A method and system for analyzing the matching of supply and demand of urban construction resources based on data mining

Abstract

The present invention is applicable to the field of urban planning, and provides a method and system for analyzing the matching of supply and demand of urban construction resources based on data mining. The system includes: a data collection and preprocessing module, a resource and demand analysis module, a resource supply and demand matching model establishment module, and a decision support and system evaluation module. The method can identify and understand the long-term trend and seasonal changes of resources and demand for the construction of children's parks in urban areas, and help grasp market dynamics and demand changes. By establishing a children's park supply and demand matching model, the method can predict the matching degree of resource supply and demand, provide a basis for resource allocation and planning, help improve resource utilization efficiency and meet market demand, and contribute to the sustainable development of urban children's park construction.

Description

Technical Field

[0001] The present invention belongs to the field of urban planning, and in particular to a method and system for analyzing supply and demand matching of urban area construction resources based on data mining. Background Art

[0002] As urban data becomes increasingly abundant, urban planning increasingly relies on data analysis. Data mining technology can extract valuable information from large amounts of data and provide a scientific basis for urban planning. The application of artificial intelligence and machine learning technology makes resource supply and demand matching analysis more intelligent and automated.

[0003] As part of the urban construction resources, children's parks not only provide a place for children's entertainment and education, but also help improve the urban environment, promote community interaction, and provide a safe and interesting place for families. Through the reasonable planning and configuration of children's parks, the needs of urban residents for construction resources such as children's parks can be met, and the livability of the city and the quality of life of residents can be improved.

[0004] And children's playground is a special type of urban construction land, which is part of green land and square land. Green land and square land are open spaces used to provide leisure, entertainment and green environment in urban planning.

[0005] Children's playgrounds are classified as a type of parkland, that is, they have special land use characteristics and functions in parkland. Parkland is an important part of urban planning, which aims to provide citizens with an open, green environment for leisure, entertainment, sports and social activities. The design and planning of parkland usually take into account the needs and interests of different groups of people, including children, adults and the elderly.

[0006] Although significant progress has been made in the resource and demand analysis of children's parks in urban areas, there are still some technical problems and challenges. Resource and demand analysis involves multiple sources and types of data, such as socioeconomic data, environmental data, demographic data, etc. Resource and demand analysis requires real-time or near real-time data analysis to respond to rapidly changing environments, but real-time data processing has high requirements for computing power and algorithm efficiency. Summary of the invention

[0007] The purpose of the present invention is to provide a metropolitan area construction resource supply and demand matching analysis method based on data mining, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0008] The present invention is implemented by a data mining-based urban construction resource supply and demand matching analysis method, the method comprising:

[0009] Collect and process data on the construction resources and demand for children's parks in metropolitan areas through public urban planning documents, community surveys, and market research reports;

[0010] Use time series analysis to identify trends and moving average methods to understand the characteristics and trends of construction resources and demand for children's parks in metropolitan areas;

[0011] Establish a supply and demand matching model for children's parks to predict the matching degree of resource supply and demand;

[0012] Provide support for decision-making on the construction of children's parks in urban areas and evaluate the effectiveness of the system.

[0013] As a further solution of the present invention, the construction resources and demand data of children's parks in urban areas are collected and processed through public urban planning documents, community surveys, and market research reports, specifically including:

[0014] Collect construction resource requirements, expected visitor volume, geographic location, and local demographic data of children's parks in metropolitan areas from public urban planning documents, community surveys, and market research reports;

[0015] Data cleansing, transformation, integration and simplification.

[0016] As a further solution of the present invention, the use of time series analysis method to identify trends and moving average method to understand the characteristics and trends of construction resources and needs of children's parks in urban areas specifically includes:

[0017] Identify the long-term trend in resource and demand data, record the time point data T with one year as a time period, and record the number of school-age children as the number of children data R. The specific formula is:

[0018] R t =β0+β1T+l

[0019] R t is the number of school-age children at time point T, β0 is the intercept term, β1 is the slope, indicating the rate of change over time, and l is the error term;

[0020] Analyze the annual changes in the data and identify the peaks and troughs of demand at different times of the year. The specific formula is:

[0021] R t =S t ·C t

[0022] R t is the number of school-age children at time point T, S t is a seasonal component, C t is the trend-cycle component after seasonal adjustment;

[0023] By calculating the moving average, random fluctuations are removed. The specific formula is:

[0024]

[0025] R t-n is the number of school-age children at time tn, w n is the weight, and N is the number of time points considered

[0026] The specific formula for predicting the demand for children's playgrounds is:

[0027]

[0028] R t The actual number of children is the predicted number of children.

[0029] As a further solution of the present invention, the establishment of a children's park supply and demand matching model and prediction of resource supply and demand matching specifically includes:

[0030] Predict the number of children's playgrounds. The specific formula is:

[0031]

[0032] Where Y represents the number of visits to the children's park, R t is the actual number of children, is the expected number of children, T is the time variable, α0, α1, α2, α3 are model parameters, representing the intercept term, the influence coefficient of the actual number of children, the influence coefficient of the expected number of children, and the influence coefficient of the time variable, respectively, and σ is the error term;

[0033] Based on the children's park forecasting model, input the latest resource and demand data to predict the resource supply and demand of the children's park in the future;

[0034] The resource supply and demand matching degree will be calculated based on the predicted resource and demand data. The specific formula is:

[0035]

[0036] Among them, SDR is the supply-demand ratio, Y represents the predicted resource supply, that is, the number of children that the children's park can accommodate, and D represents the actual demand, that is, the number of children expected to visit the children's park. When SDR=1, it means that the supply and demand are completely matched, that is, the capacity of the park just meets the expected visit demand. When SDR<1, it means that the supply is insufficient and the demand exceeds the capacity of the park. When SDR>1, it means that the supply is excessive and the capacity of the park exceeds the expected demand.

[0037] Optimize and adjust the model according to changes in actual data.

[0038] As a further solution of the present invention, the construction decision of the children's park in the metropolitan area is supported and the effect of the system is evaluated, which specifically includes:

[0039] Provide data-supported decision-making recommendations by analyzing historical data, trends, and seasonal change factors;

[0040] Evaluate the utilization rate of children's playgrounds, residents' satisfaction, and resource utilization efficiency to determine whether the implementation of the strategy has achieved the expected results.

[0041] Another object of the present invention is to provide a metropolitan area construction resource supply and demand matching analysis system based on data mining, the system comprising:

[0042] The data collection and preprocessing module is used to collect and process the construction resource and demand data of children's parks in metropolitan areas through public urban planning documents, community surveys, and market research reports;

[0043] The resource and demand analysis module is used to use time series analysis methods to identify trends and moving average methods to understand the characteristics and trends of construction resources and demand for children's parks in metropolitan areas;

[0044] Resource supply and demand matching model establishment module, used to establish the supply and demand matching model of the children's park and predict the matching degree of resource supply and demand;

[0045] The decision support and system evaluation module is used to provide support for the construction decision of children's parks in urban areas and evaluate the effectiveness of the system.

[0046] As a further solution of the present invention, the data collection and preprocessing module specifically includes:

[0047] A data collection unit is used to collect construction resource requirements, expected visitor volume, geographic location, and local demographic data of children's parks in metropolitan areas from public urban planning documents, community surveys, and market research reports;

[0048] Data preprocessing unit is used for data cleaning, conversion, integration and simplification.

[0049] As a further solution of the present invention, the resource and demand analysis module specifically includes:

[0050] The trend analysis unit is used to identify long-term trends in resource and demand data. The time period is recorded as one year as the time point data T, and the number of children of school age is recorded as the number of children data R. The specific formula is:

[0051] R t =β0+β1T+l

[0052] R t is the number of school-age children at time point T, β0 is the intercept term, β1 is the slope, indicating the rate of change over time, and l is the error term;

[0053] The annual analysis unit is used to analyze the annual changes in the data and identify the peaks and valleys of demand in different time periods of the year. The specific formula is:

[0054] R t =S t ·C t

[0055] R t is the number of school-age children at time point T, S t is a seasonal component, C t is the trend-cycle component after seasonal adjustment;

[0056] The moving average analysis unit is used to remove random fluctuations by calculating the moving average. The specific formula is:

[0057]

[0058] R t-n is the number of school-age children at time tn, w n is the weight, and N is the number of time points considered

[0059] The resource demand comparison analysis unit is used to predict the demand for children's playgrounds. The specific formula is:

[0060]

[0061] R t The actual number of children is the predicted number of children.

[0062] As a further solution of the present invention, the resource supply and demand matching model establishment module specifically includes:

[0063] The children's playground prediction model unit is used to predict the number of children's playgrounds. The specific formula is:

[0064]

[0065] Where Y represents the number of visits to the children's park, R t is the actual number of children, is the expected number of children, T is the time variable, α0, α1, α2, α3 are model parameters, representing the intercept term, the influence coefficient of the actual number of children, the influence coefficient of the expected number of children, and the influence coefficient of the time variable, respectively, and σ is the error term;

[0066] The supply and demand forecasting unit is used to input the latest resource and demand data based on the children's park forecasting model to forecast the resource supply and demand of the children's park in the future;

[0067] The matching degree calculation unit is used to calculate the resource supply and demand matching degree based on the predicted resource and demand data. The specific formula is:

[0068]

[0069] Among them, SDR is the supply-demand ratio, Y represents the predicted resource supply, that is, the number of children that the children's park can accommodate, and D represents the actual demand, that is, the number of children expected to visit the children's park. When SDR=1, it means that the supply and demand are completely matched, that is, the capacity of the park just meets the expected visit demand. When SDR<1, it means that the supply is insufficient and the demand exceeds the capacity of the park. When SDR>1, it means that the supply is excessive and the capacity of the park exceeds the expected demand.

[0070] The model optimization and adjustment unit is used to optimize and adjust the model according to changes in actual data.

[0071] As a further solution of the present invention, the decision support and system evaluation module specifically includes:

[0072] Strategy recommendation unit, which is used to provide data-supported decision-making suggestions by analyzing historical data, trends, and seasonal change factors;

[0073] The effect evaluation unit is used to evaluate the utilization rate of the children's playground, residents' satisfaction, resource utilization efficiency, and determine whether the implementation of the strategy has achieved the expected results.

[0074] The beneficial effects of the present invention are as follows: the present method can identify and understand the long-term trend and seasonal changes of resources and demand for the construction of children's parks in urban areas, and help grasp market dynamics and demand changes. By establishing a children's park supply and demand matching model, the present method can predict the matching degree of resource supply and demand, provide a basis for resource allocation and planning, help improve resource utilization efficiency and meet market demand, and contribute to the sustainable development of children's park construction in urban areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A flow chart of a method for analyzing supply and demand matching of urban construction resources based on data mining provided by an embodiment of the present invention;

[0076] Figure 2 A flowchart for collecting and processing construction resource and demand data of children's parks in metropolitan areas through public urban planning documents, community surveys, and market research reports provided by an embodiment of the present invention;

[0077] Figure 3A flowchart of using a time series analysis method to identify trends and a moving average method to understand the characteristics and trends of construction resources and needs of children's parks in urban areas provided by an embodiment of the present invention;

[0078] Figure 4 A flowchart of establishing a children's playground supply and demand matching model and predicting resource supply and demand matching provided by an embodiment of the present invention;

[0079] Figure 5 A flowchart providing support for decision-making on the construction of a children's park in an urban area provided by an embodiment of the present invention and evaluating the effectiveness of the system;

[0080] Figure 6 A structural block diagram of a metropolitan area construction resource supply and demand matching analysis system based on data mining provided by an embodiment of the present invention;

[0081] Figure 7 A structural block diagram of a data collection and preprocessing module provided in an embodiment of the present invention;

[0082] Figure 8 A structural block diagram of a resource and demand analysis module provided in an embodiment of the present invention;

[0083] Fig. 9 A structural block diagram of a resource supply and demand matching model establishment module provided in an embodiment of the present invention;

[0084] Fig.10 A structural block diagram of the decision support and system evaluation module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0086] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0087] Children's parks are one of the important components of urban construction resources. With the continuous advancement of urbanization, the population and building density have increased, and the construction of children's parks has become increasingly important. They provide a safe, interesting and educational space for children in the city, which can promote their physical and mental health development. As part of the urban construction resources, they help improve the quality of life of urban residents. They provide a green leisure space where people can get away from the hustle and bustle and stress of the city and enjoy the beauty of nature. At the same time, the construction of children's parks also helps to increase the green area of ​​the city and improve the air quality and ecological environment.

[0088] And children's playground is a special type of urban construction land, which is part of green land and square land. Green land and square land are open spaces used to provide leisure, entertainment and green environment in urban planning.

[0089] Children's playgrounds are classified as a type of parkland, that is, they have special land use characteristics and functions in parkland. Parkland is an important part of urban planning, which aims to provide citizens with an open, green environment for leisure, entertainment, sports and social activities. The design and planning of parkland usually take into account the needs and interests of different groups of people, including children, adults and the elderly.

[0090] Figure 1 A flow chart of a method for analyzing the supply and demand matching of urban construction resources based on data mining provided by an embodiment of the present invention, such as Figure 1 As shown, a method for analyzing the matching of supply and demand of urban construction resources based on data mining comprises:

[0091] S100 collects and processes data on the construction resources and demand for children's parks in metropolitan areas through public urban planning documents, community surveys, and market research reports;

[0092] S200, using time series analysis methods to identify trends and moving average methods to understand the characteristics and trends of construction resources and demand for children's parks in metropolitan areas;

[0093] This step uses a series of analysis units to deeply understand the characteristics and trends of the construction resources and needs of children's parks in urban areas. A linear regression model is used to analyze the changing trend of the number of children of school age over time. t =β0+β1T+l,R t is the number of school-age children at time point T, β0 is the intercept term, β1 is the slope, which indicates the rate of change over time, and l is the error term. Through this model, the trend of the number of school-age children in the future can be predicted, providing a basis for resource planning. t =S t ·C t , Rt is the number of school-age children at time point T, S t is a seasonal component, C t It is a trend-cycle component adjusted for seasonality, which is used to analyze annual changes in the data and identify peaks and valleys in demand at different times of the year. Through this model, the demand for children's parks in different seasons of the year can be predicted to help planners arrange resources reasonably. The moving average is calculated to remove random fluctuations and smooth the data so that long-term trends and seasonal patterns can be better observed. The moving average analysis unit is used to remove random fluctuations by calculating the moving average. The specific formula is: R t-n is the number of school-age children at time tn, w n is the weight, and N is the number of time points considered, which determines the time window size of the moving average. Through this model, a smoother series of the number of school-age children can be obtained, which helps to identify and predict the stable trend of resources and demand. Compare the actual number of children with the predicted number of children to evaluate the matching degree of resource supply and demand. R t The actual number of children It is to predict the number of children. Through this model, we can evaluate whether the current resource allocation meets the predicted demand. If not, we can make corresponding resource adjustment suggestions.

[0094] S300, establish a children's park supply and demand matching model to predict the matching degree of resource supply and demand;

[0095] This step can predict the number of visits to the children's park based on the actual number of children, the expected number of children and the time variable. This model uses regression analysis to determine the degree of influence of each variable on the number of visits to the children's park, thereby predicting future visits. The children's park prediction model can be used to input the latest resource and demand data to predict the supply and demand of resources in the children's park in the future. This unit needs to update data regularly and rerun the prediction model to ensure the accuracy and timeliness of the prediction results. This module calculates the matching degree of resource supply and demand based on the predicted resource and demand data. This module is responsible for optimizing and adjusting the model according to changes in actual data. This unit will regularly collect actual children's park visit data, compare it with the prediction model, analyze the causes of prediction errors, and adjust model parameters or structure as needed to improve the prediction accuracy of the model.

[0096] S400, provides support for decision-making on the construction of children's parks in urban areas and evaluates the effectiveness of the system.

[0097] Figure 2A flowchart of collecting and processing construction resource and demand data of children's parks in urban areas through public urban planning documents, community surveys, and market research reports provided by an embodiment of the present invention, such as Figure 2 As shown, the construction resources and demand data of children's parks in urban areas are collected and processed through public urban planning documents, community surveys, and market research reports, including:

[0098] S110, collect construction resource requirements, expected visitor volume, geographic location, and local demographic data of children's parks in metropolitan areas from public urban planning documents, community surveys, and market research reports;

[0099] S120, data cleaning, conversion, integration and simplification.

[0100] Figure 3 A flowchart of using a time series analysis method to identify trends and a moving average method to understand the characteristics and trends of construction resources and needs of children's parks in urban areas provided by an embodiment of the present invention, such as Figure 3 As shown, the use of time series analysis methods to identify trends and moving average methods to understand the characteristics and trends of construction resources and demand for children's parks in urban areas specifically includes:

[0101] S210, identify the long-term trend in resource and demand data, record one year as a time period as time point data T, and record the number of school-age children as the number of children data R. The specific formula is:

[0102] R t =β0+β1T+l

[0103] R t is the number of school-age children at time point T, β0 is the intercept term, β1 is the slope, indicating the rate of change over time, and l is the error term;

[0104] S220, analyzes the annual changes in the data and identifies the peaks and valleys of demand in different time periods of the year. The specific formula is:

[0105] R t =S t ·C t

[0106] R t is the number of school-age children at time point T, S t is a seasonal component, C t is the trend-cycle component after seasonal adjustment;

[0107] S230, removes random fluctuations by calculating the moving average. The specific formula is:

[0108]

[0109] R t-n is the number of school-age children at time tn, w n is the weight, and N is the number of time points considered

[0110] S240, predict the demand for children's playgrounds, the specific formula is:

[0111]

[0112] R t The actual number of children is the predicted number of children.

[0113] Figure 4 The present invention provides a flowchart for establishing a children's playground supply and demand matching model and predicting the matching degree of resource supply and demand, such as Figure 4 As shown, the establishment of a children's playground supply and demand matching model and prediction of resource supply and demand matching specifically includes:

[0114] S310, predict the number of children's playgrounds, the specific formula is:

[0115]

[0116] Where Y represents the number of visits to the children's park, R t is the actual number of children, is the expected number of children, T is the time variable, α0, α1, α2, α3 are model parameters, representing the intercept term, the influence coefficient of the actual number of children, the influence coefficient of the expected number of children, and the influence coefficient of the time variable, respectively, and σ is the error term;

[0117] S320, based on the children's park prediction model, input the latest resource and demand data to predict the resource supply and demand of the children's park in the future;

[0118] S330, the resource supply and demand matching degree is calculated based on the predicted resource and demand data. The specific formula is:

[0119]

[0120] Among them, SDR is the supply-demand ratio, Y represents the predicted resource supply, that is, the number of children that the children's park can accommodate, and D represents the actual demand, that is, the number of children expected to visit the children's park. When SDR=1, it means that the supply and demand are completely matched, that is, the capacity of the park just meets the expected visit demand. When SDR<1, it means that the supply is insufficient and the demand exceeds the capacity of the park. When SDR>1, it means that the supply is excessive and the capacity of the park exceeds the expected demand.

[0121] S340, optimize and adjust the model according to changes in actual data.

[0122] Figure 5 A flowchart for providing support for decision-making on the construction of a children's park in an urban area provided by an embodiment of the present invention and evaluating the effectiveness of the system, such as Figure 5 As shown, the system provides support for decision-making on the construction of children's parks in the metropolitan area and evaluates the effectiveness of the system, including:

[0123] S410, provides data-supported decision-making recommendations by analyzing historical data, trends, and seasonal change factors;

[0124] S420, evaluate the utilization rate of children's playgrounds, residents' satisfaction, and resource utilization efficiency to determine whether the implementation of the strategy has achieved the expected results.

[0125] Figure 6 The structural block diagram of the urban construction resource supply and demand matching analysis system based on data mining provided by the embodiment of the present invention is as follows: Figure 6 As shown, a metropolitan area construction resource supply and demand matching analysis system based on data mining, the system includes:

[0126] The data collection and preprocessing module 100 is used to collect and process the construction resource and demand data of children's parks in urban areas through public urban planning documents, community surveys, and market research reports;

[0127] The resource and demand analysis module 200 is used to use a time series analysis method to identify trends and a moving average method to understand the characteristics and trends of the construction resources and demands of children's parks in urban areas;

[0128] This module uses a series of analytical units to gain an in-depth understanding of the characteristics and trends of the construction resources and needs of children's parks in urban areas. A linear regression model is used to analyze the changing trend of the number of children of school age over time. t =β0+β1T+l,R t is the number of school-age children at time point T, β0 is the intercept term, β1 is the slope, which indicates the rate of change over time, and l is the error term. Through this model, the trend of the number of school-age children in the future can be predicted, providing a basis for resource planning. t =S t ·C t , R t is the number of school-age children at time point T, S t is a seasonal component, C tIt is a trend-cycle component adjusted for seasonality, which is used to analyze annual changes in the data and identify peaks and valleys in demand at different times of the year. Through this model, the demand for children's parks in different seasons of the year can be predicted to help planners arrange resources reasonably. The moving average is calculated to remove random fluctuations and smooth the data so that long-term trends and seasonal patterns can be better observed. The moving average analysis unit is used to remove random fluctuations by calculating the moving average. The specific formula is: R t-n is the number of school-age children at time tn, w n is the weight, and N is the number of time points considered, which determines the time window size of the moving average. Through this model, a smoother series of the number of school-age children can be obtained, which helps to identify and predict the stable trend of resources and demand. Compare the actual number of children with the predicted number of children to evaluate the matching degree of resource supply and demand. R t The actual number of children It is to predict the number of children. Through this model, we can evaluate whether the current resource allocation meets the predicted demand. If not, we can make corresponding resource adjustment suggestions.

[0129] The resource supply and demand matching model establishment module 300 is used to establish a children's park supply and demand matching model and predict the resource supply and demand matching degree;

[0130] This module can predict the number of visits to the children's park based on the actual number of children, the expected number of children and the time variable. This model uses regression analysis to determine the degree of influence of each variable on the number of visits to the children's park, thereby predicting future visits. The children's park prediction model can be used to input the latest resource and demand data to predict the supply and demand of resources in the children's park in the future. This unit needs to update data regularly and rerun the prediction model to ensure the accuracy and timeliness of the prediction results. This module calculates the matching degree of resource supply and demand based on the predicted resource and demand data. This module is responsible for optimizing and adjusting the model according to changes in actual data. This unit will regularly collect actual children's park visit data, compare it with the prediction model, analyze the causes of prediction errors, and adjust model parameters or structure as needed to improve the prediction accuracy of the model.

[0131] The decision support and system evaluation module 400 is used to provide support for decision making on the construction of children's parks in urban areas and to evaluate the effectiveness of the system.

[0132] Figure 7 The structural block diagram of the data collection and preprocessing module provided by the embodiment of the present invention is as follows: Figure 7 As shown, the data collection and preprocessing module specifically includes:

[0133] The data collection unit 110 is used to collect construction resource requirements, expected visitor volume, geographic location, and local demographic data of children's parks in metropolitan areas from public urban planning documents, community surveys, and market research reports;

[0134] The data pre-processing unit 120 is used for data cleaning, conversion, integration and simplification.

[0135] Figure 8 The structural block diagram of the resource and demand analysis module provided by the embodiment of the present invention is as follows: Figure 8 As shown, the resource and demand analysis module specifically includes:

[0136] The trend analysis unit 210 is used to identify the long-term trend in the resource and demand data. A time period of one year is recorded as the time point data T, and the number of children of school age is recorded as the number of children data R. The specific formula is:

[0137] R t =β0+β1T+l

[0138] R t is the number of school-age children at time point T, β0 is the intercept term, β1 is the slope, indicating the rate of change over time, and l is the error term;

[0139] The annual analysis unit 220 is used to analyze the annual changes in the data and identify the peaks and valleys of demand in different time periods of the year. The specific formula is:

[0140] R t =S t ·C t

[0141] R t is the number of school-age children at time point T, S t is a seasonal component, C t is the trend-cycle component after seasonal adjustment;

[0142] The moving average analysis unit 230 is used to remove random fluctuations by calculating the moving average. The specific formula is:

[0143]

[0144] R t-n is the number of school-age children at time tn, w n is the weight, and N is the number of time points considered

[0145] The resource demand comparison and analysis unit 240 is used to predict the demand for children's playgrounds. The specific formula is:

[0146]

[0147] R t The actual number of children is the predicted number of children.

[0148] Fig. 9 The structural block diagram of the resource supply and demand matching model establishment module provided in the embodiment of the present invention is as follows: Fig. 9 As shown, the resource supply and demand matching model establishment module specifically includes:

[0149] The children's playground prediction model unit 310 is used to predict the number of children's playgrounds. The specific formula is:

[0150]

[0151] Where Y represents the number of visits to the children's park, R t is the actual number of children, is the expected number of children, T is the time variable, α0, α1, α2, α3 are model parameters, representing the intercept term, the influence coefficient of the actual number of children, the influence coefficient of the expected number of children, and the influence coefficient of the time variable, respectively, and σ is the error term;

[0152] The supply and demand forecasting unit 320 is used to forecast the resource supply and demand of the children's park in the future by inputting the latest resource and demand data based on the children's park forecasting model;

[0153] The matching degree calculation unit 330 is used to calculate the resource supply and demand matching degree based on the predicted resource and demand data. The specific formula is:

[0154]

[0155] Among them, SDR is the supply-demand ratio, Y represents the predicted resource supply, that is, the number of children that the children's park can accommodate, and D represents the actual demand, that is, the number of children expected to visit the children's park. When SDR=1, it means that the supply and demand are completely matched, that is, the capacity of the park just meets the expected visit demand. When SDR<1, it means that the supply is insufficient and the demand exceeds the capacity of the park. When SDR>1, it means that the supply is excessive and the capacity of the park exceeds the expected demand.

[0156] The model optimization and adjustment unit 340 is used to optimize and adjust the model according to changes in actual data.

[0157] Fig.10 The structural block diagram of the decision support and system evaluation module provided by the embodiment of the present invention is as follows: Fig.10 As shown, the decision support and system evaluation module specifically includes:

[0158] A strategy recommendation unit 410 is used to provide data-supported decision-making suggestions by analyzing historical data, trends, and seasonal change factors;

[0159] The effect evaluation unit 420 is used to evaluate the utilization rate of the children's playground, residents' satisfaction, and resource utilization efficiency, and to determine whether the implementation of the strategy has achieved the expected effect.

[0160] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0161] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0162] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0163] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0164] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A data mining-based urban construction resource supply and demand matching analysis method, characterized in that: The method comprises: Collect and process data on the construction resources and demand for children's parks in metropolitan areas through public urban planning documents, community surveys, and market research reports; Use time series analysis to identify trends and moving average methods to understand the characteristics and trends of construction resources and demand for children's parks in metropolitan areas; Establish a supply and demand matching model for children's parks to predict the matching degree of resource supply and demand; Provide support for decision-making on the construction of children's parks in metropolitan areas and evaluate the effectiveness of the system; The use of time series analysis methods to identify trends and moving average methods to understand the characteristics and trends of construction resources and demand for children's parks in urban areas specifically includes: Identify the long-term trend in resource and demand data, record the time point data T with one year as a time period, and record the number of school-age children as the number of children data R. The specific formula is: R t =β0+β1T+l R t is the number of school-age children at time point T, β0 is the intercept term, β1 is the slope, indicating the rate of change over time, and l is the error term; Analyze the annual changes in the data and identify the peaks and troughs of demand at different times of the year. The specific formula is: R t =S t ·C t R t is the number of school-age children at time point T, S t is the seasonal component, C t is the trend-cycle component after seasonal adjustment; By calculating the moving average, random fluctuations are removed. The specific formula is: R t-n is the number of school-age children at time tn, w n is the weight, N is the number of time points considered, WMA t represents the weighted moving average, that is, the weighted average of the demand for children's playgrounds at time point t; The specific formula for predicting the demand for children's playgrounds is: R t The actual number of children is the predicted number of children, APE t It represents the relative error between the actual demand for children's playgrounds and the predicted demand at time point t; The establishment of a children's playground supply and demand matching model and prediction of resource supply and demand matching specifically includes: Predict the number of children's playgrounds. The specific formula is: Where Y represents the number of visits to the children's park, R t is the actual number of children, is the expected number of children, T is the time variable, α0, α1, α2, α3 are model parameters, representing the intercept term, the influence coefficient of the actual number of children, the influence coefficient of the expected number of children, and the influence coefficient of the time variable, respectively, and σ is the error term; Based on the children's park forecasting model, input the latest resource and demand data to predict the resource supply and demand of the children's park in the future; The resource supply and demand matching degree will be calculated based on the predicted resource and demand data. The specific formula is: Among them, SDR is the supply-demand ratio, Y represents the predicted resource supply, that is, the number of children that the children's park can accommodate, and D represents the actual demand, that is, the number of children expected to visit the children's park. When SDR=1, it means that the supply and demand are completely matched, that is, the capacity of the park just meets the expected visit demand. When SDR<1, it means that the supply is insufficient and the demand exceeds the capacity of the park. When SDR>1, it means that the supply is excessive and the capacity of the park exceeds the expected demand. Optimize and adjust the model according to changes in actual data; Provide support for decision-making on the construction of children's parks in the metropolitan area and evaluate the effectiveness of the system, including: Provide data-supported decision-making recommendations by analyzing historical data, trends, and seasonal change factors; Evaluate the utilization rate of children's playgrounds, residents' satisfaction, and resource utilization efficiency to determine whether the implementation of the strategy has achieved the expected results.

2. The method according to claim 1, characterized in that The aforementioned collection and processing of construction resources and demand data for children's parks in metropolitan areas through public urban planning documents, community surveys, and market research reports specifically includes: Collect construction resource requirements, expected visitor volume, geographic location, and local demographic data of children's parks in metropolitan areas from public urban planning documents, community surveys, and market research reports; Data cleansing, transformation, integration and simplification.

3. A data mining-based urban construction resource supply and demand matching analysis system, characterized by: The system comprises: The data collection and preprocessing module is used to collect and process the construction resource and demand data of children's parks in metropolitan areas through public urban planning documents, community surveys, and market research reports; The resource and demand analysis module is used to use time series analysis methods to identify trends and moving average methods to understand the characteristics and trends of construction resources and demand for children's parks in metropolitan areas; Resource supply and demand matching model establishment module, used to establish the supply and demand matching model of the children's park and predict the matching degree of resource supply and demand; Decision support and system evaluation module, used to provide support for decision-making on the construction of children's parks in urban areas and evaluate the effectiveness of the system; The resource and demand analysis module specifically includes: The trend analysis unit is used to identify long-term trends in resource and demand data. The time period is recorded as one year as the time point data T, and the number of children of school age is recorded as the number of children data R. The specific formula is: R t =β0+β1T+l R t is the number of school-age children at time point T, β0 is the intercept term, β1 is the slope, indicating the rate of change over time, and l is the error term; The annual analysis unit is used to analyze the annual changes in the data and identify the peaks and valleys of demand in different time periods of the year. The specific formula is: R t =S t ·C t R t is the number of school-age children at time point T, S t is a seasonal component, C t is the trend-cycle component after seasonal adjustment; The moving average analysis unit is used to remove random fluctuations by calculating the moving average. The specific formula is: R t-n is the number of school-age children at time tn, w n is the weight, N is the number of time points considered, WMA t represents the weighted moving average, that is, the weighted average of the demand for children's playgrounds at time point t; The resource demand comparison analysis unit is used to predict the demand for children's playgrounds. The specific formula is: R t The actual number of children is the predicted number of children, APE t It represents the relative error between the actual demand for children's playgrounds and the predicted demand at time point t; The resources provided by APE t The model building module needs to be matched, including: The children's playground prediction model unit is used to predict the number of children's playgrounds. The specific formula is: Where Y represents the number of visits to the children's park, R t is the actual number of children, is the expected number of children, T is the time variable, α0, α1, α2, α3 are model parameters, representing the intercept term, the influence coefficient of the actual number of children, the influence coefficient of the expected number of children, and the influence coefficient of the time variable, respectively, and σ is the error term; The supply and demand forecasting unit is used to input the latest resource and demand data based on the children's park forecasting model to forecast the resource supply and demand of the children's park in the future; The matching degree calculation unit is used to calculate the resource supply and demand matching degree based on the predicted resource and demand data. The specific formula is: Among them, SDR is the supply-demand ratio, Y represents the predicted resource supply, that is, the number of children that the children's park can accommodate, and D represents the actual demand, that is, the number of children expected to visit the children's park. When SDR=1, it means that the supply and demand are completely matched, that is, the capacity of the park just meets the expected visit demand. When SDR<1, it means that the supply is insufficient and the demand exceeds the capacity of the park. When SDR>1, it means that the supply is excessive and the capacity of the park exceeds the expected demand. Model optimization and adjustment unit, used to optimize and adjust the model according to changes in actual data; The decision support and system evaluation module specifically includes: Strategy recommendation unit, which is used to provide data-supported decision-making suggestions by analyzing historical data, trends, and seasonal change factors; The effect evaluation unit is used to evaluate the utilization rate of the children's playground, residents' satisfaction, resource utilization efficiency, and determine whether the implementation of the strategy has achieved the expected results.

4. The system according to claim 3, characterized in that The data collection and preprocessing module specifically includes: A data collection unit is used to collect construction resource requirements, expected visitor volume, geographic location, and local demographic data of children's parks in metropolitan areas from public urban planning documents, community surveys, and market research reports; Data preprocessing unit is used for data cleaning, conversion, integration and simplification.

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