-source urban data.

[0051] For the embodiments of the present application, due to the huge amount of data from multi-source city data, traditional data processing operations are difficult to cope with such large-scale data processing needs, and processing large amounts of data usually takes a long time. In order to significantly improve the processing efficiency of large-scale data and meet the needs of large-scale data processing, a distributed computing framework is used to pre-process multi-source city data. Of course, multiple nodes are used to perform tasks in parallel. Even if some nodes fail, other nodes can continue to perform tasks. This fault-tolerant mechanism ensures the smooth completion of data pre-processing tasks and improves the stability and reliability of data processing.

[0052] Specifically, data classification is performed based on the data source and data volume of multi-source city data, and the data is divided into multiple data sets. The data sets contain target city data of a fixed data volume from the same data source. The size of the fixed data volume is determined based on the processing capacity of the computing node and the load balancing requirements to ensure that the computing node has sufficient computing power to perform preprocessing operations on the data sets. Then, multiple data sets in the multi-source city data are distributed to multiple computing nodes in the distributed computing cluster to control a computing node to perform data processing operations on a data set. Furthermore, for each computing node, multidimensional data processing is performed based on the target city data to obtain multidimensional processed city data, wherein the multidimensional data processing includes: data cleaning, data format conversion and data transformation. Data cleaning is used to remove redundant and erroneous information, fill in missing values ​​and correct data anomalies; data format conversion is used to unify the data format for subsequent data processing and analysis. Finally, data fusion is performed based on each multidimensional processed city data to obtain preprocessed multi-source city data. After that, data fusion is performed based on the multi-dimensional processed urban data after data processing of each computing node to obtain pre-processed multi-source urban data, wherein the data fusion method can be selected from conventional data fusion methods such as data splicing, data aggregation, and data association.

[0053] It can be seen that in the embodiments of the present application, in order to significantly improve the processing efficiency of large-scale data and meet the needs of large-scale data processing, the multi-source city data is distributed to multiple computing nodes in the distributed computing cluster based on the data source and data volume of the multi-source city data. For each computing node, multi-dimensional data processing is performed based on the target city data to obtain multi-dimensional processed city data, wherein the multi-dimensional data processing includes: data cleaning and data format conversion. Finally, data fusion is performed based on each multi-dimensional processed city data to obtain pre-processed multi-source city data. A distributed computing framework is used for data processing, and multiple nodes are used to execute tasks in parallel. Even if some nodes fail, other nodes can continue to execute tasks. This fault-tolerant mechanism ensures the smooth completion of data pre-processing tasks and improves the stability and reliability of data processing.

[0054] Furthermore, in the embodiment of the present application, it also includes: The three-dimensional model display platform is used to obtain the three-dimensional model of the smart city, and to display the visual model based on multi-source city data, key city indicators and urban development trends to obtain a visual display model, in which the multi-source city data, key city indicators and urban development trends are in different display layers, so that urban planners can superimpose different display layers based on application needs.

[0055] For the embodiments of the present application, multi-source city data, key city indicators and city development trends are all in the form of data sets or two-dimensional chart visualizations, which cannot intuitively associate the data with the three-dimensional structure and spatial relationships of the city, making it impossible for city planners to have an in-depth understanding of the city's spatial structure and functional layout, which to a certain extent affects city planners' ability to discover potential problems and optimize spatial utilization. In order to enable city planners to have a deeper understanding of the city's spatial structure and functional layout, multi-source city data, key city indicators and city development trends are placed on the three-dimensional model of the smart city for intuitive display, which helps city planners to comprehensively consider the city's development status and urban spatial layout, accurately discover potential problems in the city and optimize spatial utilization.

[0056] Specifically, a smart city three-dimensional model is obtained, which is used to accurately reflect the three-dimensional structure and spatial relationship of the city. A visual model is displayed based on multi-source city data, key city indicators and city development trends to obtain a visual display model, wherein the multi-source city data, key city indicators and city development trends are respectively set in different display layers in the visual display model, so that when city planners understand the city development status and spatial layout, they can select appropriate layers according to their own needs to add to the smart city three-dimensional model. When performing layer overlay, a single display layer can be overlaid on the smart city three-dimensional model, or any two or all three items can be simultaneously overlaid on the city three-dimensional model. This is no longer limited in the embodiments of the present application.

[0057] It can be seen that in the embodiment of the present application, the three-dimensional model display platform is used to obtain the three-dimensional model of the smart city, and to perform a visual model display based on multi-source city data, key city indicators and urban development trends to obtain a visual display model. The visual display model helps urban planners to comprehensively consider the urban development status and urban spatial layout, accurately identify potential problems in the city and optimize space utilization.

[0058] The above embodiment introduces a smart city planning system based on big data analysis from the perspective of method flow. The following embodiment introduces a smart city planning method based on big data analysis from the perspective of method flow, including step S201, step S202, and step S203, wherein: Step S201: Acquire multi-source city data, pre-process the multi-source city data using a distributed computing framework to obtain pre-processed multi-source city data, and store the pre-processed multi-source city data in a big data lake of a distributed file system, wherein the multi-source city data includes: city dynamic data collected by IoT sensors, city static data accessed by public databases, and city additional data accessed by third-party API interfaces; Step S202: using a deep learning algorithm to perform data mining on all multi-source city data in the big data lake to determine key city indicators and city development trends, where key city indicators are used to reflect the city's operating status and development level, and city development trends are used to reflect the prediction of the city's future development trend; Step S203: Obtain the city indicator display mode corresponding to the key city indicators and the development trend display mode corresponding to the city development trend, and visualize the key city indicators according to the city indicator display mode and the city development trend according to the development trend display mode, so as to assist city planners in making urban planning decisions.

[0059] Technical personnel in the relevant field can clearly understand that, for the convenience and conciseness of description, the specific working process of the smart city planning method based on big data analysis described above can refer to the corresponding process in the aforementioned system embodiment, and will not be repeated here.

[0060] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0061] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0062] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0063] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0064] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0065] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0066] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0067] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method in any of the above embodiments is implemented.

[0068] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0069] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An intelligent city planning system based on big data analysis, characterized in that: include: Urban data integration platform, data analysis engine and urban planning visualization display platform, including: The city data integration platform is used to obtain multi-source city data, pre-process the multi-source city data using a distributed computing framework to obtain pre-processed multi-source city data, and store the pre-processed multi-source city data in a big data lake of a distributed file system, wherein the multi-source city data includes: city dynamic data collected by IoT sensors, city static data accessed by public databases, and city additional data accessed by third-party API interfaces; A data analysis engine, used to perform data mining on all multi-source city data in the big data lake using a deep learning algorithm to determine key city indicators and city development trends, wherein the key city indicators are used to reflect the city's operating status and development level, and the city development trends are used to reflect the prediction of the city's future development trends; The urban planning visualization display platform is used to obtain the urban indicator display mode corresponding to the key urban indicators and the development trend display mode corresponding to the urban development trend, and visualize the key urban indicators according to the urban indicator display mode and the urban development trend according to the development trend display mode, so as to assist urban planners in making urban planning decisions.

2. The smart city planning system based on big data analysis according to claim 1 is characterized in that: The data analysis engine is further used for: Obtain urban planning objectives, perform automated planning based on the key urban indicators, the urban development trends and the urban planning objectives, and determine the urban planning to be reviewed.

3. The smart city planning system based on big data analysis according to claim 2 is characterized in that: Also includes: The intelligent consulting service platform is used to receive the urban planning consulting text input by the citizens, perform intent recognition based on the urban planning consulting text, and determine the urban planning consulting intention; Obtaining an established urban planning database, performing knowledge retrieval based on the urban planning consultation intention and the established urban planning database, determining the urban planning to be responded to, and performing planning interpretation based on the urban planning to be responded to, to obtain consultation response information, wherein the planning interpretation is used to interpret the urban planning to be responded to into an answer that is easy for citizens to understand; When a suggestion instruction carrying a citizen participatory planning suggestion is detected, planning requirements are extracted based on the citizen participatory planning suggestion to determine the citizen-oriented urban planning.

4. The smart city planning system based on big data analysis according to claim 3 is characterized in that: Also includes: An urban planning simulation platform for obtaining a first simulation model corresponding to the urban planning to be reviewed and a second simulation model corresponding to the citizen-oriented urban planning; Performing automated planning simulation based on the first simulation model and the city planning to be reviewed to obtain first simulation data; Performing a participatory planning simulation based on the second simulation model and the citizen-oriented urban planning to obtain second simulation data; Based on the first simulation data and the second simulation data, the planning effectiveness of the urban planning to be reviewed and the citizen-oriented urban planning are evaluated to determine the effective urban planning.

5. The smart city planning system based on big data analysis according to claim 1 is characterized in that: When the city data integration platform performs preprocessing on the multi-source city data using the distributed computing framework to obtain the preprocessed multi-source city data, it is used to: Based on the data sources and data volumes of the multi-source city data, the multi-source city data is distributed to a plurality of computing nodes in a distributed computing cluster, wherein each computing node is used to process a fixed amount of target city data from the same data source; For each of the computing nodes, multi-dimensional data processing is performed based on the target city data to obtain multi-dimensional processed city data, wherein the multi-dimensional data processing includes: data cleaning and data format conversion; Data fusion is performed based on each of the multi-dimensionally processed urban data to obtain pre-processed multi-source urban data.

6. The smart city planning system based on big data analysis according to claim 1 is characterized in that: Also includes: The three-dimensional model display platform is used to obtain the three-dimensional model of the smart city, and to display the visual model based on the multi-source city data, the key city indicators and the city development trend to obtain a visual display model, wherein the multi-source city data, the key city indicators and the city development trend are respectively in different display layers, so that city planners can overlay different display layers based on application requirements.

7. A smart city planning method based on big data analysis, characterized in that: include: Acquire multi-source city data, pre-process the multi-source city data using a distributed computing framework to obtain pre-processed multi-source city data, and store the pre-processed multi-source city data in a big data lake of a distributed file system, wherein the multi-source city data includes: city dynamic data collected by IoT sensors, city static data accessed by a public database, and city additional data accessed by a third-party API interface; Using deep learning algorithms to perform data mining on all multi-source city data in the big data lake to determine key city indicators and city development trends, wherein the key city indicators are used to reflect the city's operating status and development level, and the city development trends are used to reflect the prediction of the city's future development trends; Obtain the city indicator display mode corresponding to the key city indicators and the development trend display mode corresponding to the city development trend, and visualize the key city indicators according to the city indicator display mode and the city development trend according to the development trend display mode, so as to assist city planners in making urban planning decisions.

8. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the smart city planning method based on big data analysis as described in claim 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the smart city planning method based on big data analysis as described in claim 7.

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