Urban intelligent drainage method and device based on big data, terminal and storage medium

Abstract

The invention provides an urban intelligent drainage method and device based on big data, a terminal and a storage medium. The method comprises the following steps: acquiring a historical waterflooding event record of a target area, wherein the historical waterflooding event record comprises historical waterflooding areas where waterflooding events occur in each historical time period and historical rainfall data; according to the historical waterflooding area, the historical rainfall data and predicted rainfall data of the target area, determining an area with waterflooding risk in the prediction time period in the target area, and recording the area as a waterflooding risk area; according to the current topographic data of the target area and the drainage pipe network siltation prediction model of the target area, correcting the waterflooding risk area to obtain a corrected waterflooding risk area; and determining a drainage control scheme of the drainage pipe network of the target area according to the corrected flooding risk area. The method can timely and effectively determine the drainage control scheme of the drainage pipe network in the target area, thereby improving the real-time performance and effectiveness of drainage pipe network control, and reducing the urban inland inundation risk.

Description

Technical Field

[0001] The present invention relates to the field of urban drainage technology, and in particular to a big data-based urban smart drainage method, device, terminal and storage medium. Background Art

[0002] With the acceleration of global urbanization, more and more people are pouring into urban areas, leading to the continuous expansion and densification of urban scale. The accompanying construction activities, such as high-rise buildings, road paving, and underground facility development, have caused the load on urban drainage systems to increase sharply. At the same time, the hardening of urban surfaces has continued to intensify, traditional natural drainage paths have been blocked, rainwater infiltration has decreased, and surface runoff has increased significantly. In addition, the complexity of urban structure has brought about the dispersion and imbalance of drainage networks, resulting in a significant increase in the difficulty of drainage system design, management, and maintenance. The drainage system not only needs to cope with the conventional domestic sewage treatment needs, but also the sudden increase in rainwater loads under extreme weather conditions. These factors have jointly increased the complexity and operating pressure of the drainage system.

[0003] At present, many urban drainage systems rely on manual control or simple rule settings. When encountering sudden rainstorms or floods, the scheduling of drainage pump stations and gates often lags behind and cannot effectively handle large amounts of rainwater in a timely manner. In this case, urban waterlogging often occurs, causing traffic paralysis, damage to infrastructure, and serious impacts on residents' lives. Summary of the invention

[0004] The embodiments of the present invention provide a big data-based urban smart drainage method, device, terminal and storage medium to solve the current problem that it is difficult to perform drainage control in a timely and effective manner according to real-time conditions.

[0005] In a first aspect, an embodiment of the present invention provides an urban smart drainage method based on big data, comprising:

[0006] Acquire historical flood event records of the target area, wherein the historical flood event records include historical flood areas and historical rainfall data where flood events occurred in each historical period;

[0007] According to the historical flooded areas, the historical rainfall data and the predicted rainfall data of the target area, determining areas in the target area that have flooding risks during the forecast period, and recording them as flooding risk areas;

[0008] According to the current topographic data of the target area and the drainage network siltation prediction model of the target area, the flood risk area is corrected to obtain a corrected flood risk area;

[0009] Based on the revised flood risk area, a drainage control plan for the drainage network in the target area is determined.

[0010] In a possible implementation, according to the historical flooded area, the historical rainfall data and the predicted rainfall data of the target area, determining an area in the target area with a flood risk in the predicted period, and recording it as a flood risk area, includes:

[0011] Calculating the similarity between the predicted rainfall data of the target area and the historical rainfall data;

[0012] According to the similarity, the area in the target area with flood risk in the forecast period is determined and recorded as the flood risk area.

[0013] In a possible implementation, the historical rainfall data includes historical rainfall intensity data and historical rainfall distribution data, and the predicted rainfall data includes predicted rainfall intensity data and predicted rainfall distribution data;

[0014] The calculating the similarity between the predicted rainfall data of the target area and the historical rainfall data includes:

[0015] For the historical flooding event records, clustering is performed according to the historical flooding areas, the historical rainfall intensity data, and the historical rainfall distribution data, and a target flooding area, target rainfall intensity data, and target rainfall distribution data of each cluster center are determined;

[0016] The distances between the predicted rainfall intensity data and the predicted rainfall distribution data and the target rainfall intensity data and the target rainfall distribution data of each cluster center are calculated as the similarity between the predicted rainfall data and each type of historical rainfall data.

[0017] In a possible implementation, according to the similarity, determining an area in the target area that has a flood risk in the forecast period, and recording it as a flood risk area, includes:

[0018] According to the similarity, the target flooded area corresponding to the cluster center with the greatest similarity is determined as the area in the target area with flooding risk in the forecast period, and is recorded as the flooding risk area.

[0019] In a possible implementation, the flood risk area is corrected according to the current terrain data of the target area and the drainage network siltation prediction model of the target area to obtain the corrected flood risk area, including:

[0020] According to the similarity, historical terrain data of the historical period corresponding to the historical rainfall data with the greatest similarity is obtained;

[0021] Determining terrain change data based on current terrain data of the target area and the historical terrain data;

[0022] According to the drainage pipe network siltation prediction model of the target area, the current siltation prediction result of the drainage pipe network in the target area and the historical siltation prediction result in the historical period are obtained;

[0023] Obtaining sedimentation change data of the drainage network in the target area according to the current sedimentation prediction result and the historical sedimentation prediction result;

[0024] The flood risk area is corrected according to the terrain change data and the siltation change data to obtain a corrected flood risk area.

[0025] In a possible implementation, the process of constructing the drainage network siltation prediction model includes:

[0026] Acquire pipeline attribute information and pipeline maintenance records of the drainage pipeline network in the target area, wherein the pipeline maintenance records include desilting time and siltation degree;

[0027] The drainage network siltation prediction model is trained by taking the pipeline attribute information and the desilting time as input and the siltation degree as output.

[0028] In a possible implementation, the method further includes:

[0029] According to the similarity, historical meteorological data of a preset period before the historical period corresponding to the historical rainfall data with the greatest similarity is obtained, and recorded as the first target meteorological data;

[0030] Acquire meteorological data of a preset time period before the time period corresponding to the predicted rainfall data, and record it as second target meteorological data;

[0031] Calculating meteorological similarity according to the first target meteorological data and the second target meteorological data;

[0032] Determining soil permeability change data of the flood risk area according to the meteorological similarity and the soil properties of the flood risk area;

[0033] The flood risk area is corrected according to the terrain change data and the siltation change data to obtain a corrected flood risk area, including:

[0034] The flood risk area is corrected according to the terrain change data, the siltation change data and the soil permeability change data to obtain a corrected flood risk area.

[0035] In a second aspect, an embodiment of the present invention provides an urban smart drainage device based on big data, comprising:

[0036] A data collection module is used to obtain historical flooding event records of the target area, wherein the historical flooding event records include historical flooding areas and historical rainfall data where flooding events occurred in each historical period;

[0037] A first data processing module is used to determine, based on the historical flooded areas, the historical rainfall data and the predicted rainfall data of the target area, areas in the target area that have flooding risks during the forecast period, and record them as flooding risk areas;

[0038] A second data processing module is used to correct the flood risk area according to the current terrain data of the target area and the drainage pipe network siltation prediction model of the target area to obtain a corrected flood risk area;

[0039] The drainage control module is used to determine the drainage control plan of the drainage network in the target area according to the modified flood risk area.

[0040] In a third aspect, an embodiment of the present invention provides a terminal, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to perform the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0041] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0042] The embodiment of the present invention provides a method, device, terminal and storage medium for smart urban drainage based on big data, which first obtains the historical flooding event records of the target area, wherein the historical flooding event records include the historical flooding areas and historical rainfall data where flooding events occurred in each historical period, and determines the areas in the target area with flooding risks in the predicted period according to the historical flooding areas, historical rainfall data and predicted rainfall data of the target area, and records them as flooding risk areas. The flooding risk areas of the target area can be identified in advance based on the big data of the historical flooding events of the target area and the predicted rainfall conditions. On this basis, the flooding risk areas are corrected according to the current terrain data of the target area and the drainage network siltation prediction model of the target area. The real-time terrain status and drainage network status of the target area can be used to obtain a more accurate correction of the flooding risk area, and then the drainage control plan of the drainage network of the target area is timely and effectively determined according to the more accurate correction of the flooding risk area, thereby improving the real-time and effectiveness of the drainage network control and reducing the risk of urban waterlogging. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1 is a flow chart of a big data-based urban smart drainage method provided by an embodiment of the present invention;

[0045] Figure 2 is a schematic diagram of an urban smart drainage device based on big data provided by an embodiment of the present invention;

[0046] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0048] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0049] See also Figure 1 , which shows a flowchart of the implementation of the urban smart drainage method based on big data provided by an embodiment of the present invention, which is described in detail as follows:

[0050] In step 101, historical flood event records of a target area are obtained, where the historical flood event records include historical flooded areas and historical rainfall data where flood events occurred in each historical period.

[0051] For example, the target area may be a city or an area in a city, and this embodiment does not limit the scope of the specific target area. Historical flooding event records may be obtained by accessing public data of local relevant departments or through geographic information systems, user feedback records, insurance claim records, etc.

[0052] For example, for a certain target area, each historical flood event record may include all areas of the target area where flood events occurred during a certain period of time and the corresponding rainfall data. For example, a historical flood event record may be shown in Table 1:

[0053] Table 1 Historical flooding event records

[0054]

[0055] In step 102, based on historical flooded areas, historical rainfall data and predicted rainfall data of the target area, areas in the target area with flooding risks during the forecast period are determined and recorded as flooding risk areas.

[0056] In this embodiment, taking into account the historical flooding event records of the target area, it can represent the area of ​​the target area that is prone to flooding events under the combined effects of rainfall, drainage and terrain. Therefore, based on the historical flooding event records, according to the historical flooding areas and historical rainfall data in the historical flooding event records, combined with the predicted rainfall data of the target area, it is possible to predict the areas in the target area that have flooding risks in the future, thereby identifying the flooding risk areas in the target area in advance based on the big data of historical flooding events and predicted rainfall conditions in the target area.

[0057] Exemplarily, based on historical flooded areas, historical rainfall data and predicted rainfall data of the target area, determining areas in the target area that have flooding risks during the forecast period and recording them as flooding risk areas can include: calculating the similarity between the predicted rainfall data and the historical rainfall data of the target area; and based on the similarity, determining areas in the target area that have flooding risks during the forecast period and recording them as flooding risk areas.

[0058] In this embodiment, in order to identify the flood risk area of ​​the target area in advance based on the big data of historical flooding events and predicted rainfall conditions in the target area, the similarity between the predicted rainfall data and the historical rainfall data of the target area can be measured, and then the most likely flood risk area can be screened out based on the similarity of the rainfall conditions in the target area.

[0059] For example, in order to maximize accuracy, the similarity between the predicted rainfall intensity data and predicted rainfall distribution data in the predicted rainfall data and the corresponding historical rainfall intensity data and historical rainfall distribution data may be calculated one by one, thereby obtaining a more accurate flood risk area.

[0060] In another embodiment, in order to improve the calculation efficiency, the historical flooding event records may be clustered according to the historical flooding areas, historical rainfall intensity data and historical rainfall distribution data to determine the target flooding area, target rainfall intensity data and target rainfall distribution data of each cluster center; then, the distance between the predicted rainfall intensity data and the predicted rainfall distribution data and the target rainfall intensity data and the target rainfall distribution data of each cluster center is calculated as the similarity between the predicted rainfall data and each type of historical rainfall data, so that according to the similarity, the target flooding area corresponding to the cluster center with the largest similarity (that is, the smallest distance) is determined as the area with flooding risk in the target area during the forecast period, and is recorded as the flooding risk area.

[0061] In step 103, the flood risk area is corrected according to the current terrain data of the target area and the drainage network siltation prediction model of the target area to obtain a corrected flood risk area.

[0062] In step 104, a drainage control plan for the drainage network in the target area is determined based on the revised flood risk area.

[0063] In this embodiment, considering the uncertainty of the terrain of the target area and the uncertainty of the drainage network status of the target area, the flood risk area determined only based on the predicted rainfall data of the target area may have deviations. Therefore, after determining the flood risk area, the flood risk area is corrected according to the real-time current terrain data of the target area and the drainage network siltation prediction model, so as to obtain a more accurate corrected flood risk area, so as to determine the drainage control plan of the drainage network in the target area based on the more accurate corrected flood risk area.

[0064] In one embodiment, the flood risk area is corrected according to the current terrain data of the target area and the drainage network siltation prediction model of the target area to obtain the corrected flood risk area, which may include:

[0065] According to the similarity, the historical terrain data of the historical period corresponding to the historical rainfall data with the greatest similarity is obtained.

[0066] Determine terrain change data based on current terrain data and historical terrain data of the target area.

[0067] According to the drainage network siltation prediction model of the target area, the current siltation prediction results of the drainage network in the target area and the historical siltation prediction results in the historical period are obtained.

[0068] Based on the current siltation prediction results and the historical siltation prediction results, the siltation change data of the drainage network in the target area is obtained.

[0069] The flood risk area is corrected according to the terrain change data and the siltation change data to obtain the corrected flood risk area.

[0070] Exemplarily, the process of constructing a drainage network siltation prediction model may include:

[0071] The pipeline attribute information and pipeline maintenance records of the drainage network in the target area are obtained. The pipeline maintenance records include desilting time and siltation degree. The drainage network siltation prediction model is trained with the pipeline attribute information and desilting time as input and the siltation degree as output.

[0072] In this embodiment, considering that in the process of correcting the flood risk area, the historical siltation conditions of the drainage network are difficult to obtain, that is, it is difficult to measure the impact of the uncertainty of the drainage network status on the flood risk area, therefore, based on the pipeline attribute information and pipeline maintenance records of the drainage network that are easily obtained in the current area, a drainage network siltation prediction model is trained, so that after determining the historical period with the greatest similarity to the predicted rainfall data, the current siltation prediction results of the drainage network in the target area and the historical siltation prediction results in the historical period are obtained based on the drainage network siltation prediction model, so as to quantitatively measure the siltation change data of the drainage network in the target area, so as to correct the flood risk area according to the real-time terrain change data and siltation change data of the target area, and obtain a more accurate corrected flood risk area.

[0073] In another embodiment, in order to further improve the accuracy of correcting the flood risk area, it is also possible to:

[0074] According to the similarity, historical meteorological data of a preset time period before the historical time period corresponding to the historical rainfall data with the greatest similarity is obtained and recorded as the first target meteorological data.

[0075] The meteorological data of a preset time period before the time period corresponding to the predicted rainfall data is obtained and recorded as the second target meteorological data.

[0076] The meteorological similarity is calculated based on the first target meteorological data and the second target meteorological data.

[0077] Based on the meteorological similarity and soil properties of the flood risk area, the soil permeability change data of the flood risk area is determined.

[0078] Accordingly, the flood risk area is corrected according to the terrain change data and the sedimentation change data to obtain the corrected flood risk area, which may include:

[0079] The flood risk area is corrected according to the terrain change data, siltation change data and soil permeability change data to obtain the corrected flood risk area.

[0080] In this embodiment, in addition to considering the impact of terrain changes and changes in the sedimentation of the drainage network on the flood risk area, the impact of changes in soil permeability in the flood risk area is also considered. Considering that soil permeability is affected by the soil properties itself on the one hand and by the external climate on the other hand, historical meteorological data of a preset period before the historical period corresponding to the historical rainfall data with the greatest similarity and meteorological data of a preset period before the period corresponding to the predicted rainfall data are obtained, thereby measuring the meteorological similarity between the predicted period and the historical period, so as to determine the soil permeability change data of the flood risk area based on the meteorological similarity and the soil properties of the flood risk area.

[0081] It should be noted that, for the convenience of processing, after acquiring relevant data, such as historical flooding event records of the target area, current terrain data and historical terrain data of the target area, pipeline attribute information and pipeline maintenance records of the drainage network of the target area, meteorological data of the target area, soil properties, etc., the present embodiment may pre-process the relevant data. Exemplarily, pre-processing the relevant data may include:

[0082] Data cleaning and filtering: Remove noise and outliers from data through data cleaning techniques. The techniques used may include: Outlier detection and processing: Detect and process outliers in data using statistical methods or machine learning models. Missing value filling: Fill in missing data using interpolation or prediction models based on historical data.

[0083] Data format conversion: Convert raw data in different formats into a unified standard format.

[0084] Data calibration and synchronization: Perform time synchronization and space calibration based on the temporal and spatial differences of the collected data.

[0085] Data compression and decompression: Compress and store massive amounts of data to reduce storage space and transmission bandwidth requirements.

[0086] For example, while determining the drainage control plan of the drainage network in the target area according to the revised flood risk area, a flood risk map can also be generated according to the revised flood risk area to facilitate more intuitive flood risk warning. In addition, the corresponding emergency response procedures can be automatically triggered according to the level of flood risk warning in different areas, including traffic control, evacuation guidance, etc., and multiple emergency departments can be coordinated through an integrated communication system so that in the event of an emergency (such as heavy rain or drainage system abnormality), the system can respond immediately and work with relevant departments to coordinate the deployment of emergency resources, significantly reduce the risk and loss of urban waterlogging, and effectively reduce the impact of disasters.

[0087] The embodiment of the present invention first obtains the historical flooding event records of the target area, wherein the historical flooding event records include the historical flooding areas and historical rainfall data where flooding events occurred in each historical period, and determines the areas in the target area with flooding risks in the predicted period according to the historical flooding areas, historical rainfall data and predicted rainfall data of the target area, and records them as flooding risk areas. Based on the big data of historical flooding events and predicted rainfall conditions of the target area, the flooding risk areas of the target area can be identified in advance. On this basis, the flooding risk areas are corrected according to the current terrain data of the target area and the drainage network siltation prediction model of the target area. The real-time terrain status and drainage network status of the target area can be used to obtain a more accurate correction of the flooding risk area, and then the drainage control plan of the drainage network of the target area is timely and effectively determined according to the more accurate correction of the flooding risk area, thereby improving the real-time and effectiveness of the drainage network control and reducing the risk of urban waterlogging.

[0088] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0089] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0090] Figure 2 The schematic diagram of the structure of the urban smart drainage device based on big data provided by an embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0091] like Figure 2 As shown, the urban smart drainage device based on big data includes: a data acquisition module 21, a first data processing module 22, a second data processing module 23 and a drainage control module 24.

[0092] The data collection module 21 is used to obtain the historical flooding event records of the target area, and the historical flooding event records include the historical flooding areas and historical rainfall data where flooding events occurred in each historical period;

[0093] The first data processing module 22 is used to determine the area in the target area with flooding risk in the forecast period according to the historical flooded area, the historical rainfall data and the forecast rainfall data of the target area, and record it as the flooding risk area;

[0094] The second data processing module 23 is used to correct the flood risk area according to the current terrain data of the target area and the drainage network siltation prediction model of the target area to obtain a corrected flood risk area;

[0095] The drainage control module 24 is used to determine the drainage control plan of the drainage network in the target area according to the modified flood risk area.

[0096] The embodiment of the present invention first obtains the historical flooding event records of the target area, wherein the historical flooding event records include the historical flooding areas and historical rainfall data where flooding events occurred in each historical period, and determines the areas in the target area with flooding risks in the predicted period according to the historical flooding areas, historical rainfall data and predicted rainfall data of the target area, and records them as flooding risk areas. Based on the big data of historical flooding events and predicted rainfall conditions of the target area, the flooding risk areas of the target area can be identified in advance. On this basis, the flooding risk areas are corrected according to the current terrain data of the target area and the drainage network siltation prediction model of the target area. The real-time terrain status and drainage network status of the target area can be used to obtain a more accurate correction of the flooding risk area, and then the drainage control plan of the drainage network of the target area is timely and effectively determined according to the more accurate correction of the flooding risk area, thereby improving the real-time and effectiveness of the drainage network control and reducing the risk of urban waterlogging.

[0097] In a possible implementation, the first data processing module 22 can be used to calculate the similarity between the predicted rainfall data and the historical rainfall data of the target area; based on the similarity, the areas in the target area with flooding risks during the forecast period are determined and recorded as flooding risk areas.

[0098] In a possible implementation, the historical rainfall data includes historical rainfall intensity data and historical rainfall distribution data, and the predicted rainfall data includes predicted rainfall intensity data and predicted rainfall distribution data; the first data processing module 22 can be used to cluster the historical flooding event records according to the historical flooding areas, historical rainfall intensity data and historical rainfall distribution data, and determine the target flooding area, target rainfall intensity data and target rainfall distribution data of each cluster center; calculate the distance between the predicted rainfall intensity data and the predicted rainfall distribution data and the target rainfall intensity data and the target rainfall distribution data of each cluster center as the similarity between the predicted rainfall data and each type of historical rainfall data.

[0099] In a possible implementation, the first data processing module 22 may be configured to determine, based on the similarity, the target flooded area corresponding to the cluster center with the greatest similarity as an area within the target area with flooding risk during the forecast period, and record it as the flooding risk area.

[0100] In one possible implementation, the second data processing module 23 can be used to obtain historical terrain data of a historical period corresponding to historical rainfall data with the greatest similarity based on similarity; determine terrain change data based on current terrain data and historical terrain data of the target area; obtain current siltation prediction results of the drainage network in the target area and historical siltation prediction results in historical periods based on a drainage network siltation prediction model of the target area; obtain siltation change data of the drainage network in the target area based on current siltation prediction results and historical siltation prediction results; and correct the flood risk area based on the terrain change data and the siltation change data to obtain a corrected flood risk area.

[0101] In one possible implementation, the construction process of the drainage network siltation prediction model includes: obtaining the pipeline attribute information and pipeline maintenance records of the drainage network in the target area, the pipeline maintenance records including the desilting time and the degree of siltation; using the pipeline attribute information and desilting time as input and the degree of siltation as output, and training to obtain the drainage network siltation prediction model.

[0102] In a possible implementation, the second data processing module 23 can also be used to obtain historical meteorological data of a preset time period before the historical time period corresponding to the historical rainfall data with the greatest similarity, based on the similarity, and record it as the first target meteorological data; obtain meteorological data of a preset time period before the time period corresponding to the predicted rainfall data, and record it as the second target meteorological data; calculate the meteorological similarity based on the first target meteorological data and the second target meteorological data; determine the soil permeability change data of the flood risk area based on the meteorological similarity and the soil properties of the flood risk area; and correct the flood risk area based on the terrain change data, the siltation change data and the soil permeability change data to obtain a corrected flood risk area.

[0103] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. Figure 3 As shown, the terminal 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in the above-mentioned embodiments of the urban smart drainage method based on big data are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 2 Functions of the modules / units 21 to 24 are shown.

[0104] Exemplarily, the computer program 32 may be divided into one or more modules / units, one or more modules / units are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the terminal 3. For example, the computer program 32 may be divided into Figure 2 Modules / units 21 to 24 are shown.

[0105] The terminal 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of terminal 3 and does not constitute a limitation on terminal 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0106] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0107] The memory 31 may be an internal storage unit of the terminal 3, such as a hard disk or memory of the terminal 3. The memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal 3. Further, the memory 31 may also include both an internal storage unit of the terminal 3 and an external storage device. The memory 31 is used to store computer programs and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0108] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0109] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0111] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0112] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0114] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned embodiments of the urban smart drainage method based on big data. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0115] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A big data-based urban smart drainage method, characterized in that: include: Acquire historical flood event records of the target area, wherein the historical flood event records include historical flood areas and historical rainfall data where flood events occurred in each historical period; According to the historical flooded areas, the historical rainfall data and the predicted rainfall data of the target area, determining areas in the target area that have flooding risks during the forecast period, and recording them as flooding risk areas; According to the current topographic data of the target area and the drainage network siltation prediction model of the target area, the flood risk area is corrected to obtain a corrected flood risk area; Based on the revised flood risk area, a drainage control plan for the drainage network in the target area is determined.

2. The urban smart drainage method based on big data according to claim 1 is characterized in that: According to the historical flooded areas, the historical rainfall data and the predicted rainfall data of the target area, the areas in the target area with flood risk in the predicted period are determined and recorded as flood risk areas, including: Calculating the similarity between the predicted rainfall data of the target area and the historical rainfall data; According to the similarity, the area in the target area with flood risk in the forecast period is determined and recorded as the flood risk area.

3. The urban smart drainage method based on big data according to claim 2 is characterized in that: The historical rainfall data includes historical rainfall intensity data and historical rainfall distribution data, and the predicted rainfall data includes predicted rainfall intensity data and predicted rainfall distribution data; The calculating the similarity between the predicted rainfall data of the target area and the historical rainfall data includes: For the historical flooding event records, clustering is performed according to the historical flooding areas, the historical rainfall intensity data, and the historical rainfall distribution data, and a target flooding area, target rainfall intensity data, and target rainfall distribution data of each cluster center are determined; The distances between the predicted rainfall intensity data and the predicted rainfall distribution data and the target rainfall intensity data and the target rainfall distribution data of each cluster center are calculated as the similarity between the predicted rainfall data and each type of historical rainfall data.

4. The urban smart drainage method based on big data according to claim 3 is characterized in that: According to the similarity, the areas in the target area with flood risk in the forecast period are determined and recorded as flood risk areas, including: According to the similarity, the target flooded area corresponding to the cluster center with the greatest similarity is determined as the area in the target area with flooding risk in the forecast period, and is recorded as the flooding risk area.

5. The urban smart drainage method based on big data according to claim 2 is characterized in that: The step of correcting the flood risk area according to the current terrain data of the target area and the drainage network siltation prediction model of the target area to obtain the corrected flood risk area includes: According to the similarity, historical terrain data of the historical period corresponding to the historical rainfall data with the greatest similarity is obtained; Determining terrain change data based on current terrain data of the target area and the historical terrain data; According to the drainage pipe network siltation prediction model of the target area, the current siltation prediction result of the drainage pipe network in the target area and the historical siltation prediction result in the historical period are obtained; Obtaining sedimentation change data of the drainage network in the target area according to the current sedimentation prediction result and the historical sedimentation prediction result; The flood risk area is corrected according to the terrain change data and the siltation change data to obtain a corrected flood risk area.

6. The urban smart drainage method based on big data according to claim 5 is characterized in that: The construction process of the drainage network siltation prediction model includes: Acquire pipeline attribute information and pipeline maintenance records of the drainage pipeline network in the target area, wherein the pipeline maintenance records include desilting time and siltation degree; The drainage network siltation prediction model is trained by taking the pipeline attribute information and the desilting time as input and the siltation degree as output.

7. The urban smart drainage method based on big data according to claim 5 is characterized in that: Also includes: According to the similarity, historical meteorological data of a preset period before the historical period corresponding to the historical rainfall data with the greatest similarity is obtained, and recorded as the first target meteorological data; Acquire meteorological data of a preset time period before the time period corresponding to the predicted rainfall data, and record it as second target meteorological data; Calculating meteorological similarity according to the first target meteorological data and the second target meteorological data; Determining soil permeability change data of the flood risk area according to the meteorological similarity and the soil properties of the flood risk area; The flood risk area is corrected according to the terrain change data and the siltation change data to obtain a corrected flood risk area, including: The flood risk area is corrected according to the terrain change data, the siltation change data and the soil permeability change data to obtain a corrected flood risk area.

8. An urban smart drainage device based on big data, characterized in that: include: A data collection module is used to obtain historical flooding event records of the target area, wherein the historical flooding event records include historical flooding areas and historical rainfall data where flooding events occurred in each historical period; A first data processing module is used to determine, based on the historical flooded areas, the historical rainfall data and the predicted rainfall data of the target area, areas in the target area that have flooding risks during the forecast period, and record them as flooding risk areas; A second data processing module is used to correct the flood risk area according to the current terrain data of the target area and the drainage pipe network siltation prediction model of the target area to obtain a corrected flood risk area; The drainage control module is used to determine the drainage control plan of the drainage network in the target area according to the modified flood risk area.

9. A terminal, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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