CN116386284B

CN116386284B - City flood warning method and system

Info

Publication number: CN116386284B
Application number: CN202310606199.1A
Authority: CN
Inventors: 赵梦琦; 张飞珍; 谢涵聪; 胡孟娴; 孙映宏; 桂发二; 于丹红; 万鹏; 张瑶伊
Original assignee: Zhejiang Guiren Information Technology Co ltd
Current assignee: Zhejiang Guiren Information Technology Co ltd
Priority date: 2023-05-26
Filing date: 2023-05-26
Publication date: 2023-08-25
Anticipated expiration: 2043-05-26
Also published as: CN116386284A

Abstract

The invention relates to a method and a system for warning and pre-warning urban flood, comprising the following steps: calculating equipment operation index data by using working state data, calculating pipe network health degree data by using real-time liquid level data, carrying out statistical classification on urban ponding depth based on real-time river network water level data and real-time surface ponding depth to obtain first target statistical classification result data, calculating a first ponding index by using real-time pavement water level flow velocity data, and carrying out blockwise classification on urban surface ponding degree to obtain first ponding degree classification result data of each urban block; when at least one of the equipment operation index data, the pipe network health degree data, the first target statistical classification result data and the first ponding degree classification result data of each city block is determined to be in accordance with the corresponding alarm condition, corresponding alarm information is generated, so that compared with the existing scheme, the problems that data are easy to lose and the like caused by manual inspection can be at least solved.

Description

City flood warning method and system

Technical Field

The invention relates to the fields of urban water safety, urban intelligent water control and refined management, in particular to a method and a system for warning and pre-warning urban flood.

Background

At present, complex river network water systems and urban underground pipe network systems exist in the areas of China, however, the traditional river network water systems and pipe network systems are required to be inspected by a large amount of manpower, and the problems that recorded data are easy to lose, supervision inspection strength is insufficient and the like exist. And the long-acting operation and maintenance management of the pipe network also has the problems of weak design bearing capacity, low perception monitoring coverage, difficult operation and maintenance supervision, low co-processing efficiency and the like.

Disclosure of Invention

First, the technical problem to be solved

In view of the above-mentioned shortcomings and disadvantages of the prior art, the present invention provides a method and a system for warning urban flood, which solve the technical problems of easy loss of recorded data in the prior art.

(II) technical scheme

In order to achieve the above purpose, the main technical scheme adopted by the invention comprises the following steps:

in a first aspect, an embodiment of the present invention provides a method for warning and early warning of urban flood, including: acquiring working state data of monitoring equipment in a target city, real-time liquid level data of a underground pipe network, real-time river network water level data and real-time pavement water level flow velocity data; the real-time road surface water level flow velocity data comprise real-time road surface water level data and real-time surface water accumulation depth;

Calculating equipment operation index data by using working state data, calculating pipe network health degree data by using real-time liquid level data, carrying out statistical classification of urban ponding depth on the basis of real-time river network water level data and real-time surface ponding depth to obtain first target statistical classification result data, calculating a first ponding index by using real-time pavement water level flow velocity data, carrying out blockwise classification on urban surface ponding degree to obtain a plurality of urban blocks, and determining first ponding degree classification result data of each urban block in the plurality of urban blocks on the basis of the first ponding index; the first target statistical classification result data refers to statistical classification result data of real-time urban ponding depth exceeding a warning line;

and when at least one of the equipment operation index data, the pipe network health degree data, the first target statistical classification result data and the first ponding degree classification result data of each city block is determined to be in accordance with the corresponding alarm condition, corresponding alarm information is generated.

In one possible embodiment, the operational status data includes a device operational status and a device duration of operation;

Wherein calculating device operation index data using the operating state data includes: respectively determining a score value of the working state of the equipment and a coefficient corresponding to the continuous working time of the equipment; and taking the product value of the score value of the equipment working state and the coefficient corresponding to the equipment continuous working time as an equipment operation index.

In one possible embodiment, the calculation formula for the health data of the pipe network is as follows:

；

wherein ,Grepresenting pipe network health degree data;G ₁ the drainage efficiency of the pipe network is represented, and the drainage efficiency of the pipe network is the ratio of real-time liquid level data to the pipe diameter of the underground pipe network;N ₁ the number of indexes for influencing the drainage efficiency of the pipe network is represented;G ₂ representing the environmental structure of a pipe network;N ₂ the number of indexes influencing the environment structure of the pipe network is represented;G ₃ representing pipe network defects;N ₃ the number of indexes affecting pipe network defects is represented;G ₄ representing pipe network fouling;N ₄ the number of indices affecting pipe network fouling is indicated.

In one possible embodiment, the real-time road surface water level flow rate data includes a real-time accumulated water depth, a real-time accumulated water time, and a real-time integrated flow rate, the first accumulated water index is determined based on a first flood factor index value and a first land property factor index value, the first land property factor index value being determined by a grade, a land use type, and a volume rate;

Wherein, utilize real-time road surface water level velocity of flow data, calculate first ponding index, include: respectively determining a risk value corresponding to the real-time accumulated water depth, a risk value corresponding to the real-time accumulated water time and a risk value corresponding to the real-time integrated flow rate, and carrying out weighted summation on the risk value corresponding to the real-time accumulated water depth, the risk value corresponding to the real-time accumulated water time and the risk value corresponding to the real-time integrated flow rate to obtain a first flood factor index value; respectively determining a risk value corresponding to the gradient, a risk value corresponding to the land use type and a risk value corresponding to the volume rate, and carrying out weighted summation on the risk value corresponding to the gradient, the risk value corresponding to the land use type and the risk value corresponding to the volume rate to obtain a first land property element index value; and carrying out weighted summation on the first flood factor index value and the first land property factor index value to obtain a first ponding index.

In one possible embodiment, the warning method further includes: acquiring real-time water level data of a water level station, real-time flow data of a flow station, real-time river network water level flow data and forecast rainfall of a target city; matching the real-time river network water level data with a pre-constructed river channel reservoir capacity curve to obtain the current water storage volume of the river channel; calculating the future water storage volume of the river by utilizing the current water storage volume of the river and the forecast rainfall, and matching the future water storage volume of the river with the river storage capacity curve again to obtain the future water level of the river network; wherein the two-dimensional hydrodynamic force full-coupling model is obtained based on a one-dimensional river network model for simulating river network water flow, and the river network future water level is provided as a boundary condition to the one-dimensional river network model; based on a pre-constructed two-dimensional hydrodynamic full-coupling model, carrying out prediction processing on real-time water level data of a water level station, real-time flow data of a flow station, real-time river network water level flow data, real-time liquid level data of an underground pipe network and forecast rainfall of a target city to obtain prediction data; the prediction data comprise the water level of a future urban river network, the depth of surface water accumulation in the future, the accumulated water accumulation time in the future and the comprehensive flow rate in the future; carrying out statistical classification of urban water logging depth based on the future urban river network water depth and the future surface water logging depth obtained through the future urban river network water level to obtain second target statistical classification result data, calculating a second water logging index by utilizing the future surface water logging depth, the future accumulated water logging time and the future comprehensive flow rate, and determining second water logging degree classification result data of each urban block based on the second water logging index; the second target statistical classification result data refers to statistical classification result data of the predicted urban ponding depth exceeding the warning line; and when at least one of the second target statistical classification result data and the second ponding degree classification result data of each city block meets the corresponding early warning condition, generating corresponding early warning prompt information.

In one possible embodiment, the process of constructing the two-dimensional hydrodynamic full-coupling model includes: constructing a one-dimensional hydrodynamic model; the one-dimensional hydrodynamic model also comprises a one-dimensional pipe network model for simulating urban underground pipe network water flow; constructing a two-dimensional hydrodynamic model for simulating urban surface water flow; the method comprises the steps of executing one-dimensional model coupling of a river network and urban underground on a one-dimensional river network model and a one-dimensional pipe network model, executing two-dimensional model coupling of the river network and urban earth surface on a one-dimensional river network model and a two-dimensional hydrodynamic model, and executing two-dimensional model coupling of the urban underground earth surface on a one-dimensional pipe network model and a two-dimensional hydrodynamic model.

In one possible embodiment, the one-dimensional river network model comprises:

；

wherein ,Brepresenting the water surface width at the section of the river network;Zrepresenting the water level at the section of the river network;Qrepresenting the flow rate at the section of the river network;Arepresenting the water passing area at the section of the river network;trepresenting time;xrepresenting the river course distance;qrepresenting a source item, and the source item includes a lateral inflow;grepresenting gravitational acceleration;S _f representing the loss of momentum along-way resistance;Rrepresents the hydraulic radius;nrepresenting the manning roughness coefficient.

In one possible embodiment, the one-dimensional pipe network model comprises:

；

wherein ,Arepresenting the cross-sectional area of the water;Qrepresenting the flow rate at the water cross section;Hrepresenting the head at the water cross section;trepresenting time;xrepresenting the distance along the pipeline network;grepresenting gravitational acceleration;S _f representing the loss of momentum along-way resistance;Rrepresents the hydraulic radius;nrepresenting the manning roughness coefficient.

In one possible embodiment, performing one-dimensional model coupling of river networks with urban subsurface for a one-dimensional river network model and a one-dimensional pipe network model, comprises: connecting an outlet of the one-dimensional pipe network model with a target section of the one-dimensional river network model to serve as a coupling node; the coupling node comprises a pipe network outlet and a river network section; intermediate steps: the one-dimensional river network model provides a water level boundary for the one-dimensional pipe network model, and provides the section water level of the river network section at the coupling node as a boundary condition of a pipe network outlet for the one-dimensional pipe network model; according to the water level boundary, updating the one-dimensional pipe network model from the current time step to the next time step; the one-dimensional pipe network model provides a lateral inflow source for the one-dimensional river network model, and the pipe network drainage flow is used as the lateral inflow source of the river network section at the coupling node according to the pipe network drainage flow at the coupling node after the one-dimensional pipe network model is updated; according to the lateral inflow source, the one-dimensional river network model is updated from the current time step to the next time step, and then the middle step is skipped to circularly calculate until the calculation is completed.

In a second aspect, an embodiment of the present invention provides a warning system for urban flooding, including: the monitoring equipment is used for acquiring working state data of the monitoring equipment in the target city, real-time liquid level data of the underground pipe network, real-time river network water level data and real-time pavement water level flow velocity data; the real-time road surface water level flow velocity data comprise real-time road surface water level data and real-time surface water accumulation depth; the system comprises an Internet of things platform, a real-time road surface water level flow rate data, a first water accumulation index, a first water accumulation degree classification result data and a second water accumulation degree classification result data, wherein the Internet of things platform is used for calculating equipment operation index data by using working state data, calculating pipe network health degree data by using real-time liquid level data, carrying out statistical classification on urban water accumulation depths on the basis of real-time river network water level data and real-time surface water accumulation depths to obtain first target statistical classification result data, calculating the first water accumulation index, carrying out blockwise classification on urban surface water accumulation degrees to obtain a plurality of urban blocks, and determining the first water accumulation degree classification result data of each urban block in the plurality of urban blocks on the basis of the first water accumulation index; the first target statistical classification result data refers to statistical classification result data of real-time urban ponding depth exceeding a warning line; the monitoring and early warning platform is used for generating corresponding warning information when at least one of the equipment operation index data, the pipe network health degree data, the first target statistical classification result data and the first ponding degree classification result data of each city block is determined to accord with the corresponding warning condition.

(III) beneficial effects

The beneficial effects of the application are as follows:

the application provides a warning method and a warning system for urban flood, which can more intuitively reflect the running state of equipment and improve the maintenance and management efficiency of a management department on monitoring equipment through the quantized index of the running index of the equipment.

The embodiment of the application also takes the quantitative index after the influence of multiple factors into consideration through the health degree of the pipe network, can more intuitively and accurately reflect the health state of the pipe network, and improves the maintenance and management efficiency of the management department on the urban underground pipe network.

And the embodiment of the application can clearly reflect the ponding degree of each area on the urban surface through the ponding index, thereby being convenient for managing and controlling the area where the urban flood is easy to occur and planning the disaster prevention early warning of the area where the flood is easy to occur.

In addition, the embodiment of the application also concentrates the monitoring, alarming and other works into one system, and optimizes the multi-department collaborative management flow.

In order to make the above objects, features and advantages of the embodiments of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 shows a schematic diagram of an urban flood warning system according to an embodiment of the present application;

FIG. 2 is a flowchart of a related method of an alarm part in a warning method of urban flood according to the present application;

FIG. 3 is a schematic diagram showing a determination of risk values of an evaluation factor according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a water accumulation index evaluation system according to an embodiment of the present application;

fig. 5 is a flowchart of a related method of an early warning part in an early warning method according to an embodiment of the present application.

Detailed Description

The application will be better explained by the following detailed description of the embodiments with reference to the drawings.

At present, urban flood disasters frequently occur in recent years, and in order to make emergency response to the flood disasters in advance, it is important to forecast and pre-warn the urban places where the flood is likely to occur, so that the life and property safety of people is guaranteed.

Therefore, how to build a set of multi-platform system integrating river network monitoring, pipe network operation and maintenance management and urban flood warning and early warning functions and a warning and early warning method is a technical management problem which needs to be solved urgently at present.

Based on the above, the embodiment of the application provides a method and a system for warning and early warning of urban flood, wherein the system comprises a comprehensive management and control platform (or called a river management and control platform), monitoring equipment, an internet of things platform, a monitoring and warning and early warning platform (or called a monitoring and warning and early warning platform, namely warning and early warning), a pushing platform, a short message system and a mobile client. The comprehensive management and control platform is connected with the monitoring equipment, the monitoring equipment can transmit monitoring information to the Internet of things platform in real time, the Internet of things platform can process and transmit the acquired monitoring information to the monitoring notification and early warning platform, the monitoring notification and early warning platform responds to data to generate notification and early warning prompt and notification and early warning event management information, the pushing platform receives the notification and early warning event management information and sends event processing prompt information to event processing personnel, and a user can check the notification and early warning prompt and event management information of the monitoring notification and early warning platform through a mobile client.

In order that the above-described aspects may be better understood, exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.

Referring to fig. 1, fig. 1 shows a schematic diagram of an urban flood warning system according to an embodiment of the present application. As shown in FIG. 1, the warning and early warning system comprises a comprehensive management and control platform, monitoring equipment, an Internet of things platform, a monitoring warning and early warning platform, a pushing platform, a short message system and a mobile client. The monitoring equipment can be respectively in communication connection with the comprehensive control platform and the Internet of things platform, the monitoring notification and early warning platform can be respectively in communication connection with the Internet of things platform, the pushing platform and the mobile client, and the pushing platform is also in communication connection with the short message system.

Specifically, in order to conveniently understand the distribution condition of urban river networks, pipe networks and monitoring equipment and view real-time and historical monitoring data of river network water levels, river network flow and pipe network liquid levels, the comprehensive management and control platform can correlate the spatial position information and monitoring data information of the monitoring equipment, display the specific positions and spatial relations of all the monitoring equipment of water level stations, flow stations, river networks, underground pipe networks and pavements in an urban area on a managed and controlled map, and display real-time monitoring data and historical monitoring data of corresponding monitoring equipment;

The monitoring equipment can acquire real-time water level data of the water level station, real-time flow data of the flow station, real-time water level flow data of the river network, real-time liquid level data of the underground pipe network, real-time water level flow speed data of the road surface and working states (such as normal, offline and fault) of the monitoring equipment, and transmit the data to the Internet of things platform and the comprehensive management and control platform in real time;

the Internet of things platform receives monitoring data of the monitoring equipment, analyzes and processes the monitoring data to obtain an equipment operation index, pipe network health and water accumulation index (for example, a first water accumulation index and a second water accumulation index), simulates and predicts future urban water accumulation depth, future accumulated water accumulation time and the like based on a two-dimensional hydrodynamic full-coupling model, and transmits a calculation result and a prediction result to the monitoring and warning platform;

the monitoring and warning platform receives the calculation result and the prediction result uploaded by the Internet of things platform, and can generate corresponding equipment operation warning prompt, pipe network health warning prompt, urban flood warning prompt and flood partition warning and warning prompt according to the two results, and the monitoring and warning platform can also generate a warning and warning event management list according to the warning and warning prompt;

The pushing platform can generate event processing prompts according to information in the warning event management list generated by the monitoring warning platform, the event processing prompts are sent to event processing personnel through the short message system, and a user can log in the mobile client to check the warning prompts and the warning event management information in real time.

It should be understood that the specific setting of the comprehensive management and control platform, the specific setting of the monitoring device, the specific setting of the internet of things platform, the specific setting of the monitoring and warning platform, the specific setting of the pushing platform, the specific setting of the short message system, the specific setting of the mobile client and the like can all be set according to actual requirements, and the embodiment of the application is not limited to this.

Optionally, in order to realize sensing and collecting of different types of data, the monitoring device in the application can be composed of a data sensing device and an RTU data collecting device. The data sensing equipment can sense the water level, the liquid level, the flow speed and the equipment working state information; the RTU data acquisition device may acquire data.

In order to facilitate network transmission of data, the application can connect the data sensing device and the RTU data acquisition device through the 485 bus module, and build a communication card (for example, a 4G communication card or a 5G communication card) in the RTU data acquisition device, and transmit the acquired data to the platform of the Internet of things in real time.

Optionally, in order to collect monitoring data at different geographic positions of the city, the monitoring device can be arranged at various positions of a water level station, a flow station, a river network and a underground pipe network in a fixed camera mode, and can also be used for carrying out inspection and monitoring at various positions of the river network in a unmanned plane and unmanned ship mode.

Optionally, referring to fig. 1, in order to evaluate the real-time working state of the monitoring device, the application may receive the relevant data information of the working state of the device collected by the monitoring device through the device management module, calculate the device operation index data based on the relevant data information of the working state of the device, and determine which working state of the monitoring device is in good, medium or poor state at the moment according to the device operation index data, thereby realizing the technical effect of convenient maintenance and management of the monitoring device, and the device management module transmits the calculation result to the monitoring notification and early warning platform.

And in order to evaluate the urban underground pipe network state, the application can receive the underground pipe network liquid level data acquired by the monitoring equipment through the pipe network management module, calculate the pipe network health degree data based on the underground pipe network liquid level data, and determine what health degree grade (for example, a first-level health degree grade, a second-level health degree grade, a third-level health degree grade, a fourth-level health degree grade and the like) the pipe network is in at the moment according to the pipe network health degree data, and each health degree grade specifically has the range of the corresponding pipe network health degree data, so that the application can determine what health degree grade is based on the current pipe network health degree data, thereby realizing the technical effect of conveniently maintaining and managing the underground pipe network, and the pipe network management module transmits the calculation result to the monitoring and early warning platform.

In order to realize the prediction of the river network water level and the urban flood, the application can receive the rainfall predicted by the weather bureau through the weather data receiving module, and take the rainfall as the data information needed to be used in the river network water level prediction module and the full-coupling calculation module.

In order to realize the prediction of the river network water level, the application can predict the future water level of the river network by using the water level station and river network water level data collected by the monitoring equipment and the forecast rainfall transmitted by the meteorological data receiving module through the river network water level prediction module, and transmit the prediction result to the full-coupling calculation module.

In order to realize the early warning of urban flood, the application can also utilize the water level station water level data, the flow station flow data, the river network water level flow data, the underground pipe network liquid level data and the river network water level prediction result sent by the river network water level prediction module and the weather forecast rainfall simulation transmitted by the weather data receiving module to predict the future urban river network water level, the future surface water accumulation depth, the future accumulated water accumulation time and the future comprehensive flow rate acquired by the monitoring equipment through the full-coupling calculation module, and transmit the result to the flood analysis module.

And in order to analyze the data, judging whether the flood exists and/or predicting the occurrence of the flood, the flood analysis module has the following two functions, in particular:

the first method is to conduct statistical classification on urban river network water depth and surface water depth obtained by utilizing real-time river network, real-time pavement water level data and/or data predicted by a full-coupling calculation module acquired by monitoring equipment so as to obtain urban water depth exceeding a warning line, and transmit the result to a monitoring and warning platform. And, the water depth warning line of the present application may be divided into a yellow warning line and a red warning line. Wherein, the red warning line of the river network is 5.75m, the northeast water system plum flood season of the yellow warning line of the river network is 3.5m, the northeast water system non-plum flood season of the yellow warning line of the river network is 3.8m, the southwest water system plum flood season of the yellow warning line of the river network is 4.3m, the southwest water system non-plum flood season of the yellow warning line of the river network is 4.5m, the red warning line of the road surface is 0.15m, and the yellow warning line of the road surface is 0.05m;

the second method is to calculate the water accumulation index by using the real-time road surface water level flow rate data collected by the monitoring equipment and/or the accumulated water accumulation time and the comprehensive flow rate predicted by the full-coupling calculation module, and to conduct block classification on the urban surface water accumulation degree so as to obtain the water accumulation degree (such as primary water accumulation degree, secondary water accumulation degree, tertiary water accumulation degree, quaternary water accumulation degree, five-level water accumulation degree and the like) of each block of the city, wherein the water accumulation degree of each level has the data range of the corresponding water accumulation degree, so that the water accumulation degree of which level is determined based on the current water accumulation degree can be in any range, and the result is transmitted to the monitoring and warning platform.

Optionally, with continued reference to fig. 1, in order to alert that the device operation state is poor, the device operation alert module of the present application may generate a device operation alert prompt according to the device operation state calculation result.

And in order to warn that the pipe network state is serious or urgent, the pipe network health warning module can generate pipe network health warning prompt according to the pipe network state calculation result.

And in order to warn that the current urban ponding reaches a warning line and the ponding degree of each block of the city, the flood inspection alarm module can generate an urban flood alarm prompt according to the first target statistical classification result data and can also generate a flood partition alarm prompt according to the first ponding degree classification result data of each urban block. And, in the present application, the block at the first-order water accumulation level may be represented in green, the block at the second-order water accumulation level may be represented in blue, the block at the third-order water accumulation level may be represented in yellow, the block at the fourth-order water accumulation level may be represented in orange, and the block at the fifth-order water accumulation level may be represented in red.

And in order to early warn that the urban ponding reaches the warning line and the ponding degree of each block of the city in advance, the urban flood early warning module can generate an urban flood early warning prompt according to the predicted second target statistical classification result data, and can also generate a flood partition early warning prompt according to the second ponding degree classification result data of each urban block.

And in order to conveniently check and manage the notice and early warning event, the notice and early warning event management module can generate an alarm event management list according to the alarm information of the equipment operation alarm module, the pipe network health alarm module and the flood inspection alarm module, and can also generate an early warning event management list according to the early warning information of the urban flood early warning module.

In order to facilitate event processing personnel to process the warning event in time, the push platform can push warning event processing prompt information to the event processing personnel in a short message sending mode.

In order to facilitate users to view the notification and early warning information and the notification and early warning event processing conditions of the platform in real time, the client terminal of the application can be arranged in a mobile phone terminal and a computer terminal.

It should be understood that the foregoing warning system is merely exemplary, and those skilled in the art can make various modifications according to actual needs, and the solutions after the modifications also belong to the protection scope of the present application.

To facilitate an understanding of the specific implementation of the warning system, the following description may be provided by way of specific method embodiments.

Referring to fig. 2, fig. 2 is a flowchart illustrating a related method of an alarm part in an urban flood warning method according to the present application. As shown in fig. 2, the related method of the alarm part includes:

Step S210, the monitoring equipment acquires working state data of the monitoring equipment in the target city, real-time liquid level data of a underground pipe network, real-time river network water level data and real-time pavement water level flow velocity data. The real-time road surface water level flow velocity data comprise real-time road surface water level data and real-time surface water accumulation depth.

Specifically, the monitoring device may obtain data of the working state (e.g., normal, offline, and fault) of the collectable device, real-time liquid level data of the underground pipe network, real-time river network water level data, and real-time road surface water level flow rate data, and may transmit the data to the internet of things platform in real time through the communication card.

Step S220, the Internet of things platform calculates equipment operation index data by using working state data, calculates pipe network health degree data by using real-time liquid level data, performs statistical classification of urban water accumulation depth based on real-time river network water level data and real-time surface water accumulation depth to obtain first target statistical classification result data, calculates a first water accumulation index by using real-time pavement water level flow velocity data, performs blockwise classification of urban surface water accumulation degree to obtain a plurality of urban blocks, and determines first water accumulation degree classification result data of each urban block in the plurality of urban blocks based on the first water accumulation index. The first target statistical classification result data refers to statistical classification result data of real-time city ponding depth exceeding a warning line.

It should be understood that the specific process of calculating the device operation index data using the operation state data may be set according to actual requirements, and embodiments of the present application are not limited thereto.

Optionally, in the case that the evaluation factor of the device operation index data includes the current operating state of the device and the continuous operating time of the device, the calculation formula of the device operation index data is as follows:

；

wherein ,Srepresenting equipment operational index data;S ₁ a score value representing a current operating state of the device, the score value may be 100 when the operating state is normal, 80 when the operating state is offline, and 50 when the operating state is faulty; alpha represents a coefficient corresponding to the continuous operation duration of the device, and the coefficient may be 1.0 when normal, 1.0 when the operation duration is 0 to 3 hours, 0.9 when the operation duration is 3 to 6 hours, 0.8 when the operation duration is 6 to 12 hours, and 0.6 when the operation duration is longer than 12 hours.

And, the equipment operation index in the application can be used as a score for evaluating the real-time operation state of the monitoring equipment, and the score range of the equipment operation index can be 0-100. More than 85 points can represent good running state of the equipment, 60-85 points can represent medium running state of the equipment, and less than 60 points can represent poor running state of the equipment, so that the equipment needs maintenance and overhaul.

It should also be understood that the specific process of calculating the health data of the pipe network by using the real-time liquid level data may be set according to actual requirements, and the embodiment of the application is not limited thereto.

Optionally, the pipe network health degree provided by the application can be used as a score for evaluating the urban underground pipe network state, and the score range of the pipe network health degree can be 0-100. Wherein, when the score of the pipe network health degree is 80~100, it belongs to first level health degree, pipe network state is good, and when the score of pipe network health degree is 60~80, it belongs to second level health degree, pipe network state is general, and when the score of pipe network health degree is 40~60, it belongs to tertiary health degree, pipe network state is serious, and when the score of pipe network health degree is 40 and below, it belongs to fourth level health degree, pipe network state is urgent. And, for three and four levels of health, it requires maintenance of the network. In addition, the application can consider the influence on the health of the pipe network from four aspects of pipe network drainage efficiency, pipe network environment structure, pipe network defects and pipe network siltation.

For the drainage efficiency of the pipe network, the ratio of the real-time liquid level of the pipe network to the design fullness (i.e. the pipe diameter of the pipe) of the pipe network can be used for judging, and the ratio can be realized by adopting buckling, wherein the higher the ratio is, the smaller the drainage capacity which can be borne by the pipe is, the lower the drainage efficiency is, and the buckling is more. When the ratio is 100%, 100 minutes are buckled, when the ratio is 0-20%, 0 minutes are buckled, and interpolation is carried out in other intervals according to linearity;

And, for the pipe network environment structure, the aspects of pipe age, pipe diameter, joint, road grade, soil covering depth and soil type can be considered, and the score can be calculated according to the deduction. Wherein, when the pipe age is 0-5 years, 0 part is buckled, when the pipe age is 5-10 years, 5 parts are buckled, when the pipe age is 10-30 years, 10 parts are buckled, and when the pipe age is more than 30 years, 15 parts are buckled; when the pipe diameter is smaller than 300mm, the buckle is 8 parts, when the pipe diameter is 300-500 mm, the buckle is 4 parts, when the pipe diameter is 500-1000 mm, the buckle is 2 parts, and when the pipe diameter is larger than 1000mm, the buckle is 0 part; the rigid interface buckle is 0 part, the flexible interface buckle is 5 parts, and the other interface buckles are 10 parts; the road is divided into 8 parts of sidewalk buckles, 4 parts of branch road buckles, 2 parts of secondary trunk road buckles and 0 part of main trunk road buckles; covering soil with the depth of less than 1m for 5 minutes, 1-3 m for 1 minute, 3-5 m for 3 minutes, and more than 5m for 5 minutes; 0 part of soil type rock soil button, 2 parts of broken stone soil button, 4 parts of sand soil button and 8 parts of silt soil button;

and, for pipe network defects, including cracking, deformation, corrosion, staggering, rolling, and derailment. Wherein, the crack buckle in the cracking defect is 3 parts, the crack buckle is 5 parts, the broken buckle is 10 parts, and the collapse buckle is 15 parts; the deformation amount in the deformation defect is less than 3 parts of the buckle with the pipe diameter of 5 percent, 5 to 15 percent of buckle with the pipe diameter of 5 percent, 15 to 25 percent of buckle with the pipe diameter of 10 percent, and more than 25 percent of buckle with the pipe diameter of 15 percent; the corrosion defect is that the moderate corrosion buckle is 3 minutes, the moderate corrosion buckle is 5 minutes and the heavy corrosion buckle is 10 minutes; in the dislocation defect, the slight dislocation is buckled for 3 minutes, the moderate dislocation is buckled for 5 minutes, the severe dislocation is buckled for 10 minutes, and the severe dislocation is buckled for 15 minutes; the fluctuation defect takes the ratio of the fluctuation height to the pipe diameter as an index, the ratio is less than 20 percent of buckle 3, 20-35 percent of buckle 5, 35-50 percent of buckle 10 and more than 50 percent of buckle 15; the derailment defect is divided into 3 parts of moderate derailment buckles, 5 parts of moderate derailment buckles, 10 parts of heavy derailment buckles and 15 parts of heavy derailment buckles;

And, for pipe network fouling, the mild fouling buckles 10 points, the moderate fouling buckles 30 points, the severe fouling buckles 50 points, and the severe fouling buckles 70 points.

Based on the four evaluation factors, the health degree data of the pipe network can be calculated by the following formula:

；

wherein ,Grepresenting pipe network health degree data;G ₁ the drainage efficiency of the pipe network is represented, and the drainage efficiency of the pipe network is the ratio of real-time liquid level data to the pipe diameter of the underground pipe network;N ₁ the number of indexes affecting the drainage efficiency of the pipe network is expressed, andN ₁ may be 1 (i.e., the ratio of real-time liquid level to design fullness);G ₂ representing the environmental structure of a pipe network;N ₂ indicating the number of indexes affecting the environmental structure of the pipe network, andN ₂ the specific value of (a) may be 6 (e.g., pipe age, pipe diameter, interface, road grade, depth of coverage, soil type);G ₃ representing pipe network defects;N ₃ indicating the number of indexes affecting pipe network defects, andN ₃ the specific value of (1) can be 6 (fracture, deformation, corrosion, dislocation,Heave and derailment);G ₄ representing pipe network fouling;N ₄ indicating the number of indicators affecting pipe network fouling, andN ₄ may be 1 (i.e. sludge);representing the total fraction deducted by considering the drainage efficiency of the pipe network; / >Representing the total score deducted by considering the environmental structure of the pipe network; />Representing a total score taking into account pipe network defect deductions; />Representing the total fraction that is taken into account for pipe network fouling.

The method is characterized in that the data required for calculating the health degree of the pipe network can be obtained by municipal administration departments except that the liquid level of the pipe network is the real-time monitoring data of monitoring equipment.

It should also be understood that the specific process of performing statistical classification of urban ponding depth based on real-time river network water level data and real-time surface water depth to obtain the first target statistical classification result data may also be set according to actual requirements, and the embodiment of the application is not limited thereto.

Optionally, since the water depth refers to a water depth above the terrain elevation and the water level is equal to the water depth plus the terrain elevation, the real-time urban river network water depth can be determined by the real-time river network water level data, and statistical classification of urban ponding depth can be performed based on the real-time urban river network water depth and the real-time surface ponding water depth to obtain the first target statistical classification result data.

It should also be understood that, with the real-time road surface water level flow rate data, the specific process of calculating the first water accumulation index may also be set according to actual requirements, and embodiments of the present application are not limited thereto.

Optionally, the water accumulation index (for example, the first water accumulation index or the second water accumulation index) provided by the application can be used as an evaluation index for reflecting the surface water accumulation degree, and the water accumulation index can be in a value range of 0-100, and is in a first-level water accumulation degree (the water accumulation degree is the lightest) when the water accumulation index is in a value of 0-20, and is in a second-level water accumulation degree when the water accumulation index is in a value of 20-40, and is in a third-level water accumulation degree when the water accumulation index is in a value of 40-60, and is in a fourth-level water accumulation degree when the water accumulation index is in a value of 60-80, and is in a fifth-level water accumulation degree (the water accumulation degree is the heaviest) when the water accumulation index is in a value of 80-100.

And considering that under the condition of the same water accumulation depth, accumulated water accumulation time and comprehensive flow rate, risks caused by water accumulation to commercial, residential and public land with large gradient and high volume rate are larger than those of green land and square land with small gradient and low volume rate, the application constructs water accumulation indexes by taking flood element indexes and land property element indexes as classification indexes. The flood factor index can adopt the accumulated water depth, accumulated water accumulation time and comprehensive flow rate as evaluation factors; the land property factor index may use the gradient, the land use type, and the volume rate as evaluation factors.

In addition, the gradient represents the degree of surface steepness, the gradient can be represented by using a degree number, and the gradient=elevation difference/horizontal distance is calculated by adopting an inverse trigonometric function; the land utilization type can represent land resources with the same land utilization mode, and the classification of the land utilization type can adopt a classification mode in the urban land classification and planning construction land standard (GB 50137-2011); the volumetric rate may represent the ratio of the total building area of the earth's surface to the net floor area in an area.

In addition, the flood factor indexes (accumulated water depth, accumulated water time and comprehensive flow rate) can be calculated by the road surface water level flow rate and/or the full-coupling calculation module acquired by the monitoring equipment, and the land property factors (gradient, land utilization type and volume rate) can be based on the data information acquired by the municipal administration of the local city.

And the water accumulation index is calculated as follows:

firstly, risk values (the range of values is 0-100) corresponding to the accumulated water depth, accumulated water time, comprehensive flow rate, gradient, land utilization type and volume rate evaluation factors can be respectively determined.

For example, referring to fig. 3, fig. 3 shows a schematic diagram of determining a risk value of an evaluation factor according to an embodiment of the present application. As shown in fig. 3, when the surface water is 0.1m at a certain time, the risk value of the water depth of the accumulated water is 12.5 minutes by linear interpolation calculation, which is between low risk (< 0.05,0 minutes) and low risk (< 0.15, 25 minutes), as shown by the determination standard of the risk value of the evaluation factor shown in fig. 3. And if the surface water exceeds 0.5m, namely, the risk is large (< 0.5, 100 points), the risk value is taken as 100 points.

It should be noted that, other evaluation factors except the land use type evaluation factor can be calculated according to the method, and the land use type evaluation factor is classified according to the land type to directly take value.

It should be noted that, in the case where the water accumulation coefficient is the first water accumulation index, the water accumulation depth may be the real-time water accumulation depth, the accumulated water accumulation time may be the real-time accumulated water accumulation time, and the integrated flow rate may be the real-time integrated flow rate; under the condition that the calculated water accumulation coefficient is the second water accumulation index, the water accumulation depth can be the future water accumulation depth, the accumulated water accumulation time can be the future accumulated water accumulation time, and the comprehensive flow rate can be the future comprehensive flow rate.

Secondly, a ponding index evaluation system can be constructed, and the weight coefficient of each layer in the evaluation system can be determined. As shown in FIG. 4, the water accumulation index evaluation system of the application can be divided into three layers, namely a target layer, a criterion layer and an index layer. Wherein the target layer may be a ponding index; the criterion layer can be flood factor indexes and land property factor indexes; the index layer is composed of six evaluation factors, namely accumulated water depth, accumulated water time, comprehensive flow rate, gradient, land utilization type and volume rate.

And, with continued reference to fig. 4, the constructed ponding index evaluation system also provides the weight coefficient of each layer of the system, and also provides a correction method of the weight coefficient, namely, the weight coefficient of each layer in the ponding index evaluation system can be modified according to the actual situation of the city.

Specifically, when the index layer can obtain all the evaluation factor data, the ponding index evaluation system is determined according to fig. 4; if there is missing evaluation factor data in the index layer, for example, the volume rate evaluation factor data is missing, the land property element index does not consider the volume rate evaluation factor any more, and the land property element index weight and the flood element index weight are corrected, the land property element index weight is corrected to 30% -20% ×30% =24%, and the flood element index weight is corrected to 70+20% ×30% =76%.

Correspondingly, when other average factors are missing, the weight can be revised according to the steps, and the details are not repeated here.

Finally, the water accumulation index can be calculated by the following formula:

A=B ₁ *70%+B ₂ *30%；

wherein ,Arepresenting the ponding index;B ₁ representing flood factor indexes;B ₂ the index of the property element of the representation land.

and ,B ₁ the calculation formula of (2) is as follows:

B ₁ =C ₁₁ *70%+C ₁₂ *20%+C ₁₃ %*10%；

B ₂ =C ₂₁ *10%+C ₂₂ *70%+C ₂₃ %*20%；

wherein ,C ₁₁ indicating the depth of accumulated water Evaluating a risk value of the factor;C ₁₂ a risk value representing an accumulated water accumulation time evaluation factor;C ₁₃ a risk value representing the integrated flow rate assessment factor;C ₂₁ a risk value representing a gradient evaluation factor;C ₂₂ a risk value representing a land use type evaluation factor;C ₂₃ a risk value representing the volume rate evaluation factor.

Step S230, the monitoring and early warning platform generates corresponding warning information when determining that at least one of the equipment operation index data, the pipe network health degree data, the first target statistical classification result data and the first ponding degree classification result data of each city block accords with the corresponding warning condition.

It should be understood that the specific alarm condition corresponding to each data may be set according to actual requirements, and embodiments of the present application are not limited thereto.

For example, upon determining that the score of the device operational index data is less than 60 minutes, a device operational alert prompt may be generated; for another example, when the score of the pipe network health degree data is less than 40 hours, a pipe network health warning prompt is generated.

In addition, after the alarm information is generated, the push platform can also send alarm event processing prompt information to event processing personnel, the event processing personnel can process the alarm event according to the assigned work task, and the user can also log in the client to check the alarm prompt and the alarm event processing condition in real time.

It should be noted that, although the steps S210 to S230 show the execution subjects of the respective steps, those skilled in the art should understand that the execution subjects of the respective steps may be modified according to actual requirements, and the embodiment of the present application is not limited thereto.

The urban flood warning system of the application has the functions of warning and the like, and the specific process can be described in the following.

Referring to fig. 5, fig. 5 is a flowchart illustrating a related method of an early warning part in an early warning method according to an embodiment of the present application. Specifically, the related method of the early warning part comprises the following steps:

step S510, the monitoring equipment collects real-time water level data of the water level station, real-time flow data of the flow station, real-time river network water level flow data and forecast rainfall of the target city.

Step S520, a river network water level prediction module of the Internet of things platform matches real-time river network water level data with a pre-constructed river channel reservoir capacity curve to obtain the current water storage volume of the river channel, calculates the future water storage volume of the river channel by using the current water storage volume of the river channel and the forecast rainfall, and matches the future water storage volume of the river channel with the river channel reservoir capacity curve again to obtain the future water level of the river network. The river reservoir capacity curve can be a water level-volume relation curve.

Specifically, under the condition that the river network is formed by a plurality of river channels, each river channel can be fitted with a river channel reservoir capacity curve according to the river channel water storage volumes corresponding to different water levels of the river channel.

And the current water storage volume of the river channel can be obtained by matching the real-time river network water level data with the fitted river channel storage capacity curve according to the real-time river network water level data acquired by the monitoring equipment (or, the current water storage volume of the river channel is calculated by utilizing the river channel storage capacity curve).

And according to the forecast rainfall received by the meteorological data receiving module of the Internet of things platform, the future water storage volume of the river channel can be calculated according to the following formula:

；

wherein ,V _t representing the future water storage volume of the river channel;Vrepresenting the current water storage volume of the river channel;Rrepresenting a forecast rainfall;representing the surface area of the river channel; />The net flow coefficient is represented as such,the value can be 0-1.

And after the future water storage volume of the river is determined, the future water storage volume of the river and the fitted river storage capacity curve can be matched to obtain the future water level of the river network. Wherein, the future water level of the river network refers to the boundary condition of the river network.

In step S530, the internet of things platform predicts the real-time water level data of the water level station, the real-time flow data of the flow station, the real-time river network water level flow data, the real-time liquid level data of the underground pipe network and the forecast rainfall of the target city based on the pre-constructed two-dimensional hydrodynamic force full-coupling model to obtain the predicted data. The prediction data comprise the future urban river network water level, the future surface water accumulation depth, the future accumulated water accumulation time and the future comprehensive flow rate. Wherein the two-dimensional hydrodynamic force full-coupling model is obtained based on a one-dimensional river network model for simulating river network water flow, and the river network future water level is provided as a boundary condition to the one-dimensional river network model; the water level of the future urban river network refers to the water level at each section.

It should be understood that the process of constructing the two-dimensional hydrodynamic full-coupling model may be set according to actual requirements, and embodiments of the present application are not limited thereto.

Optionally, constructing a one-dimensional hydrodynamic model; the one-dimensional hydrodynamic model comprises a one-dimensional river network model for simulating river network water flow and a one-dimensional pipe network model for simulating urban underground pipe network water flow; constructing a two-dimensional hydrodynamic model for simulating urban surface water flow; the method comprises the steps of executing one-dimensional model coupling of a river network and urban underground on a one-dimensional river network model and a one-dimensional pipe network model, executing two-dimensional model coupling of the river network and urban earth surface on a one-dimensional river network model and a two-dimensional hydrodynamic model, and executing two-dimensional model coupling of the urban underground earth surface on a one-dimensional pipe network model and a two-dimensional hydrodynamic model.

Wherein, the control equation of the one-dimensional river network model can adopt the san Vena equation, and is specifically as follows:

；

wherein ,Brepresenting the water surface width at the section of the river network;Zrepresenting the water level at the section of the river network;Qrepresenting the flow rate at the section of the river network;Arepresenting the water passing area at the section of the river network;trepresenting time;xrepresenting the river course distance;qrepresenting a source item, and the source item includes a lateral inflow; gRepresenting gravitational acceleration;S _f representing the loss of momentum along the way and its expression；RRepresents the hydraulic radius;nrepresenting the manning roughness coefficient.

According to the application, a Godunov finite volume method is adopted to solve a one-dimensional river network control equation, MUSCL-Hancock prediction correction is adopted to ensure that the solution has second-order precision in time and space, the numerical flux (mass flux and momentum flux) at a unit interface is adopted to solve an approximate Riemann solution in an HLL format, and the solution method has good shock wave capturing capability and can simulate constant flow and non-constant flow under the condition of complex topography. In addition, the method adopts the OpenMP parallel computing technology when constructing the one-dimensional river network model, and is used for improving the computing speed of the model. Meanwhile, the one-dimensional river network model also receives a river network water level prediction result of the river network water level prediction module as a water level boundary condition. The technology and method for constructing the one-dimensional river network model are known in the academy, so the application is not described in detail.

And, the control equation of the one-dimensional pipe network model may include a continuous equation and a momentum equation, which are specifically as follows:

；

wherein ,Arepresenting the cross-sectional area of the water;Qrepresenting the flow rate at the water cross section; HRepresenting the head at the water cross section;trepresenting time;xrepresenting the distance along the pipeline network;grepresenting gravitational acceleration;S _f representing the loss of momentum along the way and its expression；RRepresents the hydraulic radius;nrepresenting the manning roughness coefficient.

In addition, the one-dimensional pipe network model constructed in the application can adopt a SWMM urban drainage system pipe network model, and because the model is water conservancy professional commercial software, the control equation solving technology and method are known in the academy, and therefore, the application is not described in detail.

And a control equation of the two-dimensional hydrodynamic model adopts a two-dimensional shallow water equation, and is specifically as follows:

；

wherein ,hrepresenting the water depth;uis thatxA directional flow rate;vis thatyA directional flow rate;ttime is;ggravitational acceleration;qis a quality source item, comprising a point source and lateral inflow;s _x ands _y is a momentum source item. The method comprises the steps of,s _x ands _y the expression of (2) is:

；

wherein ,Z_b Representing the elevation of the bed surface bottom; c (C) _f The friction coefficient is expressed as，nRepresenting the Manning roughness coefficient; />Represents the density of water; />Representing the density of air;Pis the atmospheric pressure of the water surface;C _D drag coefficient for wind stress;V _w is the wind speed at 10m above the water surface;V _x at a wind speed of 10m above the water surfacexA wind speed component in the direction; V _y At a wind speed of 10m above the water surfaceyA wind speed component in the direction;fis a Coriolis coefficient, and the expression is +.>，，/>Is latitude.

In addition, the construction of the two-dimensional hydrodynamic model in the application can adopt an inclined triangular grid, and the grid has the advantage of adapting to two-dimensional terrains. The method for solving the control equation adopts a Godunov finite volume method, and adopts two steps of MUSCL-Hancock prediction correction to ensure that the solution has second-order precision in time and space, and the numerical flux (mass flux and momentum flux) at a cell interface solves the approximate Riemann solution adopting an HLLC format.

In addition, the method adopts a GPU parallel acceleration computing technology when constructing the two-dimensional hydrodynamic model, and is used for improving the computing speed of the model. Meanwhile, the two-dimensional hydrodynamic model receives weather forecast rainfall of the weather data receiving module as a quality source item. The technology and method used to construct the two-dimensional hydrodynamic model are known to the academy, and therefore, the application is not described in detail.

And for executing one-dimensional model coupling of river network and urban underground on the one-dimensional river network model and the one-dimensional pipe network model, the application provides a coupling method for river network and pipe network, which carries out water flow exchange through one-dimensional river network section and one-dimensional pipe network discharge, realizes one-dimensional coupling of river network and urban underground, and comprises the following steps:

Connecting an outlet of the one-dimensional pipe network model with a target section of the one-dimensional river network model to serve as a coupling node; the coupling node comprises a pipe network outlet and a river network section;

intermediate steps: the one-dimensional river network model provides a water level boundary for the one-dimensional pipe network model, and provides the section water level of the river network section at the coupling node as a boundary condition of a pipe network outlet for the one-dimensional pipe network model;

according to the water level boundary, updating the one-dimensional pipe network model from the current time step to the next time step;

the one-dimensional pipe network model provides a lateral inflow source for the one-dimensional river network model, and the pipe network drainage flow is used as the lateral inflow source of the river network section at the coupling node according to the pipe network drainage flow at the coupling node after the one-dimensional pipe network model is updated;

according to the lateral inflow source, the one-dimensional river network model is updated from the current time step to the next time step, and then the middle step is skipped to circularly calculate until the calculation is completed.

In addition, for two steps of executing two-dimensional model coupling of the river network and the urban earth surface on the one-dimensional river network model and the two-dimensional hydrodynamic model and executing two-dimensional model coupling of the urban underground earth surface on the one-dimensional pipe network model and the two-dimensional hydrodynamic model, the two-dimensional model coupling of the river network and the urban earth surface and the two-dimensional model coupling of the urban underground earth surface are known in the academic circles, the coupling technology and the method adopted in the two-dimensional model coupling are not described in detail.

In addition, on the basis of constructing the two-dimensional hydrodynamic force full-coupling model, the real-time water level data of the water level station, the real-time flow data of the flow station, the real-time river network water level flow data, the real-time liquid level data of the underground pipe network and the forecast rainfall of the target city can be predicted based on the pre-constructed two-dimensional hydrodynamic force full-coupling model so as to obtain prediction data.

Therefore, after the construction of the two-dimensional hydrodynamic force full-coupling model is completed, the two-dimensional hydrodynamic force full-coupling model after the construction can be utilized for prediction processing.

In step S540, the internet of things platform performs statistical classification of urban water depth based on the future urban river network water depth and the future surface water depth obtained by the future urban river network water level to obtain second target statistical classification result data, calculates a second water accumulation index by using the future surface water depth, the future accumulated water accumulation time and the future integrated flow rate, and determines second water accumulation degree classification result data of each urban block based on the second water accumulation index. The second target statistical classification result data refers to the statistical classification result data of the predicted urban ponding depth exceeding the warning line.

It should be understood that, the specific process of the internet of things platform for performing statistical classification of urban water accumulation depth based on the future urban river network water depth and the future surface water accumulation depth obtained by the future urban river network water level to obtain the second target statistical classification result data may be set according to actual requirements, and the embodiment of the application is not limited thereto.

Optionally, the internet of things platform may obtain a future urban river network water depth based on the future urban river network water level, and perform statistical classification of the urban water depth based on the future urban river network water depth and the future surface water depth, so as to obtain second target statistical classification result data.

It should be understood that the calculation process of the second water accumulation index may refer to the calculation process of the water accumulation index, and will not be repeated here.

Step S550, the monitoring and early warning platform generates corresponding early warning prompt information when determining that the second target statistical classification result data and the second ponding degree classification result data of each city block meet the corresponding early warning conditions.

In addition, after the early warning information is generated, the pushing platform can also send early warning event processing prompt information to event processing personnel, the event processing personnel can process the early warning event according to the assigned work task, and the user can also log in the client to check the early warning prompt and the early warning event processing condition in real time.

It should be noted that, although the steps S510 to S550 show the execution subjects of the respective steps, those skilled in the art should understand that the execution subjects of the respective steps may be modified according to actual requirements, and the embodiment of the present application is not limited thereto.

In summary, the embodiment of the application can more intuitively reflect the running state of the equipment through the quantized index of the running index of the equipment, and improve the maintenance and management efficiency of the monitoring equipment by a management department.

In addition, the embodiment of the application also concentrates monitoring, alarming and early warning work into one system, and optimizes the multi-department collaborative management flow.

And the two-dimensional hydrodynamic force full-coupling model comprehensively considers the mutual water flow exchange among the urban river network, the underground pipe network and the earth surface, and improves the accuracy of urban flood disaster early warning.

It should be understood that the foregoing method for warning urban flood is merely exemplary, and those skilled in the art may make various modifications according to actual needs, and the solutions after the modifications also belong to the protection scope of the present application.

It should be noted that, in the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described as different from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other. For the apparatus class embodiments, the description is relatively simple as it is substantially similar to the method embodiments, and reference is made to the description of the method embodiments for relevant points.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes. It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.

Claims

1. The urban flood warning method is characterized by comprising the following steps of:

acquiring working state data of monitoring equipment in a target city, real-time liquid level data of a underground pipe network, real-time river network water level data and real-time pavement water level flow velocity data; the real-time road surface water level flow velocity data comprise real-time road surface water level data and real-time surface water accumulation depth;

Calculating equipment operation index data by using the working state data, calculating pipe network health degree data by using the real-time liquid level data, carrying out statistical classification of urban water accumulation depth on the basis of the real-time river network water level data and the real-time surface water accumulation depth to obtain first target statistical classification result data, calculating a first water accumulation index serving as an evaluation index reflecting the surface water accumulation degree by using the real-time pavement water level flow rate data, carrying out blockwise classification on the urban surface water accumulation degree to obtain a plurality of urban blocks, and determining first water accumulation degree classification result data of each urban block in the plurality of urban blocks on the basis of the first water accumulation index; the first target statistical classification result data refers to statistical classification result data of real-time urban water accumulation depth exceeding a warning line, the value range of the first water accumulation index can be 0-100, the first water accumulation index is a primary water accumulation degree when the value of the first water accumulation index is 0-20, the first water accumulation index is a secondary water accumulation degree when the value of the first water accumulation index is 20-40, the first water accumulation index is a tertiary water accumulation degree when the value of the first water accumulation index is 40-60, the first water accumulation index is a quaternary water accumulation degree when the value of the first water accumulation index is 60-80, the first water accumulation index is a five-level water accumulation degree when the value of the first water accumulation index is 80-100, the first water accumulation index is obtained by constructing a water accumulation index evaluation system, the water accumulation index evaluation system is divided into three layers, namely a target layer, a criterion layer and an index layer, wherein each index in the criterion layer is obtained by weighting calculation, and each index in the criterion layer is obtained by weighting calculation of each factor value in the corresponding index layer;

Generating corresponding alarm information when at least one of the equipment operation index data, the pipe network health degree data, the first target statistical classification result data and the first ponding degree classification result data of each city block is determined to accord with the corresponding alarm condition;

the real-time pavement water level flow rate data comprise real-time accumulated water depth, real-time accumulated water accumulation time and real-time comprehensive flow rate, the first accumulated water index is determined based on a first flood factor index value and a first land property factor index value, and the first land property factor index value is determined through gradient, land utilization type and volume rate;

wherein, utilize real-time road surface water level velocity of flow data, calculate first ponding index, include:

respectively determining a risk value corresponding to the real-time accumulated water depth, a risk value corresponding to the real-time accumulated water time and a risk value corresponding to the real-time integrated flow rate, and carrying out weighted summation on the risk value corresponding to the real-time accumulated water depth, the risk value corresponding to the real-time accumulated water time and the risk value corresponding to the real-time integrated flow rate to obtain the first flood factor index value; wherein the real-time accumulated water depth, the real-time accumulated water time and the real-time comprehensive flow rate are respectively the real-time accumulated water depth, the real-time accumulated water time and the real-time comprehensive flow rate which are obtained by monitoring equipment;

Respectively determining a risk value corresponding to the gradient, a risk value corresponding to the land use type and a risk value corresponding to the volume rate, and carrying out weighted summation on the risk value corresponding to the gradient, the risk value corresponding to the land use type and the risk value corresponding to the volume rate to obtain the first land property element index value;

and carrying out weighted summation on the first flood factor index value and the first land property factor index value to obtain the first ponding index.

2. The warning method of claim 1, wherein the operating state data comprises an operating state of the device and a duration of operation of the device;

wherein calculating device operation index data using the operating state data includes:

respectively determining a score value of the working state of the equipment and a coefficient corresponding to the continuous working time of the equipment;

and taking the product value of the coefficient corresponding to the continuous working time length of the equipment and the score value of the working state of the equipment as an equipment operation index.

3. The warning method according to claim 1, wherein the calculation formula of the pipe network health degree data is as follows:

；

wherein ,Grepresenting the health degree data of the pipe network; G ₁ The drainage efficiency of the pipe network is represented, and the drainage efficiency of the pipe network is the ratio of the real-time liquid level data to the pipe diameter of the underground pipe network;N ₁ the number of indexes for influencing the drainage efficiency of the pipe network is represented;G ₂ representing the environmental structure of a pipe network;N ₂ the number of indexes affecting the environment structure of the pipe network is represented;G ₃ representing pipe network defects;N ₃ the number of indexes affecting the pipe network defects is represented;G ₄ representing pipe network fouling;N ₄ the number of indices affecting the pipe network fouling is indicated.

4. A warning method according to any one of claims 1 to 3, characterized in that the warning method further comprises:

acquiring real-time water level data of a water level station, real-time flow data of a flow station, real-time river network water level flow data and forecast rainfall of the target city;

matching the real-time river network water level data with a pre-constructed river channel reservoir capacity curve to obtain the current water storage volume of the river channel;

calculating the future water storage volume of the river by utilizing the current water storage volume of the river and the forecast rainfall, and matching the future water storage volume of the river with the river storage capacity curve again to obtain the future water level of the river network;

based on a pre-constructed two-dimensional hydrodynamic full-coupling model, carrying out prediction processing on the real-time water level data of the water level station, the real-time flow data of the flow station, the real-time river network water level flow data, the real-time liquid level data of the underground pipe network and the predicted rainfall of the target city to obtain prediction data; the prediction data comprise a future urban river network water level, a future surface water accumulation depth, a future accumulated water accumulation time and a future comprehensive flow rate; wherein the two-dimensional hydrodynamic force full-coupling model is obtained based on a one-dimensional river network model for simulating river network water flow, and the river network future water level is provided to the one-dimensional river network model as a boundary condition;

Carrying out statistical classification of urban water accumulation depth based on the future urban river network water depth obtained through the future urban river network water level and the future surface water accumulation depth to obtain second target statistical classification result data, calculating a second water accumulation index by utilizing the future surface water accumulation depth, the future accumulated water accumulation time and the future comprehensive flow rate, and determining second water accumulation degree classification result data of each urban block based on the second water accumulation index; the second target statistical classification result data refers to statistical classification result data of predicted urban ponding depth exceeding a warning line;

and when at least one of the second target statistical classification result data and the second ponding degree classification result data of each city block meets the corresponding early warning condition, generating corresponding early warning prompt information.

5. The warning method according to claim 4, wherein the construction process of the two-dimensional hydrodynamic full-coupling model comprises:

constructing a one-dimensional hydrodynamic model; the one-dimensional hydrodynamic model also comprises a one-dimensional pipe network model for simulating urban underground pipe network water flow;

Constructing a two-dimensional hydrodynamic model for simulating urban surface water flow;

executing one-dimensional model coupling of river network and urban underground on the one-dimensional river network model and the one-dimensional pipe network model, executing two-dimensional model coupling of river network and urban earth surface on the one-dimensional river network model and the two-dimensional hydrodynamic model, and executing two-dimensional model coupling of urban underground earth surface on the one-dimensional pipe network model and the two-dimensional hydrodynamic model.

6. The warning method according to claim 5, wherein the one-dimensional river network model comprises:

；

wherein ,Brepresenting the water surface width at the section of the river network;Zrepresenting the water level at the section of the river network;Qrepresenting the flow rate at the section of the river network;Arepresenting the water passing area at the section of the river network;trepresenting time;xrepresenting the river course distance;qrepresenting a source item, and the source item includes a lateral inflow;grepresenting gravitational acceleration;S _f representing the loss of momentum along the way.

7. The warning method of claim 5, wherein the one-dimensional pipe network model comprises:

；

wherein ,Arepresenting the cross-sectional area of the water;Qrepresenting the flow rate at the water cross section;Hrepresenting the head at the water cross section; tRepresenting time;xrepresenting the distance along the pipeline network;grepresenting gravitational acceleration;S _f representing the loss of momentum along the way.

8. The warning method of claim 5, wherein said performing one-dimensional model coupling of river networks to urban underground for said one-dimensional river network model and said one-dimensional pipe network model comprises:

connecting an outlet of the one-dimensional pipe network model with a target section of the one-dimensional river network model to serve as a coupling node; wherein the coupling node comprises a pipe network outlet and a river network section;

intermediate steps: the one-dimensional river network model provides a water level boundary for the one-dimensional pipe network model, and the section water level of the river network section at the coupling node is provided as a boundary condition of a pipe network outlet for the one-dimensional pipe network model;

according to the water level boundary, the one-dimensional pipe network model is updated from the current time step to the next time step;

the one-dimensional pipe network model provides a lateral inflow source for the one-dimensional river network model, and the pipe network drainage flow is used as the lateral inflow source of the river network section at the coupling node according to the pipe network drainage flow at the coupling node updated by the one-dimensional pipe network model;

and according to the lateral inflow source, the one-dimensional river network model is updated from the current time step to the next time step, and then the middle step is skipped to circularly calculate until the calculation is completed.

9. An urban flood warning system, comprising:

the monitoring equipment is used for acquiring working state data of the monitoring equipment in the target city, real-time liquid level data of the underground pipe network, real-time river network water level data and real-time pavement water level flow velocity data; the real-time road surface water level flow velocity data comprise real-time road surface water level data and real-time surface water accumulation depth;

the internet of things platform is used for calculating equipment operation index data by using the working state data, calculating pipe network health degree data by using the real-time liquid level data, carrying out statistical classification on urban water accumulation depth on the basis of the real-time river network water level data and the real-time surface water accumulation depth to obtain first target statistical classification result data, calculating a first water accumulation index which is used as an evaluation index for reflecting the surface water accumulation degree by using the real-time pavement water level flow rate data, carrying out blockwise classification on the urban surface water accumulation degree to obtain a plurality of urban blocks, and determining first water accumulation degree classification result data of each urban block in the plurality of urban blocks on the basis of the first water accumulation index; the first target statistical classification result data refers to statistical classification result data of real-time urban water accumulation depth exceeding a warning line, the value range of the first water accumulation index can be 0-100, the first water accumulation index is a primary water accumulation degree when the value of the first water accumulation index is 0-20, the first water accumulation index is a secondary water accumulation degree when the value of the first water accumulation index is 20-40, the first water accumulation index is a tertiary water accumulation degree when the value of the first water accumulation index is 40-60, the first water accumulation index is a quaternary water accumulation degree when the value of the first water accumulation index is 60-80, the first water accumulation index is a five-level water accumulation degree when the value of the first water accumulation index is 80-100, the first water accumulation index is obtained by constructing a water accumulation index evaluation system, the water accumulation index evaluation system is divided into three layers, namely a target layer, a criterion layer and an index layer, wherein each index in the criterion layer is obtained by weighting calculation, and each index in the criterion layer is obtained by weighting calculation of each factor value in the corresponding index layer;

The monitoring and early warning platform is used for generating corresponding warning information when at least one of the equipment operation index data, the pipe network health degree data, the first target statistical classification result data and the first ponding degree classification result data of each city block is determined to accord with the corresponding warning condition;

the internet of things platform is specifically configured to: respectively determining a risk value corresponding to the real-time accumulated water depth, a risk value corresponding to the real-time accumulated water time and a risk value corresponding to the real-time integrated flow rate, and carrying out weighted summation on the risk value corresponding to the real-time accumulated water depth, the risk value corresponding to the real-time accumulated water time and the risk value corresponding to the real-time integrated flow rate to obtain the first flood factor index value; wherein the real-time accumulated water depth, the real-time accumulated water time and the real-time comprehensive flow rate are respectively the real-time accumulated water depth, the real-time accumulated water time and the real-time comprehensive flow rate which are obtained by monitoring equipment; respectively determining a risk value corresponding to the gradient, a risk value corresponding to the land use type and a risk value corresponding to the volume rate, and carrying out weighted summation on the risk value corresponding to the gradient, the risk value corresponding to the land use type and the risk value corresponding to the volume rate to obtain the first land property element index value; and carrying out weighted summation on the first flood factor index value and the first land property factor index value to obtain the first ponding index.