Sponge engineering management system based on road drainage data

Abstract

The invention belongs to the technical field of data processing, and particularly relates to a sponge engineering management system based on road drainage data, and the system comprises a collection module which is used for deploying sensors and monitoring equipment at key drainage points of urban roads in a target area, and collecting related data according to a preset collection period; the processing module analyzes the related data, obtains an evaluation value of the related data and judges whether the related data is sent to the storage module or not based on the evaluation value, the storage module selects and stores effective target data through analysis, and the analysis module formulates a design scheme of drainage facilities in a target area by establishing a simulation model; when the drainage facility is found to be abnormal, a manager is notified in time, a prediction model is also established, and an early warning notification is given out when potential risks are predicted to exist. According to the invention, the drainage efficiency of the target area can be improved while network resources and storage space are saved.

Description

Technical Field

[0001] The invention belongs to the technical field of data processing, and in particular relates to a sponge engineering management system based on road drainage data. Background Art

[0002] With the acceleration of urbanization, urban drainage problems are becoming increasingly serious, especially in the rainy season, when urban waterlogging occurs frequently. Traditional drainage systems can no longer meet the needs of modern cities, and a more intelligent and efficient management system is needed to solve this problem. A similar prior art Chinese patent application with publication number CN111079212A provides a method for applying BIM technology in sponge city construction, including receiving data related to sponge city construction collected in real time by a storage acquisition module; combining a construction drawing deepening design model for obtaining professional and detailed construction drawings that meet the needs of construction and engineering management, and using data to modify and optimize the construction drawing deepening design model; decomposing construction activities into various schedules according to work tasks, listing the activity content of each schedule, determining each construction process and logical relationship according to the construction plan, and formulating a preliminary construction schedule; associating the preliminary construction schedule with the optimized construction drawing deepening design model to generate a construction schedule simulation model for visual construction simulation; and performing dynamic construction simulation on sponge city projects. However, when collecting and storing relevant data, this solution does not consider the problem that a large amount of redundant data will be stored due to the large amount of relevant data collected. Similar prior art also includes a Chinese patent application with publication number CN116049933A, which discloses a system for building an ecological integrated rainwater garden in a sponge city, including (1) data collection, which is collected according to the current status of the site in the design project area, including site distribution data, soil characteristic data, groundwater level data, underlying surface distribution characteristics data, rainwater pipe network data, local climate type data, local native plant information data, local rainfall information data in the past 50 years and soil moisture data information of the design project area; (2) design planning, which divides the design project area into areas, plans the rainwater garden in the area, and the boundary line of the rainwater garden is greater than 2.5m from the building foundation and away from the water supply system and wells; determines the structure and depth in the planning area; determines the area; comprehensively considers the collected data; (3) determines the laying; (4) plant landscape; (5) later monitoring and maintenance. However, this invention also does not consider the problem of storing a large amount of redundant data due to the large amount of relevant data collected in the data collection stage. Therefore, the present invention provides a sponge engineering management system based on road drainage data. Summary of the invention

[0003] The present invention reduces the number of data transmissions and improves data transmission efficiency through a processing module, optimizes data storage space through a storage module, and establishes a design scheme for sponge engineering drainage facilities that can improve drainage efficiency in a target area through an analysis module.

[0004] In order to achieve the above-mentioned purpose of the invention, the present invention provides a sponge engineering management system based on road drainage data as described below, including the following modules:

[0005] A collection module deploys sensors and monitoring equipment at key drainage points of urban roads in the target area, collects relevant data at a preset collection cycle, the relevant data including rainfall data, water level data, flow data, flow velocity data and meteorological data, and sends the relevant data to a processing module;

[0006] a processing module, analyzing the relevant data, obtaining an evaluation value of the relevant data, determining whether the evaluation value is greater than a preset first threshold, and if so, sending the relevant data to a storage module for storage by the storage module; if not, not sending the relevant data, and setting a data value corresponding to the time of the relevant data in the storage module as a predetermined mark;

[0007] A storage module, after receiving the relevant data from the processing module, first stores the received relevant data in a first storage unit, obtains an extraction interval by analyzing the relevant data stored in the first storage unit, extracts target data from the first storage unit based on the extraction interval, and stores the target data in a second storage unit;

[0008] An analysis module, which obtains the historical relevant data from the storage module, analyzes the rainfall pattern, water level change and drainage demand of the target area in combination with the historical relevant data, establishes a simulation model of the target area based on the analysis results combined with the land use data and topographic data of the target area, and formulates a design plan for the drainage facilities of the target area based on the simulation model;

[0009] The early warning module monitors the drainage facilities in the target area in real time and obtains drainage data, analyzes whether there are any abnormalities in the drainage facilities based on the drainage data, and promptly notifies management personnel when abnormalities in the drainage facilities are found. A prediction model is established in combination with the relevant data and the drainage data, and an early warning notification is issued when potential risks are predicted.

[0010] As a preferred technical solution of the present invention, the processing module is configured as follows:

[0011] The relevant data are normalized and saved, and the normalized relevant data are used as learning data for training to generate a prediction model, and the prediction model is used to obtain prediction data, and the relevant data are used as first data, and the relevant data collected in the previous collection cycle are obtained as second data, and the error between the predicted data and the first data is calculated, and the change rate of the first data and the second data is calculated. If the error is greater than a preset second threshold, the evaluation value is set to a value greater than the first threshold. If the error is less than or equal to the preset second threshold, it is determined whether the change rate is greater than a preset third threshold. If the change rate is greater than the preset third threshold, the error is set to a value greater than the first threshold, otherwise the evaluation value is set to a value less than or equal to the first threshold.

[0012] As a preferred technical solution of the present invention, the processing module is configured as follows:

[0013] The error between the predicted data and the first data is calculated by a first formula, where the first formula is: Wherein, E is the error, n is the number of data items included in the first data, μ i is the weight value corresponding to the i-th data item of the first data, X i is the value of the i-th data item of the first data, Y i is the value of the i-th data item of the predicted data.

[0014] As a preferred technical solution of the present invention, the storage module is configured as follows:

[0015] After receiving the relevant data, the relevant data is stored in the first storage unit, a plurality of analysis methods used for data analysis are obtained, a corresponding analysis threshold is set for each analysis method, the analysis threshold refers to the accuracy of the analysis result obtained by using the corresponding analysis method for analysis, experimental data with different data densities are obtained, different experimental data are analyzed using each analysis method, the data density required to reach the analysis threshold of each analysis method is obtained, the maximum value of the data density is obtained, the optimal collection period for collecting the relevant data is determined based on the maximum value, the data collection interval is obtained based on the optimal collection period, after the amount of data stored in the first storage unit reaches a preset proportion of the storage space of the first storage unit, the target data is extracted from the first storage unit based on the data collection interval, and the extracted target data is stored in the second storage unit.

[0016] As a preferred technical solution of the present invention, the storage module is configured as follows:

[0017] In the process of storing the target data, the storage module also matches the target data with normal data. If the data items of the target data match the data items of the normal data, the target data is judged to be correct and stored in the second storage unit. Otherwise, the target data is judged to be wrong, and unmatched data items are obtained. The first value collected in the previous collection cycle and the second value collected in the next collection cycle of the same sensor with the same data item source are collected, and the average value of the first value and the second value is calculated, and the average value is added to the data item corresponding to the target data, and the added target data is stored in the second storage unit.

[0018] As a preferred technical solution of the present invention, the analysis module is configured as follows:

[0019] Acquire spatial data of the target area, the spatial data including land use data and topographic data, divide the target area into multiple sub-areas based on the spatial data, establish a simulation model based on the spatial data of the sub-areas, apply the relevant data to the simulation model, acquire water conservancy data of the sub-areas, the water conservancy data including rainfall infiltration, surface runoff, water storage and evaporation, calculate the water accumulation data of the sub-areas based on the water conservancy data and relevant data, acquire a variety of drainage facilities used in designing sponge engineering, design the layout and planning scheme of the drainage facilities for the target area based on the drainage data of the various drainage facilities combined with the water accumulation data, the drainage facilities including rain gardens, infiltration ditches and reservoirs.

[0020] As a preferred technical solution of the present invention, the analysis module is configured as follows:

[0021] Acquire feature-related data from the storage module, apply the feature-related data and the drainage data of the drainage facilities to the simulation model, and acquire water accumulation data of the target area when the feature-related data occurs based on the simulation model; if the water accumulation data is greater than a preset water accumulation threshold, optimize the layout and planning of the drainage facilities in the target area.

[0022] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0023] In the present invention, firstly, relevant data are collected efficiently through the collection module, and the relevant data are sent to the processing module. The processing module performs preliminary analysis and processing on the relevant data, and only sends relevant data with a large amount of information to the storage module to reduce the utilization of network resources. Then, in the storage module, the relevant data is analyzed to obtain a reasonable data density of the relevant data, which reduces the storage space of the storage module and ensures the accuracy of data analysis. The analysis module simulates and models the relevant data and the spatial data of the target area, designs a reasonable design plan for the sponge engineering drainage facility, and evaluates and further optimizes the design plan. Finally, the drainage facility is monitored by the early warning module. When an abnormality is detected in the drainage facility, the relevant management personnel are notified in time to conduct an inspection. A prediction model is also established to issue an early warning notification when a potential risk is predicted in the target area. The present invention not only saves network resources and storage space, but also improves the drainage efficiency of the target area and optimizes the water environment of the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a structural diagram of the sponge engineering management system based on road drainage data of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0026] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0027] The present invention provides a sponge engineering management system based on road drainage data, such as Figure 1 As shown, the system includes:

[0028] The collection module deploys sensors and monitoring equipment at key drainage points of urban roads in the target area to collect relevant data at a preset collection cycle. The relevant data includes rainfall data, water level data, flow data, flow velocity data and meteorological data, and sends the relevant data to the processing module.

[0029] Specifically, in order to build a sponge engineering management system, and achieve efficient management and risk minimization of urban water cycles through detection technology, data analysis and intelligent control, it is first necessary to deploy sensors and monitoring equipment at key drainage points on urban roads in the target area to collect relevant data with a preset collection cycle. The collection cycle can be set to different collection cycles according to the current meteorological data. The time interval for setting the collection cycle is usually short, such as 10 minutes or 5 minutes. The rainfall data includes the accumulated rainfall and rainfall intensity. The water level data includes the real-time water level of the drainage facilities and the real-time water level of the rivers in the city, etc. The flow data refers to the water flow in and out of the drainage facilities, including the flow in pipes and rivers. The flow rate data refers to the water flow rate of the drainage facilities (such as drainage pipes). The meteorological data refers to temperature, humidity, wind speed, air pressure, etc. These data affect the rainfall pattern and the evaporation rate of water. After collecting the relevant data, the relevant data will be sent to the processing module.

[0030] The processing module analyzes the relevant data, obtains the evaluation value of the relevant data, and determines whether the evaluation value is greater than a preset first threshold. If so, the relevant data is sent to the storage module for storage by the storage module; if not, the relevant data is not sent, and the data value of the relevant data corresponding to the time in the storage module is set as a predetermined mark.

[0031] Specifically, in order to improve the efficiency of data collection and to manage data more intelligently, ensure that relevant data is sent to the storage module only when it meets specific conditions, so as to reduce the amount of data transmission and optimize the use of network resources and server resources. The data storage is dynamically adjusted based on the evaluation value of the relevant data. The specific calculation method of the evaluation value will be explained in detail later. By judging whether the evaluation value is greater than a preset first threshold, the first threshold is a reference value of the evaluation value obtained based on historical experimental data. If it is greater than the first threshold, it means that the relevant data has a higher amount of information and needs to be saved in the storage module. Otherwise, it means that the amount of information of the relevant data is small and the relevant data is not sent. The amount of information refers to the contribution of the corresponding data prediction of the relevant data. The data value corresponding to the relevant data is set to a predetermined mark in the storage module, such as set to WITHPREV. When performing subsequent analysis based on the relevant data in the storage module, the value of the previous data can be obtained according to the predetermined mark as a reference for analysis.

[0032] The storage module, after receiving relevant data from the processing module, first stores the received relevant data in the first storage unit, obtains the extraction interval by analyzing the relevant data stored in the first storage unit, extracts the target data from the first storage unit based on the extraction interval, and stores the target data in the second storage unit.

[0033] Specifically, the storage module saves the relevant data received from the processing module. When saving the relevant data, since the collection cycle preset in the collection module is usually short, the data density of the collected relevant data is dense, resulting in a very large amount of relevant data, which may lead to the storage of a large amount of redundant data and occupy precious storage space. However, sometimes analysis of relevant data does not require data with very dense data density. Therefore, a reasonable data extraction interval is used to ensure that the stored relevant data can reflect the actual situation. Data is extracted from the collected relevant data by analyzing the relevant data. The specific extraction method will be explained in detail later.

[0034] The analysis module obtains relevant historical data from the storage module, analyzes the rainfall pattern, water level changes and drainage requirements of the target area based on the relevant historical data, and establishes a simulation model of the target area based on the analysis results combined with the land use data and topographic data of the target area, and formulates a design plan for the drainage facilities in the target area based on the simulation model.

[0035] Specifically, in order to design a reasonable design scheme for the sponge project, the rainfall pattern, water level changes and drainage needs of the target area are analyzed in combination with the relevant data of the target area, and a simulation model of the target area is established based on the analysis results and the land use data and topographic data of the target area to simulate the rainfall infiltration, surface runoff, water storage and evaporation in the target area. A design scheme for the drainage facilities in the target area is also formulated based on the above data. Drainage facilities refer to the drainage facilities related to the sponge project, including rain gardens, infiltration ditches and reservoirs. The design scheme of the drainage facilities is intelligently applied to the simulation model, simulating the water conservancy data of the target area after using the sponge project, and optimizing the drainage facilities based on the water conservancy data.

[0036] The early warning module monitors the drainage facilities in the target area in real time and obtains drainage data. It analyzes whether there are any abnormalities in the drainage facilities based on the drainage data and promptly notifies management personnel when abnormalities are found in the drainage facilities. It also establishes a prediction model based on relevant data and drainage data and issues early warning notifications when potential risks are predicted.

[0037] Specifically, after the design scheme of the drainage facilities is implemented in the target area, in order to monitor the drainage conditions of the drainage facilities and whether there are any abnormalities in the drainage facilities, it is necessary to monitor the drainage facilities in the target area in real time and obtain drainage data. Based on the drainage data, it is analyzed whether there are any abnormalities in the drainage facilities. For example, if the drainage data of a drainage pipe is lower than the normal value at a certain time point due to being blocked by foreign objects, the management personnel will be notified through the early warning module after the abnormality is found, and the management personnel will inspect the abnormal drainage facilities to prevent water accumulation from affecting normal travel. A prediction model is also established in combination with relevant data and drainage data to predict whether there are potential risks. For example, through the analysis and prediction of relevant data and drainage data, it is found that there may be water accumulation on the road at a certain time point in the future. Excessive water accumulation on the road will affect normal travel. Therefore, when it is predicted that there are potential risks, an early warning notification is issued to notify relevant personnel to take sampling measures and adjust the operating status of the drainage facilities, such as adding connecting switches between reservoirs to increase water storage capacity, or increasing the volume of the reservoir and the flow of the drainage pipe, so that the water on the road can be discharged in time and the possibility of water accumulation on the road can be reduced.

[0038] The present invention collects relevant data efficiently through a collection module, and sends the relevant data to a processing module. The processing module performs preliminary analysis and processing on the relevant data, and only sends relevant data with a large amount of information to a storage module, thereby reducing the utilization of network resources. Afterwards, in the storage module, by analyzing the relevant data, a reasonable data density of the relevant data is obtained, which reduces the storage space of the storage module and ensures the accuracy of data analysis. The analysis module simulates and models the relevant data and the spatial data of the target area, designs a reasonable design plan for the sponge engineering drainage facility, and also evaluates and further optimizes the design plan. Finally, the drainage facility is monitored through the early warning module. When an abnormality is detected in the drainage facility, the relevant management personnel are notified in time to conduct an inspection. A prediction model is also established to issue an early warning notification when a potential risk is predicted in the target area.

[0039] The present invention not only saves network resources and storage space, but also improves the drainage efficiency of the target area to optimize the water environment of the target area.

[0040] Further, the processing module is configured to: normalize and save the relevant data, use the normalized relevant data as learning data for training to generate a prediction model, use the prediction model to obtain prediction data, use the relevant data as the first data, obtain the relevant data collected in the previous collection cycle as the second data, calculate the error between the prediction data and the first data, calculate the rate of change of the first data and the second data, if the error is greater than a preset second threshold, set the evaluation value to a value greater than the first threshold, if the error is less than or equal to the preset second threshold, determine whether the rate of change is greater than a preset third threshold, if the rate of change is greater than the preset third threshold, set the error to a value greater than the first threshold, otherwise set the evaluation value to a value less than or equal to the first threshold.

[0041] Specifically, the evaluation value is used to determine the amount of information in the relevant data. A higher evaluation value often indicates that the relevant data has a higher amount of information. First, in order to generate learning data that is more conducive to training, the relevant data in the storage module is normalized, and the normalized relevant data is used as learning data to generate a prediction model through training, and then the prediction model is used to generate prediction data. The relevant data is collected according to the collection cycle. The first data is the relevant data collected in the latest collection cycle. The latest collection cycle refers to the collection cycle closest to the current time. The first data is compared with the predicted data to obtain the error. The amount of information in the first data can be determined by the error. A larger error means that the first data has a higher amount of information. The relevant data collected in the previous collection cycle is also obtained as the second data. The previous collection cycle refers to the previous collection cycle of the latest collection cycle. By calculating the change rate of the first data and the second data, if the change rate exceeds a certain threshold , indicating that the first data has a higher amount of information. The above steps first judge the error. If the error is large, it means that the first data has a higher amount of information, which means that the first data has value to be collected, so the corresponding evaluation value is set to a value greater than the first threshold. If the error is small, it means that the prediction model can be used to accurately predict the first data, further indicating that the amount of information of the first data is relatively low. Then continue to judge the change rate of the first data and the second data. If the change rate of the first data and the second data is large, it means that the first data has obvious changes compared with the second data, such as a sudden increase or decrease in certain data items, further indicating that the first data has a higher amount of information, so the corresponding evaluation value is set to a value greater than the first threshold. If the change rate is small, it means that the first data has no obvious change compared with the second data, and the prediction error is also small, indicating that the amount of information of the first data is small, so the corresponding evaluation value is set to a value less than or equal to the first threshold.

[0042] Further, the processing module is configured to calculate the error between the predicted data and the first data by a first formula, where the first formula is: Where E is the error, n is the number of data items included in the relevant data, μ i is the weight value corresponding to the i-th data item of the first data, X i is the value of the i-th data item of the first data, Y i is the value of the i-th data item of the predicted data.

[0043] Specifically, the error between the predicted data and the first data is calculated by the above formula, and different weight values ​​are set for each data item according to the importance of each data item. If the data item has little impact on the subsequent analysis, the corresponding weight value is set to be smaller, and even if the prediction error of the data item is larger, the corresponding error can be reduced. If the data item has a larger impact on the subsequent analysis, the corresponding weight value is set to be larger, and even if the prediction error of the data item is smaller, the corresponding error can be magnified, so that the calculated error is more scientific.

[0044] Furthermore, the storage module is configured as follows:

[0045] After receiving the relevant data, the relevant data is stored in the first storage unit, a plurality of analysis methods used for data analysis are obtained, a corresponding analysis threshold is set for each analysis method, the analysis threshold refers to the accuracy of the analysis result obtained by using the corresponding analysis method for analysis, experimental data with different data densities are obtained, different experimental data are analyzed using each analysis method, the data density required to reach the analysis threshold of each analysis method is obtained, the maximum value of the data density is obtained, the optimal collection period for collecting relevant data is determined based on the maximum value, the data collection interval is obtained based on the optimal collection period, after the amount of data stored in the first storage unit reaches a preset proportion of the storage space of the first storage unit, the target data is extracted from the first storage unit based on the data collection interval, and the extracted target data is stored in the second storage unit.

[0046] Specifically, in order to save the storage space of the storage module, after the storage module receives the relevant data, the relevant data is first stored in the first storage unit. The first storage unit is a temporary storage unit and usually has a small storage space. A variety of analysis methods used for data analysis are obtained. The multiple analysis methods include neural network analysis methods, Bayesian network analysis methods, and random forest analysis methods. Different analysis thresholds are set for each analysis method. Since the collection cycle set by the collection module when collecting data is usually short, the data density of the collected data is usually large. For example, the data density is 12 times / hour. However, when performing data analysis, it may not be necessary to obtain data with such a large data density in order to make the data analysis more accurate. Therefore, experimental data with different data densities are obtained. For example, 12 data items are collected 12 times in one hour, and 6 data items are evenly selected from the 12 data items. 4 pieces of data or 3 pieces of data, use different analysis methods to analyze experimental data of different densities, obtain the data density required to reach the analysis threshold of each analysis method, and obtain the maximum value of the data density, such as 6 times / hour, and determine the optimal collection period for collecting related data based on the maximum value, such as 10 minutes, and determine the data collection interval based on the optimal collection period, such as the collection interval is to select one piece of data from two adjacent data, and when a certain proportion of the storage in the first storage unit is reached in the first storage unit, such as 80% of the storage space, then extract the target data from the first storage unit based on the collection interval, and store the extracted target data in the second storage unit, the second storage unit is a permanent storage space and has a large storage space. Through the above method, the storage space of the storage module can be saved while the storage efficiency can be improved, and the accuracy of subsequent data analysis can be guaranteed.

[0047] Furthermore, the storage module is configured as follows:

[0048] In the process of storing the target data, the storage module also matches the target data and the normal data. If the data items of the target data match the data items of the normal data, the target data is judged to be correct and the target data is stored in the second storage unit. Otherwise, the target data is judged to be wrong, and the unmatched data items are obtained. The first value collected in the previous collection cycle and the second value collected in the next collection cycle of the same sensor with the same data item source are collected, and the average of the first value and the second value is calculated, and the average value is added to the data item corresponding to the target data, and the added target data is stored in the second storage unit.

[0049] Specifically, in order to ensure that the data stored in the storage module are all normal data, in the process of storing the target data, the target data and normal data are matched for data items. If the data items of the target data and the normal data match, the target data is judged to be correct and the target data is directly stored in the second storage unit. Otherwise, it means that the data items of the target data and the normal data do not match, and some data items may be lost in the process of data transmission. The data items that do not match the target data and the normal data are obtained. For example, the normal data has 10 items of data and the target data has only 9 items, which means that one item of data is lost in the process of data transmission. The lost data is obtained, and the first value collected in the previous collection cycle of the same sensor with the same source as the data and the second value collected in the next collection cycle are obtained, and the average of the first value and the second value is calculated, and the average value is supplemented in the target data as the lost data item, and the supplemented target data is stored in the second storage unit. The above method can ensure that the data stored in the storage module are all normal data, and further ensure the accuracy of the subsequent data analysis.

[0050] Furthermore, the analysis module is configured as follows:

[0051] Acquire spatial data of the target area, including land use data and topographic data; divide the target area into multiple sub-areas based on the spatial data; establish a simulation model based on the spatial data of the sub-areas; apply relevant data to the simulation model; acquire water conservancy data of the sub-areas, including rainfall infiltration, surface runoff, water storage and evaporation; calculate the water accumulation data of the sub-areas based on the water conservancy data and relevant data; acquire a variety of drainage facilities used in the design of sponge engineering; design the layout and planning scheme of drainage facilities for the target area based on the drainage data of a variety of drainage facilities combined with the water accumulation data; the drainage facilities include rain gardens, infiltration ditches and reservoirs.

[0052] Specifically, in order to obtain a reasonable design plan for sponge engineering drainage facilities, the spatial data of the target area is obtained. The spatial data includes land use data and topographic data. Land use data refers to the land coverage type, such as impervious surfaces, green spaces and water bodies, and topographic and geomorphic data refer to ground height, slope, soil type and land area. In order to establish a simulation model more accurately, the target area is divided into multiple sub-areas based on the spatial data, such as separating areas with different land use data and topographic data. A simulation model is established based on the spatial data of the sub-areas. The simulation model is a simulation model of each sub-area. The relevant data is applied to the simulation model. The simulation model can simulate the changes in water level and flow in the sub-area under different conditions, evaluate the drainage capacity of the sub-area, and obtain the sub-area. Water conservancy data. Water conservancy data includes multiple data. The total drainage data of the sub-area can be obtained by adding the water conservancy data. The water accumulation data of the sub-area can be calculated based on the water conservancy data and related data. The water accumulation data is obtained by subtracting the total drainage data from the total precipitation data. Based on the obtained water accumulation data and the drainage data of various drainage facilities that can be used for sponge work, a corresponding drainage facility design scheme is set for the sub-area. The drainage facilities include rain gardens, infiltration ditches and reservoirs, etc., so that the sum of the drainage data of each drainage facility can be greater than the water accumulation data of the sub-area. By using the same method to design corresponding drainage facilities for each sub-area, the target area can improve its drainage efficiency through natural accumulation, natural infiltration and other means, and the water environment of the target area can also be optimized.

[0053] Furthermore, the analysis module is configured as follows:

[0054] Acquire feature-related data from the storage module, apply the feature-related data and drainage data of the drainage facilities to the simulation model, and acquire water accumulation data of the target area when the feature-related data occurs based on the simulation model; if the water accumulation data is greater than a preset water accumulation threshold, optimize the layout and planning of the drainage facilities in the target area.

[0055] Specifically, in order to optimize the design of drainage facilities in the target area so that the target area can better cope with the risk of waterlogging, feature-related data is obtained from the storage module. The feature-related data refers to the relevant data when waterlogging occurred in the target area in history. The feature-related data and the drainage facilities used above are applied to the simulation model to simulate the rainwater regulation effect of the sponge engineering drainage facilities in the target area when waterlogging occurs. Based on the simulation model, the water accumulation data of the target area under the occurrence of feature-related data is obtained. If the water accumulation data is less than or equal to the preset water accumulation threshold, it means that the target area can cope with the risk of waterlogging. If the water accumulation data is greater than the preset water accumulation threshold, it means that the design of the drainage facilities in the target area needs to be optimized. At this time, the target area can be enabled to cope with the risk of waterlogging by increasing drainage facilities, such as increasing the size of the reservoir, increasing the flow rate of the drainage pipe, and other measures.

[0056] In summary, the sponge engineering management system based on road drainage data of the present invention includes a collection module that deploys sensors and monitoring equipment at key drainage points of urban roads in the target area to collect relevant data in a preset collection cycle; a processing module that analyzes the relevant data, obtains the evaluation value of the relevant data, and determines whether to send the relevant data to the storage module based on the evaluation value. The storage module selects and saves valid target data through analysis, and the analysis module formulates the design plan of the drainage facilities in the target area by establishing a simulation model. The early warning module notifies the management personnel in time when abnormalities are found in the drainage facilities, and also establishes a prediction model to issue an early warning notification when potential risks are predicted. While saving network resources and storage space, the present invention can also improve the drainage efficiency of the target area.

[0057] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0058] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), 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).

[0059] The technical features of the above-mentioned embodiments can be combined arbitrarily. 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 there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0060] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0061] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A sponge engineering management system based on road drainage data, characterized in that: The system comprises: A collection module deploys sensors and monitoring equipment at key drainage points of urban roads in the target area, collects relevant data at a preset collection cycle, the relevant data including rainfall data, water level data, flow data, flow velocity data and meteorological data, and sends the relevant data to a processing module; a processing module, analyzing the relevant data, obtaining an evaluation value of the relevant data, determining whether the evaluation value is greater than a preset first threshold, and if so, sending the relevant data to a storage module for storage by the storage module; if not, not sending the relevant data, and setting a data value corresponding to the time of the relevant data in the storage module as a predetermined mark; A storage module, after receiving the relevant data from the processing module, first stores the received relevant data in a first storage unit, obtains an extraction interval by analyzing the relevant data stored in the first storage unit, extracts target data from the first storage unit based on the extraction interval, and stores the target data in a second storage unit; An analysis module, which obtains the historical relevant data from the storage module, analyzes the rainfall pattern, water level change and drainage demand of the target area in combination with the historical relevant data, establishes a simulation model of the target area based on the analysis results combined with the land use data and topographic data of the target area, and formulates a design plan for the drainage facilities of the target area based on the simulation model; The early warning module monitors the drainage facilities in the target area in real time and obtains drainage data, analyzes whether there are any abnormalities in the drainage facilities based on the drainage data, and promptly notifies management personnel when abnormalities in the drainage facilities are found. A prediction model is established in combination with the relevant data and the drainage data, and an early warning notification is issued when potential risks are predicted.

2. The system according to claim 1, characterized in that The processing module is configured as follows: The relevant data are normalized and saved, and the normalized relevant data are used as learning data for training to generate a prediction model, and the prediction model is used to obtain prediction data, and the relevant data are used as first data, and the relevant data collected in the previous collection cycle are obtained as second data, and the error between the predicted data and the first data is calculated, and the change rate of the first data and the second data is calculated. If the error is greater than a preset second threshold, the evaluation value is set to a value greater than the first threshold. If the error is less than or equal to the preset second threshold, it is determined whether the change rate is greater than a preset third threshold. If the change rate is greater than the preset third threshold, the error is set to a value greater than the first threshold, otherwise the evaluation value is set to a value less than or equal to the first threshold.

3. The system according to claim 2, characterized in that The processing module is configured as follows: The error between the predicted data and the first data is calculated by a first formula, where the first formula is: Wherein, E is the error, n is the number of data items included in the first data, μ i is the weight value corresponding to the i-th data item of the first data, X i is the value of the i-th data item of the first data, Y i is the value of the i-th data item of the predicted data.

4. The system according to claim 1, characterized in that The storage module is configured as follows: After receiving the relevant data, the relevant data is stored in the first storage unit, a plurality of analysis methods used for data analysis are obtained, a corresponding analysis threshold is set for each analysis method, the analysis threshold refers to the accuracy of the analysis result obtained by using the corresponding analysis method for analysis, experimental data with different data densities are obtained, different experimental data are analyzed using each analysis method, the data density required to reach the analysis threshold of each analysis method is obtained, the maximum value of the data density is obtained, the optimal collection period for collecting the relevant data is determined based on the maximum value, the data collection interval is obtained based on the optimal collection period, after the amount of data stored in the first storage unit reaches a preset proportion of the storage space of the first storage unit, the target data is extracted from the first storage unit based on the data collection interval, and the extracted target data is stored in the second storage unit.

5. The system according to claim 4, characterized in that The storage module is configured as follows: In the process of storing the target data, the storage module also matches the target data with normal data. If the data items of the target data match the data items of the normal data, the target data is judged to be correct and stored in the second storage unit. Otherwise, the target data is judged to be wrong, and unmatched data items are obtained. The first value collected in the previous collection cycle and the second value collected in the next collection cycle of the same sensor with the same data item source are collected, and the average value of the first value and the second value is calculated, and the average value is added to the data item corresponding to the target data, and the added target data is stored in the second storage unit.

6. The system according to claim 1, characterized in that The analysis module is configured as follows: Acquire spatial data of the target area, the spatial data including land use data and topographic data, divide the target area into multiple sub-areas based on the spatial data, establish a simulation model based on the spatial data of the sub-areas, apply the relevant data to the simulation model, acquire water conservancy data of the sub-areas, the water conservancy data including rainfall infiltration, surface runoff, water storage and evaporation, calculate the water accumulation data of the sub-areas based on the water conservancy data and relevant data, acquire a variety of drainage facilities used in designing sponge engineering, design the layout and planning scheme of the drainage facilities for the target area based on the drainage data of the various drainage facilities combined with the water accumulation data, the drainage facilities including rain gardens, infiltration ditches and reservoirs.

7. The system according to claim 6, characterized in that The analysis module is configured as follows: Acquire feature-related data from the storage module, apply the feature-related data and the drainage data of the drainage facilities to the simulation model, and acquire the water accumulation data of the target area when the feature-related data occurs based on the simulation model; if the water accumulation data is greater than a preset water accumulation threshold, optimize the layout and planning of the drainage facilities in the target area.

Images