Land resource planning data real-time analysis system

Abstract

The invention relates to the technical field of land resource analysis, in particular to a territorial resource planning data real-time analysis system, which comprises a resource dynamic change analysis module for collecting territorial resource planning data, establishing a territorial resource planning time sequence, extracting resource change characteristics of adjacent time points in the territorial resource planning time sequence, and analyzing the resource change characteristics; and identifying abnormal fluctuation points according to a preset change rate threshold value, and generating a territorial resource feature analysis result. According to the method and the device, the abnormal points in the resource fluctuation are identified, and unstable factors in the resource change are found in time, so that a basis is provided for prediction of the resource change trend. The method not only can capture the potential problem of over-development of resources or imbalance of supply and demand, but also can provide accurate data support for resource planning by analyzing historical and future resource change trends, and can effectively screen out high-risk resource regions and provide reference suggestions by evaluating resource risk regions and ranking priorities.

Description

Technical Field

[0001] The present invention relates to the technical field of land resource analysis, and in particular to a real-time analysis system for land resource planning data. Background Art

[0002] In the analysis of land and resources data, existing technologies mainly rely on trend prediction of static historical data, which cannot capture subtle fluctuations in resource changes in a timely manner, resulting in a relatively delayed identification of resource change trends. For example, existing technologies usually only analyze existing historical data and cannot effectively predict future changes in resource demand or supply, which limits the ability to predict resource changes. In addition, existing technologies have low flexibility and intelligence when processing complex, multi-source data, and fail to deeply explore the correlation and potential risks between data. For example, when faced with complex changes in resource supply and demand, it is difficult to accurately identify possible resource pressure areas and anomalies in the future, which in turn leads to a lack of foresight in the analysis of land and resources planning data. This affects the accuracy of land and resources data analysis. Summary of the invention

[0003] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a real-time analysis system for land and resources planning data.

[0004] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A real-time analysis system for land and resources planning data comprises: The resource dynamic change analysis module collects land and resources planning data, establishes a land and resources planning time series, extracts resource change characteristics at adjacent time points in the land and resources planning time series, identifies abnormal fluctuation points based on a preset change rate threshold, and generates land and resources characteristic analysis results; The resource utilization trend prediction module performs segmented processing on the time series of land and resources planning data based on the land and resources characteristic analysis results, captures the abnormal change trend of resources, predicts the future changes of land and resources planning data, and generates land and resources utilization trend prediction results; The comprehensive risk assessment module collects the historical supply of resources in the land and resources planning data, combines the future changes of the land and resources planning data in the land and resources utilization trend forecast results, analyzes the supply and demand relationship of resources in the future, divides each risk area into multiple types and resource utilization pressure levels according to the preset risk threshold, and generates land and resources risk data assessment results; The resource allocation optimization module determines the priority of resources in each risk area based on the land and resources risk data assessment results, re-screens the resources according to the priority, and generates real-time analysis results of land and resources planning data.

[0005] As a further solution of the present invention, the step of acquiring the resource change characteristics is specifically: Collect land and resources planning data, organize them based on geographic information systems, classify and summarize each type of resource data according to the time dimension, and establish a time series for land and resources planning; Based on the land and resources planning time series, the changes in land utilization rate, water resource reserves and mineral resource mining intensity at adjacent time points are analyzed through sliding windows and marked according to time windows to generate resource change characteristics.

[0006] As a further solution of the present invention, the steps for obtaining the land and resources characteristic analysis results are specifically as follows: Based on the resource change characteristics, the formula is adopted: ; Computing resource change rate , get the change rate data between adjacent time points; in, It is The resource change characteristic value at a time point, It is The resource change characteristic values ​​at adjacent time points, It is The adjacent time points The time interval between time points; Based on the change rate data between the adjacent time points, the preset change rate thresholds are compared item by item, abnormal fluctuation points in resource types are identified, and the time series trend of each resource type data is analyzed to generate land and resources characteristic analysis results.

[0007] As a further solution of the present invention, the step of acquiring the abnormal change trend of the captured resources is specifically as follows: Based on the analysis results of the land and resources characteristics, multiple stages are divided according to the comparison results of the change rate between adjacent time points and the change rate threshold, and the time point when the change rate exceeds the change rate threshold or the time point when the trend changes significantly is set as the stage boundary, and the key data points in each period are extracted, including extreme points and trend inflection points, and the start and end time and key point information of each section are recorded to generate segmented processing results; Based on the segmented processing results, the key data points are sorted into a key data point sequence in chronological order, and a smooth Bezier curve is generated by fitting segment by segment. The data points affected by abnormal fluctuation points in the fitting are adjusted to reflect the dynamic change state of resource utilization and generate abnormal resource change trend analysis results.

[0008] As a further solution of the present invention, the steps for obtaining the land and resources utilization trend prediction results are specifically as follows: Based on the analysis results of the abnormal change trend of resources, the formula is adopted: ; Calculate the Bezier curve at a point in time The derivative of , get the derivative record of the Bezier curve; in, is the normalized time parameter, It is The resource utilization characteristic values ​​of key data points, Indicates the first key data point in the sequence The resource utilization characteristic value of the point, represents the order of the Bezier curve, Indicates the current time point For The weight decay value of key data points, is the power index, which determines The weighted intensity in the decrement is an offset value that determines the global influence of the curve segment. Indicates time The relative distance from the starting point increases with As increases, the initial weight gradually decreases. Indicates the current time point For The growth weight of key data points, : Used to indicate the previous key point, emphasizing the progressive order of weight distribution; Based on the derivative records of the Bezier curve, the resource utilization value is updated at each time point through time recursion, and the phased changes in the growth rate are analyzed to predict the future resource utilization trend and generate the land resource utilization trend prediction result.

[0009] As a further solution of the present invention, the steps for obtaining the supply and demand relationship of the analysis resources in the future are specifically as follows: Based on the land and resources utilization trend prediction results and the historical supply of resources in the collected land and resources planning data, the historical average supply is obtained from the historical supply data of resources, and the historical average supply is compared with the future predicted utilization to determine the supply and demand difference of each resource type, thereby obtaining supply and demand difference data; Based on the supply and demand difference data, the supply and demand ratio of each resource type in the future period is analyzed, and the supply and demand ratio of each resource type is compared with the preset shortage risk threshold to determine whether there is a risk area of ​​resource shortage and obtain the risk area analysis result.

[0010] As a further solution of the present invention, the steps for obtaining the land and resources risk data assessment results are specifically as follows: Based on the preset shortage risk threshold in the risk area analysis result, the supply-demand ratios of multiple areas are compared, the resource shortage levels are classified according to the ratio ranges, and the risk area resources are divided into multiple types to generate a classification list of risk areas; Based on the classification list of the risk areas, a corresponding resource utilization pressure level is set for each risk area resource, and the pressure level is recorded to generate a land and resources risk data assessment result.

[0011] As a further solution of the present invention, the steps for obtaining the real-time analysis results of the land and resources planning data are specifically as follows: Based on the land resources risk data assessment results, the formula is used: ; Calculate the weighted average resource , and obtain the resource weighted average result; in, It is The resource utilization pressure level weight of each partition, It is The amount of resources in a partition, is the number of resource partitions; Based on the weighted average result of the resources, the resources are reallocated according to the priority of the resource-scarce areas, the resource allocation amount is determined, the resource status of the region after allocation is updated, and the areas that still have resource gaps after allocation are marked, providing reference resource data configuration and generating real-time analysis results of land and resources planning data.

[0012] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, through dynamic data analysis based on time series, abnormal points in resource fluctuations can be identified, unstable factors in resource changes can be discovered in time, and a basis for predicting resource change trends can be provided. Not only can potential problems of over-exploitation of resources or imbalance between supply and demand be captured, but also accurate data support can be provided for resource planning by analyzing historical and future resource change trends. By evaluating and prioritizing resource risk areas, high-risk resource areas can be effectively screened out and reference opinions can be provided, which can assist in optimizing resource allocation strategies and improve the accuracy of decision-making support for land and resources planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a system flow chart of the present invention; Figure 2 A flow chart for obtaining resource change characteristics for the present invention; Figure 3 A flow chart for obtaining land and resources characteristic analysis results for the present invention; Figure 4 A flow chart for capturing abnormal change trends of resources for the present invention; Figure 5 A flow chart for obtaining land and resources utilization trend prediction results for the present invention; Figure 6 A flow chart for analyzing the supply and demand relationship of resources in the future for the present invention; Figure 7 It is a flow chart of the land resources risk data assessment results of the present invention; Figure 8 The figure is a flow chart of the real-time analysis results of land resources planning data of the present invention. DETAILED DESCRIPTION

[0014] 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.

[0015] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0016] See also Figure 1 , a real-time analysis system for land and resources planning data includes: The resource dynamic change analysis module collects land and resources planning data, including planning data of land resources, water resources, and mineral resources, organizes the land and resources planning data in chronological order, establishes a land and resources planning time series, and uses a sliding window to extract the resource change characteristics of adjacent time points in the land and resources planning time series. The resource change characteristics include land utilization rate, water resource reserve changes, and mineral resource mining intensity. The resource change characteristic data of adjacent time points are differentially calculated to obtain the change rate between every two adjacent time points. The abnormal fluctuation points are identified according to the preset change rate threshold, and the land and resources characteristic analysis results are generated by extracting the change law of land and resources in the time dimension; The resource utilization trend prediction module is based on the analysis results of land and resources characteristics, and refers to the change rate between every two adjacent time points. It segments the time series of land and resources planning data, determines the boundary of each time period, and extracts key data points in each stage. The key data points include extreme points and trend inflection points. The extreme points are the maximum and minimum values ​​in each stage. The trend inflection points are the turning points where resource utilization changes from increase to decrease or from decrease to increase. Bezier curve fitting is performed on the key data points, and the fitted Bezier curve is adjusted in combination with abnormal fluctuation points to capture the abnormal change trend of resources. By calculating the derivative of the Bezier curve at each time point, the future change of land and resources planning data is predicted, and the land and resources utilization trend prediction results are generated; The comprehensive risk assessment module collects the historical supply of resources in the land and resources planning data, combines the future changes of the land and resources planning data in the land and resources utilization trend forecast results, calculates the difference between the future forecast utilization and the historical average supply, analyzes the supply and demand relationship of resources in the future, and determines whether there will be risk areas with resource shortages in the future. According to the preset risk threshold, each risk area is divided into multiple types and resource utilization pressure levels to generate land and resources risk data assessment results; The resource allocation optimization module is based on the results of land and resources risk data assessment. It performs weighted average of each type of resources according to the resource utilization pressure level, determines the priority of resources in each risk area, re-screens resources according to the priority, and obtains the results of regional resource reconfiguration. Based on the results of regional resource reconfiguration, it updates and marks the risk areas that still have resource shortages after resource reconfiguration in real time, and generates real-time analysis results of land and resources planning data; The results of the land and resources characteristic analysis include the changing characteristics of land resource utilization rate, the fluctuation characteristics of water resource reserves and the changing characteristics of mineral resource exploitation intensity. The results of the land and resources utilization trend forecast include the distribution of resource utilization extreme points, trend inflection point distribution and trend line characteristics of Bezier curve fitting in each future time stage. The results of the land and resources risk data assessment include the future resource supply and demand difference distribution map, the classification of resource shortage risk areas and the classification of resource utilization pressure levels. The results of the real-time analysis of land and resources planning data include the optimized resource priority ranking, the regional resource distribution characteristics after resource reconfiguration and the marking results of the remaining high-risk areas.

[0017] See also Figure 2 , the specific steps for obtaining resource change characteristics are: Collect land and resources planning data, organize them based on geographic information systems, classify and summarize each type of resource data according to the time dimension, and establish a time series for land and resources planning; Based on the Geographic Information System (GIS), the latest land use data in the region is called, the land resource planning data is divided according to administrative regions and classified and counted according to the land use classification standard, the land area, land type and corresponding utilization rate of each region are extracted, and the land use change table is established according to the time dimension with these data; for water resource reserve data, the water area distribution and area data are obtained through the GIS system, and the time series are sorted out in combination with historical reserve records, the mineral resource planning data are classified and summarized, and the location of the mining area, the mining area and the annual change data are extracted in particular, and the sorted land resources, water resources and mineral resources planning data are summarized in chronological order into a complete land resources planning time series for subsequent analysis and processing.

[0018] Based on the time series of land and resources planning, the changes in land utilization rate, water resource reserves and mineral resource mining intensity at adjacent time points are analyzed through sliding windows and marked according to time windows to generate resource change characteristics; The sliding window is used to extract the resource change characteristics of adjacent time points in the land and resources planning time series. First, the land use rate, water resource reserves and mineral resource mining intensity data that have been sorted are imported through the geographic information system (GIS), and these data are aligned in time order. The time span of the sliding window is set to two months, and the sliding window is calculated and analyzed in sequence starting from the first time node. For land use rate, the GIS tool counts the total area of ​​different types of land according to the land use type classification data in the time window, and compares the area difference of the same land type at the start and end time of the window. Then, the area change is compared with the total area to obtain the percentage change of land use rate, and it is recorded in the characteristic data table. For water resource reserve changes, the GIS tool superimposes water distribution data and water depth data, and accumulates the water reserves at the start and end time nodes of the window, and records the reserve difference between the two nodes as the reserve change feature; for mineral resource mining intensity, the mining volume of each mineral is extracted from the mineral resource mining record, and the difference is calculated after summarizing the total mining volume at the start and end time of the window, and recorded as the mining intensity change feature. All data of change features are marked according to the time window.

[0019] See also Figure 3 , the specific steps for obtaining the results of land and resources characteristic analysis are as follows: Based on the resource change characteristics, the formula is used: ; Computing resource change rate , get the change rate data between adjacent time points; in, It is The resource change characteristic value at a time point, It is The resource change characteristic values ​​at adjacent time points, It is The adjacent time points The time interval between time points is calculated directly based on the timestamps recorded in the time series.

[0020] For example, if a region At the next time point (the ) The cultivated land area is 1,010 hectares and the forest area is 490 hectares.

[0021] No. Changes in land use rate at a given time point: ; No. The water area at that time was 500 hectares, the average water depth was 2 meters, and the total reserves were calculated as: ; No. At that time, the coal mining volume was 200 tons, the iron ore mining volume was 300 tons, and the total mining intensity was: ; Assume Resource change characteristics at a certain point in time: Land use rate change , water resources reserve change value , the change value of mineral resource mining intensity .

[0022] like , ,but: .

[0023] Land utilization rate: ; Water resource reserves: ; Mineral resource mining intensity: ; The results show that the rate of change of land use , indicating that the daily change in land use rate is about 0.0167%, and the change rate of water resources reserves is , showing that the daily reserves increase by 1.67 cubic meters, the rate of change in the mining intensity of mineral resources , indicating a decrease of 0.333 tons in daily mining.

[0024] Based on the change rate data between adjacent time points, the preset change rate thresholds are compared item by item, abnormal fluctuation points in resource types are identified, and the time series trend of each resource type data is analyzed to generate land and resources characteristic analysis results; According to the preset change rate threshold, the abnormal fluctuation points are identified. Combined with the calculated resource change rate example, the reasonable thresholds are assumed as follows: the land utilization rate change rate threshold is 0.0003, the water resource reserve change rate threshold is 2.0, and the mineral resource mining intensity change rate threshold is -0.5. The calculated rates are compared with the thresholds one by one, and the abnormal point identification and feature analysis process is completed through the following steps: The calculated land utilization rate change rate is 0.000167, which is lower than the preset change rate threshold of 0.0003. Therefore, the change in land utilization during this period is within the normal range and is not identified as an abnormal fluctuation point. Combined with the time series, further observation shows that the land utilization rate shows a steady growth trend, indicating that the land use activities in the area are proceeding normally within the planning scope. The calculated result of the water resource reserve change rate is 1.67, which is higher than the preset change rate threshold of 2.0. The change rate of water resource reserves during this period is close to the threshold range but does not exceed it, and is not marked as a significant anomaly. However, time series analysis shows that this rate may be affected by continuous precipitation and water storage projects, and it is necessary to closely track whether it deviates further from the normal value in subsequent observations. The rate of change of mineral resource mining intensity is -0.333, which is higher than the preset change rate threshold of -0.5. This rate did not trigger the judgment criteria for abnormal fluctuation points, but the trend of change shows a significant slowdown in mining activities, which is related to the maintenance of equipment in the mining area. It is recommended to pay attention to whether there will be further declines in future time nodes. According to the changing rules of land use, water resource reserves, and mineral resource mining intensity in the time dimension, the following analysis results are generated: The rate of change of land utilization is within the normal range, and there are no abnormal fluctuations, indicating that land development activities are stable and controllable. The change in water resource reserves is close to the threshold range, and there may be potential risks of short-term growth. It is necessary to combine subsequent data to confirm whether there is a sustained impact. The change in mineral resource mining intensity is normal, but it is showing a slowing trend, and long-term resource development plans need to be evaluated in a timely manner.

[0025] See also Figure 4 ,The specific steps for capturing abnormal change trends of resources are: Based on the results of land and resources characteristic analysis, the data are divided into multiple stages according to the comparison results of the change rate between adjacent time points and the change rate threshold. The time point when the change rate exceeds the change rate threshold or the time point when the trend changes significantly is set as the stage boundary. The key data points in each period are extracted, including extreme points and trend inflection points. The start and end time and key point information of each period are recorded to generate segmented processing results. Based on the previous change rate threshold judgment results, the time series data of resource utilization changes are first segmented. According to the comparison results of the change rate and the threshold at every two adjacent time points, the time series is divided into different stages. The stage boundary is set at the time point when the change rate exceeds the threshold or the trend changes significantly. Taking the change of land utilization rate as an example, if at time point arrive If the rate of change is 0.000167 and below the threshold of 0.0003, it is considered to be a stage of stable growth; if If the rate rises to 0.00035 and exceeds the threshold, then set For the extraction of key data points in each period, we first calculate the resource change characteristic values ​​at all time points in the period and extract the extreme points, including the maximum and minimum values. arrive In the period, the land use rate change values ​​are 0.50, 0.51, and 0.52, respectively. 0.52 is the maximum value of this period, and 0.50 is the minimum value. At the same time, analyze the rate change trend and determine the location of the trend inflection point. For example, in the period to If the resource change rate changes from positive (increasing) to negative (decreasing) within a period, the time point at which the trend reverses is Finally, the information of stage segmentation, extreme points and trend inflection points is combined to form the segmentation processing results and a list of key data points, including the start and end time of each stage, extreme points and their corresponding resource change characteristic values, the time point of the trend inflection point and the change direction record, to ensure that the segmentation and key point information are clear and complete.

[0026] Based on the segmented processing results, the key data points are sorted into a key data point sequence in chronological order, and a smooth Bezier curve is generated by fitting segment by segment. The data points affected by abnormal fluctuation points in the fitting are adjusted to reflect the dynamic change state of resource utilization and generate abnormal resource change trend analysis results; Bezier curve fitting is performed on the extracted key data points. First, the key data points (including extreme points and trend inflection points) of each stage are sorted into a sequence of key data points in chronological order. The data of these key data points include time points and corresponding resource change characteristic values, such as the maximum value, minimum value and inflection point position of a certain stage. MATLAB or ArcGIS is selected to load the key data points and perform Bezier curve fitting. The key points in each period are processed in turn to generate a continuous and smooth curve. In the fitting process, it is necessary to ensure that the curve can reflect the actual change trend of each stage. For abnormal fluctuation points, the key data points are adjusted before fitting to reduce the impact of abnormal values ​​on the overall curve, such as using the average value of adjacent data points as a substitute. After fitting, the curve presents a continuous and smooth change state on the time axis, and each segment of the curve is naturally connected at the boundary point. The curve as a whole reflects the change trend of resource utilization characteristics in the time dimension. The Bezier curve that is finally fitted shows the overall change state of resource utilization, such as land utilization rate shows a steady upward trend in some stages, water resource reserves grow rapidly but tend to be flat in a specific stage, and the mineral resource mining intensity curve shows a downward trend with short-term fluctuations.

[0027] See also Figure 5 , the specific steps for obtaining the results of land and resources utilization trend prediction are as follows: Based on the analysis results of abnormal resource change trends, the formula is used: ; Calculate the Bezier curve at a point in time The derivative of , get the derivative record of the Bezier curve; in, is a normalized time parameter, ranging from 0 to 1, indicating the position of the current time point within the stage time range. The calculation formula is For example, if the time period is from 2023-01-01 to 2023-02-01, and the current time point is 2023-01-15, then , It is The resource utilization characteristic values ​​of key data points, including extreme points and trend inflection points, are extracted by analyzing the maximum and minimum values ​​of time series data and the direction of rate change. For example, if the maximum value of land utilization rate in a stage is 0.52 and the minimum value is 0.50, then , , the trend turning point is determined by the positive and negative conversion of the rate change, Indicates the first key data point in the sequence The resource utilization characteristic value of the point is The adjacent previous point of Also derived from the time series key data points, for example, if the current key data point , then the previous point , Indicates the order of the Bezier curve, which is defined as the number of key data points minus one and is directly determined by the number of key data points. For example, if there are 3 key data points, then , Indicates the current time point For The weight decay value of the control point, the size of the weight increases with The closer the current time point is to the next control point, the smaller the impact on the previous control point is. Power of To quantify the time point The contribution of each control point is Indicates the current time point For The growth weight of the control point, the size of the weight increases with The closer the current time point is to the next control point, the greater its influence on it. Power of To quantify the time point The contribution of each control point.

[0028] For key data points and :For example, the key data point of land utilization is , , For the normalized time :Get time period , , current time point ,calculate .

[0029] Key Data Points: , , ; Order ; Normalized time . Substituting into the formula: ; Difference calculation: ; Substituting weights: ; ; The results show that the derivative Indicates at a point in time (2023-01-15), the rate of change of land utilization is 0.01096. Combined with the derivative values ​​at other time points, the trend changes of resource utilization can be fully described.

[0030] Based on the derivative records of the Bezier curve, the resource utilization value is updated at each time point through time recursion, and the phased changes in the growth rate are analyzed to predict the future resource utilization trend and generate the land resource utilization trend prediction results; Based on the calculated derivative values , assuming that there are other derivative values, the detailed process of analyzing and predicting the future changes of land and resources planning data is as follows. Assuming that the current time is 2023-01-15, the current value of land utilization rate is , the prediction of the change trend of land utilization rate in the next month is carried out in steps: the next month (2023-01-16 to 2023-02-15) is divided into daily time points, assuming that the change rate (derivative) of land utilization rate Maintain a linear trend in the short term. Recursively calculate resource utilization values ​​at future time points, for example: 2023-01-16: , 2023-01-17: Assume that on 2023-01-25, affected by resource conditions, the derivative changes to From this point in time, use the new derivative value to predict future resource utilization values, for example: 2023-01-26: , 2023-01-27: Based on the daily calculation results, a trend chart of future land utilization rate is drawn to clarify the changes in the growth rate at each stage. It is predicted that by 2023-02-15, the land utilization rate will reach about , which means an increase of 11.57% in one month. If the current trend of change is maintained for a long time, combined with the rate adjustment points in other periods of the year, it is expected that by 2023-12-31, the land utilization rate may be close to 0.8. For the early rapid growth stage (the stage with higher derivatives), resource development and allocation should be optimized to ensure efficient use of land resources; in the rate decline stage, it is necessary to analyze the restrictions of resources and environment on the growth trend and adjust the land use plan in a timely manner; combined with the annual forecast results, a flexible land use management strategy should be formulated to support regional sustainable development. Through the above process, the future trend of land and resources planning data was quantitatively predicted, and specific planning basis for optimizing resource management was obtained.

[0031] See also Figure 6 , the specific steps for analyzing the supply and demand relationship of resources in the future are: Based on the results of land and resources utilization trend forecast and the historical supply of resources in the collected land and resources planning data, the historical average supply is obtained from the historical supply data of resources, and the historical average supply is compared with the future forecast utilization to clarify the supply and demand difference of each resource type and obtain the supply and demand difference data; Combine the forecast results of land resource utilization trends and the collected historical supply data to calculate the difference between the future forecast utilization and the historical average supply. Taking land resources, water resources and mineral resources as examples, land resources: Historical supply: Historical data shows that the average annual supply of land development from 2010 to 2020 is 40,000 hectares. Future forecast utilization: According to trend forecasts, land utilization will increase to 50,000 hectares in 2025. Supply and demand difference: Compare the forecast utilization and the historical average supply, and calculate the difference 10,000 hectares, indicating that the future demand for land resources will exceed the historical supply by 25%. Water resources: Historical supply: Historical data show that the average water supply over the years is 21 million cubic meters / year. Future forecast utilization: According to forecasts, 20 million cubic meters of water resources will be needed in 2025. Supply and demand difference: The supply and demand difference is 10,000 cubic meters, indicating that water supply is slightly higher than demand, and demand has dropped by about 4.76%. Mineral resources: Historical supply: The historical average supply is 2.8 million tons / year. Future forecast utilization: The future forecast for mineral resource demand in 2025 is 3 million tons. Supply and demand difference: The difference is 10,000 tons, and the future demand will be 7.14% higher than the historical supply. Through the above comparative analysis, the future supply and demand differences of different resource types are clarified, providing a quantitative basis for further evaluation of resource supply and demand balance and shortage risks.

[0032] Based on the supply and demand difference data, analyze the supply and demand ratio of each resource type in the future, compare the supply and demand ratio of each resource type with the preset shortage risk threshold, determine whether there is a risk area of ​​resource shortage, and obtain the risk area analysis results; Based on the calculated supply and demand differences, the supply and demand relationship of resources in the future is analyzed, and whether there are risk areas of resource shortage. Taking land resources as an example, the future predicted utilization is 50,000 hectares, the historical average supply is 40,000 hectares, and the supply-demand ratio is , which is higher than the preset risk threshold of 1.2, so the area is judged as a potential risk area for land resource shortage; for water resources, the predicted utilization is 20 million cubic meters, the historical supply is 21 million cubic meters, and the supply-demand ratio is , which is lower than the risk threshold of shortage, indicating that supply and demand are balanced and there is no risk of shortage; the predicted utilization of mineral resources is 3 million tons, the historical supply is 2.8 million tons, and the supply-demand ratio is , close to but not exceeding the risk threshold of shortage, it is judged to be close to equilibrium, without obvious risk of shortage. By calculating the supply-demand ratio and comparing it with the risk threshold of shortage, the possible risk areas of land resources are identified.

[0033] See also Figure 7 , the specific steps for obtaining the land and resources risk data assessment results are as follows: Based on the preset shortage risk threshold in the risk area analysis results, the supply-demand ratios of multiple areas are compared, the resource shortage levels are classified according to the ratio range, and the resources in the risk areas are divided into multiple types to generate a classification list of risk areas; According to the preset shortage risk threshold, each risk area is divided into multiple types. Combined with the calculation results of the supply-demand ratio, the possibility of resource shortage in each area is classified. For example, if the land resource supply-demand ratio is 1.25, which exceeds the shortage risk threshold of 1.2, the area can be classified as a medium shortage type. If the mineral resource supply-demand ratio of a certain area is 1.5, it can be classified as a high shortage type. For areas with a supply-demand ratio lower than the threshold, such as a water resource supply-demand ratio of 0.95, it is classified as a supply-demand balance type. By comparing the supply-demand ratio of each area with the shortage risk threshold and setting different ratio intervals, such as 1.0-1.2 for mild shortage, 1.2-1.5 for moderate shortage, and >1.5 for high shortage, the classification of each risk area is finally completed to form a preliminary data table of the classification results.

[0034] Based on the classification list of risk areas, set the corresponding resource utilization pressure level for each risk area resource, record the pressure level, and generate the land and resources risk data assessment results; Divide the corresponding resource utilization pressure level and generate the land and resources risk data assessment results. First, set the resource utilization pressure level according to the risk area classification results in paragraph 1. For example, lightly scarce areas are set to low pressure, moderately scarce areas are set to medium pressure, and highly scarce areas are set to high pressure. For supply and demand balance areas, they are set to no pressure. Combined with the resource type, the supply and demand situation of each area is comprehensively summarized. For example, for land resources, lightly scarce areas are set to level one pressure, moderately scarce areas are set to level two pressure, and highly scarce areas are set to level three pressure. The pressure levels are graded and recorded in the assessment result table. Finally, the risk level of each area is associated and integrated with the pressure level to generate a comprehensive risk data assessment table including area name, resource type, supply and demand ratio, shortage type, and utilization pressure level.

[0035] See also Figure 8 ,The specific steps for obtaining the real-time analysis results of land and resources planning data are as follows: Based on the results of land and resources risk data assessment, the formula is used: ; Calculate the weighted average resource , and obtain the resource weighted average result; in, It is The resource utilization pressure level weight of each zone reflects the resource shortage degree of different regions, such as slightly shortage, moderately shortage or highly shortage areas, which is obtained through the pressure level distribution in the land and resources risk assessment results. It is The resource volume of each zone represents the total amount of resources in the zone (such as land use area, water resource reserves or mineral resource mining volume), which is obtained through historical data statistics or land resource utilization trend forecast results. It is the number of resource zones, indicating the total number of different areas involved in the calculation, which is determined according to the standards for the division of land and resources management areas.

[0036] If the number of partitions The specific resource quantities are , , . Weight : Assign weights according to resource utilization pressure levels. For example, lightly scarce areas are set to 1, moderately scarce areas are set to 2, and highly scarce areas are set to 3. , , .

[0037] Substituting into the formula: ; ; ; ; The results show that It means that after considering the pressure level, the average amount of resources per partition is about 36.67.

[0038] Based on the weighted average results of resources, resources are reallocated according to the priority of resource-scarce areas, the specific allocation amount of resources is determined, the resource status after regional allocation is updated, and areas with resource gaps after allocation are marked, providing reference resource data configuration and generating real-time analysis results of land and resources planning data; Calculated based on weighted average , determine the priority of resources in each risk area, and reallocate resources according to the priority. The process is as follows: According to the shortage type and weight level of the risk area, the resource needs of the highly scarce areas are met first. For example, assuming that the partition The resource requirements are 60, 40 and 20 respectively, and the corresponding shortage weights are 3 (high shortage), 2 (moderate shortage), and 1 (slight shortage). The priority order is . Allocate the total resources according to priority, configure the total amount to 100, and the allocation rule is based on weight ratio: Total weight: . Dispensing amount: , , . After allocation, calculate the remaining resource requirements: Allocate 50, the gap is , which is still in high shortage; Allocate 33.33, the gap is , updated to moderate shortage; Allocate 16.67, which fully meets the demand and has no shortage risk. Organize the updated resource allocation and risk status, and generate a data analysis table containing the region name, resource demand, allocated resource volume, remaining gap, and current shortage level. For example: Region : Resource demand 60, allocation 50, gap 10, highly scarce; region : Resource demand 40, allocation 33.33, gap 6.67, moderate shortage; region :Resource demand 20, allocation 16.67, no gap, no risk of shortage. Through the above process, the determination of resource priorities, the rational reconfiguration of resources and the real-time update of risk areas are completed.

[0039] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A real-time analysis system for land and resources planning data, characterized in that: The system comprises: The resource dynamic change analysis module collects land and resources planning data, establishes a land and resources planning time series, extracts resource change characteristics at adjacent time points in the land and resources planning time series, identifies abnormal fluctuation points based on a preset change rate threshold, and generates land and resources characteristic analysis results; The resource utilization trend prediction module performs segmented processing on the time series of land and resources planning data based on the land and resources characteristic analysis results, captures the abnormal change trend of resources, predicts the future changes of land and resources planning data, and generates land and resources utilization trend prediction results; The comprehensive risk assessment module collects the historical supply of resources in the land and resources planning data, combines the future changes of the land and resources planning data in the land and resources utilization trend forecast results, analyzes the supply and demand relationship of resources in the future, divides each risk area into multiple types and resource utilization pressure levels according to the preset risk threshold, and generates land and resources risk data assessment results; The resource allocation optimization module determines the priority of resources in each risk area based on the land and resources risk data assessment results, re-screens the resources according to the priority, provides reference resource data configuration, and generates real-time analysis results of land and resources planning data.

2. The real-time analysis system for land and resources planning data according to claim 1 is characterized in that: The steps for obtaining the resource change characteristics are specifically as follows: Collect land and resources planning data, organize them based on geographic information systems, classify and summarize each type of resource data according to the time dimension, and establish a time series for land and resources planning; Based on the land and resources planning time series, the changes in land utilization rate, water resource reserves and mineral resource mining intensity at adjacent time points are analyzed through sliding windows and marked according to time windows to generate resource change characteristics.

3. The real-time analysis system for land and resources planning data according to claim 2 is characterized in that: The steps for obtaining the land and resources characteristic analysis results are specifically as follows: Based on the resource change characteristics, the formula is adopted: ； Computing resource change rate , get the change rate data between adjacent time points; in, It is The resource change characteristic value at a time point, It is The resource change characteristic values ​​at adjacent time points, It is The adjacent time points The time interval between time points; Based on the change rate data between the adjacent time points, the preset change rate thresholds are compared item by item, abnormal fluctuation points in resource types are identified, and the time series trend of each resource type data is analyzed to generate land and resources characteristic analysis results.

4. The real-time analysis system for land and resources planning data according to claim 3 is characterized in that: The steps for acquiring the abnormal change trend of the captured resources are specifically as follows: Based on the analysis results of the land and resources characteristics, multiple stages are divided according to the comparison results of the change rate between adjacent time points and the change rate threshold, and the time point when the change rate exceeds the change rate threshold or the time point when the trend changes significantly is set as the stage boundary, and the key data points in each period are extracted, including extreme points and trend inflection points, and the start and end time and key point information of each section are recorded to generate segmented processing results; Based on the segmented processing results, the key data points are sorted into a key data point sequence in chronological order, and a smooth Bezier curve is generated by fitting segment by segment. The data points affected by abnormal fluctuation points in the fitting are adjusted to reflect the dynamic change state of resource utilization and generate abnormal resource change trend analysis results.

5. The real-time analysis system for land and resources planning data according to claim 4 is characterized in that: The steps for obtaining the land and resources utilization trend prediction results are specifically as follows: Based on the analysis results of the abnormal change trend of resources, the formula is adopted: ； Calculate the Bezier curve at a point in time The derivative of , get the derivative record of the Bezier curve; in, is the normalized time parameter, It is The resource utilization characteristic value of each key data point, Indicates the first key data point in the sequence The resource utilization characteristic value of the point, represents the order of the Bezier curve, Indicates the current time point For The weight decay value of key data points, is the power index, which determines The weighted intensity in the decrement is an offset value that determines the global influence of the curve segment. Indicates time The relative distance from the starting point increases with As increases, the initial weight gradually decreases. Indicates the current time point For The growth weight of key data points, : Used to indicate the previous key point, emphasizing the progressive order of weight distribution; Based on the derivative records of the Bezier curve, the resource utilization value is updated at each time point through time recursion, and the phased changes in the growth rate are analyzed to predict the future resource utilization trend and generate the land resource utilization trend prediction result.

6. The real-time analysis system for land and resources planning data according to claim 5 is characterized in that: The steps for obtaining the supply and demand relationship of the analysis resources in the future are specifically as follows: Based on the land and resources utilization trend prediction results and the historical supply of resources in the collected land and resources planning data, the historical average supply is obtained from the historical supply data of resources, and the historical average supply is compared with the future predicted utilization to determine the supply and demand difference of each resource type, thereby obtaining supply and demand difference data; Based on the supply and demand difference data, the supply and demand ratio of each resource type in the future period is analyzed, and the supply and demand ratio of each resource type is compared with the preset shortage risk threshold to determine whether there is a risk area of ​​resource shortage and obtain the risk area analysis result.

7. The real-time analysis system for land and resources planning data according to claim 6 is characterized in that: The specific steps for obtaining the land and resources risk data assessment results are as follows: Based on the preset shortage risk threshold in the risk area analysis result, the supply-demand ratios of multiple areas are compared, the resource shortage levels are classified according to the ratio ranges, and the risk area resources are divided into multiple types to generate a classification list of risk areas; Based on the classification list of the risk areas, a corresponding resource utilization pressure level is set for each risk area resource, and the pressure level is recorded to generate a land and resources risk data assessment result.

8. The real-time analysis system for land and resources planning data according to claim 7 is characterized in that: The steps for obtaining the real-time analysis results of the land and resources planning data are specifically as follows: Based on the land resources risk data assessment results, the formula is used: ； Calculate the weighted average resource , and obtain the resource weighted average result; in, It is The resource utilization pressure level weight of each partition, It is The amount of resources in a partition, is the number of resource partitions; Based on the weighted average result of the resources, the resources are reallocated according to the priority of the resource-scarce areas, the resource allocation amount is determined, the resource status of the region after allocation is updated, and the areas that still have resource gaps after allocation are marked, providing reference resource data configuration and generating real-time analysis results of land and resources planning data.

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