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

Abstract

The application is suitable for the technical field of sponge city rainwater resource management, and provides a sponge city rainfall analysis method and system based on super-long duration data, which comprises the following steps: obtaining real-time predicted precipitation of a target city expansion area in a predetermined time period, a preliminary rainwater management efficiency formulated for the target city expansion area, and simultaneously obtaining underground water level fluctuation records, historical land cover change data and 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 cover change information; firstly, the primary correction factor of the rainwater management efficiency is accurately adjusted by analyzing the underground water level change rate and combining the soil permeability rate deviation; and secondly, the average slope of the historical land change trend is used as a secondary correction factor to further optimize the rainwater management efficiency.

Description

TECHNICAL FIELD

[0001] The application 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 super-long duration data. BACKGROUND

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

[0003] In the process of urbanization, rainwater management has been an important means to solve the problems of urban waterlogging, floods and water resource shortage; the concept of sponge city emerges as the times require, aiming to increase the infiltration, retention and purification capacity of urban rainwater through natural or quasi-natural methods, so as to improve the utilization efficiency of urban rainwater; however, the existing sponge city water resource management system has certain limitations, especially in efficiency optimization, most methods still rely on static models and single precipitation prediction; the existing technology often does not comprehensively consider the influence of factors such as groundwater level, soil permeability and land use change in the process of urban expansion, which makes the dynamic adjustment ability of water resource management efficiency insufficient, and cannot effectively respond to the changing hydrological conditions of the city. SUMMARY

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

[0005] The present application is implemented as follows: a sponge city rainfall analysis method based on super-long duration data, the method comprising:

[0006] obtaining real-time predicted precipitation of the target urban expansion area within a predetermined time period, preliminary rainwater management efficiency formulated for the target urban expansion area, and simultaneously obtaining groundwater level fluctuation records, historical land cover change data and soil permeability historical data table of the target urban expansion area;

[0007] analyzing the groundwater level fluctuation records to determine whether the rising rate of the groundwater level of the target urban expansion area within a specified time period exceeds a preset threshold value, if so, obtaining the current soil permeability, and finding the historical soil permeability of the historical time period closest to the current time and within the same preset range of the current real-time predicted precipitation from the soil permeability historical data table;

[0008] calculating the deviation quantitative value between the current soil permeability and the historical soil permeability, and taking it as the primary correction factor;

[0009] analyze historical land cover change data, extract natural land use change information, and draw a natural land change trend graph, calculate the average slope of the change trend of the natural land change trend graph, and take the average slope as a secondary correction factor;

[0010] In combination with the primary correction factor and the secondary correction factor, the preliminary rainwater management efficiency is double corrected to obtain the optimized rainwater management efficiency.

[0011] As a further limitation of the technical scheme of the embodiment of the application, the specified time period refers to a time interval covered from the start of the construction of the target urban expansion area to the current time.

[0012] As a further limitation of the technical scheme of the embodiment of the application, the underground water level fluctuation record is analyzed to determine whether the rising change rate of the underground water level of the target urban expansion area in the specified time period exceeds a preset threshold, if so, the current soil permeability is obtained, and the historical soil permeability of the historical time period closest to the current time and within the same preset range of the current real-time predicted precipitation is found from the soil permeability historical data table.

[0013] The underground water level data of each sub-time interval is extracted by analyzing the underground water level fluctuation record and dividing the specified time period into several sub-time intervals equal in length to the predetermined time period.

[0014] Based on the underground water level data of each sub-time interval, the rising change rate of the underground water level of the target urban expansion area in the specified time period is calculated, and it is determined whether the rising change rate exceeds a preset threshold.

[0015] If the rising change rate exceeds the preset threshold, the current soil permeability is obtained, and the historical soil permeability of the historical time period closest to the current time and within the same preset range of the current real-time predicted precipitation is selected from the soil permeability historical data table.

[0016] As a further limitation of the technical scheme of the embodiment of the application, the historical land cover change data is analyzed, the natural land use change information is extracted, and the natural land change trend graph is drawn, the average slope of the change trend of the natural land change trend graph is calculated, and the average slope is taken as a secondary correction factor.

[0017] The natural land residual value of each sub-time interval in the specified time period is extracted from the analysis of the historical land cover change data.

[0018] The natural land change trend graph of the target urban expansion area in the specified time period is drawn with each sub-time interval as the X-axis and the natural land residual value as the Y-axis.

[0019] An average slope of the change trend of the natural land change trend map is calculated, and the average slope is taken as a secondary correction factor.

[0020] As a further limitation of the technical solutions of the embodiments of the present application, the preliminary rainwater management efficiency is double corrected by combining the primary correction factor and the secondary correction factor to obtain the optimized rainwater management efficiency.

[0021] The rainwater management efficiency optimization formula is called, and the preliminary rainwater management efficiency is double corrected by combining the primary correction factor and the secondary correction factor to obtain the optimized rainwater management efficiency.

[0022] The optimized rainwater management efficiency is applied to the rainwater collection system of the target urban expansion area.

[0023] As a further limitation of the technical solutions of the embodiments of the present application, the rainwater management efficiency optimization formula is , wherein E optimized represents the optimized rainwater management efficiency, E initial represents the preliminary rainwater management efficiency, D bias represents the primary correction factor, that is, the deviation quantitative value between the current soil permeability and the historical soil permeability, K1 represents the adjustment coefficient of the primary correction factor, S avg represents the secondary correction factor, that is, the average slope of the change trend of the natural land change trend map, and K2 represents the adjustment coefficient of the secondary correction factor.

[0024] In the rainwater management efficiency optimization formula:

[0025] , wherein F current represents the current soil permeability, F historical represents the historical soil permeability.

[0026] , wherein N represents the total number of sub-time intervals, R i represents the natural land residual value corresponding to the i-th sub-time interval, T i represents the time median of the i-th sub-time interval.

[0027] A sponge city rainfall analysis system based on super-long duration data, the system comprises 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, wherein:

[0028] The data acquisition module is configured to acquire real-time predicted rainfall of the target urban expansion area in a predetermined time period, a preliminary rainwater management efficiency formulated for the target urban expansion area, and simultaneously acquire underground water level fluctuation records, historical land cover change data and soil permeability historical data table of the target urban expansion area;

[0029] The data analysis module is configured to analyze the underground water level fluctuation records, determine whether the rising change rate of the underground water level of the target urban expansion area in the specified time period exceeds a preset threshold value, and if so, acquire the current soil permeability and find the historical soil permeability of a historical time period closest to the current time and within the same preset range of the current real-time predicted rainfall from the soil permeability historical data table.

[0030] The specified time period refers to a time interval from the start of the construction of the target urban expansion area to the current time;

[0031] The primary correction factor determination module is configured to calculate a deviation quantitative value between the current soil permeability and the historical soil permeability and take the deviation quantitative value as a primary correction factor;

[0032] The secondary correction factor determination module is configured to analyze the historical land cover change data, extract natural land use change information, and draw a natural land change trend graph, calculate an average slope of the change trend of the natural land change trend graph, and take the average slope as a secondary correction factor;

[0033] The rainwater management efficiency optimization module is configured to combine the primary correction factor and the secondary correction factor to double correct the preliminary rainwater management efficiency and obtain an optimized rainwater management efficiency.

[0034] As a further limitation of the technical scheme of the embodiment of the present application, the data analysis module specifically comprises:

[0035] The first data analysis unit is configured to analyze the underground water level fluctuation records, divide the specified time period into a plurality of sub-time intervals equal in length to the predetermined time period, and extract underground water level data of each sub-time interval;

[0036] The rising change rate calculation unit is configured to calculate the rising change rate of the underground water level of the target urban expansion area in the specified time period based on the underground water level data of each sub-time interval, and determine whether the rising change rate exceeds the preset threshold value;

[0037] The second data analysis unit is configured to acquire the current soil permeability and filter the historical soil permeability of the historical time period closest to the current time and within the same preset range of the current real-time predicted rainfall from the soil permeability historical data table if the rising change rate exceeds the preset threshold value.

[0038] As a further limitation of the technical scheme of the embodiment of the present application, the secondary correction factor determination module specifically comprises:

[0039] The natural land residual value extraction unit is configured to analyze historical land cover change data and extract the natural land residual value of each sub-time interval within a specified time period from the historical land cover change data;

[0040] The change trend graph drawing unit is configured to draw a natural land change trend graph of the target urban expansion area within a specified time period, with each sub-time interval as the X-axis and the natural land residual value as the Y-axis.

[0041] The secondary correction factor generation unit is configured to calculate the average slope of the change trend of the natural land change trend graph and take the average slope as the secondary correction factor.

[0042] As a further limitation of the technical scheme of the embodiment of the present application, the rainwater management efficiency optimization module specifically comprises:

[0043] The rainwater management efficiency optimization unit is configured to call 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, so as to obtain the optimized rainwater management efficiency.

[0044] The optimized rainwater management efficiency application unit is configured to apply the optimized rainwater management efficiency to the rainwater collection system of the target urban expansion area.

[0045] The rainwater management efficiency optimization formula is , wherein E optimized represents the optimized rainwater management efficiency, E initial represents the preliminary rainwater management efficiency, D bias represents the primary correction factor, which is the deviation quantitative value between the current soil permeability and the historical soil permeability, and K1 represents the adjustment coefficient of the primary correction factor, S avg represents the secondary correction factor, which is the average slope of the change trend of the natural land change trend graph, and K2 represents the adjustment coefficient of the secondary correction factor.

[0046] In the rainwater management efficiency optimization formula:

[0047] , wherein F current represents the current soil permeability, F historical represents the historical soil permeability.

[0048] , wherein N represents the total number of sub-time intervals, R i represents the natural land residual value corresponding to the i-th sub-time interval, and T i represents the time median of the i-th sub-time interval.

[0049] Compared with the prior art, the present application has the following beneficial effects:

[0050] By combining the fluctuation of underground water level, historical data of soil permeability, historical land cover change information and other multi-dimensional data, the rainwater management efficiency is optimized; first, by analyzing the change rate of underground water level, combined with the deviation of soil permeability, the primary correction factor of rainwater management efficiency is accurately adjusted; second, the average slope of the historical land change trend is used as the secondary correction factor to further optimize the rainwater management efficiency; the double correction of the two can comprehensively consider the influence of land permeability and land use change on rainwater collection, and improve the accuracy and adaptability of the rainwater collection system.

[0051] The method can dynamically adjust the rainwater management efficiency according to the comparison of real-time data and historical data, significantly improve the rainwater recycling capacity of the city under different climate conditions; at the same time, through the fusion analysis of long-term data, the response ability of the system to the change of hydrological conditions in the urban expansion area is enhanced, the urban rainwater management is optimized, and the sustainable development of green infrastructure is promoted; the technology has wide application prospect, especially in the fields of sponge city construction, rainwater resource utilization and urban environmental protection. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 The flowchart of the method provided for the embodiment of the present application;

[0053] Figure 2 The flowchart of acquiring the current soil permeability and the historical soil permeability in the method provided for the embodiment of the present application;

[0054] Figure 3 The flowchart of generating the secondary correction factor in the method provided for the embodiment of the present application;

[0055] Figure 4 The flowchart of double correction of the preliminary rainwater management efficiency in the method provided for the embodiment of the present application;

[0056] Figure 5 The application architecture diagram of the system provided for the embodiment of the present application;

[0057] Figure 6 The structure block diagram of the data analysis module in the system provided for the embodiment of the present application;

[0058] Figure 7 The structure block diagram of the secondary correction factor determination module in the system provided for the embodiment of the present application;

[0059] Figure 8 The structure block diagram of the rainwater management efficiency optimization module in the system provided for the embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0061] Figure 1 A flowchart of the method provided by the embodiment of the present application is shown.

[0062] Specifically, a sponge city rainfall analysis method based on ultra-long duration data, the method specifically comprises the following steps:

[0063] Step S100, obtaining real-time predicted precipitation of the target city expansion area in a predetermined time period, preparing a preliminary rainwater management efficiency for the target city expansion area, and simultaneously obtaining groundwater level fluctuation records, historical land cover change data and soil permeability historical data table of the target city expansion area.

[0064] In the embodiment of the present application, the reason for taking the city expansion area as the research object is that the city expansion area is usually a key area in urban development, and 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, and accurate prediction and adjustment of rainwater management efficiency can help improve the utilization efficiency of urban water resources, reduce the risk of flood disasters, and promote sustainable urban development; the "rainwater management" indicated in the present application can specifically cover multiple aspects such as "rainwater collection, storage, permeation, retention, purification and utilization".

[0065] In the prior art, real-time prediction of precipitation usually relies on meteorological prediction models, such as numerical prediction provided by the meteorological bureau, radar observation data and satellite remote sensing data, etc., and these methods have been widely used in precipitation prediction; in addition, the preliminary rainwater management efficiency prepared for the target city expansion area usually relies on historical meteorological data, precipitation statistical analysis and big data AI model, and with the help of modern meteorological observation technology and data collection equipment (such as rain gauge, weather station, etc.) to set the preliminary rainwater management efficiency.

[0066] Rainwater management efficiency specifically refers to the calculation and adjustment of the collection, storage and utilization capacity of rainwater in urban areas; it involves the design and optimization of rainwater pipe network, underground water storage facilities and other infrastructure, aiming to maximize rainwater recycling and effective utilization; for example, for a certain city expansion area, it may be necessary to adjust the working efficiency of rainwater collection equipment (such as rainwater collection pool, permeation well or permeable pavement) according to the predicted precipitation, so as to ensure that more rainwater can be effectively collected and stored when the precipitation increases, for subsequent utilization.

[0067] The groundwater level fluctuation record should include the groundwater level data at each observation time, the groundwater level change rate, and its periodic change trend, usually from long-term geological survey, groundwater monitoring station data record, or real-time data obtained by remote sensing technology and groundwater monitoring instrument.

[0068] The historical land cover change data should cover the land use types in the region (such as green land, agricultural land, construction land, etc.) and their change records at different time periods; these data usually come from remote sensing image analysis, geographic information system (GIS) data, city planning and land management department databases.

[0069] The soil permeability historical data table should contain soil permeability data at different periods, reflecting the change trend of the soil permeability in the region; the data usually come from soil survey, laboratory analysis, agricultural monitoring, etc.

[0070] The association of these data sources with "ultra-long duration data" is that they collectively provide long-term, continuous historical data support for studying the hydrological changes in the target urban expansion area, enabling the system to analyze the correlation between land use change, groundwater level fluctuation, and soil permeability based on duration data, and then optimize rainwater management efficiency.

[0071] Further, the sponge city rain analysis method based on ultra-long duration data further comprises the following steps:

[0072] Step S200, analyze the groundwater level fluctuation record, 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 yes, obtain the current soil permeability, and find the historical soil permeability of the historical time period closest to the current time and within the same preset range of the current real-time predicted rainfall from the soil permeability historical data table.

[0073] The specified time period refers to the time interval from the start of the construction of the target urban expansion area to the current time.

[0074] Specifically, Figure 2 The flowchart for obtaining the current soil permeability and the historical soil permeability is shown.

[0075] Among them, analyzing the groundwater level fluctuation record, determining 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 yes, obtaining the current soil permeability, and finding the historical soil permeability of the historical time period closest to the current time and within the same preset range of the current real-time predicted rainfall from the soil permeability historical data table specifically includes the following steps:

[0076] Step S201, analyze the groundwater level fluctuation record, divide the specified time period into several sub-time intervals equal in length to the predetermined time period, and extract the groundwater level data of each sub-time interval;

[0077] Step S202, based on the groundwater level data of each sub-time interval, calculate the rising rate of the groundwater level in the target urban expansion area in the specified time period, and determine whether the rising rate exceeds the preset threshold;

[0078] Step S203, if the rising rate exceeds the preset threshold, obtain the current soil permeability, and select the historical soil permeability of the historical time period closest to the current time and within the same preset range of the current real-time predicted precipitation from the soil permeability historical data table.

[0079] In the embodiment of the present application, when the rising rate of the groundwater level in the target urban expansion area in the specified time period exceeds the preset threshold, it can be determined from the side that the land permeability of the target urban expansion area has changed; 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 permeability of the soil is poor, and water cannot effectively penetrate into the ground, but stays on the surface or in the shallow soil layer; therefore, the change of the rising rate of the groundwater level can indirectly reflect the change of the soil permeability.

[0080] When the groundwater level rises gradually, the efficiency of rainwater management needs to be increased; because the rise of the groundwater level often means that the saturation of the soil is improved, and water cannot effectively penetrate into the ground, resulting in surface water or water accumulation; at this time, if rainwater collection and utilization are not strengthened, it may cause waste of water resources or flooding in the region; by increasing the efficiency of rainwater management, excessive precipitation can be effectively collected and stored, reducing surface water, thereby reducing the pressure brought by the rise of the groundwater level.

[0081] 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 in each sub-interval can uniformly represent the change of the groundwater level in the whole 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 result.

[0082] The selected "historical soil permeability from the soil permeability historical data table closest in time to the current time and within the same preset range as the current real-time predicted rainfall" is selected as the comparison object because the historical data close in time can more accurately reflect the change trend of the current soil permeability, and the similar rainfall ensures the consistency of the comparison data in the environmental factors; this selection can minimize the deviation caused by time or rainfall differences, making the comparison of soil permeability more meaningful and valuable, thereby improving the accuracy of rainwater management efficiency optimization.

[0083] Further, the sponge city rainfall analysis method based on the super-long duration data further includes the following steps:

[0084] Step S300, calculate the deviation quantization value between the current soil permeability and the historical soil permeability, and take it as the primary correction factor.

[0085] In the embodiment of the application, the deviation quantization value is taken as the correction factor for optimizing the preliminary rainwater management efficiency, which 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 soil permeability deviates greatly from the historical value, it means that the water absorption capacity of the current soil has changed, which will directly affect the permeation, storage and utilization efficiency of rainwater; by calculating the deviation quantization value and taking it as the correction factor, the change can be reflected in real time during the rainwater collection process, ensuring that the response to rainfall is more accurate and efficient; ultimately, the rainwater management efficiency can be dynamically adjusted to improve the utilization rate of rainwater.

[0086] Further, the sponge city rainfall analysis method based on the super-long duration data further includes the following steps:

[0087] Step S400, analyze the historical land cover change data, extract the natural land use change information, and draw a natural land change trend graph, calculate the average slope of the change trend of the natural land change trend graph, and take the average slope as the secondary correction factor.

[0088] Specifically, Figure 3 A flowchart for generating the secondary correction factor is shown.

[0089] Among them, analyzing the historical land cover change data, extracting the natural land use change information, and drawing a natural land change trend graph, calculating the average slope of the change trend of the natural land change trend graph, and taking the average slope as the secondary correction factor specifically includes the following steps:

[0090] Step S401, analyze the historical land cover change data, and extract the natural land remaining value of each sub-time interval within a specified time period from the historical land cover change data;

[0091] Step S402, draw a natural land change trend graph of the target urban expansion area in the specified time period, with each sub-time interval as the X-axis and the natural land residual value as the Y-axis.

[0092] Step S403, calculate the average slope of the change trend of the natural land change trend graph, and take the average slope as the secondary correction factor.

[0093] In the embodiments of the present application, the historical land cover change data is analyzed and the natural land change trend graph is drawn to better understand the changes in the natural land use of the target urban expansion area; by extracting the natural land residual value and drawing the trend graph, the change trend of land use can be clearly shown, and basis can be provided for subsequent rainwater management efficiency optimization; the significance of drawing the natural land change trend graph lies in that it can reveal the potential correlation between the change of natural land and precipitation and permeability; especially when the rising rate of 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 graph, effective information can be further provided for optimizing the rainwater management efficiency.

[0094] Taking the average slope of the natural land change trend graph as the secondary correction factor can dynamically reflect the change trend of the natural land and adjust the rainwater management efficiency based thereon; the average slope can quantify the speed of land change, and then accurately adjust the influence of the land change on the 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 more gentle the land change, and the adjustment intensity of the rainwater management efficiency can be correspondingly reduced; therefore, by adjusting the slope value, the change of the 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.

[0095] Further, the method for analyzing rainwater of a sponge city based on super-long duration data further comprises the following steps:

[0096] Step S500, combining the primary correction factor and the secondary correction factor, double-correcting the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency.

[0097] Specifically, Figure 4 A flow chart for double-correcting the preliminary rainwater management efficiency is shown.

[0098] Among them, combining the primary correction factor and the secondary correction factor, double-correcting the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency specifically comprises the following steps:

[0099] Step S501, call 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;

[0100] Step S502, apply the optimized rainwater management efficiency to the rainwater collection system of the target urban expansion area.

[0101] The rainwater management efficiency optimization formula is , wherein 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 quantitative 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 graph, K2 refers to the adjustment coefficient of the secondary correction factor.

[0102] In the rainwater management efficiency optimization formula:

[0103] , wherein F current refers to the current soil permeability, F historical refers to the historical soil permeability;

[0104] , wherein N represents the total number of sub-time intervals, R i represents the natural land residual value corresponding to the i-th sub-time interval, T i represents the time median of the i-th sub-time interval.

[0105] In the embodiment of the application, the reason why the primary correction factor and the secondary correction factor are used for double correction is that the two can optimize the rainwater management efficiency from different dimensions, 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, reflects the influence of land permeability adjustment on rainwater management efficiency, and the secondary correction factor considers the long-term influence of natural land use change on hydrological conditions through the slope of the land change trend; the two can complement each other and jointly promote the improvement of the efficiency of the rainwater collection system.

[0106] Specifically, the primary correction factor assesses 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; and the secondary correction factor provides information on the trend of land use change by calculating the slope of natural land use change, thereby further fine-tuning the rainwater management efficiency; the linkage between the two lies in that soil permeability and land use change are closely related in hydrological processes, and an increase in land permeability often means an increase in water infiltration capacity, while land use change can affect the water accumulation and drainage capacity of the region; through the comprehensive correction of the two, the efficiency of the rainwater collection system can be more accurately predicted and adjusted.

[0107] In the formula, since both the "deviation between the current soil permeability and the historical soil permeability" and the "average slope" are negative values, but they reflect the positive adjustment 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, so as to ensure that the initial rainwater management efficiency is effectively improved; this way makes the negative correction factor correctly converted into a positive adjustment to the initial efficiency, ensuring that the optimized rainwater management efficiency is improved within a suitable range.

[0108] Further, Figure 5 The application architecture diagram of the system provided by the embodiment of the application is shown.

[0109] In another preferred embodiment provided by the application, a sponge city rainfall analysis system based on ultra-long duration data comprises:

[0110] The data acquisition module 100 is configured to acquire real-time predicted precipitation of the target urban expansion area within a predetermined time period, acquire the preliminary rainwater management efficiency formulated for the target urban expansion area, and simultaneously acquire the underground water level fluctuation record, historical land cover change data, and soil permeability historical data table of the target urban expansion area.

[0111] In the embodiment of the application, 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, and its land use type and infrastructure construction change rapidly, leading to significant changes in hydrological conditions; in these areas, the infiltration of precipitation and the management of water resources are particularly important, and accurate prediction and adjustment of rainwater management efficiency can help improve the utilization efficiency of urban water resources, reduce the risk of flood disasters, and promote sustainable urban development.

[0112] In the prior art, real-time prediction of precipitation usually relies on meteorological prediction models such as numerical prediction provided by meteorological bureaus, radar observation data, satellite remote sensing data, etc. These methods have been widely used in precipitation prediction. In addition, the preliminary rainwater management efficiency for the target city expansion area is usually based on historical meteorological data, precipitation statistical analysis and big data AI model, with the help of modern meteorological observation technology and data collection equipment (such as rain gauge, weather station, etc.) to set the preliminary rainwater management efficiency. The "rainwater management" mentioned in this invention can specifically include "rainwater collection, storage, infiltration, retention, purification and utilization" and other aspects.

[0113] Rainwater management efficiency specifically refers to the calculation and adjustment of the collection, storage and utilization capacity of rainwater in urban areas. It involves the design and optimization of rainwater pipe network, underground water storage facilities and other infrastructure, aiming to maximize rainwater recycling and effective utilization. For example, for a certain city expansion area, it may be necessary to adjust the working efficiency of rainwater collection equipment (such as rainwater collection pool, infiltration well or permeable pavement) according to the predicted precipitation to ensure that more rainwater can be effectively collected and stored when the precipitation increases, so as to be used later.

[0114] The underground water level fluctuation record should include the underground water level data, underground water level change rate and periodic change trend of each observation time, usually from long-term geological survey, underground water monitoring station data record, or real-time data obtained by remote sensing technology and underground water monitoring instrument.

[0115] The historical land cover change data should cover the land use types (such as green land, agricultural land, construction land, etc.) in the region and their change records at different time periods. These data usually come from remote sensing image analysis, geographic information system (GIS) data, city planning and land management department database.

[0116] The soil permeability historical data table should contain soil permeability data at different periods, reflecting the change trend of soil permeability in the region. The data usually comes from soil survey, laboratory analysis, agricultural monitoring, etc.

[0117] The association of these data sources with "ultra-long duration data" is that they together provide long-term, continuous historical data support for the study of hydrological changes in the target city expansion area, so that the system can analyze the correlation between land use change, underground water level fluctuation and soil permeability based on the duration data, and then optimize the rainwater management efficiency.

[0118] Further, the sponge city rain analysis system based on ultra-long duration data further comprises:

[0119] The data analysis module 200 is configured to analyze the underground water level fluctuation record, determine whether the rising change rate of the underground water level in the target urban expansion area in a specified time period exceeds a preset threshold, if yes, obtain the current soil permeability, and find the historical soil permeability of a historical time period closest to the current time and within a same preset range of the current real-time predicted precipitation from a soil permeability historical data table.

[0120] The specified time period refers to a time interval from the start of the construction of the target urban expansion area to the current time.

[0121] Specifically, Figure 6 The structure block diagram of the data analysis module 200 in the system provided by the embodiment of the application is shown.

[0122] In the preferred embodiment provided by the application, the data analysis module 200 specifically comprises:

[0123] The first data analysis unit 201 is configured to analyze the underground water level fluctuation record, divide the specified time period into a plurality of sub-time intervals equal in length to a predetermined time period, and extract the underground water level data of each sub-time interval.

[0124] The rising change rate calculation unit 202 is configured to calculate the rising change rate of the underground water level in the target urban expansion area in the specified time period based on the underground water level data of each sub-time interval, and determine whether the rising change rate exceeds a preset threshold.

[0125] The second data analysis unit 203 is configured to, if the rising change rate exceeds the preset threshold, obtain the current soil permeability, and filter out the historical soil permeability of a historical time period closest to the current time and within a same preset range of the current real-time predicted precipitation from a soil permeability historical data table.

[0126] In the embodiment of the application, whether the rising change rate of the underground water level in the target urban expansion area in the specified time period exceeds the preset threshold can indirectly determine that the land permeability of the target urban expansion area has changed; this is because the change rate of the underground water level is closely related to the permeability of the land; if the underground water level rises too fast, it usually means that the permeability of the soil is poor, and the water cannot effectively penetrate into the ground, but stays on the ground surface or in the shallow soil layer; therefore, the change of the rising rate of the underground water level can indirectly reflect the change of the soil permeability.

[0127] When the groundwater level gradually rises, it is necessary to increase the efficiency of rainwater management; this is because the rise of the groundwater level often means that the soil saturation is increased, and water is difficult to effectively penetrate into the ground, causing surface water or water accumulation; at this time, if rainwater collection and utilization are not strengthened, it may cause waste of water resources or flooding in the region; by increasing the efficiency of rainwater management, excessive precipitation can be effectively collected and stored, reducing surface water, thereby reducing the pressure brought by the rise of the groundwater level.

[0128] 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 in each sub-interval can uniformly represent the change of the groundwater level in 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.

[0129] The selected "historical soil permeability in the historical time period closest to the current time and within the same predetermined range of current real-time predicted precipitation from the soil permeability historical data table" as the comparison object is because the historical data close in time can more accurately reflect the change trend of the current soil permeability, and the 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 meaningful and valuable, thereby improving the accuracy of rainwater management efficiency optimization.

[0130] Further, the sponge city rainfall analysis system based on super-long duration data further comprises:

[0131] The primary correction factor determination module 300 is used to calculate the deviation quantitative value between the current soil permeability and the historical soil permeability, and take it as the primary correction factor.

[0132] In the embodiment of the present application, the deviation quantitative value is taken as the correction factor for optimizing the preliminary rainwater management efficiency, which 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 soil permeability deviates greatly from the historical value, it means that the water absorption capacity of the current soil has changed, which will directly affect the efficiency of rainwater penetration, storage and utilization; by calculating the deviation quantitative value and taking it as the correction factor, the change can be reflected in real time during the rainwater collection process, ensuring that the response to precipitation is more accurate and efficient; ultimately, the rainwater management efficiency can be dynamically adjusted to improve the utilization rate of rainwater.

[0133] Further, the sponge city rainfall analysis system based on super-long duration data further comprises:

[0134] The secondary correction factor determination module 400 is configured to analyze the historical land cover change data, extract natural land use change information, draw a natural land change trend graph, calculate an average slope of a change trend of the natural land change trend graph, and take the average slope as the secondary correction factor.

[0135] Specifically, Figure 7 A structure block diagram of the secondary correction factor determination module 400 in the system provided by the embodiment of the application is shown.

[0136] In the preferred embodiment provided by the application, the secondary correction factor determination module 400 specifically comprises:

[0137] The natural land residual value extraction unit 401 is configured to analyze the historical land cover change data and extract a natural land residual value of each sub-time interval in a specified time period from the historical land cover change data;

[0138] The change trend graph drawing unit 402 is configured to draw a natural land change trend graph of the target urban expansion area in the specified time period with each sub-time interval as an X axis and the natural land residual value as a Y axis;

[0139] The secondary correction factor generation unit 403 is configured to calculate an average slope of a change trend of the natural land change trend graph and take the average slope as the secondary correction factor.

[0140] In the embodiment of the application, the historical land cover change data is analyzed and the natural land change trend graph is drawn to help better understand the change of the natural land use of the target urban expansion area. By extracting the natural land residual value and drawing the trend graph, the change trend of the land use can be clearly shown, and a basis can be provided for subsequent rainwater management efficiency optimization. The significance of drawing the natural land change trend graph lies in that it can reveal the potential correlation between the change of the natural land and the precipitation and the permeability. Especially, when the rising change rate of the underground water level exceeds a preset threshold, the permeability and the use mode of the land can change, and these changes are often closely related to the change of the natural land cover. Therefore, through the analysis of the trend graph, effective information can be further provided for optimizing the rainwater management efficiency.

[0141] The average slope of the natural land change trend graph is taken as the secondary correction factor, which can dynamically reflect the change trend of the natural land and adjust the rainwater management efficiency based on this; the average slope can quantify the speed of land change, and then accurately adjust the influence of the land change on the hydrological conditions; when the absolute value of the average slope is large, it means that the change of land use is relatively violent, and the utilization of land resources becomes lower or changes greatly, at this time, the adjustment of the 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 is, the more gentle the land change is, and the adjustment strength of the rainwater management efficiency can be correspondingly reduced; therefore, by adjusting the slope value, the change of the 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.

[0142] Further, the sponge city rainwater analysis system based on the super-long duration data further comprises:

[0143] The rainwater management efficiency optimization module 500 is configured to correct the preliminary rainwater management efficiency twice in combination with the primary correction factor and the secondary correction factor to obtain the optimized rainwater management efficiency.

[0144] Specifically, Figure 8 A structural block diagram of the rainwater management efficiency optimization module 500 in the system provided by the embodiment of the application is shown.

[0145] In the preferred embodiment provided by the application, the rainwater management efficiency optimization module 500 specifically comprises:

[0146] The rainwater management efficiency optimization unit 501 is configured to call the rainwater management efficiency optimization formula and correct the preliminary rainwater management efficiency twice in combination with the primary correction factor and the secondary correction factor to obtain the optimized rainwater management efficiency.

[0147] The optimized rainwater management efficiency application unit 502 is configured to apply the optimized rainwater management efficiency to the rainwater collection system of the target urban expansion area.

[0148] The rainwater management efficiency optimization formula is , wherein E optimized E represents the optimized rainwater management efficiency, E initial D represents the preliminary rainwater management efficiency, D bias K1 represents the adjustment coefficient of the primary correction factor, and S avg K2 represents the adjustment coefficient of the secondary correction factor.

[0149] In the rainwater management efficiency optimization formula:

[0150] wherein F current refers to the current soil permeability, F historical refers to the historical soil permeability;

[0151] wherein, N represents the total number of sub-time intervals, R i represents the natural land residual value corresponding to the i-th sub-time interval, T i represents the time median of the i-th sub-time interval.

[0152] In the embodiments of the present application, the reason for adopting the primary correction factor and the secondary correction factor for double correction is that the two can respectively optimize the rainwater management efficiency from different dimensions, 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, reflects the influence of land permeability adjustment on rainwater management efficiency, and the secondary correction factor considers the long-term influence of natural land use change on hydrological conditions through the slope of land change trend; the two can complement each other and jointly promote the improvement of rainwater collection system efficiency.

[0153] Specifically, the primary correction factor evaluates the change of land permeability by comparing the deviation between the current soil permeability and the historical soil permeability, so as to adjust the rainwater management efficiency; and the secondary correction factor provides information of land use change trend by calculating the slope of natural land use change, and further fine-tunes the rainwater management efficiency; the linkage of the two lies in that soil permeability and land use change are closely related in hydrological process, and increase of land permeability often means enhancement of water permeability, while land use change may affect the water accumulation and drainage capacity of the region; through the comprehensive correction of the two, the efficiency of the rainwater collection system can be more accurately predicted and adjusted.

[0154] In the formula, since the "deviation quantitative value between the current soil permeability and the historical soil permeability" and the "average slope" are both negative values, but they reflect the positive regulation 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, so as to ensure that the initial rainwater management efficiency is effectively improved; this way makes the negative correction factor correctly converted into positive regulation of the initial efficiency, ensuring that the optimized rainwater management efficiency is improved within a proper range.

[0155] It should be understood that although the steps in the flowcharts of the embodiments of the present application are shown in a certain order following the arrows, the steps do not have to be executed in the order shown by the arrows; unless otherwise specified herein, the execution of the steps does not have strict order limitation, and the steps can be executed in other orders; and at least some of the steps in the embodiments can include multiple sub-steps or multiple stages, which do not have to be executed at the same time, but can be executed at different times, and the execution order of the sub-steps or stages does not have to be sequential, but can be executed in rotation or alternation with at least some of the other steps or sub-steps or stages of the other steps.

[0156] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiments can be included; any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory; the non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory; the volatile memory can include random access memory (RAM) or external cache memory; as an illustration but 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 (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM) and the like.

[0157] The technical features of the above-mentioned embodiments can be combined in any way, and in order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0158] The above embodiments only express several implementation manners of the present application, which are described in a more specific and detailed manner, but cannot be understood as a limitation on the patent scope of the present application; it should be noted that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, which all belong to the protection scope of the present application; therefore, the protection scope of the present application should be subject to the appended claims.

[0159] The above only describes the preferred embodiments of the present application and is not used to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for sponge city rainfall analysis based on ultra-long duration data, characterized in that, The method comprises: acquiring real-time predicted rainfall of the target urban expansion area in a predetermined time period, a preliminary rainwater management efficiency formulated for the target urban expansion area, simultaneously acquiring groundwater level fluctuation records of the target urban expansion area, historical land cover change data and historical soil permeability data table; analyzing the groundwater level fluctuation records to determine whether the rising rate of the groundwater level of the target urban expansion area in the specified time period exceeds a preset threshold, if so, acquiring the current soil permeability, and finding the historical soil permeability of the historical time period closest to the current time and within the same preset range of the current real-time predicted rainfall from the historical soil permeability data table; calculating the deviation quantification value between the current soil permeability and the historical soil permeability, and taking it as the primary correction factor; analyzing the historical land cover change data, extracting natural land use change information, and drawing a natural land change trend graph, calculating the average slope of the change trend of the natural land change trend graph, and taking the average slope as the secondary correction factor; combining the primary correction factor and the secondary correction factor to double correct the preliminary rainwater management efficiency to obtain the optimized rainwater management efficiency.

2. The sponge city rain analysis method based on ultra-long duration data according to claim 1, characterized in that, The specified time period refers to the time interval from the start of the construction of the target urban expansion area to the current time.

3. The sponge city rain analysis method based on ultra-long duration data according to claim 2, characterized in that, The steps of analyzing the groundwater level fluctuation records to determine whether the rising rate of the groundwater level of the target urban expansion area in the specified time period exceeds a preset threshold, if so, acquiring the current soil permeability, and finding the historical soil permeability of the historical time period closest to the current time and within the same preset range of the current real-time predicted rainfall from the historical soil permeability data table comprise: analyzing the groundwater level fluctuation records, dividing the specified time period into a plurality of sub-time intervals equal in length to 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 rate of the groundwater level of the target urban expansion area in the specified time period, and determining whether the rising rate exceeds the preset threshold; if the rising rate exceeds the preset threshold, acquiring the current soil permeability, and selecting the historical soil permeability of the historical time period closest to the current time and within the same preset range of the current real-time predicted rainfall from the soil permeability historical data table.

4. The sponge city rain 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 natural land use change information, and drawing a natural land change trend graph, calculating the average slope of the change trend of the natural land change trend graph, and taking the average slope as the secondary correction factor comprise: analyzing the historical land cover change data to extract the natural land remaining value of each sub-time interval in the specified time period; taking each sub-time interval as the X axis and the natural land remaining value as the Y axis, drawing a natural land change trend graph of the target urban expansion area in the specified time period; calculating the average slope of the change trend of the natural land change trend graph, and taking the average slope as the secondary correction factor.

5. The sponge city rain analysis method based on ultra-long duration data according to claim 1, characterized in that, The steps of double-modifying the preliminary rainwater management efficiency by combining the primary modification factor and the secondary modification factor to obtain the optimized rainwater management efficiency include: The rainwater management efficiency optimization formula is called, and the preliminary rainwater management efficiency is double-modified by combining the primary modification factor and the secondary modification factor to obtain the optimized rainwater management efficiency. The optimized rainwater management efficiency is applied to the rainwater collection system of the target urban expansion area.

6. The sponge city rain analysis method based on ultra-long duration data according to claim 5, characterized in that, The rainwater management efficiency optimization formula is Wherein 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, i.e. the deviation quantitative 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, i.e. the average slope of the change trend of the natural land change trend graph, 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 by, The system comprises a data acquisition module, a data analysis module, a primary modification factor determination module, a secondary modification factor determination module, and a rainwater management efficiency optimization module, wherein: The data acquisition module is configured to acquire real-time predicted precipitation of the target urban expansion area in a predetermined time period, a preliminary rainwater management efficiency formulated for the target urban expansion area, underground water level fluctuation records of the target urban expansion area, historical land cover change data, and historical soil permeability data. The data analysis module is configured to analyze the underground water level fluctuation records, determine whether the rising change rate of the underground water level of the target urban expansion area in a specified time period exceeds a preset threshold, and if so, acquire a current soil permeability and find a historical soil permeability of a historical time period closest to the current time and within a same preset range of the current real-time predicted precipitation from the historical soil permeability data. The specified time period refers to a time interval from the start of the construction of the target urban expansion area to the current time. The primary modification factor determination module is configured to calculate a deviation quantization value between the current soil permeability and the historical soil permeability and take the deviation quantization value as the primary modification factor. The secondary modification factor determination module is configured to analyze the historical land cover change data, extract natural land use change information, draw a natural land change trend graph, calculate an average slope of the change trend of the natural land change trend graph, and take the average slope as the secondary modification factor. The rainwater management efficiency optimization module is configured to double-modify the preliminary rainwater management efficiency by combining the primary modification factor and the secondary modification factor to obtain the optimized rainwater management efficiency. 8.The sponge city rain analysis system based on ultra-long duration data of claim 7, wherein, The data analysis module specifically comprises: A first data analysis unit is configured to analyze the underground water level fluctuation records, divide the specified time period into a plurality of sub-time intervals equal in length to the predetermined time period, and extract underground water level data of each sub-time interval. A rising change rate calculation unit is configured to calculate the rising change rate of the underground water level of the target urban expansion area in the specified time period based on the underground water level data of each sub-time interval and determine whether the rising change rate exceeds the preset threshold. A second data analysis unit is configured to acquire the current soil permeability and find the historical soil permeability of the historical time period closest to the current time and within the same preset range of the current real-time predicted precipitation from the historical soil permeability data if the rising change rate exceeds the preset threshold. 9.The sponge city rain analysis system based on ultra-long duration data of claim 8, wherein, The secondary modification factor determination module specifically comprises: A natural land residual value extraction unit is configured to analyze the historical land cover change data and extract a natural land residual value of each sub-time interval in the specified time period. The change trend chart drawing unit is configured to draw a natural land change trend chart of the target urban expansion area in a designated time period with each sub-time interval as an X axis and a natural land residual value as a Y axis. The secondary correction factor generation unit is configured to calculate an average slope of the change trend of the natural land change trend chart and take the average slope as a secondary correction factor.

10. The sponge city rain analysis system based on ultra-long duration data according to claim 9, characterized in that, The rainwater management efficiency optimization module specifically comprises: The rainwater management efficiency optimization unit is configured to call a rainwater management efficiency optimization formula and perform double corrections on the preliminary rainwater management efficiency in combination with the primary correction factor and the secondary correction factor to obtain an optimized rainwater management efficiency. The optimized rainwater management efficiency application unit is configured to apply the optimized rainwater management efficiency to a rainwater collection system of the target urban expansion area. The rainwater management efficiency optimization formula is Wherein 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, i.e. the deviation quantitative 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, i.e. 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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