A big data and internet-based sponge city data management system

Abstract

The application discloses a kind of sponge city data management systems based on big data and Internet, it is related to big data technical field;Through sponge city digital image generation module, sponge city structure data is collected, and according to sponge city structure data, sponge city is digitally modeled to generate sponge city digital image;In sponge city digital image deployment data acquisition node, and real-time data acquisition data acquisition node corresponding basic penetration data is collected;Send to data management library, each basic penetration data is classified and corresponding sub data management layer is obtained, and each sub data management layer is arranged to obtain data management layer;And the sponge city data of each sub data management layer is handled to obtain corresponding penetration abnormal value, according to each penetration abnormal value, the waterlogging grade value corresponding to sponge body area is obtained;According to waterlogging grade value, each data management layer is early warned;Effectively improve the timeliness of sponge city data.

Description

Technical Field

[0001] This invention relates to the field of big data technology, specifically a sponge city data management system based on big data and the Internet. Background Technology

[0002] Sponge city, a new generation of urban stormwater management concept, refers to a city's ability to adapt to environmental changes and cope with natural disasters caused by rainwater, much like a sponge. It can also be called a "water-resilient city." The practical application of "sponge city" materials demonstrates excellent permeability, pressure resistance, wear resistance, slip resistance, as well as being environmentally friendly, aesthetically pleasing, comfortable, easy to maintain, and sound-absorbing and noise-reducing. This results in "breathing" urban landscape pavements, effectively mitigating the urban heat island effect and preventing urban roads from overheating.

[0003] Due to the variability and importance of sponge city data, it is necessary to monitor and manage it. However, the diversity of sponge city-related data leads to insufficient coverage of data collection points, resulting in incomplete data collection. Furthermore, a large amount of sponge city data cannot be promptly alerted during management. Therefore, to solve the above problems, this invention provides a sponge city data management system based on big data and the Internet. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a sponge city data management system based on big data and the Internet;

[0005] The objective of this invention can be achieved through the following technical solution: a sponge city data management system based on big data and the Internet, including a data management library, wherein the data management library is connected to a sponge city digital image generation module, a sponge city data acquisition module, a sponge city data processing module, and a data monitoring module;

[0006] The sponge city digital image generation module is used to collect sponge city structural data and generate a sponge city digital image based on the sponge city structural data.

[0007] The sponge city data acquisition module is used to deploy data acquisition nodes on the digital image of the sponge city and collect the basic permeability data corresponding to the data acquisition nodes in real time, and then send it to the data management library.

[0008] The data management library is used to classify each basic penetration data and obtain the corresponding sub-data management layer, and arrange each sub-data management layer to obtain the data management layer;

[0009] The sponge city data processing module processes the sponge city data of each sub-data management layer to obtain the corresponding infiltration anomaly values, and obtains the waterlogging level value corresponding to the sponge body area based on each infiltration anomaly value.

[0010] The data monitoring module is used to issue early warnings to each data management layer based on the flood level value.

[0011] Furthermore, the process by which the sponge city digital image generation module generates a sponge city digital image includes:

[0012] The maximum infiltration distance of rainwater in the sponge city was determined, and several target points were set within the sponge city. Using the target point with the maximum infiltration distance as its side length as the center point, square ground areas corresponding to each target point were obtained and marked as sponge body areas. Different sponge body areas in the sponge city were labeled with different colors. Geospatial data corresponding to each sponge body area was obtained through GIS, and remote sensing data corresponding to each sponge body area was obtained through remote sensing technology. Finally, a 3D spatial model was used to combine the geospatial data and remote sensing data of each sponge body area to generate a digital image of the sponge city.

[0013] Furthermore, the process of combining geospatial data and remote sensing data of various sponge regions through a three-dimensional spatial model includes:

[0014] A three-dimensional spatial model is constructed based on the geospatial data corresponding to each sponge body in the sponge city area. The remote sensing data corresponding to each sponge body is then transmitted to the geospatial data corresponding to the three-dimensional spatial model and displayed in real time, thereby generating a digital image of the sponge city.

[0015] Furthermore, the process of deploying data collection nodes for sponge city data includes:

[0016] Sensors are installed at all locations of sponge bodies within the sponge city area, and automatic on / off terminals and planned acquisition cycles are set on these sensors. Remote sensing data threshold ranges are set for each sponge body type. Based on the digital image of the sponge city, if no sponge body in the remote sensing data area corresponds to the threshold, the corresponding sponge body location is marked as the first acquisition node, and a first acquisition node signal is generated. Conversely, if a sponge body location is found to be present, it is marked as the second acquisition node, and a second acquisition node signal is generated. The first and second acquisition node signals are simultaneously sent to the corresponding sensors.

[0017] Furthermore, the process of real-time acquisition of basic penetration data corresponding to data acquisition nodes includes:

[0018] If a signal from the first acquisition node is received, the sensor automatically turns on according to the automatic switch terminal to collect the basic infiltration data corresponding to the first acquisition node in real time. If a signal from the second acquisition node is received, the sensor automatically turns on the corresponding automatic switch terminal according to the planned acquisition cycle to collect the basic infiltration data corresponding to the second acquisition node. The hydrological data is then transmitted to the corresponding sponge body location on the digital image of the sponge city for real-time display.

[0019] Furthermore, the process by which the data management library obtains the sub-data management layer includes:

[0020] Acquire the acquisition time corresponding to the basic infiltration data. Based on the acquisition time, associate the basic infiltration data and the corresponding remote sensing data of each sponge in the sponge region to generate a sponge data set. Merge the sponge data sets corresponding to the same sponge type to generate a sponge data set library. Arrange the sponge data sets in the sponge data set library in descending order of the difference between the corresponding remote sensing data and the remote sensing data threshold range. Place the sponge data sets with the same difference in parallel.

[0021] Acquire climate data corresponding to rainy weather conditions, including rainfall intensity and duration; obtain basic rainwater data based on the climate data, including rainfall amount and water quality parameters; associate the climate data with the basic rainwater data and map it with the corresponding sponge city data set library to obtain the corresponding sub-data management layer under rainy weather conditions.

[0022] Furthermore, the process of acquiring the data management layer includes:

[0023] Arrange the sub-data management layers in the order of the first data acquisition node and the second data acquisition node to obtain the corresponding data management layer under rainy weather conditions.

[0024] Furthermore, the process by which the sponge city data processing module obtains the corresponding infiltration anomaly values ​​includes:

[0025] The basic rainwater data of each sponge city data set in the scheduling sub-data management layer is used to obtain the basic rainwater condition group corresponding to each sponge city data set. The basic infiltration data of each sponge city data set under the corresponding rainwater condition group is then scheduled, and the average basic infiltration value of each sponge city under each rainwater condition based on the collection time is obtained. Furthermore, the maximum and minimum average basic infiltration values ​​under the rainwater condition are obtained, leading to the determination of the basic infiltration value range for each sponge city, denoted as... Where L represents the location of the corpus cavernosum. Represented as the maximum basic permeability average. It is represented as the minimum basic permeability average; and is correlated with the corresponding sponge body data set.

[0026] Furthermore, the process of obtaining the waterlogging level value corresponding to the sponge city area includes:

[0027] Basic permeability data that is less than the basic permeability value threshold is marked as abnormal permeability value. Then, the number of abnormal permeability values ​​corresponding to the spongy body region is obtained, and the ratio of this number to the total number of spongy bodies in the spongy body region is obtained and marked as the abnormal permeability ratio. Otherwise, no processing is performed.

[0028] When the abnormal infiltration rate is less than or equal to 30%, the waterlogging level of the corresponding sponge area will be marked as Level 1.

[0029] When the abnormal infiltration rate is greater than 30% but less than 80%, the waterlogging level of the corresponding sponge area will be marked as Level II.

[0030] When the abnormal infiltration rate is greater than or equal to 80%, the waterlogging level of the corresponding sponge area will be marked as Level III.

[0031] The abnormal infiltration ratio is correlated with the corresponding sponge body data set, and the waterlogging level value is correlated with the corresponding data management layer. The sponge body data sets corresponding to each sub-data management layer are rearranged in ascending order of basic infiltration value range, and the data management layers are rearranged in descending order of waterlogging level value.

[0032] Furthermore, the process by which the data monitoring module issues early warnings to each data management layer based on the flood level value includes:

[0033] Based on digital images of sponge cities, the colors of sponge areas corresponding to different levels of waterlogging are converted into corresponding warning colors and sent to preset management terminals for warning purposes.

[0034] The relevant administrators on the management terminal adjust the geospatial data of the sponge corresponding to the abnormal infiltration value and update the remote sensing data of the sponge in real time according to the corresponding warning color.

[0035] Compared with existing technologies, the beneficial effects of this invention are as follows: It collects sponge city structural data through a sponge city digital image generation module, and generates a digital image of the sponge city by digitally modeling the sponge city based on this structural data; it deploys data acquisition nodes on the sponge city digital image and collects basic infiltration data corresponding to these nodes in real time; it sends this data to a data management database, classifies each basic infiltration data, obtains corresponding sub-data management layers, and arranges these sub-data management layers to obtain a data management layer; it processes the sponge city data in each sub-data management layer to obtain corresponding infiltration anomalies, and obtains the corresponding waterlogging level value for each sponge area based on these anomalies; it provides early warnings for each data management layer based on the waterlogging level value; thus effectively improving the timeliness of sponge city data. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0037] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] like Figure 1 As shown, a sponge city data management system based on big data and the Internet includes a data management library, which is connected to a sponge city digital image generation module, a sponge city data acquisition module, a sponge city data processing module, and a data monitoring module.

[0040] The sponge city digital image generation module is used to collect sponge city structural data and generate a sponge city digital image based on the sponge city structural data.

[0041] The sponge city data acquisition module is used to deploy data acquisition nodes on the digital image of the sponge city and collect the basic permeability data corresponding to the data acquisition nodes in real time, and then send it to the data management library.

[0042] The data management library is used to classify each basic penetration data and obtain the corresponding sub-data management layer, and arrange each sub-data management layer to obtain the data management layer;

[0043] The sponge city data processing module processes the sponge city data of each sub-data management layer to obtain the corresponding infiltration anomaly values, obtains the waterlogging level value corresponding to the sponge body area based on each infiltration anomaly value, and sends it to the data management database for data management.

[0044] The data monitoring module is used to issue early warnings to each data management layer based on the flood level value;

[0045] It should be further explained that, in the specific implementation process, the process of the sponge city digital image generation module collecting sponge city structural data and generating a digital image of the sponge city based on the sponge city structural data includes:

[0046] Under sunny conditions, the furthest distance at which rainwater can infiltrate the sponge city is set, and several target points are set within the sponge city. Using the target point with the furthest infiltration distance as the side length as the center point, square ground areas corresponding to each target point are obtained and marked as sponge areas. The sponge city is divided into several sponge areas, and different sponge areas are marked with different colors. Geospatial data corresponding to each sponge area is obtained through GIS, and remote sensing data corresponding to each sponge area is obtained through remote sensing technology. Finally, the geospatial data and remote sensing data of each sponge area are combined through a 3D spatial model to generate a digital image of the sponge city.

[0047] Furthermore, the setting of the furthest infiltration distance is based on an ideal sponge city environment, such as sandy soil, flat terrain, dense vegetation, and low groundwater level; sponge city structural data includes geospatial data and remote sensing data;

[0048] In the above embodiments, it should be further explained that the process of combining the geospatial data and remote sensing data of each sponge region through a three-dimensional spatial model includes:

[0049] A three-dimensional spatial model is constructed based on the geospatial data corresponding to each sponge body in the sponge city area. The remote sensing data corresponding to each sponge body is then transmitted to the geospatial data corresponding to the three-dimensional spatial model and displayed in real time, thereby generating a digital image of the sponge city.

[0050] The geospatial data includes the location, morphology, and type of sponge bodies; the remote sensing data includes vegetation cover, terrain slope, soil type, and groundwater level.

[0051] Furthermore, the sponge body includes not only water systems such as rivers, lakes, and ponds, but also green plants, soil, and permeable ground; for example, if the sponge body is green plants, the corresponding remote sensing data is vegetation cover; if it is permeable ground, the corresponding remote sensing data is terrain slope.

[0052] It should be further explained that, in the specific implementation process, the sponge city data acquisition module deploys data acquisition nodes on the sponge city digital image and collects the basic permeability data corresponding to the data acquisition nodes in real time, including:

[0053] Sensors are installed at all locations of sponge bodies within the sponge city area, and automatic on / off terminals and planned acquisition cycles are set on these sensors. Remote sensing data threshold ranges are set for each sponge body type. Based on the digital image of the sponge city, if no sponge body is found in the remote sensing data of the sponge city area corresponding to the remote sensing data threshold, the location of that sponge body is marked as a first acquisition node, and a first acquisition node signal is generated. Conversely, if a sponge body is found to be located within the threshold range, the location is marked as a second acquisition node, and a second acquisition node signal is generated. The first and second acquisition node signals are simultaneously sent to the corresponding sensors.

[0054] In rainy weather, if a signal is received from the first acquisition node, the sensor will automatically turn on according to the automatic switch terminal to collect the basic infiltration data corresponding to the first acquisition node in real time; if a signal is received from the second acquisition node, the sensor will automatically turn on the corresponding automatic switch terminal according to the planned acquisition cycle to collect the basic infiltration data corresponding to the second acquisition node; and transmit the hydrological data to the corresponding sponge body location on the digital image of the sponge city for real-time display.

[0055] The basic infiltration data includes water level, water flow velocity, and soil content.

[0056] In the above embodiments, it should be further noted that the sensors include, but are not limited to, rainfall sensors, water level sensors, water quality sensors, flow sensors, soil moisture sensors, etc. It should also be noted that each sensor is set in a different location and is set to collect corresponding basic infiltration data. There is only one such basic infiltration data, that is, the sensor set at each collection node can only collect one type of basic infiltration data.

[0057] It should be further explained that, in the specific implementation process, the data management library classifies each basic penetration data and obtains the corresponding sub-data management layers, and arranges each sub-data management layer to obtain the data management layer. The process includes:

[0058] Acquire the acquisition time corresponding to the basic infiltration data. Based on the acquisition time, associate the basic infiltration data and the corresponding remote sensing data of each sponge in the sponge region to generate a sponge data set. Merge the sponge data sets corresponding to the same sponge type to generate a sponge data set library. Arrange the sponge data sets in the sponge data set library in descending order of the difference between the corresponding remote sensing data and the remote sensing data threshold range. Place the sponge data sets with the same difference in parallel.

[0059] Acquire climate data corresponding to rainy weather conditions, including rainfall intensity and duration; obtain basic rainwater data based on the climate data, including rainfall amount and water quality parameters; associate the climate data with the basic rainwater data and map it with the corresponding sponge city data set library to obtain the corresponding sub-data management layer under rainy weather conditions.

[0060] Arrange the sub-data management layers in the order of the first data acquisition node and the second data acquisition node to obtain the corresponding data management layer under rainy weather conditions.

[0061] It should be further explained that, in the specific implementation process, the sponge city data processing module processes each sub-data management layer to obtain the corresponding infiltration anomaly values, obtains the corresponding waterlogging level value of the sponge body area based on each infiltration anomaly value, and sends it to the data management database for data management. The process includes:

[0062] The basic rainwater data of each sponge city data set in the scheduling sub-data management layer is used to obtain the basic rainwater condition group corresponding to each sponge city data set. This rainwater condition group includes (Ymax, Smax), (Ymax, Smin), (Ymin, Smax), and (Ymin, Smin), where Ymax, Smax, Ymin, and Smin represent the maximum rainfall, maximum water quality parameter, minimum rainfall, and minimum water quality parameter, respectively. The basic infiltration data of each sponge city data set corresponding to the rainwater condition group is scheduled, and the average basic infiltration value of the sponge city under each rainwater condition is obtained based on the collection time. Then, the maximum and minimum average basic infiltration values ​​are obtained within the rainwater condition, and the range of basic infiltration values ​​corresponding to the sponge city is obtained, denoted as [missing information]. Where L represents the location of the corpus cavernosum. Represented as the maximum basic permeability average. It is represented as the minimum basic permeability average; and data is correlated with the corresponding sponge body dataset.

[0063] Basic permeability data that is less than the basic permeability value threshold is marked as abnormal permeability value. Then, the number of abnormal permeability values ​​corresponding to the spongy body region is obtained, and the ratio of this number to the total number of spongy bodies in the spongy body region is obtained and marked as the abnormal permeability ratio. Otherwise, no processing is performed.

[0064] When the abnormal infiltration rate is less than or equal to 30%, the waterlogging level of the corresponding sponge area will be marked as Level 1.

[0065] When the abnormal infiltration rate is greater than 30% but less than 80%, the waterlogging level of the corresponding sponge area will be marked as Level II.

[0066] When the abnormal infiltration rate is greater than or equal to 80%, the waterlogging level of the corresponding sponge area will be marked as Level III.

[0067] The abnormal infiltration ratio is correlated with the corresponding sponge body data set, and the waterlogging level value is correlated with the corresponding data management layer.

[0068] In the above embodiments, it should be further explained that the process of updating the data management layer includes:

[0069] The sponge body data sets corresponding to each sub-data management layer are rearranged in ascending order of basic infiltration value range, and the data management layers are rearranged in descending order of urban flooding level value.

[0070] It should be further explained that, in the specific implementation process, the process by which the data monitoring module issues early warnings to each data management layer based on the flood level value includes:

[0071] Based on digital images of sponge cities, the colors of sponge areas corresponding to different levels of waterlogging are converted into corresponding warning colors and sent to preset management terminals for warning purposes.

[0072] The relevant administrators on the management terminal adjust the geospatial data of the sponge corresponding to the abnormal infiltration value and update the remote sensing data of the sponge in real time according to the corresponding warning color.

[0073] The features and exemplary embodiments of various aspects of this application will be described in detail above. In order to make the purpose, technical solution and advantages of this application clearer, the application will be further described in detail above with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit this application. For those skilled in the art, this application can be implemented without some of the details in these specific details. The above description of the embodiments is only to provide a better understanding of this application by showing examples of this application.

[0074] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A sponge city data management system based on big data and the Internet, comprising a data management database, characterized in that, The data management library is connected to a sponge city digital image generation module, a sponge city data acquisition module, a sponge city data processing module, and a data monitoring module. The sponge city digital image generation module is used to collect sponge city structural data and generate a sponge city digital image based on the sponge city structural data. The sponge city data acquisition module is used to deploy data acquisition nodes on the digital image of the sponge city and collect the basic permeability data corresponding to the data acquisition nodes in real time, and then send it to the data management library. The data management library is used to classify each basic penetration data and obtain the corresponding sub-data management layer, and arrange each sub-data management layer to obtain the data management layer; The sponge city data processing module processes the sponge city data of each sub-data management layer to obtain the corresponding infiltration anomaly values, and obtains the waterlogging level value corresponding to the sponge body area based on each infiltration anomaly value. The data monitoring module is used to issue early warnings to each data management layer based on the flood level value; The process of generating digital images of sponge cities by the sponge city digital image generation module includes: The maximum infiltration distance of rainwater in the sponge city is set, and several target points are set in the sponge city. The square ground area corresponding to each target point is obtained with the maximum infiltration distance as the side length and the target point as the center point, and marked as the sponge body area. Different sponge body areas in the sponge city are marked with different colors. The geospatial data corresponding to each sponge body area is obtained through GIS, and the remote sensing data corresponding to each sponge body area is obtained through remote sensing technology. Then, the geospatial data and remote sensing data of each sponge body area are combined through a three-dimensional spatial model to generate a digital image of the sponge city. The process of combining geospatial data and remote sensing data of various sponge regions using a 3D spatial model includes: A three-dimensional spatial model is constructed based on the geospatial data corresponding to each sponge body in the sponge city area. The remote sensing data corresponding to each sponge body is then transmitted to the geospatial data corresponding to the three-dimensional spatial model and displayed in real time, thereby generating a digital image of the sponge city. The process of deploying data collection nodes for the sponge city data collection module includes: Sensors are installed at all locations of sponge bodies within the sponge city area, and automatic on / off terminals and planned acquisition cycles are set on these sensors. Remote sensing data threshold ranges are set for each sponge body type. Based on the digital image of the sponge city, if no sponge body is found in the remote sensing data of the sponge city area corresponding to the remote sensing data threshold, the location of that sponge body is marked as a first acquisition node, and a first acquisition node signal is generated. Conversely, if a sponge body is found to be located within the threshold range, the location is marked as a second acquisition node, and a second acquisition node signal is generated. The first and second acquisition node signals are simultaneously sent to the corresponding sensors. The process of real-time acquisition of basic penetration data corresponding to data acquisition nodes includes: If a signal from the first acquisition node is received, the sensor will automatically turn on according to the automatic switch terminal to collect the basic infiltration data corresponding to the first acquisition node in real time; if a signal from the second acquisition node is received, the sensor will automatically turn on the corresponding automatic switch terminal according to the planned acquisition cycle to collect the basic infiltration data corresponding to the second acquisition node; and transmit the hydrological data to the corresponding sponge body location on the digital image of the sponge city for real-time display. The process by which the data management library obtains the sub-data management layer includes: Acquire the acquisition time corresponding to the basic infiltration data. Based on the acquisition time, associate the basic infiltration data and the corresponding remote sensing data of each sponge in the sponge region to generate a sponge data set. Merge the sponge data sets corresponding to the same sponge type to generate a sponge data set library. Arrange the sponge data sets in the sponge data set library in descending order of the difference between the corresponding remote sensing data and the remote sensing data threshold range. Place the sponge data sets with the same difference in parallel. Obtain the corresponding climate data under rainy weather conditions, and obtain basic rainwater data based on the climate data; after associating the climate data with the basic rainwater data, map it with the corresponding sponge city data set library to obtain the corresponding sub-data management layer under rainy weather conditions; The process of acquiring data from the management layer includes: Arrange the sub-data management layers in the order of the first data acquisition node and the second data acquisition node to obtain the corresponding data management layer under rainy weather conditions; The process by which the sponge city data processing module obtains the corresponding infiltration anomaly values ​​includes: The basic rainwater data of each sponge city data set in the scheduling sub-data management layer is used to obtain the basic rainwater condition group corresponding to each sponge city data set. The basic infiltration data of each sponge city data set under the corresponding rainwater condition group is then scheduled, and the average basic infiltration value of each sponge city under each rainwater condition based on the collection time is obtained. Furthermore, the maximum and minimum average basic infiltration values ​​under the rainwater condition are obtained, leading to the determination of the basic infiltration value range for each sponge city, denoted as... Where L represents the location of the corpus cavernosum. Represented as the maximum basic permeability average. It is represented as the minimum basic permeability average; and data is correlated with the corresponding sponge body dataset. The process of obtaining the waterlogging level value corresponding to the sponge city area includes: Basic permeability data that is less than the minimum basic permeability average is marked as abnormal permeability values. Then, the number of abnormal permeability values ​​corresponding to the spongy body region is obtained, and the ratio of this number to the total number of spongy bodies in the spongy body region is marked as the abnormal permeability ratio. Otherwise, no processing is performed. When the abnormal infiltration rate is less than or equal to 30%, the waterlogging level of the corresponding sponge area will be marked as Level 1. When the abnormal infiltration rate is greater than 30% but less than 80%, the waterlogging level of the corresponding sponge area will be marked as Level II. When the abnormal infiltration rate is greater than or equal to 80%, the waterlogging level of the corresponding sponge area will be marked as Level III. The abnormal infiltration ratio is correlated with the corresponding sponge body data set, and the waterlogging level value is correlated with the corresponding data management layer; the sponge body data sets corresponding to each sub-data management layer are rearranged in ascending order of basic infiltration value range, and the data management layers are rearranged in descending order of waterlogging level value. The process by which the data monitoring module issues early warnings to each data management layer based on the flood level value includes: Based on digital images of sponge cities, the colors of sponge areas corresponding to different levels of waterlogging are converted into corresponding warning colors and sent to preset management terminals for warning purposes. The relevant administrators on the management terminal adjust the geospatial data of the sponge corresponding to the abnormal infiltration value and update the remote sensing data of the sponge in real time according to the corresponding warning color.

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