Urban river embankment maintenance management and monitoring system based on big data

Abstract

The invention relates to the technical field of river channel maintenance management, in particular to an urban river channel embankment maintenance management and monitoring system based on big data. The segmented visual detection module is used for monitoring key parameters in N river channel areas and then calculating a comprehensive hydrological health score. And a segmentation change factor is calculated based on a time interval key parameter. And the dynamic early warning module is used for respectively displaying the key parameters according to the river channel regions, calculating an average value of the key parameters based on the key parameters, then calculating to obtain dynamic early warning levels, traversing and judging that the dynamic early warning levels contain the river channel regions exceeding the preset dynamic early warning level, and then emitting warning light in the corresponding river channel regions. And the segmented collaborative scheduling module calculates a plurality of path risk values of the initial starting point and the maintenance end point based on the dynamic early warning levels in the N river channel regions, then judges an optimal path based on the plurality of path risk values, marks the optimal path in the overall river channel model, and displays the optimal path.

Description

Technical Field

[0001] The present invention belongs to the technical field of river maintenance management, and in particular to an urban river embankment maintenance management and monitoring system based on big data. Background Art

[0002] In urban river management, traditional management methods usually face problems such as incomplete management, insufficient real-time monitoring, and difficulty in timely response. These problems often lead to managers being unable to fully grasp the real-time status of each river area, especially when the river area is large and the environment is complex, it is difficult to accurately locate potential hidden dangers or high-risk areas. In addition, there is a certain correlation between the various areas of the river. Changes in the upstream area will directly affect factors such as the water level and water flow speed in the downstream area. However, traditional monitoring methods are difficult to capture this dynamic impact between upstream and downstream, making it impossible for managers to timely predict and judge the fluctuations in the health status of local areas and their development trends, increasing the risk of river management. At the same time, in water rescue and river maintenance work, path selection also has great safety hazards. Due to the lack of systematic data support, rescue and maintenance personnel often find it difficult to effectively plan paths during the operation, and may mistakenly enter high-risk areas, increasing the uncertainty and safety hazards of operations. Especially when responding to sudden dangerous situations, traditional path selection methods may delay the efficiency of rescue and maintenance work, further exacerbating disaster losses. Summary of the invention

[0003] The present invention aims to solve the technical problems of incomplete river management and failure to timely capture the impact of upstream and downstream on the region, causing losses, and insufficient support for water rescue and maintenance route planning, and to provide an urban river embankment maintenance management and monitoring system based on big data.

[0004] The technical solution adopted by the present invention to solve the technical problem is: an urban river embankment maintenance management and monitoring system based on big data, including a segmented visualization detection module, a dynamic early warning module, and a segmented collaborative scheduling module.

[0005] The segmented visualization detection module is used for managers to divide the overall river model into N river areas, and then send it to the segmented collaborative scheduling module. It is used to monitor the key parameters in N river areas, and then calculate the comprehensive hydrological health score based on the key parameters and the preset key weight coefficients, and then send the comprehensive hydrological health score and key parameters to the dynamic early warning module. It is also used to calculate the segmented change factor based on the key parameters stored at a time interval, and then send the segmented change factor to the dynamic early warning module. It is also used to display the key parameters according to the river area after monitoring the key parameters in N river areas.

[0006] The dynamic early warning module is network-connected to the segmented visualization detection module. It is used to calculate the average value of the key parameters after receiving the key parameters, the segmented change factor, and the comprehensive hydrological health score. Then, based on the segmented change factor, the comprehensive hydrological health score, the average value of the key parameters, and the preset initial early warning level, it calculates the dynamic early warning level and sends it to the segmented collaborative scheduling module. And it is used to traverse and determine whether the river channel areas exceeding the preset dynamic early warning level are included in the dynamic early warning level after calculating the dynamic early warning level. And it is used to emit warning lights in the corresponding river channel areas after traversing and determining that the river channel areas exceeding the preset dynamic early warning level are included in the dynamic early warning level.

[0007] The segmented collaborative scheduling module is network-connected to the segmented visualization detection module and the dynamic early warning module. It is used to store the N river channel areas after receiving them. And it is used for the manager to set the initial starting point and the maintenance end point. And it is used to calculate a number of path risk values of the initial starting point and the maintenance end point based on the dynamic early warning levels in the N river channel areas after receiving the dynamic early warning levels in the N river channel areas. Then, based on the number of path risk values, it determines the optimal path, marks the optimal path in the overall river channel model, and then displays the optimal path.

[0008] Furthermore, the segmented visualization detection module includes a river channel division unit, a data monitoring unit, a monitoring unit, and a first edge server.

[0009] The river channel division unit is used for the manager to store the overall river channel model. And it is used for the manager to equally divide the overall river channel model into N river channel areas and then transmit the N river channel areas to the segmented collaborative scheduling module.

[0010] The data monitoring unit is network-connected to the dynamic early warning module and is set in the N river channel areas. It is used to monitor the water level height, water flow velocity, and dike pressure in the N river channel areas, and then send the water level height, water flow velocity, and dike pressure in the N river channel areas to the first edge server, the monitoring unit, and the dynamic early warning module. Then, based on the water level height, water flow velocity, and dike pressure in the N river channel areas and the preset key weight coefficients, it calculates the comprehensive hydrological health scores in the N river channel areas and sends the comprehensive hydrological health scores in the N river channel areas to the dynamic early warning module.

[0011] The first edge server is network - connected to the data monitoring unit and the dynamic warning module. It is used to store the water level heights, water flow velocities, and dike pressures in N river regions separately according to the river regions after receiving them. And it is used to read the water level heights, water flow velocities, and dike pressures in N river regions stored within a certain time interval, then obtain the water level height differences, water level velocity differences, and dike pressure differences in N river regions within the time interval based on the water level heights, water flow velocities, and dike pressures stored within the time interval. Then, calculate N sectional change factors based on the water level height differences, water level velocity differences, and dike pressure differences in N river regions, and send the N sectional change factors to the dynamic warning module.

[0012] The monitoring unit is network - connected to the data monitoring unit and is used to display the water level heights, water flow velocities, and dike pressures in N river regions separately according to the river regions after receiving them.

[0013] Furthermore, the data monitoring unit includes a water level monitoring sensor, a flow velocity meter, a stress sensor, and a microprocessor.

[0014] The water level monitoring sensor is set in N river regions and is network - connected to the dynamic warning module. It is used to monitor the water level heights in N river regions and then send the water level heights in N river regions to the first edge server, the monitoring unit, the microprocessor, and the dynamic warning module.

[0015] The flow velocity meter is set in N river regions and is network - connected to the dynamic warning module. It is used to monitor the water flow velocities in N river regions and then send the water flow velocities in N river regions to the first edge server, the monitoring unit, the microprocessor, and the dynamic warning module.

[0016] The stress sensor is set in N river regions and is network - connected to the dynamic warning module. It is used to monitor the dike pressures in N river regions and then send the dike pressures in N river regions to the first edge server, the monitoring unit, the microprocessor, and the dynamic warning module.

[0017] The microprocessor is network - connected to the water level monitoring sensor, the flow velocity meter, the stress sensor, and the dynamic warning module. It is used to calculate the comprehensive hydrological health scores in N river regions based on the water level heights, water flow velocities, and dike pressures in N river regions and the preset key weight coefficients after receiving the water level heights, water flow velocities, and dike pressures in N river regions, and then send the comprehensive hydrological health scores in N river regions to the dynamic warning module.

[0018] Further, the formula for the microprocessor to calculate the comprehensive hydrological health scores of N river regions based on the water level heights, water flow velocities, and levee pressures in the N river regions and the preset key weight coefficients is: S i = w 1 ·W i + w 2 ·V i + w 3 ·T i ,

[0019] where S i is the comprehensive hydrological health score of the i-th river region, W i is the water level height in the i-th river region, in m, i is the number of the river region, i ∈ [1, N], V i is the water flow velocity in the i-th river region, in m / s, T i is the levee pressure in the i-th river region, in kPa, ω 1 , ω 2 , ω 3 are the preset key weight coefficients.

[0020] Further, the formula for the first edge server to calculate N sectional change factors based on the water level height differences, water level velocity differences, and levee pressure differences in the N river regions is: F i = ΔW i + ΔV i + ΔT i ,

[0021] where ΔW i is the water level height difference in the i-th river region, N i is the water level velocity difference in the i-th river region, in g / kg, T i is the levee pressure difference in the i-th river region, F i is the sectional change factor in the i-th river region.

[0022] Further, the dynamic warning module includes a second edge server and a warning device,

[0023] The second edge server is connected to a water level monitoring sensor, a flow velocity meter, a microprocessor, a stress sensor, the first edge server, and a segmented cooperative scheduling module. It is used to store the water level height, water flow velocity, embankment pressure, N segmented change factors, and the comprehensive hydrological health scores in N river regions respectively based on the river regions after receiving them. And it is used to calculate the average water level height in N river regions according to the water level height in the current river region and the water level heights in the previous and next river regions within the current river region. And it is used to calculate the average water flow velocity in N river regions according to the water flow velocity in the current river region and the previous and next water flow velocities within the current river region. And it is used to calculate the average embankment pressure in N river regions according to the embankment pressure in the current river region and the previous and next embankment pressures within the current river region. And it is used to calculate the dynamic warning levels in N river regions based on the N segmented change factors, the comprehensive hydrological health scores in N river regions, the water level heights, water flow velocities, embankment pressures in N river regions, a preset initial warning level, and a preset warning weight coefficient, and then send them to the segmented cooperative scheduling module and the warning device. And it is used to, after calculating the dynamic warning levels in N river regions, traverse and judge the river regions among the dynamic warning levels in N river regions that exceed the preset dynamic warning level, then generate a warning signal, and then send the warning signal to the warning device of the corresponding river region.

[0024] The warning device is connected to the second edge server and the warning light board network, and is set in N river regions. It is used to emit warning lights after receiving the warning signal.

[0025] Further, the formula for the second edge server to calculate the average water level height in N river regions according to the water level height in the current river region and the water level heights in the previous and next river regions within the current river region is:

[0026] Where, W avg is the average water level height in the i-th river region, with the unit of m, W i+1 is the water level height in the next river region based on the i-th river region, with the unit of m, W i-1 is the water level height in the previous river region based on the i-th river region, with the unit of m, W i is the water level height in the i-th river region, with the unit of m,

[0027] The formula for calculating the average water flow velocity in N river channel areas based on the water flow velocity in the current river channel area, the previous water flow velocity in the current river channel area, and the subsequent water flow velocity in the current river channel area is as follows:

[0028]

[0029] where V avg is the average water flow velocity in the i-th river channel area, with the unit of m, V i+1 is the subsequent water flow velocity based on the i-th river channel area, with the unit of m / s, V i-1 is the previous water flow velocity based on the i-th river channel area, with the unit of m, V i is the water flow velocity in the i-th river channel area, with the unit of m / s.

[0030] The formula for calculating the average dike pressure in N river channel areas based on the dike pressure in the current river channel area, the previous dike pressure in the current river channel area, and the subsequent dike pressure in the current river channel area is as follows:

[0031] where T avg is the average dike pressure in the i-th river channel area, with the unit of kPa, T i+1 is the subsequent dike pressure based on the i-th river channel area, with the unit of kPa, T i-1 is the previous dike pressure based on the i-th river channel area, with the unit of kPa, T i is the dike pressure in the i-th river channel area, with the unit of kPa.

[0032] Furthermore, the formula for the second edge server to calculate the dynamic warning levels in N river channel areas based on N sectional change factors, the comprehensive hydrological health scores in N river channel areas, the average dike pressures in N river channel areas, the average water flow velocities, the average water flow velocities, the preset initial warning level, and the preset warning weight coefficients is as follows:

[0033]

[0034] where L i is the dynamic warning level in the i-th river channel area, is the preset initial warning level, S i is the comprehensive hydrological health score in the i-th river channel area, F i is the sectional change factor in the i-th river channel area, a 1 , a 2 , b 1 , b 2 , b 3 are the preset warning weight coefficients.

[0035] Furthermore, the segmented collaborative scheduling module includes a path planning module and a risk calculation module.

[0036] The risk calculation module is network-connected to the river channel division unit and the second edge server, and is used to store the N river channel areas after receiving them. It is also used for the manager to set the initial starting point and the maintenance end point. And it is used to calculate a number of path risk values of the initial starting point and the maintenance end point based on the dynamic warning levels within the N river channel areas after receiving the dynamic warning levels within the N river channel areas, then determine the optimal path based on the number of path risk values, and then send the optimal path to the path planning module.

[0037] The path planning module is network-connected to the river channel division unit and the second edge server, and is used for the manager to store the overall river channel model. It is also used to store the N river channel areas after receiving them. And it is used to mark the optimal path in the overall river channel model after receiving the optimal path, and then display the optimal path.

[0038] Furthermore, the formula for the risk calculation module to calculate a number of path risk values of the initial starting point and the maintenance end point based on the dynamic warning levels within the N river channel areas is:

[0039] where path is a number of paths, and K path is the number of path risk values of the initial starting point and the maintenance end point.

[0040] Advantages of the present invention:

[0041] 1. The segmented visualization detection module equally divides the overall river channel model into N river channel areas, providing river channel structured information at the segmented level. Through the graphical interface, the manager can clearly monitor each river channel area, facilitating refined management. It can collect and monitor key parameters in real time within each river channel area and display them on the interface, helping the manager obtain real-time risk information, calculate the segmented change factor based on the change of key parameters at a certain time interval, and provide the real-time change trend of each river channel area for the dynamic warning module. It not only considers the situation of each area, but also considers the impact of the upstream and downstream change trends on this area, so as to judge the fluctuation situation and development trend of the regional health.

[0042] 2. After the dynamic warning module aggregates information such as the key parameters, section change factors, and health scores of the segmented visualization detection module, it processes the key parameter data by calculating the average value, etc. Then, in combination with the section change factors and the comprehensive health score, it dynamically adjusts the warning level. This dynamic warning can timely reflect the changes in each area of the river channel, improve the accuracy and timeliness of the warning, and can trigger warning lights within the river sections where the system detects that the safety threshold is exceeded, reminding on-site personnel or patrol personnel to identify high-risk areas.

[0043] 3. The segmented collaborative scheduling module can provide basic data for path calculation, can receive and store the dynamic warning information of each river channel area, and provide basic data for path calculation. Mark and display the optimal path in the river channel model, which is convenient for managers or executors to carry out maintenance work in the safe area and reduce the safety hazards of path selection. Description of the Drawings

[0044] Figure 1 is a schematic diagram of the system module of the present invention;

[0045] Figure 2 is a schematic diagram of the data monitoring unit of the present invention. Detailed Embodiment

[0046] The following will clearly and completely describe the concept and technical effects generated by the present invention in combination with embodiments to fully understand the purpose, features, and effects of the present invention. Please refer to Figure 1 、 Figure 2 。

[0047] The urban river channel dike maintenance management and monitoring system based on big data includes a segmented visualization detection module, a dynamic warning module, and a segmented collaborative scheduling module.

[0048] The segmented visualization detection module is used for the manager to divide the overall river channel model into N river channel areas with equal area, and then send them to the segmented collaborative scheduling module. It is used to monitor the key parameters in the N river channel areas, then calculate the comprehensive hydrological health score based on the key parameters and the preset key weight coefficients, and then send the comprehensive hydrological health score and key parameters to the dynamic warning module. And it is used to calculate the section change factor based on the key parameters stored at a certain time interval, and then send the section change factor to the dynamic warning module. And it is used to display the key parameters separately according to the river channel areas after monitoring the key parameters in the N river channel areas.

[0049] The dynamic warning module is network-connected to the segmented visualization detection module and is used to calculate the average value of the key parameters based on the key parameters after receiving the key parameters, segmented change factors, and comprehensive hydrological health scores. Then, based on the segmented change factors, comprehensive hydrological health scores, average value of the key parameters, and preset initial warning levels, it calculates the dynamic warning levels and sends them to the segmented collaborative scheduling module. And it is used to traverse and determine whether there are river channel areas exceeding the preset dynamic warning levels in the calculated dynamic warning levels after calculating the dynamic warning levels. And it is used to emit warning lights in the corresponding river channel areas after traversing and determining that there are river channel areas exceeding the preset dynamic warning levels in the dynamic warning levels.

[0050] The segmented collaborative scheduling module is network-connected to the segmented visualization detection module and the dynamic warning module and is used to store the received N river channel areas. And it is used for the manager to set the initial starting point and maintenance end point. And it is used to calculate several path risk values of the initial starting point and maintenance end point based on the dynamic warning levels in the N river channel areas after receiving the dynamic warning levels in the N river channel areas, then determine the optimal path based on the several path risk values, then mark the optimal path in the overall river channel model, and then display the optimal path.

[0051] In this embodiment, the segmented visualization detection module equally divides the overall river channel model into N river channel areas and provides river channel structured information at the segmented level. Through the graphical interface, the manager can clearly monitor each river channel area, facilitating refined management. It can collect and monitor key parameters in real time in each river channel area and display them on the interface to help the manager obtain real-time risk information. It calculates the segmented change factors based on the changes in key parameters at a certain time interval and provides the real-time change trends of each river channel area for the dynamic warning module. It not only considers the situation of each area but also the influence of the upstream and downstream change trends on this area, thereby judging the fluctuation situation and development trend of regional health. After summarizing the information such as key parameters, segmented change factors, and health scores of the segmented visualization detection module, the dynamic warning module processes the key parameter data by calculating the average value, etc., and then combines the segmented change factors with the comprehensive health score to dynamically adjust the warning levels. This dynamic warning can timely reflect the changes in each area of the river channel, improve the accuracy and timeliness of the warning, and can trigger warning lights in the river sections where the system detects that the safety threshold is exceeded to remind on-site personnel or patrol personnel to identify high-risk areas. The segmented collaborative scheduling module can provide basic data for path calculation, can receive and store the dynamic warning information of each river channel area, and provide basic data for path calculation. Mark and display the optimal path in the river channel model, facilitating the manager or executor to carry out maintenance work in the safe area and reducing the safety hazards of path selection.

[0052] In this embodiment, the segmented visualization detection module includes a river channel division unit, a data monitoring unit, a monitoring unit, and a first edge server.

[0053] The river channel division unit is used for the manager to store the overall river channel model. And it is used for the manager to equally divide the overall river channel model into N river channel areas, and then transmit the N river channel areas to the segmented collaborative scheduling module.

[0054] The data monitoring unit is network-connected to the dynamic warning module and is set in the N river channel areas. It is used to monitor the water level height, water flow velocity, and levee pressure in the N river channel areas, and then send the water level height, water flow velocity, and levee pressure in the N river channel areas to the first edge server, the monitoring unit, and the dynamic warning module. Then, based on the water level height, water flow velocity, and levee pressure in the N river channel areas and the preset key weight coefficients, it calculates the comprehensive hydrological health scores in the N river channel areas, and then sends the comprehensive hydrological health scores in the N river channel areas to the dynamic warning module.

[0055] The first edge server is network-connected to the data monitoring unit and the dynamic warning module. It is used to store the water level height, water flow velocity, and levee pressure in the N river channel areas separately according to the river channel areas after receiving them. And it is used to read the water level height, water flow velocity, and levee pressure in the N river channel areas stored at an interval of time, and then obtain the water level height differences, water level velocity differences, and levee pressure differences in the N river channel areas at an interval of time based on the water level height, water flow velocity, and levee pressure in the N river channel areas stored at an interval of time. Then, it calculates N segmented change factors based on the water level height differences, water level velocity differences, and levee pressure differences in the N river channel areas, and then sends the N segmented change factors to the dynamic warning module.

[0056] The monitoring unit is network-connected to the data monitoring unit. It is used to display the water level height, water flow velocity, and levee pressure in the N river channel areas separately according to the river channel areas after receiving them.

[0057] In this embodiment, the overall river channel model is divided into N river channel areas of equal area by the river channel division unit to achieve modular management of the river channel, which facilitates independent monitoring and regulation of each area. Such division enables each area to be managed in real time based on personalized parameters, contributing to improving the accuracy and pertinence of monitoring. The data monitoring unit set in each river channel area can collect the water level height, water flow velocity, and dike pressure of each river channel area in real time, ensuring accurate capture of key hydrological parameters. The collected data is transmitted to multiple modules simultaneously, ensuring that each sub-module can perform synchronous calculations and judgments based on the latest data. The first edge server is connected to the monitoring units of each river channel area and has distributed data processing and storage capabilities. After receiving the key parameter data in each river channel area, the first edge server stores and manages them according to area classification, reducing the data load of the central server and improving the data transmission efficiency. By reading the key parameters stored in each area within a time interval through the first edge server, the water level height difference, water flow velocity difference, and dike pressure difference are calculated, thereby obtaining the sectional change factor. The sectional change factor reflects the hydrological change trend in each river channel area, enhancing the response ability of the early warning system to sudden changes. Based on the key parameters such as water level height, water flow velocity, and dike pressure in the river channel area and the preset weight coefficients, a comprehensive hydrological health score is generated in real time. This score reflects the overall health of the current hydrological condition and can provide an intuitive basis for adjusting the subsequent risk early warning level. The sectional change factor of each area is generated using the parameter differences within the time interval. The change factor can help the dynamic early warning module judge the hydrological change trend in the river channel area to identify potential safety hazards in advance. The monitoring unit is directly connected to the data monitoring unit and can display data such as water level height, water flow velocity, and dike pressure according to each river channel area. Managers can observe the distribution of hydrological parameters in each area in real time through the visualization interface, enhancing the insight and control ability of the river channel state.

[0058] In this embodiment, the data monitoring unit includes a water level monitoring sensor, a flow velocity meter, a stress sensor, and a microprocessor.

[0059] The water level monitoring sensor is set in N river channel areas and is network-connected to the dynamic early warning module. It is used to monitor the water level height in the N river channel areas and then send the water level height in the N river channel areas to the first edge server, the monitoring unit, the microprocessor, and the dynamic early warning module.

[0060] The flow velocity meter is set in N river channel areas and is network-connected to the dynamic early warning module. It is used to monitor the water flow velocity in the N river channel areas and then send the water flow velocity in the N river channel areas to the first edge server, the monitoring unit, the microprocessor, and the dynamic early warning module.

[0061] The stress sensors are arranged in N river channel areas and are network-connected to the dynamic early warning module, and are used to monitor the dike pressures in the N river channel areas, and then send the dike pressures in the N river channel areas to the first edge server, the monitoring unit, the microprocessor, and the dynamic early warning module.

[0062] The microprocessor is network-connected to the water level monitoring sensor, the flow velocity meter, the stress sensor, and the dynamic early warning module, and is used to calculate the comprehensive hydrological health scores in the N river channel areas based on the water level heights, water flow velocities, and dike pressures in the N river channel areas after receiving them, and then send the comprehensive hydrological health scores in the N river channel areas to the dynamic early warning module.

[0063] In this embodiment, the formula for the microprocessor to calculate the comprehensive hydrological health scores in the N river channel areas based on the water level heights, water flow velocities, and dike pressures in the N river channel areas and the preset key weight coefficients is: S i =w 1 ·W i +w 2 ·V i +w 3 ·T i ,

[0064] Among them, S i is the comprehensive hydrological health score in the i-th river channel area, W i is the water level height in the i-th river channel area, with the unit of m, i is the number of the river channel area, i ∈ [1, N], V i is the water flow velocity in the i-th river channel area, with the unit of m / s, T i is the dike pressure in the i-th river channel area, with the unit of kPa, ω 1 , ω 2 , ω 3 are the preset key weight coefficients.

[0065] For example, the water level height in the i-th river channel area is 3.5 m, the water flow velocity in the i-th river channel area is 1.2 m / s, the dike pressure in the i-th river channel area is 2 kPa, ω 1 is 0.4, ω 2 is 0.3, ω 3 is 0.3, and the comprehensive hydrological health score in the i-th river channel area is 2.4.

[0066] In this embodiment, the formula for the first edge server to calculate N sectional change factors based on the water level height differences, water level flow velocity differences, and dike pressure differences in the N river channel areas is: F i =ΔW i +ΔV i +ΔTi ,

[0067] where ΔW i is the water level height difference in the i-th river channel area, N i is the water flow velocity difference of the water level in the i-th river channel area, with the unit g / kg, T i is the dike pressure difference in the i-th river channel area, F i is the sectional change factor in the i-th river channel area.

[0068] where ΔW i is 0.3, N i is 0.5, T i is 0.2, F i is 1.

[0069] In this embodiment, the dynamic warning module includes a second edge server and a warning device.

[0070] The second edge server is connected to a water level monitoring sensor, a flow velocity meter, a microprocessor, a stress sensor, a first edge server, and a sectional collaborative scheduling module. It is used to store the water level height, water flow velocity, dike pressure, N sectional change factors, and the comprehensive hydrological health scores in N river channel areas respectively based on the river channel areas after receiving them. And it is used to calculate the average water level height in N river channel areas according to the water level height in the current river channel area and the water level heights in the previous and next river channel areas in the current river channel area. And it is used to calculate the average water flow velocity in N river channel areas according to the water flow velocity in the current river channel area and the previous and next water flow velocities in the current river channel area. And it is used to calculate the average dike pressure in N river channel areas according to the dike pressure in the current river channel area and the previous and next dike pressures in the current river channel area. And it is used to calculate the dynamic warning levels in N river channel areas based on N sectional change factors, the comprehensive hydrological health scores in N river channel areas, the water level heights, water flow velocities, dike pressures in N river channel areas, a preset initial warning level, and a preset warning weight coefficient, and then send them to the sectional collaborative scheduling module and the warning device. And it is used to traverse and judge the river channel areas in the dynamic warning levels in N river channel areas that exceed the preset dynamic warning level after calculating the dynamic warning levels in N river channel areas, then generate a warning signal, and then send the warning signal to the warning device of the corresponding river channel area.

[0071] The warning device is connected to the second edge server and the warning light board network, and is set in N river areas, and is used to emit warning lights after receiving warning signals.

[0072] In this embodiment, to improve the accuracy and rationality of the warning, the system introduces a dynamic regional average calculation method. The average value is calculated based on the water level, flow rate, and pressure data of the current, upstream, and downstream of each river area, so that the dynamic warning level of each area can be adjusted in combination with the situations of the upstream and downstream areas. This dynamic calculation method effectively enhances the response sensitivity of the warning system to the overall hydrological conditions of each area. The second edge server calculates the dynamic warning level based on the water level, flow rate, pressure, sectional change factor, and hydrological health score of each river area, in combination with the preset initial warning level and weight coefficient. This grading process can make the warning level more accurately reflect the real-time hydrological conditions and can be adaptively adjusted with the changes in water level and pressure. Based on the weight adjustment of the sectional change factor and hydrological health score, this adaptive coefficient makes the warning system more flexible, and in different areas, it can make reasonable adjustments to the warning in combination with the current hydrological conditions, improving the effectiveness and accuracy of the warning. After calculating the dynamic warning level, it automatically detects whether the warning level of any area exceeds the set threshold and generates the corresponding warning signal in real time. The warning signal is transmitted to the warning devices in each area through the second edge server to achieve instant response to the over-limit area. Each area is equipped with a warning device that can directly trigger the on-site warning lights after receiving the warning signal, reminding nearby personnel with eye-catching light signals and assisting in rescue dispatching. This design greatly shortens the time from data analysis to warning response, enabling the system to quickly issue an alarm when the water level or pressure suddenly changes and improving the efficiency of emergency handling.

[0073] In this embodiment, the formula for the second edge server to calculate the average water level height in N river areas based on the water level height in the current river area, the water level height in the previous river area within the current river area, and the water level height in the subsequent river area within the current river area is:

[0074] Among them, W avg is the average water level height in the i-th river area, with the unit of m, W i+1 is the water level height in the subsequent river area within the i-th river area, with the unit of m, W i-1 is the water level height in the previous river area within the i-th river area, with the unit of m, W i is the water level height in the i-th river area, with the unit of m.

[0075] For example, W i+1 is 4m, W i-1 , is 4.2m, W avg is 3.9.

[0076] The formula for calculating the average water flow velocity in N river channel areas based on the water flow velocity in the current river channel area, the previous water flow velocity in the current river channel area, and the subsequent water flow velocity in the current river channel area is as follows:

[0077] Among them, V avg is the average water flow velocity in the i-th river channel area, with the unit of m, V i+1 is the subsequent water flow velocity based on the i-th river channel area, with the unit of m / s, V i-1 is the previous water flow velocity based on the i-th river channel area, with the unit of m, V i is the water flow velocity in the i-th river channel area, with the unit of m / s.

[0078] For example, V i+1 is 1.5 m / s, V i-1 , is 1.4 m / s, V avg is 1.37 m / s.

[0079] The formula for calculating the average embankment pressure in N river channel areas based on the embankment pressure in the current river channel area, the previous embankment pressure in the current river channel area, and the subsequent embankment pressure in the current river channel area is as follows:

[0080] Among them, T avg is the average embankment pressure in the i-th river channel area, with the unit of kPa, T i+1 is the subsequent embankment pressure based on the i-th river channel area, with the unit of kPa, T i-1 is the previous embankment pressure based on the i-th river channel area, with the unit of kPa, T i is the embankment pressure in the i-th river channel area, with the unit of kPa.

[0081] For example, T i+1 is 2.3 kPa, T i is 2.5 kPa, T avg is 2.27 kPa.

[0082] In this embodiment, the formula for the second edge server to calculate the dynamic warning levels in N river channel areas based on N sectional change factors, the comprehensive hydrological health scores in N river channel areas, the average embankment pressures, the average water flow velocities, the average water flow velocities, the preset initial warning level, and the preset warning weight coefficients is as follows:

[0083]

[0084] Among them, L iis the dynamic warning level within the i-th river channel area, L i 0 is the preset initial warning level, S i is the comprehensive hydrological health score within the i-th river channel area, F i is the sectional change factor within the i-th river channel area,

[0085] For example, a 1 ,a 2 ,a 3 ,b 1 ,b 2 ,b 3 is the preset warning weight coefficient, a 1 ,a 2 ,b 1 ,b 2 ,b 3 are 0.5, 0.3, 0.2, 0.2, 0.1 respectively, L i 0 is 1, L i is 3.

[0086] In this embodiment, the sectional collaborative scheduling module includes a path planning module and a risk calculation module,

[0087] The risk calculation module is network-connected to the river channel division unit and the second edge server, and is used for storing the N river channel areas after receiving them. And it is used for the manager to set the initial starting point and the maintenance end point. And it is used for calculating a number of path risk values of the initial starting point and the maintenance end point based on the dynamic warning levels within the N river channel areas after receiving the dynamic warning levels within the N river channel areas, then judging the optimal path based on the number of path risk values, and then sending the optimal path to the path planning module.

[0088] The path planning module is network-connected to the river channel division unit and the second edge server, and is used for the manager to store the overall river channel model. And it is used for storing the N river channel areas after receiving them. And it is used for marking the optimal path in the overall river channel model after receiving the optimal path, and then displaying the optimal path.

[0089] In this embodiment, the risk calculation module can dynamically calculate the risk value of each path based on the current hydrological and environmental factors by obtaining the dynamic warning levels of each river channel area. The system realizes the safety condition assessment of the entire path by converting the dynamic warning levels of each section area into path risk values. Through the calculation of the risk values of multiple paths, the risk calculation module can automatically identify the path with the lowest risk value between the initial starting point and the maintenance end point, generate and lock the optimal path. This optimal path determination logic ensures that the system can still provide a safest travel path under complex hydrological conditions. The Dijkstra algorithm can be used to quickly query the optimal path. After receiving the optimal path, the path planning module can mark and display the optimal path on the overall river channel model in real time. This dynamic planning and display function not only simplifies the path selection process, but also provides clear and real-time visual guidance for the managers, which can significantly improve the path safety and scheduling efficiency. The path planning module not only stores the overall river channel model, but also supports the regional display of the marked path on the model, making the sectional safety condition of the path more intuitive, thereby improving the managers' ability to identify and prevent potential risks of the path. By connecting the starting point and the maintenance end point, the system realizes the automatic scheduling of the path planning module, and at the same time allows for more intelligent allocation of maintenance tasks, enabling the managers to quickly and reasonably schedule the maintenance tasks and execute the optimal path.

[0090] In this embodiment, the formula for the risk calculation module to calculate the risk values of several paths from the initial starting point to the maintenance end point based on the dynamic warning levels in N river channel areas is as follows:

[0091] where path is several paths, and K path is the risk values of several paths from the initial starting point to the maintenance end point.

[0092] For example, the river channel division unit divides five river channel areas. Assume that the dynamic warning level of the first river channel area is 3, the dynamic warning level of the second river channel area is 4, the dynamic warning level of the third river channel area is 2, the dynamic warning level of the fourth river channel area is 1, and the dynamic warning level of the fifth river channel area is 5. It is stipulated that there are two paths to go from the first river channel to the fifth river channel. Path 1: from the first river channel area to the second river channel area to the fourth river channel area to the fifth river channel area,

[0093] Path 2: from the first river channel area to the third river channel area to the fifth river channel area.

[0094] Then the risk value of the path from the initial starting point to the maintenance end point of Path 1 is 13, and that of Path 2 is 10. Path 2 is the optimal path.

[0095] The above embodiments are only a part of the embodiments of the present invention, rather than all of them. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

Claims

1. The urban river embankment maintenance management and monitoring system based on big data is characterized by: It includes segmented visual detection module, dynamic warning module, and segmented collaborative scheduling module. The segmented visualization detection module is used for the manager to divide the overall river model into N river areas, and then send it to the segmented collaborative scheduling module; it is used to monitor the key parameters in the N river areas, and then calculate the comprehensive hydrological health score based on the key parameters and the preset key weight coefficients, and then send the comprehensive hydrological health score and key parameters to the dynamic early warning module; and it is used to calculate the segmented change factor based on the key parameters stored at a time interval, and then send the segmented change factor to the dynamic early warning module; and after monitoring the key parameters in the N river areas, the key parameters are displayed separately according to the river areas; The dynamic warning module is connected to the segmented visualization detection module network, and is used to calculate the average value of the key parameters based on the key parameters after receiving the key parameters, the segmented change factor, and the comprehensive hydrological health score, and then calculate the dynamic warning level according to the segmented change factor, the comprehensive hydrological health score, the average value of the key parameters, and the preset initial warning level, and then send it to the segmented collaborative scheduling module; and after calculating the dynamic warning level, traverse and determine whether the dynamic warning level contains a river area that exceeds the preset dynamic warning level; and after traversing and determining that the dynamic warning level contains a river area that exceeds the preset dynamic warning level, emit a warning light in the corresponding river area; The segmented collaborative scheduling module is connected to the segmented visualization detection module and the dynamic early warning module through a network, and is used to store N river channel areas after receiving them; It is also used for managers to set the initial starting point and maintenance end point; And it is used to calculate several path risk values ​​of the initial starting point and the maintenance end point based on the dynamic warning levels in the N river areas after receiving the dynamic warning levels in the N river areas, and then determine the optimal path based on the several path risk values, and then mark the optimal path in the overall river model, and then display the optimal path.

2. The urban river embankment maintenance management and monitoring system based on big data according to claim 1 is characterized by: The segmented visualization detection module includes a river channel division unit, a data monitoring unit, a monitoring unit, and a first edge server. The river channel division unit is used for the manager to store the overall river channel model; and is used for the manager to divide the overall river channel model into N river channel areas of equal area, and then transmit the N river channel areas to the segmented collaborative scheduling module; The data monitoring unit is connected to the dynamic early warning module network and is set in N river channel areas to monitor the water level, water flow velocity, and levee pressure in the N river channel areas, and then send the water level, water flow velocity, and levee pressure in the N river channel areas to the first edge server, the monitoring unit, and the dynamic early warning module, and then calculate the comprehensive hydrological health score in the N river channel areas based on the water level, water flow velocity, levee pressure and preset key weight coefficients in the N river channel areas, and then send the comprehensive hydrological health score in the N river channel areas to the dynamic early warning module; The first edge server is connected to the data monitoring unit and the dynamic early warning module network, and is used to store the water level, water flow velocity and levee pressure in the N river channel areas according to the river channel areas after receiving the water level, water flow velocity and levee pressure in the N river channel areas; and is used to read the water level, water flow velocity and levee pressure in the N river channel areas stored at a time interval, and then obtain the water level height difference, water level flow velocity difference and levee pressure difference in the N river channel areas at a time interval based on the water level, water flow velocity and levee pressure in the N river channel areas stored at a time interval, and then calculate N segmented change factors based on the water level height difference, water level flow velocity difference and levee pressure difference in the N river channel areas, and then send the N segmented change factors to the dynamic early warning module; The monitoring unit is connected to the data monitoring unit through a network, and is used to display the water level, water flow velocity and levee pressure in the N river channel areas respectively according to the river channel areas after receiving the water level, water flow velocity and levee pressure in the N river channel areas.

3. The urban river embankment maintenance management and monitoring system based on big data according to claim 2 is characterized by: The data monitoring unit includes a water level monitoring sensor, a flow meter, a stress sensor, and a microprocessor. The water level monitoring sensor is arranged in N river channel areas and is connected to the dynamic early warning module network to monitor the water level in the N river channel areas, and then sends the water level in the N river channel areas to the first edge server, the monitoring unit, the microprocessor, and the dynamic early warning module; The flow meter is arranged in N river areas, connected to the network of the dynamic early warning module, and is used to monitor the water flow velocity in the N river areas, and then send the water flow velocity in the N river areas to the first edge server, the monitoring unit, the microprocessor, and the dynamic early warning module; The stress sensors are arranged in N river channel areas and are connected to the dynamic early warning module network to monitor the levee pressure in the N river channel areas, and then send the levee pressure in the N river channel areas to the first edge server, the monitoring unit, the microprocessor, and the dynamic early warning module; The microprocessor is network-connected to the water level monitoring sensor, the flow meter, the stress sensor, and the dynamic early warning module, and is used to calculate the comprehensive hydrological health scores of the N river channel areas based on the water level heights, water flow velocities, and levee pressures in the N river channel areas and preset key weight coefficients after receiving the water level heights, water flow velocities, and levee pressures in the N river channel areas, and then send the comprehensive hydrological health scores of the N river channel areas to the dynamic early warning module.

4. The urban river embankment maintenance management and monitoring system based on big data according to claim 3 is characterized by: The microprocessor calculates the comprehensive hydrological health score in N river areas based on the water level, water flow velocity, levee pressure and preset key weight coefficients in N river areas: i =w1·W i +w2·V i +w3·T i , Among them, S i is the comprehensive hydrological health score in the ith river area, W i is the water level in the ith river area, in meters, i is the number of the river area, i∈[1,N], V i is the water velocity in the ith river channel, in m / s, T i The levee pressure in the ith river channel area is in kPa, and ω1, ω2, and ω3 are preset key weight coefficients.

5. The urban river embankment maintenance management and monitoring system based on big data according to claim 4 is characterized by: The first edge server calculates the N segment change factors based on the water level height difference, water level velocity difference, and embankment pressure difference in the N river areas: Fi = ΔW i +ΔV i +ΔT i , Where, ΔW i is the water level difference in the ith river channel area, N i is the water level velocity difference in the ith river channel area, in g / kg, T i is the levee pressure difference in the i-th river channel area, F i is the segment variation factor within the ith river channel area.

6. The urban river embankment maintenance management and monitoring system based on big data according to claim 5 is characterized by: The dynamic warning module includes a second edge server and a warning device. The second edge server is connected to the water level monitoring sensor, the flow meter, the microprocessor, the stress sensor, the first edge server, and the segmented collaborative scheduling module, and is used to store the water level, flow velocity, levee pressure, N segmented change factors, and comprehensive hydrological health scores in the N river channel areas based on the river channel areas after receiving the water level heights, water flow velocity, levee pressure, N segmented change factors, and comprehensive hydrological health scores in the N river channel areas; and is used to calculate the average water level height in the N river channel areas based on the water level height in the current river channel area, the water level height in the previous river channel area based on the current river channel area, and the water level height in the next river channel area based on the current river channel area. Mean; and used to calculate the average value of water flow velocity in N river channel areas according to the water flow velocity in the current river channel area and the previous water flow velocity based on the current river channel area and the next water flow velocity based on the current river channel area; and used to calculate the average value of levee pressure in N river channel areas according to the levee pressure in the current river channel area and the previous levee pressure based on the current river channel area and the next levee pressure based on the current river channel area; and used to calculate the dynamic warning level in N river channel areas according to N segment change factors, comprehensive hydrological health scores in N river channel areas, water level heights, water flow velocities, levee pressures, preset initial warning levels and preset warning weight coefficients in N river channel areas, and then send them to the segment collaborative scheduling module and the warning device; And after calculating the dynamic warning levels in the N river areas, it is used to traverse and determine the river areas whose dynamic warning levels in the N river areas exceed the preset dynamic warning levels, then generate a warning signal, and then send the warning signal to the warning device of the corresponding river area; The early warning device is connected to the second edge server and the warning light network, and is set in N river areas, and is used to emit a warning light after receiving a warning signal.

7. The urban river embankment maintenance management and monitoring system based on big data according to claim 6 is characterized by: The second edge server calculates the average water level in N river areas according to the water level in the current river area, the water level in the previous river area based on the current river area, and the water level in the next river area based on the current river area: Among them, W avg is the average water level in the ith river channel, in meters, W i+1 is the water level in the next river channel area based on the ith river channel area, in meters. W i-1 is the water level in the previous river area based on the ith river area, in meters, W i is the water level in the ith river channel, in meters. The formula for calculating the average water flow velocity in N river areas based on the water flow velocity in the current river area, the previous water flow velocity based on the current river area, and the next water flow velocity based on the current river area is: Among them, V avg is the average water velocity in the ith river channel, in m, V i+1 is the next water velocity based on the ith river channel area, in m / s, V i-1 is the previous water velocity based on the ith river channel area, in m, V i is the water flow velocity in the ith river channel area, in m / s, The formula for calculating the average levee pressure in N river channel areas based on the levee pressure in the current river channel area, the previous levee pressure in the current river channel area, and the next levee pressure in the current river channel area is: Among them, T avg is the average levee pressure in the ith river channel area, in kPa, T i+1 is the next levee pressure based on the ith river channel area, in kPa, T i-1 is the previous levee pressure based on the ith river channel area, in kPa, T i is the levee pressure in the ith river channel area, in kPa.

8. The urban river embankment maintenance management and monitoring system based on big data according to claim 6 is characterized by: The second edge server calculates the dynamic warning level in the N river areas according to the N segment change factors, the comprehensive hydrological health scores in the N river areas, the average levee pressure in the N river areas, the average water flow velocity, the average water flow velocity, the preset initial warning level and the preset warning weight coefficient. The formula is: Among them, L i is the dynamic warning level in the ith river area, is the preset initial warning level, S i is the comprehensive hydrological health score of the ith river area, F i is the segment change factor in the ith river area, and a1, a2, b1, b2, and b3 are the preset warning weight coefficients.

9. The urban river embankment maintenance management and monitoring system based on big data according to claim 8 is characterized by: The segmented collaborative scheduling module includes a path planning module and a risk calculation module. The risk calculation module is connected to the river division unit and the second edge server network, and is used to store N river areas after receiving them; It is also used for managers to set the initial starting point and maintenance end point; And used for calculating a plurality of path risk values ​​of an initial starting point and a maintenance end point based on the dynamic warning levels in the N river channel areas after receiving the dynamic warning levels in the N river channel areas, and then determining an optimal path based on the plurality of path risk values, and then sending the optimal path to the path planning module; The path planning module is connected to the river division unit and the second edge server network, and is used for the manager to store the overall river model; and is used to store the N river areas after receiving them; And it is used to mark the optimal path in the overall river model after receiving the optimal path, and then display the optimal path.

10. The urban river embankment maintenance management and monitoring system based on big data according to claim 9 is characterized by: The risk calculation module calculates the risk values ​​of several paths between the initial starting point and the maintenance end point based on the dynamic warning levels in N river areas. The formula is: Among them, path is a number of paths, K path are the risk values ​​of several paths between the initial starting point and the maintenance end point.

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