Sponge city rainfall analysis method and system based on super-long duration data

Abstract

The invention is suitable for the technical field of sponge city rainwater resource management, and provides a sponge city rainfall analysis method and system based on ultra-long duration data, and the method comprises the steps: obtaining the real-time predicted precipitation of a target city extension region in a preset time period, setting the initial rainwater management efficiency for the target city extension region, and obtaining the initial rainwater management efficiency of the target city extension region; meanwhile, acquiring an underground water level fluctuation record, historical land coverage change data and a soil permeability historical data table of the target city expansion area; the rainwater management efficiency is optimized by combining multi-dimensional data such as underground water level fluctuation, soil permeability historical data and historical land coverage change information; the method comprises the following steps: firstly, accurately adjusting a primary correction factor of rainwater management efficiency by analyzing an underground water level change rate and combining with soil permeability deviation; secondly, the average slope of the historical land change trend is used as a secondary correction factor, and the rainwater management efficiency is further optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of sponge city rainwater resource management, and particularly relates to a sponge city rainfall analysis method and system based on ultra-long duration data. Background Art

[0002] Sponge city rainfall analysis, as an important means to optimize urban water resource management, involves the comprehensive analysis of multi-dimensional data such as precipitation, soil permeability, groundwater level, and land use changes to accurately predict and adjust the management efficiency of rainwater; through the effective utilization of these data, the management efficiency of water resources can be dynamically adjusted to adapt to changing hydrological and climatic conditions.

[0003] In the process of urbanization, rainwater management has always been an important means to solve urban waterlogging, flood, and water resource shortage problems; the concept of sponge city emerged, aiming to increase the infiltration, retention, and purification capacity of urban rainwater through natural or quasi-natural means, thereby improving the utilization efficiency of urban rainwater; however, the existing sponge city water resource management systems have certain limitations, especially in terms of efficiency optimization, and most methods still rely on static models and single precipitation predictions; the existing technologies often do not comprehensively consider the impacts of factors such as groundwater level, soil permeability, and land use changes during the urban expansion process, which results in insufficient dynamic adjustment ability of water resource management efficiency and inability to effectively respond to the changing hydrological conditions of the city. Summary of the Invention

[0004] The purpose of the present invention is to provide a sponge city rainfall analysis method and system based on ultra-long duration data, aiming to solve the problems raised in the background art.

[0005] The present invention is implemented as follows. A sponge city rainfall analysis method based on ultra-long duration data, the method includes: Obtain the real-time predicted precipitation in the target urban expansion area within a predetermined time period, the preliminary rainwater management efficiency formulated for the target urban expansion area, and at the same time obtain the groundwater level fluctuation record, historical land cover change data, and historical soil permeability data table of the target urban expansion area; Analyze the groundwater level fluctuation record, and judge whether the rising change rate of the groundwater level in the target urban expansion area within the specified time period exceeds a preset threshold. If so, obtain the current soil permeability, and find the historical soil permeability in the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation from the historical soil permeability data table; Calculate the deviation quantization value between the current soil permeability and the historical soil permeability, and use it as the primary correction factor; Analyze historical land cover change data, extract natural land use change information, draw a natural land change trend graph, calculate the average slope of the change trend of the natural land change trend graph, and use the average slope as the secondary correction factor; Combine the primary correction factor and the secondary correction factor to perform double correction on the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency.

[0006] As a further limitation of the technical solution of the embodiment of the present invention, the specified time period refers to the time interval from the start of the construction of the target urban expansion area to the current moment.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, the steps of analyzing the groundwater level fluctuation record, judging whether the rising change rate of the groundwater level in the target urban expansion area exceeds a preset threshold within the specified time period, and if so, obtaining the current soil permeability and finding the historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation from the historical soil permeability data table include: Analyze the groundwater level fluctuation record, divide the specified time period into several sub-time intervals of the same length as the predetermined time period, and extract the groundwater level data of each sub-time interval; Based on the groundwater level data of each sub-time interval, calculate the rising change rate of the groundwater level in the target urban expansion area within the specified time period, and judge whether the rising change rate exceeds the preset threshold; If the rising change rate exceeds the preset threshold, obtain the current soil permeability, and screen out the historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation from the historical soil permeability data table.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, the steps of analyzing historical land cover change data, extracting natural land use change information, drawing a natural land change trend graph, calculating the average slope of the change trend of the natural land change trend graph, and using the average slope as the secondary correction factor include: Analyze historical land cover change data and extract the natural land remaining value of each sub-time interval within the specified time period from it; Taking each sub-time interval as the X-axis and the natural land remaining value as the Y-axis, draw the natural land change trend graph of the target urban expansion area within the specified time period; Calculate the average slope of the change trend of the natural land change trend graph, and use the average slope as the secondary correction factor.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, the steps of double-correcting the preliminary rainwater management efficiency by combining the primary correction factor and the secondary correction factor to obtain the optimized rainwater management efficiency include: Retrieve the rainwater management efficiency optimization formula, and double-correct the preliminary rainwater management efficiency by combining the primary correction factor and the secondary correction factor to obtain the optimized rainwater management efficiency; Apply the optimized rainwater management efficiency to the rainwater collection system in the target urban expansion area.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the rainwater management efficiency optimization formula is , where E optimized refers to the optimized rainwater management efficiency, E initial refers to the preliminary rainwater management efficiency, D bias refers to the primary correction factor, that is, the deviation quantification value between the current soil permeability and the historical soil permeability, K1 refers to the adjustment coefficient of the primary correction factor, S avg refers to the secondary correction factor, that is, the average slope of the change trend of the natural land change trend map, K2 refers to the adjustment coefficient of the secondary correction factor.

[0011] In the rainwater management efficiency optimization formula: , where F current refers to the current soil permeability, F historical refers to the historical soil permeability; , where N represents the total number of sub-time intervals, R i represents the remaining value of natural land corresponding to the i-th sub-time interval, T i represents the time median of the i-th sub-time interval.

[0012] A sponge city rainfall analysis system based on ultra-long duration data, the system includes: a data acquisition module, a data parsing module, a primary correction factor determination module, a secondary correction factor determination module, and a rainwater management efficiency optimization module, where: The data acquisition module is used to obtain the real-time predicted precipitation in the target urban expansion area within a predetermined time period, the preliminary rainwater management efficiency formulated for the target urban expansion area, and at the same time obtain the groundwater level fluctuation record, historical land cover change data, and soil permeability historical data table of the target urban expansion area; A data parsing module, which is used to parse the groundwater level fluctuation records, determine whether the rising change rate of the groundwater level in the target urban expansion area exceeds a preset threshold within a specified time period. If so, obtain the current soil permeability, and find the historical soil permeability in the historical soil permeability data table for the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation; The specified time period refers to the time interval covering from the start of the construction of the target urban expansion area to the current moment; A primary correction factor determination module, which is used to calculate the deviation quantization value between the current soil permeability and the historical soil permeability, and use it as the primary correction factor; A secondary correction factor determination module, which is used to analyze the historical land cover change data, extract the natural land use change information, draw a natural land change trend graph, calculate the average slope of the change trend of the natural land change trend graph, and use the average slope as the secondary correction factor; A rainwater management efficiency optimization module, which is used to combine the primary correction factor and the secondary correction factor to perform double correction on the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the data parsing module specifically includes: A first data parsing unit, which is used to parse the groundwater level fluctuation records, divide the specified time period into several sub-time intervals of the same length as the predetermined time period, and extract the groundwater level data of each sub-time interval; A rising change rate calculation unit, which is used to calculate the rising change rate of the groundwater level in the target urban expansion area within the specified time period based on the groundwater level data of each sub-time interval, and determine whether the rising change rate exceeds the preset threshold; A second data parsing unit, which is used to, if the rising change rate exceeds the preset threshold, obtain the current soil permeability, and screen out the historical soil permeability in the historical soil permeability data table for the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the secondary correction factor determination module specifically includes: A natural land remaining value extraction unit, which is used to analyze the historical land cover change data and extract the natural land remaining value of each sub-time interval within the specified time period; A change trend graph drawing unit, which is used to draw a natural land change trend graph of the target urban expansion area within the specified time period with each sub-time interval as the X-axis and the natural land remaining value as the Y-axis; A secondary correction factor generation unit for calculating the average slope of the change trend of the natural land change trend map and using the average slope as the secondary correction factor.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the rainwater management efficiency optimization module specifically includes: A rainwater management efficiency optimization unit for retrieving the rainwater management efficiency optimization formula and combining the primary correction factor and the secondary correction factor to perform double correction on the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency; An optimized rainwater management efficiency application unit for applying the optimized rainwater management efficiency to the rainwater collection system in the target urban expansion area; The rainwater management efficiency optimization formula is , where E optimized refers to the optimized rainwater management efficiency, E initial refers to the preliminary rainwater management efficiency, D bias refers to the primary correction factor, that is, the deviation quantification value between the current soil permeability and the historical soil permeability, K1 refers to the adjustment coefficient of the primary correction factor, S avg refers to the secondary correction factor, that is, the average slope of the change trend of the natural land change trend map, K2 refers to the adjustment coefficient of the secondary correction factor.

[0016] In the rainwater management efficiency optimization formula: , where F current refers to the current soil permeability, F historical refers to the historical soil permeability; , where N represents the total number of sub-time intervals, R i represents the remaining value of natural land corresponding to the i-th sub-time interval, T i represents the time median of the i-th sub-time interval.

[0017] Compared with the prior art, the present invention has the following beneficial effects: By combining multi-dimensional data such as groundwater level fluctuations, historical soil permeability data, and historical land cover change information, the rainwater management efficiency is optimized; first, by analyzing the groundwater level change rate and combining the soil permeability deviation, the primary correction factor of the rainwater management efficiency is accurately adjusted; secondly, using the average slope of the historical land change trend as the secondary correction factor to further optimize the rainwater management efficiency; the double correction of the two can comprehensively consider the impact of land permeability and land use change on rainwater collection, improving the accuracy and adaptability of the rainwater collection system.

[0018] This method can dynamically adjust the rainwater management efficiency based on the comparison between real-time data and historical data, significantly improving the rainwater recycling capacity of cities under different climate conditions. At the same time, through the integrated analysis of long-term data, the response capacity of the system to changes in hydrological conditions in urban expansion areas is enhanced, optimizing urban rainwater management and promoting the sustainable development of green infrastructure. This technology has broad application prospects, especially in the fields of sponge city construction, rainwater resource utilization, and urban environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of the method provided by an embodiment of the present invention; Figure 2 It is a flowchart of obtaining the current soil permeability and historical soil permeability in the method provided by an embodiment of the present invention; Figure 3 It is a flowchart of generating a secondary correction factor in the method provided by an embodiment of the present invention; Figure 4 It is a flowchart of double-correcting the preliminary rainwater management efficiency in the method provided by an embodiment of the present invention; Figure 5 It is an application architecture diagram of the system provided by an embodiment of the present invention; Figure 6 It is a structural block diagram of the data parsing module in the system provided by an embodiment of the present invention; Figure 7 It is a structural block diagram of the secondary correction factor determination module in the system provided by an embodiment of the present invention; Figure 8 It is a structural block diagram of the rainwater management efficiency optimization module in the system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0021] Figure 1 It shows a flowchart of the method provided by an embodiment of the present invention.

[0022] Specifically, a sponge city rainfall analysis method based on ultra-long duration data, the method specifically includes the following steps: Step S100, obtain the real-time predicted precipitation in the target urban expansion area within a predetermined time period, the preliminary rainwater management efficiency formulated for the target urban expansion area, and at the same time obtain the groundwater level fluctuation records, historical land cover change data, and soil permeability historical data tables of the target urban expansion area.

[0023] In the embodiments of the present invention, the reason for taking the urban expansion area as the research object is that the urban expansion area is usually a key area in urban development. The rapid changes in its land use type and infrastructure construction have led to significant changes in hydrological conditions. In these areas, the permeability of precipitation and the management of water resources are particularly important. Accurately predicting and adjusting the rainwater management efficiency helps to improve the utilization efficiency of urban water resources, reduce the risk of flood disasters, and promote sustainable urban development. The "rainwater management" mentioned in the present invention can specifically cover multiple aspects such as "rainwater collection, storage, infiltration, retention, purification and utilization".

[0024] In the prior art, the real-time prediction of precipitation usually relies on meteorological prediction models, such as numerical forecasts provided by meteorological bureaus, radar observation data, and satellite remote sensing data, etc. These methods have been widely used in precipitation prediction. In addition, the preliminary rainwater management efficiency formulated for the target urban expansion area usually relies on historical meteorological data, precipitation statistical analysis, and big data AI models, and uses modern meteorological observation technologies and data collection devices (such as rain gauges, weather stations, etc.) to set the preliminary rainwater management efficiency.

[0025] The rainwater management efficiency specifically refers to calculating and adjusting the rainwater collection, storage, and utilization capabilities within the urban area. It involves the design and optimization of infrastructure such as rainwater pipe networks and underground water storage facilities, aiming to achieve maximum rainwater recovery and effective utilization. For example, for a certain urban expansion area, it may be necessary to adjust the working efficiency of rainwater collection devices (such as rainwater collection ponds, infiltration wells, or permeable pavements) according to the predicted precipitation to ensure that more rainwater can be effectively collected and stored when the precipitation increases for subsequent utilization.

[0026] The underground water level fluctuation records should include the underground water level data, the change rate of the underground water level, and its periodic change trend at each observation moment, which usually come from the data records of long-term geological surveys, groundwater monitoring stations, or real-time data obtained through remote sensing technology and groundwater monitoring instruments.

[0027] The historical land cover change data should cover the land use types within the region (such as green spaces, agricultural lands, construction lands, etc.) and their change records at different time periods. These data usually come from remote sensing image analysis, geographic information system (GIS) data, and the databases of urban planning and land management departments.

[0028] The historical data table of soil permeability should contain the soil permeability data at different times, reflecting the change trend of the soil permeability in the region. The data usually come from soil surveys, laboratory analyses, agricultural monitoring, etc.

[0029] The connection between these data sources and the "ultra-long duration data" is that they jointly provide long-term and continuous historical data support for studying the hydrological changes in the urban expansion area of the target city, enabling the system to analyze the correlations among land use changes, groundwater level fluctuations, and soil permeability based on the duration data, and then optimizing the rainwater management efficiency.

[0030] Furthermore, the sponge city rainfall analysis method based on ultra-long duration data further includes the following steps: Step S200: Analyze the groundwater level fluctuation records, and determine whether the rising change rate of the groundwater level in the urban expansion area of the target city within a specified time period exceeds a preset threshold. If so, obtain the current soil permeability, and find the historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation from the historical soil permeability data table.

[0031] The specified time period refers to the time interval covering from the start of the construction of the urban expansion area of the target city to the current moment.

[0032] Specifically, Figure 2 shows a flowchart for obtaining the current soil permeability and the historical soil permeability.

[0033] Among them, analyzing the groundwater level fluctuation records, determining whether the rising change rate of the groundwater level in the urban expansion area of the target city within a specified time period exceeds a preset threshold, and if so, obtaining the current soil permeability and finding the historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation specifically includes the following steps: Step S201: Analyze the groundwater level fluctuation records, divide the specified time period into several sub-time intervals of the same length as a predetermined time period, and extract the groundwater level data of each sub-time interval; Step S202: Based on the groundwater level data of each sub-time interval, calculate the rising change rate of the groundwater level in the urban expansion area of the target city within the specified time period, and determine whether the rising change rate exceeds the preset threshold; Step S203: If the rising change rate exceeds the preset threshold, obtain the current soil permeability, and screen out the historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation from the historical soil permeability data table.

[0034] In the embodiments of the present invention, when it is determined whether the rising change rate of the groundwater level in the target urban expansion area exceeds a preset threshold within a specified time period, it can be judged from the side that the land permeability in the target urban expansion area has changed; this is because the change rate of the groundwater level is closely related to the land permeability; if the groundwater level rises too fast, it usually means that the soil permeability is poor, and the water cannot effectively penetrate into the ground, but stays on the surface or in the shallow soil layer; therefore, the change in the rising rate of the groundwater level can indirectly reflect the change in soil permeability.

[0035] When the groundwater level gradually rises, it is necessary to increase the rainwater management efficiency; this is because the rising of the groundwater level often means an increase in soil saturation, and the water is difficult to effectively penetrate into the ground, resulting in waterlogging or water accumulation on the ground; at this time, if rainwater collection and utilization are not strengthened, it may lead to problems such as waste of water resources or flooding in the area; by increasing the rainwater management efficiency, excessive precipitation can be effectively collected and stored, reducing surface waterlogging, thereby reducing the pressure brought by the rising groundwater level.

[0036] The reason for dividing the time length of the sub-time interval into the same length as the predetermined time period is to ensure that the data within each sub-interval can evenly represent the change of the groundwater level during the overall time period; this division method makes the observation data of each sub-time interval have the same time scale, which is convenient for subsequent calculation and analysis. Especially when calculating the change rate of the groundwater level, it can reduce the error caused by different time intervals, thereby improving the accuracy and consistency of the calculation results.

[0037] Selecting "the historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation from the historical soil permeability data table" as the comparison object is because historical data that is close in time can more accurately reflect the current change trend of soil permeability, and similar precipitation ensures the consistency of the comparison data in environmental factors; this selection can minimize the deviation caused by time or precipitation differences, making the comparison of soil permeability more realistic and reference-worthy, thereby improving the accuracy of optimizing rainwater management efficiency.

[0038] Further, the sponge city rainfall analysis method based on ultra-long duration data further includes the following steps: Step S300, calculate the deviation quantization value between the current soil permeability and the historical soil permeability, and use it as the primary correction factor.

[0039] In the embodiments of the present invention, taking the deviation quantization value as a correction factor for optimizing the efficiency of preliminary rainwater management can effectively reflect the difference between the current soil permeability and the historical soil permeability, and further provide a scientific basis for adjusting the rainwater management efficiency; when the deviation between the soil permeability and the historical value is large, it means that the water absorption capacity of the current soil has changed, which will directly affect the efficiency of rainwater infiltration, storage and utilization; by calculating the deviation quantization value and using it as a correction factor, this change can be reflected in real time during the rainwater collection process, ensuring a more accurate and efficient response to precipitation; ultimately, the rainwater management efficiency can be dynamically adjusted to improve the utilization rate of rainwater.

[0040] Further, the sponge city rainfall analysis method based on ultra-long duration data further includes the following steps: Step S400, analyze the historical land cover change data, extract the natural land use change information, draw a natural land change trend map, calculate the average slope of the change trend of the natural land change trend map, and use the average slope as a secondary correction factor.

[0041] Specifically, Figure 3 The flowchart for generating the secondary correction factor is shown.

[0042] Among them, analyzing the historical land cover change data, extracting the natural land use change information, drawing a natural land change trend map, calculating the average slope of the change trend of the natural land change trend map, and using the average slope as a secondary correction factor specifically include the following steps: Step S401, analyze the historical land cover change data, and extract the remaining value of natural land in each sub-time interval within the specified time period; Step S402, taking each sub-time interval as the X-axis and the remaining value of natural land as the Y-axis, draw a natural land change trend map of the target urban expansion area within the specified time period; Step S403, calculate the average slope of the change trend of the natural land change trend map, and use the average slope as a secondary correction factor.

[0043] In the embodiments of the present invention, analyzing historical land cover change data and drawing a natural land change trend chart is to help better understand the changes in natural land use in the target urban expansion area; by extracting the remaining value of natural land and drawing a trend chart, the change trend of land use can be clearly shown, and it can provide a basis for optimizing the subsequent rainwater management efficiency; the significance of drawing a natural land change trend chart is that it can reveal the potential relationship between the change of natural land and precipitation and permeability; in particular, when the rising change rate of the groundwater level exceeds the preset threshold, the permeability and utilization mode of the land may change, and these changes are often closely related to the change of natural land cover. Therefore, through the analysis of the trend chart, it can further provide effective information for optimizing the rainwater management efficiency.

[0044] Taking the average slope of the natural land change trend chart as the secondary correction factor can dynamically reflect the change trend of natural land and adjust the rainwater management efficiency based on this; the average slope can quantify the speed of land change, and then precisely adjust its impact on hydrological conditions; when the absolute value of the average slope is large, it means that the change of land use is relatively drastic, and the utilization of land resources becomes lower or changes greatly. At this time, the adjustment of rainwater management efficiency should be strengthened to cope with the increasingly changing land characteristics; on the contrary, the smaller the absolute value of the average slope, the flatter the land change, and the adjustment intensity of rainwater management efficiency can be reduced accordingly; therefore, by adjusting the slope value, the change of rainwater management efficiency can be precisely controlled, and then the utilization rate of rainwater and the sustainable development level of the city can be improved.

[0045] Furthermore, the sponge city rainfall analysis method based on ultra-long duration data further includes the following steps: Step S500, combining the primary correction factor and the secondary correction factor to perform double correction on the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency.

[0046] Specifically, Figure 4 The flowchart showing the double correction of the preliminary rainwater management efficiency is shown.

[0047] Among them, combining the primary correction factor and the secondary correction factor to perform double correction on the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency specifically includes the following steps: Step S501, retrieve the rainwater management efficiency optimization formula, and combine the primary correction factor and the secondary correction factor to perform double correction on the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency; Step S502, apply the optimized rainwater management efficiency to the rainwater collection system in the target urban expansion area.

[0048] The rainwater management efficiency optimization formula is , where Eoptimized Refers to the optimized rainwater management efficiency, E initial Refers to the preliminary rainwater management efficiency, D bias Refers to the primary correction factor, which is the quantification of the deviation between the current soil permeability and the historical soil permeability. K1 refers to the adjustment coefficient of the primary correction factor, S avg Refers to the secondary correction factor, which is the average slope of the change trend of the natural land change trend map. K2 refers to the adjustment coefficient of the secondary correction factor.

[0049] In the rainwater management efficiency optimization formula: , where F current Refers to the current soil permeability, F historical Refers to the historical soil permeability; , where N represents the total number of sub-time intervals, R i Represents the remaining value of natural land corresponding to the i-th sub-time interval, T i Represents the time median of the i-th sub-time interval.

[0050] In the embodiments of the present invention, the reason for using the primary correction factor and the secondary correction factor for double correction is that the two can optimize the rainwater management efficiency from different dimensions respectively, so as to more comprehensively improve the rainwater collection capacity of the target urban expansion area; the primary correction factor is based on the change of soil permeability and reflects the impact of the adjustment of land permeability on the rainwater management efficiency, while the secondary correction factor considers the long-term impact of natural land use changes on hydrological conditions through the slope of the land change trend; the combination of the two can complement each other and jointly promote the improvement of the efficiency of the rainwater collection system.

[0051] Specifically, the primary correction factor evaluates the change of land infiltration capacity by comparing the deviation between the current soil permeability and the historical soil permeability, so as to adjust the rainwater management efficiency; while the secondary correction factor provides information on the land use change trend by calculating the slope of the natural land use change, and further fine-tunes the rainwater management efficiency; the linkage between the two lies in that the soil permeability and the land use change are closely related in the hydrological process. An increase in land permeability often means an increase in the water infiltration capacity, while the land use change may affect the water accumulation and drainage capacity of the area; through the comprehensive correction of the two, the efficiency of the rainwater collection system can be predicted and adjusted more accurately.

[0052] In the formula, since both the "quantification value of the deviation between the current soil permeability and the historical soil permeability" and the "average slope" are negative values, but they reflect the positive regulatory effect of soil permeability change and land change trend on rainwater management efficiency. Therefore, the form of "1 minus the product of the primary correction factor and the corresponding adjustment coefficient" and "1 minus the product of the secondary correction factor and the corresponding adjustment coefficient" is adopted to ensure that the final correction factor is positive, thereby ensuring that the initial rainwater management efficiency is effectively improved. This method enables the negative correction factor to be correctly converted into a positive regulation of the initial efficiency, ensuring that the optimized rainwater management efficiency is improved within an appropriate range.

[0053] Furthermore, Figure 5 Fig. shows the application architecture diagram of the system provided by the embodiment of the present invention.

[0054] Among them, in another preferred embodiment provided by the present invention, a sponge city rainfall analysis system based on ultra-long duration data includes: A data acquisition module 100, configured to acquire the real-time predicted precipitation in the target urban expansion area within a predetermined time period, the preliminary rainwater management efficiency formulated for the target urban expansion area, and at the same time acquire the groundwater level fluctuation record, historical land cover change data, and soil permeability historical data table of the target urban expansion area.

[0055] In the embodiment of the present invention, the reason for taking the urban expansion area as the research object is that the urban expansion area is usually a key area in urban development, where the land use type and infrastructure construction change rapidly, resulting in significant changes in hydrological conditions. In these areas, the permeability of precipitation and the management of water resources are particularly important. Accurately predicting and adjusting the rainwater management efficiency helps to improve the utilization efficiency of urban water resources, reduce the risk of flood disasters, and promote sustainable urban development.

[0056] In the prior art, the real-time predicted precipitation usually relies on meteorological prediction models, such as numerical forecasts provided by meteorological bureaus, radar observation data, and satellite remote sensing data, etc. These methods have been widely used in precipitation prediction. In addition, the preliminary rainwater management efficiency formulated for the target urban expansion area usually relies on historical meteorological data, precipitation statistical analysis, and big data AI models, and modern meteorological observation technologies and data acquisition devices (such as rain gauges, weather stations, etc.) are used to set the preliminary rainwater management efficiency. The "rainwater management" pointed out in the present invention can specifically cover multiple aspects such as "rainwater collection, storage, infiltration, retention, purification, and utilization".

[0057] Rainwater management efficiency specifically refers to calculating and adjusting the ability to collect, store, and utilize rainwater within urban areas; it involves the design and optimization of infrastructure such as rainwater pipe networks and underground water storage facilities, aiming to achieve maximum rainwater recovery and effective utilization; for example, for a certain urban expansion area, it may be necessary to adjust the working efficiency of rainwater collection equipment (such as rainwater collection ponds, infiltration wells, or permeable pavements) according to the predicted precipitation to ensure that more rainwater can be effectively collected and stored when the precipitation increases for subsequent use.

[0058] The groundwater level fluctuation records should include the groundwater level data, the rate of change of the groundwater level, and its periodic change trend at each observation time, usually from the data records of long-term geological surveys, groundwater monitoring stations, or real-time data obtained through remote sensing technology and groundwater monitoring instruments.

[0059] The historical land cover change data should cover the land use types within the region (such as green spaces, agricultural lands, construction lands, etc.) and their change records at different time periods; these data usually come from remote sensing image analysis, Geographic Information System (GIS) data, and the databases of urban planning and land management departments.

[0060] The historical data table of soil permeability should contain the soil permeability data at different times, reflecting the change trend of the soil permeability in the region; the data usually come from soil surveys, laboratory analyses, agricultural monitoring, etc.

[0061] The relevance of these data sources to the "ultra-long duration data" lies in that they jointly provide long-term and continuous historical data support for studying the hydrological changes in the target urban expansion area, enabling the system to analyze the correlations among land use changes, groundwater level fluctuations, and soil permeability based on the duration data, and then optimize the rainwater management efficiency.

[0062] Furthermore, the sponge city rainfall analysis system based on ultra-long duration data further includes: A data parsing module 200, which is used to parse the groundwater level fluctuation records, determine whether the rising change rate of the groundwater level in the target urban expansion area within a specified time period exceeds a preset threshold. If so, obtain the current soil permeability, and find the historical soil permeability of the historical time period that is closest to the current time and within the same preset range as the current real-time predicted precipitation from the historical data table of soil permeability.

[0063] The specified time period refers to the time interval covering from the start of construction of the target urban expansion area to the current time.

[0064] Specifically, Figure 6 Fig. shows the structural block diagram of the data parsing module 200 in the system provided by the embodiment of the present invention.

[0065] Among them, in the preferred embodiment provided by the present invention, the data parsing module 200 specifically includes: A first data parsing unit 201, configured to parse the groundwater level fluctuation record, divide a specified time period into a plurality of sub-time intervals of the same length as a predetermined time period, and extract the groundwater level data of each sub-time interval; A rising change rate calculation unit 202, configured to calculate the rising change rate of the groundwater level in the target urban expansion area within a specified time period based on the groundwater level data of each sub-time interval, and determine whether the rising change rate exceeds a preset threshold; A second data parsing unit 203, configured to, if the rising change rate exceeds the preset threshold, obtain the current soil permeability, and screen out the historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation from the historical soil permeability data table.

[0066] In the embodiment of the present invention, when it is determined whether the rising change rate of the groundwater level in the target urban expansion area within a specified time period exceeds the preset threshold, it can be judged from the side that the land permeability of the target urban expansion area has changed; this is because the change rate of the groundwater level is closely related to the permeability of the land; if the groundwater level rises too fast, it usually means that the soil permeability is poor, and the water cannot effectively penetrate into the ground, but stays on the surface or in the shallow soil layer; therefore, the change in the rising rate of the groundwater level can indirectly reflect the change in soil permeability.

[0067] When the groundwater level gradually rises, it is necessary to increase the rainwater management efficiency; this is because the rising of the groundwater level often means an increase in soil saturation, and the water is difficult to effectively penetrate into the ground, resulting in surface water accumulation or waterlogging; at this time, if rainwater collection and utilization are not strengthened, it may lead to problems such as waste of water resources or floods in the area; by increasing the rainwater management efficiency, excessive precipitation can be effectively collected and stored, reducing surface water accumulation, thereby reducing the pressure brought by the rising groundwater level.

[0068] The reason for dividing the time length of the sub-time interval into the same length as the predetermined time period is to ensure that the data within each sub-interval can evenly represent the change of the groundwater level within the overall time period; this division method makes the observation data of each sub-time interval have the same time scale, which is convenient for subsequent calculation and analysis. Especially when calculating the change rate of the groundwater level, it can reduce the error caused by different time intervals, thereby improving the accuracy and consistency of the calculation results.

[0069] The "historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation selected from the historical data table of soil permeability" is selected as the comparison object because historical data that is close in time can more accurately reflect the current change trend of soil permeability, and similar precipitation ensures the consistency of the comparison data in terms of environmental factors; this selection can minimize the deviation caused by differences in time or precipitation, making the comparison of soil permeability more practically significant and reference-worthy, thereby improving the accuracy of optimizing rainwater management efficiency.

[0070] Furthermore, the sponge city rainfall analysis system based on ultra-long duration data further includes: A primary correction factor determination module 300, configured to calculate a deviation quantization value between the current soil permeability and the historical soil permeability, and use it as the primary correction factor.

[0071] In the embodiment of the present invention, using the deviation quantization value as a correction factor for optimizing the initial rainwater management efficiency can effectively reflect the difference between the current soil permeability and the historical soil permeability, and further provide a scientific basis for adjusting the rainwater management efficiency; when the deviation between the soil permeability and the historical value is large, it means that the water absorption capacity of the current soil has changed, which will directly affect the efficiency of rainwater infiltration, storage, and utilization; by calculating the deviation quantization value and using it as a correction factor, this change can be reflected in real time during the rainwater collection process, ensuring a more accurate and efficient response to precipitation; ultimately, the rainwater management efficiency can be dynamically adjusted to improve the utilization rate of rainwater.

[0072] Furthermore, the sponge city rainfall analysis system based on ultra-long duration data further includes: A secondary correction factor determination module 400, configured to analyze historical land cover change data, extract natural land use change information, draw a natural land change trend graph, calculate the average slope of the change trend of the natural land change trend graph, and use the average slope as the secondary correction factor.

[0073] Specifically, Figure 7 shows a structural block diagram of the secondary correction factor determination module 400 in the system provided by the embodiment of the present invention.

[0074] Among them, in the preferred embodiment provided by the present invention, the secondary correction factor determination module 400 specifically includes: A natural land remaining value extraction unit 401, configured to analyze historical land cover change data and extract the natural land remaining value of each sub-time interval within a specified time period therefrom; A change trend graph plotting unit 402 is configured to plot a natural land change trend graph of an expanded area of a target city within a specified time period, with each sub-time interval as the X-axis and the remaining value of natural land as the Y-axis; A secondary correction factor generation unit 403 is configured to calculate an average slope of the change trend of the natural land change trend graph and use the average slope as the secondary correction factor.

[0075] In an embodiment of the present invention, analyzing historical land cover change data and plotting a natural land change trend graph is to help better understand the changes in the use of natural land in the expanded area of the target city; by extracting the remaining value of natural land and plotting a trend graph, the change trend of land use can be clearly shown, and it provides a basis for optimizing the subsequent rainwater management efficiency; the significance of plotting a natural land change trend graph is that it can reveal the potential relationship between the change of natural land and precipitation and permeability; in particular, when the rising change rate of the groundwater level exceeds a preset threshold, the infiltration capacity and utilization mode of the land may change, and these changes are often closely related to the change of natural land cover. Therefore, through the analysis of the trend graph, it can further provide effective information for optimizing the rainwater management efficiency.

[0076] Taking the average slope of the natural land change trend graph as the secondary correction factor can dynamically reflect the change trend of natural land and adjust the rainwater management efficiency based on this; the average slope can quantify the speed of land change, and then precisely adjust its impact on hydrological conditions; when the absolute value of the average slope is relatively large, it means that the change of land use is relatively drastic, and the utilization of land resources becomes lower or changes greatly. At this time, the adjustment of rainwater management efficiency should be strengthened to cope with the increasingly changing land characteristics; on the contrary, when the absolute value of the average slope is smaller, the land change is relatively gentle, and the adjustment intensity of rainwater management efficiency can be correspondingly reduced; therefore, by adjusting the slope value, the change of rainwater management efficiency can be accurately controlled, and then the utilization rate of rainwater and the sustainable development level of the city can be improved.

[0077] Furthermore, the sponge city rainfall analysis system based on ultra-long duration data further includes: A rainwater management efficiency optimization module 500 is configured to perform double correction on the preliminary rainwater management efficiency by combining the primary correction factor and the secondary correction factor to obtain the optimized rainwater management efficiency.

[0078] Specifically, Figure 8 FIG. shows a structural block diagram of the rainwater management efficiency optimization module 500 in the system provided by the embodiment of the present invention.

[0079] Wherein, in a preferred embodiment provided by the present invention, the rainwater management efficiency optimization module 500 specifically includes: The rainwater management efficiency optimization unit 501 is used to retrieve the rainwater management efficiency optimization formula, and combined with the primary correction factor and the secondary correction factor, double-correct the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency; The optimized rainwater management efficiency application unit 502 is used to apply the optimized rainwater management efficiency to the rainwater collection system in the target urban expansion area; The rainwater management efficiency optimization formula is , where E optimized refers to the optimized rainwater management efficiency, and E initial refers to the preliminary rainwater management efficiency, D bias refers to the primary correction factor, that is, the deviation quantification value between the current soil permeability and the historical soil permeability, and K1 refers to the adjustment coefficient of the primary correction factor, S avg refers to the secondary correction factor, that is, the average slope of the change trend of the natural land change trend map, and K2 refers to the adjustment coefficient of the secondary correction factor.

[0080] In the rainwater management efficiency optimization formula: , where F current refers to the current soil permeability, and F historical refers to the historical soil permeability; , where N represents the total number of sub-time intervals, and R i represents the remaining value of natural land corresponding to the i-th sub-time interval, and T i represents the time median of the i-th sub-time interval.

[0081] In the embodiments of the present invention, the reason for double-correcting with the primary correction factor and the secondary correction factor is that these two can optimize the rainwater management efficiency from different dimensions respectively, so as to more comprehensively improve the rainwater collection capacity of the target urban expansion area; the primary correction factor is based on the change of soil permeability and reflects the impact of the adjustment of land permeability on the rainwater management efficiency, while the secondary correction factor considers the long-term impact of natural land use changes on hydrological conditions through the slope of the land change trend; the combination of these two can complement each other and jointly promote the improvement of the efficiency of the rainwater collection system.

[0082] Specifically, the primary correction factor evaluates the change in land infiltration capacity by comparing the deviation between the current soil permeability and the historical soil permeability, thereby adjusting the rainwater management efficiency; while the secondary correction factor provides information on the trend of land use change by calculating the slope of natural land use change, and further fine-tunes the rainwater management efficiency; the linkage between the two lies in that soil permeability and land use change are closely related in the hydrological process. An increase in land permeability often means an enhanced infiltration capacity of water, while land use change may affect the water accumulation and drainage capacity of the area; through the combined correction of the two, the efficiency of the rainwater collection system can be predicted and adjusted more precisely.

[0083] In the formula, since both the "quantification value of the deviation between the current soil permeability and the historical soil permeability" and the "average slope" are negative values, but they reflect the positive regulatory effect of soil permeability change and land change trend on the rainwater management efficiency, therefore, the forms of "1 minus the product of the primary correction factor and the corresponding adjustment coefficient" and "1 minus the product of the secondary correction factor and the corresponding adjustment coefficient" are adopted to ensure that the final correction factor is positive, so as to effectively improve the initial rainwater management efficiency; this way enables the negative correction factor to be correctly converted into a positive regulation of the initial efficiency, ensuring that the optimized rainwater management efficiency is improved within a suitable range.

[0084] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown sequentially according to the indication of the arrows, these steps do not necessarily need to be executed sequentially according to the order indicated by the arrows; unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders; moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages, and these sub-steps or stages do not necessarily need to be executed at the same moment, but can be executed at different moments, and the execution order of these sub-steps or stages does not necessarily need to be sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0085] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0086] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0087] The above embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

[0088] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A rainstorm analysis method for sponge cities based on ultra-long duration data, characterized in that The method includes: Obtaining the real-time predicted precipitation in the extended area of the target city within a predetermined time period, the preliminary rainwater management efficiency formulated for the extended area of the target city, and simultaneously obtaining the groundwater level fluctuation record, historical land cover change data, and historical soil permeability data sheet of the extended area of the target city; Analyzing the groundwater level fluctuation record to determine whether the rising change rate of the groundwater level in the extended area of the target city within the specified time period exceeds a preset threshold. If so, obtaining the current soil permeability and finding the historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation from the historical soil permeability data sheet; Calculating the deviation quantization value between the current soil permeability and the historical soil permeability and using it as the primary correction factor; Analyzing the historical land cover change data, extracting the natural land use change information, drawing the natural land change trend graph, calculating the average slope of the change trend of the natural land change trend graph, and using the average slope as the secondary correction factor; Combining the primary correction factor and the secondary correction factor to perform double correction on the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency.

2. The method for analyzing sponge city rainfall based on ultra-long duration data according to claim 1, wherein The specified time period refers to the time interval covering from the start of construction of the extended area of the target city to the current moment.

3. The method for analyzing sponge city rainfall based on ultra-long duration data according to claim 2, characterized in that, The steps of analyzing the groundwater level fluctuation record to determine whether the rising change rate of the groundwater level in the extended area of the target city within the specified time period exceeds a preset threshold. If so, obtaining the current soil permeability and finding the historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation from the historical soil permeability data sheet include: Analyzing the groundwater level fluctuation record, dividing the specified time period into several sub-time intervals of the same length as the predetermined time period, and extracting the groundwater level data of each sub-time interval; Based on the groundwater level data of each sub-time interval, calculating the rising change rate of the groundwater level in the extended area of the target city within the specified time period and determining whether the rising change rate exceeds the preset threshold; If the rising change rate exceeds the preset threshold, obtaining the current soil permeability and screening out the historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real-time predicted precipitation from the historical soil permeability data sheet.

4. The sponge city rainfall analysis method based on ultra-long duration data according to claim 3, characterized in that, The steps of analyzing the historical land cover change data, extracting the natural land use change information, drawing the natural land change trend graph, calculating the average slope of the change trend of the natural land change trend graph, and using the average slope as the secondary correction factor include: Analyzing the historical land cover change data and extracting the remaining natural land value of each sub-time interval within the specified time period; Taking each sub-time interval as the X-axis and the remaining natural land value as the Y-axis to draw the natural land change trend graph of the extended area of the target city within the specified time period; Calculating the average slope of the change trend of the natural land change trend graph and using the average slope as the secondary correction factor.

5. The method for analyzing sponge city rainfall based on ultra-long duration data according to claim 1, wherein, Steps for double - correcting the preliminary rainwater management efficiency by combining the primary correction factor and the secondary correction factor to obtain the optimized rainwater management efficiency include: Retrieve the rainwater management efficiency optimization formula, and combine the primary correction factor and the secondary correction factor to double - correct the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency; Apply the optimized rainwater management efficiency to the rainwater collection system in the target urban expansion area.

6. The method for analyzing sponge city rainfall based on ultra-long duration data according to claim 5, characterized in that, The optimized formula for rainwater management efficiency is , where E optimized refers to the optimized rainwater management efficiency, E initial refers to the preliminary rainwater management efficiency, D bias refers to the primary correction factor, which is the quantification value of the deviation between the current soil permeability and the historical soil permeability. K1 refers to the adjustment coefficient of the primary correction factor, S avg refers to the secondary correction factor, which is the average slope of the change trend of the natural land change trend map. K2 refers to the adjustment coefficient of the secondary correction factor.

7. A sponge city rainfall analysis system based on ultra-long duration data, characterized in that, The system includes: a data acquisition module, a data analysis module, a primary correction factor determination module, a secondary correction factor determination module, and a rainwater management efficiency optimization module, where: The data acquisition module is used to obtain the real - time predicted precipitation in the target urban expansion area within a predetermined time period, the preliminary rainwater management efficiency formulated for the target urban expansion area, and at the same time obtain the groundwater level fluctuation record, historical land cover change data, and historical soil permeability data table of the target urban expansion area; The data analysis module is used to analyze the groundwater level fluctuation record, and judge whether the rising change rate of the groundwater level in the target urban expansion area within the specified time period exceeds a preset threshold. If so, obtain the current soil permeability, and find the historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real - time predicted precipitation from the historical soil permeability data table; The specified time period refers to the time interval covering from the start of construction of the target urban expansion area to the current moment; The primary correction factor determination module is used to calculate the deviation quantization value between the current soil permeability and the historical soil permeability, and use it as the primary correction factor; The secondary correction factor determination module is used to analyze the historical land cover change data, extract the natural land use change information, draw a natural land change trend graph, calculate the average slope of the change trend of the natural land change trend graph, and use the average slope as the secondary correction factor; The rainwater management efficiency optimization module is used to double - correct the preliminary rainwater management efficiency by combining the primary correction factor and the secondary correction factor to obtain the optimized rainwater management efficiency.

8. The sponge city rainfall analysis system based on ultra-long duration data according to claim 7, characterized in that The data analysis module specifically includes: The first data analysis unit is used to analyze the groundwater level fluctuation record, divide the specified time period into several sub - time intervals of the same length as the predetermined time period, and extract the groundwater level data of each sub - time interval; The rising change rate calculation unit is used to calculate the rising change rate of the groundwater level in the target urban expansion area within the specified time period based on the groundwater level data of each sub - time interval, and judge whether the rising change rate exceeds the preset threshold; The second data analysis unit is used to, if the rising change rate exceeds the preset threshold, obtain the current soil permeability, and screen out the historical soil permeability of the historical time period that is closest to the current moment in time and within the same preset range as the current real - time predicted precipitation from the historical soil permeability data table.

9. The sponge city rainfall analysis system based on ultra-long duration data according to claim 8, characterized in that, The secondary correction factor determination module specifically includes: The natural land remaining value extraction unit is used to analyze the historical land cover change data and extract the natural land remaining value of each sub - time interval within the specified time period; A changing trend graph plotting unit, which is used to plot a natural land changing trend graph of the target urban expansion area within a specified time period, with each sub-time interval as the X-axis and the remaining value of natural land as the Y-axis; A secondary correction factor generating unit, which is used to calculate the average slope of the changing trend of the natural land changing trend graph and use the average slope as the secondary correction factor.

10. The sponge city rainfall analysis system based on ultra-long duration data according to claim 9, characterized in that, The rainwater management efficiency optimization module specifically includes: A rainwater management efficiency optimization unit, which is used to retrieve the rainwater management efficiency optimization formula and, in combination with the primary correction factor and the secondary correction factor, perform double correction on the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency; An optimized rainwater management efficiency application unit, which is used to apply the optimized rainwater management efficiency to the rainwater collection system of the target urban expansion area; The rainwater management efficiency optimization formula is , where E optimized refers to the optimized rainwater management efficiency, E initial refers to the preliminary rainwater management efficiency, D bias refers to the primary correction factor, that is, the quantification value of the deviation between the current soil permeability and the historical soil permeability. K1 refers to the adjustment coefficient of the primary correction factor, S avg refers to the secondary correction factor, that is, the average slope of the change trend of the natural land change trend map. K2 refers to the adjustment coefficient of the secondary correction factor.

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