System and method for comprehensive monitoring of complete period of land use planning and construction

Abstract

The invention provides a land use planning and construction full-period comprehensive monitoring system and method, and the system comprises a data obtaining module which obtains spatial geographic image data at historical moments, and carries out the time span detection of the spatial geographic image data; the influence factor determining module is used for determining a geographic image change influence factor of the target area according to the time span detection result; the change degree calculation module is used for analyzing linear relations between the r1, the r2, the r3 and the r4 and the comprehensive change degree z of the geographic image according to a multiple linear regression model; and the prediction result module is used for predicting a future value of the comprehensive change degree z by using the fitted ARIMA model, and monitoring land use planning construction according to a prediction result.

Description

Technical Field

[0001] The present invention relates to the field of comprehensive land planning, and in particular to a comprehensive monitoring system and method for the entire cycle of land use planning and construction. Background Art

[0002] Empower investment promotion with digitalization, strengthen the digitalization, visualization, and full-process management services of construction land, create a full-cycle construction in the land planning process, maximize the utilization of land construction resources, accurately locate data, and efficiently feedback abnormalities. Many research and development projects also attempt to conduct real-time management and control of land construction throughout the entire life cycle, and monitor land implementation from all directions and angles. A development prediction model is set up in CN 113850350A, and the information data is further analyzed, and an optimization algorithm is proposed for urban construction planning. However, the parameters considered by the fitted deep learning model do not conform to the actual situation of urban construction. This urgently requires technical personnel in this field to solve the corresponding technical problems. Summary of the invention

[0003] The present invention aims to at least solve the technical problems existing in the prior art, and in particular innovatively proposes a comprehensive monitoring system and method for the entire cycle of land use planning and construction.

[0004] In order to achieve the above-mentioned object of the present invention, the present invention provides a comprehensive monitoring system for the whole cycle of land use planning and construction, comprising:

[0005] The data acquisition module acquires spatial geographic image data at historical moments and performs time span detection on the spatial geographic image data;

[0006] The influencing factor determination module determines the influencing factors of geographic image changes in the target area based on the time span detection results;

[0007] The change calculation module is used to analyze r according to the multivariate linear regression model. 1 , r 2 , r 3 , r 4 The linear relationship between the comprehensive change degree z of the geographic image;

[0008] The prediction result module uses the fitted ARIMA model to predict the future value of the comprehensive change degree z, and monitors the land use planning and construction based on the prediction results.

[0009] Preferably, the data acquisition module comprises:

[0010] Analyze the layers according to the changes of geographic images, call the relevant data of the layers of the geographic images that have changed through the GIS engine, and obtain all the changed layers of the detected geographic images to form the detection results;

[0011] The current layer is used for overlay analysis according to the time span to obtain the geographic image changes corresponding to the adjacent time span; thereby integrating all change layers to form an integrated geographic image.

[0012] Preferably, the above technical solution, the module for determining the impact factor includes:

[0013] According to the standardized set of target areas (s 1 ,s 2 ,s 3 ,s 4 ,H),s 1 Normalize data for commercial areas, s 2 Normalized data for industrial areas, s 3 Normalized data for residential areas, s 4 is the standardized data of the public area, and H is the set of dynamic change degrees of the target area;

[0014] H = {r 1 ,r 2 ,r 3 ,r 4}, r 1 is the dynamic change degree between the planned area of ​​the commercial district and the actual target area, r 2 is the dynamic change degree between the planned area of ​​the industrial zone and the actual target area, r 3 is the dynamic change degree between the planned residential area and the actual target area, r 4 The dynamic change degree between the public area planning area and the actual target area.

[0015] Preferably, the above technical solution, the module for determining the impact factor includes:

[0016] Calculate r 1 The dynamic change degree between the planned area of ​​the commercial district and the actual target area is:

[0017]

[0018] Among them, μ 1 is the incremental area adjustment coefficient of the target area of ​​the commercial district, ΔS i-i′ is the incremental area of ​​land in the target area of ​​the commercial district from the i-th plot to the i′th plot, S i is the total area of ​​the i-th plot of land in the target area of ​​the commercial district, and T is the monitoring time for changes in the target area;

[0019] Where S commerce is the preset area of ​​the target area in the commercial district, f(d) is the Gaussian attenuation function, and d is the attenuation distance variable of the target area.

[0020] Preferably, the above technical solution, the module for determining the impact factor includes:

[0021] Calculate r 2 The dynamic change degree between the planned area of ​​the industrial zone and the actual target area is:

[0022]

[0023] Among them, μ 2 is the incremental area adjustment coefficient of the target area of ​​the industrial zone, ΔI i-i′ is the incremental area of ​​land in the target area of ​​the industrial zone from the i-th plot to the i′th plot, I i is the total area of ​​the i-th plot of land in the target area of ​​the industrial zone, and T is the monitoring time for changes in the target area;

[0024] Where S industry is the preset area of ​​the target area in the commercial district, f(d) is the Gaussian attenuation function, and d is the attenuation distance variable of the target area.

[0025] Preferably, the above technical solution, the module for determining the impact factor includes:

[0026] Calculate r 3 The dynamic change degree between the planned residential area and the actual target area is:

[0027]

[0028] Among them, μ 3 is the incremental area adjustment coefficient of the target residential area, ΔR i-i′ is the incremental area of ​​land in the target residential area from the i-th plot to the i′th plot, R i is the total area of ​​the i-th plot of land in the target area of ​​the residential area, and T is the monitoring time for changes in the target area;

[0029] Where S resident is the preset area of ​​the target residential area, f(d) is the Gaussian attenuation function, and d is the attenuation distance variable of the target area.

[0030] Preferably, the above technical solution, the module for determining the impact factor includes:

[0031] Calculate r 4 The dynamic change degree between the public area planning area and the actual target area is,

[0032]

[0033] Among them, μ 4 is the incremental area adjustment coefficient of the public area target area, ΔPi-i′ is the incremental area of ​​land in the public area target area from the i-th plot to the i′th plot, P i is the total area of ​​the i-th plot of land in the target area of ​​the public area, and T is the target area change monitoring time;

[0034] Where S pub is the preset area of ​​the target area in the public area, f(d) is the Gaussian attenuation function, and d is the attenuation distance variable of the target area

[0035] Preferably, the above technical solution, the degree of change calculation module includes:

[0036] The specific formula is as follows:

[0037] z=β 0 +β 1 × 1 +β 2 × 2 +β 3 × 3 +β 4 × 4

[0038] Among them, β 0 is a constant term, β 1 ,β 2 ,β 3 ,β 4 They are r 1 , r 2 , r 3 , r 4 The regression coefficient is obtained by fitting historical data.

[0039] The multiple linear regression model can take into account the interactions between more factors and is suitable for more complex data relationship analysis.

[0040] Preferably, the prediction result module of the above technical solution includes:

[0041] Use the comprehensive change observation z at a certain time t t , the observed value of the comprehensive change at time t forms is the fitted value,

[0042]

[0043] Among them, μ is the mean of the differenced sequence, φ p is the pth autoregressive coefficient, θ q is the qth moving average coefficient, p and q are positive integers and are not equal, z t-p is the comprehensive change degree at time p before time t; εt-q Refers to the residual at time tq.

[0044] For monitoring purposes, we can calculate the difference between the predicted and actual values ​​and set thresholds to determine if there are anomalies or trends that need attention. The monitoring indicators are set to:

[0045]

[0046] Among them, σ z It is the standard deviation of the residual of the comprehensive change degree z, which is used to standardize the monitoring indicators so that they can be compared with the preset thresholds.

[0047] The present invention also discloses a comprehensive monitoring method for the whole cycle of land use planning and construction, comprising the following steps:

[0048] S1, obtaining spatial geographic image data at historical moments and performing time span detection on the spatial geographic image data;

[0049] S2, determining the influencing factors of geographic image changes in the target area based on the time span detection results;

[0050] S3, using the multivariate linear regression model to analyze the dynamic change between the planned area of ​​the commercial district and the actual target area 1 , the dynamic change degree between the planned area of ​​the industrial zone and the actual target area 2 , the dynamic change degree between the planned residential area and the actual target area 3 , the dynamic change degree between the public area planning area and the actual target area 4 The linear relationship between the comprehensive change degree of the geographic image;

[0051] S4, use the fitted ARIMA model to predict the future value of the comprehensive change degree z, and monitor the land use planning and construction based on the prediction results.

[0052] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0053] By analyzing the dynamic changes of different planned areas (commercial areas, industrial areas, residential areas, public areas) and actual target areas through the multivariate linear regression model, we can comprehensively evaluate the implementation effect of land use planning and reveal the differences and dynamic changes between planning and actual use.

[0054] The use of the fitted ARIMA model to predict the future value of the comprehensive change degree provides a forward-looking monitoring method for land use planning and construction. This helps to timely discover potential deviations and risks and provide a scientific basis for adjusting and optimizing land use planning.

[0055] Overall, these steps provide a scientific and systematic analysis and monitoring framework for land use planning and construction. Through historical data analysis, influencing factor identification, dynamic change assessment and prediction model application, it can provide decision makers with comprehensive and accurate information support, helping them make more scientific and reasonable land use planning decisions.

[0056] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0058] Figure 1 It is an overall schematic diagram of the present invention;

[0059] Figure 2 It is a schematic diagram of the full cycle monitoring of the present invention;

[0060] Figure 3 It is a schematic diagram of the dynamic fusion of planning and construction data of the present invention. DETAILED DESCRIPTION

[0061] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0062] like Figure 1-3 As shown, the present invention discloses a comprehensive monitoring system for the whole cycle of land use planning and construction, comprising:

[0063] The data acquisition module acquires spatial geographic image data at historical moments and performs time span detection on the spatial geographic image data.

[0064] like Figure 2 As shown, land acquisition images, agricultural land conversion images, land reserves, land acquisition, land consolidation, land planning conditions, land pre-examination, land supply, land registration, land use permit, project permit, and completion verification are dynamically integrated to extract features from real-time acquired land data;

[0065] Analyze the layers according to the changes of geographic images, call the relevant data of the layers of the geographic images that have changed through the GIS engine, and obtain all the changed layers of the detected geographic images to form the detection results;

[0066] Perform overlay analysis using the current layer according to the time span to obtain the geographic image changes corresponding to the adjacent time span; thereby integrating all change layers to form an integrated geographic image;

[0067] The influencing factor determination module determines the influencing factors of geographic image changes in the target area based on the time span detection results;

[0068] Mark the functional zoning of the target area with geographic images, cluster them according to the marked attributes, and form statistical data of the target area according to commercial areas, industrial areas, residential areas and public areas (parks, green spaces, trails, roads); use ArcGIS overlay tools to overlay the statistical data sets and judge the target areas after overlay; the dimensional differences of the data sets require the calculation of standardized attributes of the scale and mean of the data;

[0069] According to the standardized set of target areas (s 1 ,s 2 ,s 3 ,s 4 ,H),s 1 Normalize data for commercial areas, s 2 Normalized data for industrial areas, s 3 Normalized data for residential areas, s 4 is the standardized data of the public area, and H is the set of dynamic change degrees of the target area;

[0070] H = {r 1 ,r 2 ,r 3 ,r 4}, r 1 is the dynamic change degree between the planned area of ​​the commercial district and the actual target area, r 2 is the dynamic change degree between the planned area of ​​the industrial zone and the actual target area, r 3 is the dynamic change degree between the planned residential area and the actual target area, r 4 The dynamic change degree between the public area planning area and the actual target area,

[0071] Calculate r 1 The dynamic change degree between the planned area of ​​the commercial district and the actual target area is:

[0072]

[0073] Among them, μ 1 is the incremental area adjustment coefficient of the target area of ​​the commercial district, ΔS i-i′ is the incremental area of ​​land in the target area of ​​the commercial district from the i-th plot to the i′th plot, S iis the total area of ​​the i-th plot of land in the target area of ​​the commercial district, T is the monitoring time of the change in the target area, which can also be understood as the time span;

[0074] Where S commerce is the preset area of ​​the target area in the commercial district, f(d) is the Gaussian attenuation function, d is the attenuation distance variable of the target area,

[0075] Calculate r 2 The dynamic change degree between the planned area of ​​the industrial zone and the actual target area is:

[0076]

[0077] Among them, μ 2 is the incremental area adjustment coefficient of the target area of ​​the industrial zone, ΔI i-i′ is the incremental area of ​​land in the target area of ​​the industrial zone from the i-th plot to the i′th plot, I i is the total area of ​​the i-th plot of land in the target area of ​​the industrial zone, T is the monitoring time of the change in the target area, which can also be understood as the time span;

[0078] Where S industry is the preset area of ​​the target area in the commercial district, f(d) is the Gaussian attenuation function, d is the attenuation distance variable of the target area,

[0079] Calculate r 3 The dynamic change degree between the planned residential area and the actual target area is:

[0080]

[0081] Among them, μ 3 is the incremental area adjustment coefficient of the target residential area, ΔR i-i′ is the incremental area of ​​land in the target residential area from the i-th plot to the i′th plot, R i is the total area of ​​the i-th plot of land in the target area of ​​the residential area, T is the monitoring time of the change in the target area, which can also be understood as the time span;

[0082] Where S resident is the preset area of ​​the target residential area, f(d) is the Gaussian attenuation function, d is the target area attenuation distance variable,

[0083] Calculate r 4 The dynamic change degree between the public area planning area and the actual target area is,

[0084]

[0085] Among them, μ 4 is the incremental area adjustment coefficient of the public area target area, ΔP i-i′ is the incremental area of ​​land in the public area target area from the i-th plot to the i′th plot, P i is the total area of ​​the i-th plot of land in the target area of ​​the public area, T is the monitoring time of the change in the target area, which can also be understood as the time span;

[0086] Where S pub is the preset area of ​​the target area in the public area, f(d) is the Gaussian attenuation function, d is the attenuation distance variable of the target area,

[0087] The change calculation module is used to analyze r according to the multivariate linear regression model. 1 , r 2 , r 3 , r 4 The linear relationship between the comprehensive change degree z of the geographic image is as follows:

[0088] z=β 0 +β 1 × 1 +β 2 × 2 +β 3 × 3 +β 4 × 4

[0089] Among them, β 0 is a constant term, β 1 ,β 2 ,β 3 ,β 4 They are r 1 , r 2 , r 3 , r 4 The regression coefficient is obtained by fitting historical data.

[0090] The multiple linear regression model can take into account the interactions between more factors and is suitable for more complex data relationship analysis.

[0091] The prediction result module uses the fitted ARIMA model to predict the future value of the comprehensive change degree z, and monitors the land use planning and construction based on the prediction results.

[0092] Use the comprehensive change observation z at a certain time t t , the observed value of the comprehensive change at time t forms is the fitted value,

[0093]

[0094] Among them, μ is the mean of the differenced sequence, φ p is the pth autoregressive coefficient, θ q is the qth moving average coefficient, p and q are positive integers and are not equal, z t-p is the comprehensive change at time p before time t; in the context of the ARIMA model, z t-p represents the p-th observation value before time t in the time series, that is, the comprehensive change degree after p periods of lag. Similarly, z t-1 、z t-2 etc. respectively represent the first and second observations before time t, that is, the comprehensive degree of change of 1-period and 2-period lags. These lags are the basis of the autoregressive part and are used to model the dynamics of time series. t-q Refers to the residual at time tq. In the ARIMA model, the residual is the difference between the observed value and the model fitted value.

[0095] For monitoring purposes, we can calculate the difference between the predicted and actual values ​​and set thresholds to determine if there are anomalies or trends that need attention. The monitoring indicators are set to:

[0096]

[0097] Among them, σ z It is the standard deviation of the residual of the comprehensive change degree z, which is used to standardize the monitoring indicators so that they can be compared with the preset thresholds.

[0098] When M t When M is small, it means that the predicted value of the model is closer to the actual observed value, and the prediction accuracy is high; on the contrary, when M t When it is large, it means that there is a large difference between the predicted value of the model and the actual observed value, and it may be necessary to further adjust the model or take corrective measures.

[0099] The factors that need to be considered in setting the threshold are setting M t When the threshold

[0100] The following factors need to be considered: 1. Specific requirements for land planning and construction (pre-set data for commercial areas, industrial areas, residential areas and public areas, as well as areas planned in GIS, displayed through geographic images): Different land planning and construction projects have different requirements and standards. Therefore, when setting thresholds, it is necessary to fully consider the specific requirements and goals of the project to ensure that the thresholds can reflect the actual situation and needs of the project.

[0101] 2. Through the analysis and summary of historical data, we can understand the changes and rules in the process of land use planning and construction. These historical data and experience provide a useful reference for setting thresholds. The prediction accuracy of the ARIMA model is the factor that affects M t When setting the threshold, it is necessary to fully consider the prediction performance of the model to ensure that the threshold can reflect the prediction accuracy of the model. t The threshold needs to be real-time and sensitive, and be able to detect deviations in the construction process in a timely manner. Therefore, when setting the threshold, it is necessary to fully consider the requirements of real-time and sensitivity to ensure that the threshold can reflect changes in the construction process in a timely manner.

[0102] 3. In the actual application process, the threshold can be adjusted and optimized in real time according to the real-time changes of the monitoring indicator_t. By constantly adjusting and optimizing the threshold, the accuracy and sensitivity of the monitoring indicator_t can be improved, and the comprehensive monitoring of land use planning and construction can be better served. Applying the set threshold to the comprehensive monitoring of land use planning and construction can timely discover deviations. t By comparing with the threshold, deviations in land use planning and construction can be discovered in a timely manner. t When the threshold is exceeded, it indicates that there is a large difference between the actual observed value and the predicted value, and corrective measures need to be taken. t The threshold setting is crucial for comprehensive monitoring in the process of land planning and construction. A reasonable threshold range can be set by comprehensively considering the specific requirements of land planning and construction, historical data and experience, the prediction accuracy of the model, and factors such as real-time and sensitivity. Applying the set threshold to the comprehensive monitoring of land planning and construction can timely discover deviations, provide a basis for correction, optimize land planning and construction plans, and improve management efficiency. This provides strong guarantee and support for the smooth progress of land planning and construction.

[0103] The present invention also discloses a comprehensive monitoring method for the whole cycle of land use planning and construction, comprising the following steps:

[0104] S1, obtain spatial geographic image data at historical moments, perform time span detection on the spatial geographic image data, S2, determine the influencing factors of geographic image changes in the target area based on the time span detection results; S3, analyze the dynamic change degree r between the planned area of ​​the commercial area and the actual target area based on the multivariate linear regression model 1 , the dynamic change degree between the planned area of ​​the industrial zone and the actual target area 2 , the dynamic change degree between the planned residential area and the actual target area 3 , the dynamic change degree between the public area planning area and the actual target area 4The linear relationship between the comprehensive change degree of the geographic image and the comprehensive change degree of the geographic image; S4, use the fitted ARIMA model to predict the future value of the comprehensive change degree z, and monitor the land use planning and construction based on the prediction results.

[0105] Establish a dynamic data update mechanism: use modern information technology to achieve real-time data monitoring and updating.

[0106] By analyzing the dynamic changes of different planned areas (commercial areas, industrial areas, residential areas, public areas) and actual target areas through the multivariate linear regression model, we can comprehensively evaluate the implementation effect of land use planning and reveal the differences and dynamic changes between planning and actual use.

[0107] The use of the fitted ARIMA model to predict the future value of the comprehensive change degree provides a forward-looking monitoring method for land use planning and construction. This helps to timely discover potential deviations and risks and provide a scientific basis for adjusting and optimizing land use planning.

[0108] Overall, these steps provide a scientific and systematic analysis and monitoring framework for land use planning and construction. Through historical data analysis, influencing factor identification, dynamic change assessment and prediction model application, it can provide decision makers with comprehensive and accurate information support, helping them make more scientific and reasonable land use planning decisions.

[0109] like Figure 2 As shown, the monitoring system is applied to the national land space big data analysis platform. The platform collects data in the early stage, and makes comprehensive data collection on population, national land space, control plan implementation status, public service facilities, real estate development, and road traffic. By establishing status quo analysis and evaluation and maturity assessment, the planning implementation project library extracted from the planning level is optimized, and the key development areas of the city are optimized in combination with the national land space content.

[0110] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A comprehensive monitoring system for the whole cycle of land use planning and construction, characterized in that: include: The data acquisition module acquires spatial geographic image data at historical moments and performs time span detection on the spatial geographic image data; The influencing factor determination module determines the influencing factors of geographic image changes in the target area based on the time span detection results; A change degree calculation module is used to analyze the linear relationship between r1, r2, r3, r4 and the comprehensive change degree z of the geographic image according to a multivariate linear regression model; The prediction result module uses the fitted ARIMA model to predict the future value of the comprehensive change degree z, and monitors the land use planning and construction based on the prediction results.

2. The land use planning and construction full-cycle comprehensive monitoring system according to claim 1 is characterized in that: The data acquisition module comprises: Analyze the layers according to the changes of geographic images, call the relevant data of the layers of the geographic images that have changed through the GIS engine, and obtain all the changed layers of the detected geographic images to form the detection results; The current layer is used for overlay analysis according to the time span to obtain the geographic image changes corresponding to the adjacent time span; thereby integrating all change layers to form an integrated geographic image.

3. The land use planning and construction full-cycle comprehensive monitoring system according to claim 1 is characterized in that: The impact factor determination module comprises: According to the standardized set of target areas (s1, s2, s3, s4, H), s1 is the standardized data of commercial areas, s2 is the standardized data of industrial areas, s3 is the standardized data of residential areas, s4 is the standardized data of public areas, and H is the dynamic change degree set of the target area; H={r1, r2, r3, r4}, r1 is the dynamic change degree between the planned commercial area and the actual target area, r2 is the dynamic change degree between the planned industrial area and the actual target area, r3 is the dynamic change degree between the planned residential area and the actual target area, and r4 is the dynamic change degree between the planned public area and the actual target area.

4. The land use planning and construction full-cycle comprehensive monitoring system according to claim 3 is characterized in that: The impact factor determination module comprises: The dynamic change degree between the planned area and the actual target area of ​​the r1 commercial area is calculated as follows: Among them, μ1 is the incremental area adjustment coefficient of the target area of ​​the commercial district, ΔS i-i′ is the incremental area of ​​land in the target area of ​​the commercial district from the i-th plot to the i′th plot, S i is the total area of ​​the i-th plot of land in the target area of ​​the commercial district, and T is the monitoring time for changes in the target area; Where S commerce is the preset area of ​​the target area in the commercial district, f(d) is the Gaussian attenuation function, and d is the attenuation distance variable of the target area.

5. The land use planning and construction full-cycle comprehensive monitoring system according to claim 3 is characterized in that: The impact factor determination module comprises: The dynamic change degree between the planned area and the actual target area of ​​the r2 industrial zone is calculated as follows: Among them, μ2 is the incremental area adjustment coefficient of the target area of ​​the industrial zone, ΔI i-i′ is the incremental area of ​​land in the target area of ​​the industrial zone from the i-th plot to the i′th plot, I i is the total area of ​​the i-th plot of land in the target area of ​​the industrial zone, and T is the monitoring time for changes in the target area; Where S industry is the preset area of ​​the target area in the commercial district, f(d) is the Gaussian attenuation function, and d is the attenuation distance variable of the target area.

6. The land use planning and construction full-cycle comprehensive monitoring system according to claim 3 is characterized in that: The impact factor determination module comprises: The dynamic change degree between the planned area of ​​the r3 residential area and the actual target area is calculated as follows: Among them, μ3 is the incremental area adjustment coefficient of the target residential area, ΔR i-i′ is the incremental area of ​​land in the target residential area from the i-th plot to the i′th plot, R i is the total area of ​​the i-th plot of land in the target area of ​​the residential area, and T is the monitoring time for changes in the target area; Where S resident is the preset area of ​​the target residential area, f(d) is the Gaussian attenuation function, and d is the attenuation distance variable of the target area.

7. The land use planning and construction full-cycle comprehensive monitoring system according to claim 3 is characterized in that: The impact factor determination module comprises: Calculate the dynamic change degree between the planned area and the actual target area of ​​r4 public area as follows: Among them, μ4 is the incremental area adjustment coefficient of the public area target area, ΔP i-i′ is the incremental area of ​​land in the public area target area from the i-th plot to the i′th plot, P i is the total area of ​​the i-th plot of land in the target area of ​​the public area, and T is the target area change monitoring time; Where S pub is the preset area of ​​the target area in the public area, f(d) is the Gaussian attenuation function, and d is the attenuation distance variable of the target area.

8. The land use planning and construction full-cycle comprehensive monitoring system according to claim 1 is characterized in that: The degree of change calculation module includes: The specific formula is as follows: z＝β0+β1×r1+β2×r2+β3×r3+β4×r4 Among them, β0 is a constant term, β1, β2, β3, β4 are the regression coefficients of r1, r2, r3, r4 respectively, and the regression coefficients are obtained by fitting historical data. The multiple linear regression model can take into account the interactions between more factors and is suitable for more complex data relationship analysis.

9. The land use planning and construction full-cycle comprehensive monitoring system according to claim 1 is characterized in that: The prediction result module comprises: Use the comprehensive change observation z at a certain time t t , the observed value of the comprehensive change at time t forms is the fitted value, Among them, μ is the mean of the differenced sequence, φ p is the pth autoregressive coefficient, θ q is the qth moving average coefficient, p and q are positive integers and are not equal, z t-p is the comprehensive change degree at time p before time t; ε t-q Refers to the residual at time tq. For monitoring purposes, we can calculate the difference between the predicted and actual values ​​and set thresholds to determine if there are anomalies or trends that need attention. The monitoring indicators are set to: Among them, σ z It is the standard deviation of the residual of the comprehensive change degree z, which is used to standardize the monitoring indicators so that they can be compared with the preset thresholds.

10. A comprehensive monitoring method for the whole cycle of land use planning and construction, characterized in that: The steps include: S1, obtaining spatial geographic image data at historical moments and performing time span detection on the spatial geographic image data; S2, determining the influencing factors of geographic image changes in the target area based on the time span detection results; S3, using a multivariate linear regression model to analyze the linear relationship between the dynamic change degree r1 between the planned commercial area and the actual target area, the dynamic change degree r2 between the planned industrial area and the actual target area, the dynamic change degree r3 between the planned residential area and the actual target area, the dynamic change degree r4 between the planned public area and the actual target area, and the comprehensive change degree of the geographic image; S4, use the fitted ARIMA model to predict the future value of the comprehensive change degree z, and monitor the land use planning and construction based on the prediction results.

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