CN117173871A

CN117173871A - Flood prevention monitoring method and system

Info

Publication number: CN117173871A
Application number: CN202311439336.3A
Authority: CN
Inventors: 张�荣; 张桐
Original assignee: Zhaohu Jiangsu Smart Technology Co ltd
Current assignee: Zhaohu Jiangsu Smart Technology Co ltd
Priority date: 2023-11-01
Filing date: 2023-11-01
Publication date: 2023-12-05
Anticipated expiration: 2043-11-01
Also published as: CN117173871B

Abstract

The invention provides a flood prevention monitoring method and a flood prevention monitoring system, wherein the method comprises the following steps: collecting actual rainfall, comparing the actual rainfall with predicted data of rainfall of various models, and selecting a final rainfall prediction model according to comparison results; dividing road information of a monitoring area into a plurality of subareas; the plurality of sub-regions includes a first sub-region and a second sub-region; acquiring the road surface ponding information of the subareas and calculating the danger coefficient of the second subarea; obtaining a second sub-region comprehensive index according to the risk coefficient; acquiring a second region danger level according to the comprehensive index; the monitoring system comprises an acquisition module, a model selection module, a region division module, a risk coefficient acquisition module, a risk level determination module and an early warning module, and the rainfall, the water accumulation depth, the risk level and the like of a monitoring region are accurately monitored and early warned by the method and the system, so that the efficiency and the quality of flood prevention work are improved.

Description

Flood prevention monitoring method and system

Technical Field

The invention relates to the technical field of flood control monitoring, in particular to a flood control monitoring method and a flood control monitoring system.

Background

With the acceleration of climate change and urbanization, flood disasters frequently become a global problem. Flood control monitoring methods are widely studied and applied in order to improve flood control and reduce losses due to flooding. However, in some cases, there may be problems such as insufficient prediction accuracy, insufficient precision, and poor timeliness, and in particular, there is a limitation in fine management of some specific areas.

Disclosure of Invention

The invention provides a flood prevention monitoring method and a flood prevention monitoring system, which comprehensively apply various models and technical means, accurately monitor and early warn rainfall, ponding depth, danger level and the like of a monitored area, and generate and release evacuation instructions according to real-time position information, so that the efficiency and quality of flood prevention work are improved.

The invention provides a flood prevention monitoring method, which comprises the following steps:

s1, monitoring rainfall information by a preset sampling frequency and adjusting the sampling frequency according to the actual rainfall;

s2, respectively acquiring prediction data of rainfall at different places through multiple models; comparing the actual rainfall with predicted rainfall data to obtain a comparison result; selecting a final rainfall prediction model according to the comparison result;

S3, obtaining and updating road and ground information of the monitoring area through a geographic information system; dividing road information of a monitoring area into a plurality of first subareas; dividing the plurality of first subregions into second subregions;

s4, acquiring pavement ponding information of the second subarea through a traffic monitoring system; obtaining a risk coefficient of the second subarea through road surface ponding information;

s5, predicting the pavement ponding depth of the next period of time corresponding to the second subarea according to the ponding depth of the current second subarea and the rainfall prediction result of the corresponding second subarea; obtaining a second sub-region comprehensive index according to the pavement ponding depth and the second sub-region danger coefficient; acquiring a second sub-region danger level according to the second sub-region comprehensive index;

further, a flood prevention monitoring method, the S1 includes:

according to the climate information and the historical rainfall information of the monitoring area; dividing a plurality of stages; the multiple stages are divided into a rainy stage, a normal stage and a rainless stage;

according to different stages, respectively setting sampling frequencies of rainfall monitoring stations, and monitoring rainfall information; and adjusting the sampling frequency according to the actual monitoring result and the prediction information.

Further, a flood prevention monitoring method, the S2 includes:

obtaining prediction data of rainfall at different places through multiple models; the prediction data comprise average rainfall, total rainfall and maximum rainfall in unit time in a preset time period;

monitoring actual rainfall data through a rainfall monitoring station; arranging sampling points of different monitoring stations according to a sampling time sequence, and grouping according to a preset time period; counting the actual rainfall in each time period of each monitoring point; the actual rainfall comprises actual average rainfall, actual total rainfall and actual maximum rainfall in unit time in a preset time period;

comparing the actual rainfall with predicted rainfall data to obtain a comparison result; and selecting a final rainfall prediction model according to the comparison result.

Further, in the flood prevention monitoring method, the actual rainfall is compared with predicted rainfall data to obtain a comparison result; the selecting of the final rainfall prediction model according to the comparison result comprises the following steps:

the comparison result comprises a first difference value、/>、/>；

First difference:

，

second difference:

，

third difference:

，

wherein the method comprises the steps ofThe actual average rainfall of a certain prediction model in a certain preset time period of the same monitoring station; / >The predicted average rainfall amount of a preset time period for the corresponding prediction model; />The actual total rainfall of the same monitoring station in a certain preset time period corresponds to the prediction model; />The method comprises the steps of presetting a predicted total rainfall for a time period corresponding to a prediction model; />The actual maximum rainfall of unit time in a certain preset time period of the same monitoring station is corresponding to the prediction model; />Presetting a maximum rainfall in a prediction unit time of a time period for a corresponding prediction model; />The number of the preset time periods is set; />The number of the monitoring stations; />Obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>A maximum of the values of (2); />Obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>The minimum of the values of (2); />Obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>A maximum of the values of (2); />Obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>The minimum of the values of (2); />Obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>A maximum of the values of (2);obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>The minimum of the values of (2); />、/>Is a weight, in the range of (0, 1);

obtaining a comprehensive difference value according to the first difference value, the second difference value and the third difference value;

The comprehensive difference is as follows:，

wherein,、/>、/>coefficients, ranges (0, 1);

taking the first two models corresponding to the value with the minimum comprehensive difference value as rainfall prediction models;

and taking the maximum value of each of the average rainfall, the total rainfall and the maximum rainfall in unit time in a period of time predicted in the two selected rainfall prediction models as a final rainfall prediction result.

Further, a flood prevention monitoring method, the S3 includes:

obtaining and updating road and ground information of a monitoring area through a geographic information system;

dividing road information of a monitoring area into a plurality of first subareas; the first subarea comprises a ground road, a ground parking lot, an underground parking lot and an underground passage;

dividing the first subarea into a plurality of second subareas through position information;

the second sub-region is marked with a plurality of pothole regions.

Further, a flood prevention monitoring method, the step S4 includes:

acquiring pavement ponding information of each second subarea through a traffic monitoring system; obtaining a dangerous coefficient of the second area through the road surface ponding information; the ponding information comprises ponding depth;

，

a risk factor for the second zone;

the water accumulation depth of the second subarea under the preset rainfall threshold value is set;

The average value of the accumulated water depth of the pavement of all the second subareas under the preset rainfall threshold value is set;

the number of the hollow areas in the second subarea;

for the depth of each hollow area, +.>The area of each hollow area; />Judging a depth threshold value of the hollow area; />Judging the area threshold value of the hollow area; />Coefficients, ranges (0, 1);

and establishing a corresponding function relation between the accumulated water depth and the rainfall of each sub-area road surface through historical data.

Further, a flood prevention monitoring method, the step S5 includes:

predicting the pavement water accumulation depth of the second subarea in a next period of time according to the water accumulation depth of the current second subarea and the rainfall prediction result of the corresponding second subarea;，

to predict the second sub-area road surface water depth, +.>The current water accumulation depth of the second subarea; />For the estimated rainfall period->Average rainfall per unit time is estimated; />The accumulated water depth value is obtained according to the corresponding functional relation of the accumulated water depth of the pavement of the corresponding second subarea and the rainfall;

obtaining a second sub-region comprehensive index according to the pavement ponding depth and the second sub-region danger coefficient;，

wherein,for the pavement water accumulation depth of the second subarea, < > in- >Is a dangerous depth threshold; />Is constant, range (0, 1),>is a risk factor for the second subregion;

and acquiring the danger level of the second subarea according to the comprehensive index of the second subarea.

Further, a flood prevention monitoring method, the step S6 includes:

acquiring real-time position information of personnel and vehicles;

according to the real-time position information of the vehicle and the personnel, evaluating the ponding depth and the danger level of the area;

generating a corresponding evacuation instruction according to the estimated ponding depth and the risk level;

sending the evacuation instruction to the user in various modes;

continuously tracking the position information of personnel and vehicles, and the rainfall, the ponding depth and the danger level of the area;

and regenerating an evacuation instruction according to the latest data, and timely sending the evacuation instruction to a user.

The invention provides a flood prevention monitoring system, which comprises:

and the acquisition module is used for: monitoring rainfall information by presetting a sampling frequency and adjusting the sampling frequency according to the actual rainfall;

model selection module: respectively acquiring prediction data of rainfall at different places through multiple models; comparing the actual rainfall with predicted rainfall data to obtain a comparison result; selecting a final rainfall prediction model according to the comparison result;

Region dividing module: obtaining and updating road and ground information of a monitoring area through a geographic information system; dividing road information of a monitoring area into a plurality of first subareas; dividing the plurality of first subregions into second subregions;

the risk coefficient acquisition module: acquiring pavement ponding information of the second subarea through a traffic monitoring system; obtaining a risk coefficient of the second subarea through road surface ponding information;

the risk level determining module: predicting the pavement water accumulation depth of the next period of time corresponding to the second subarea according to the water accumulation depth of the current second subarea and the rainfall prediction result of the corresponding second subarea; obtaining a second sub-region comprehensive index according to the pavement ponding depth and the second sub-region danger coefficient; acquiring a second sub-region danger level according to the second sub-region comprehensive index;

and the early warning module is used for: acquiring position information of personnel and vehicles, and according to the position information of the vehicles and the personnel; and sending the ponding depth and the danger level corresponding to the nearby second subarea to a user and evacuating instructions.

The invention has the beneficial effects that: the invention provides a flood prevention monitoring method and a flood prevention monitoring system. This helps to improve the accuracy of rainfall prediction. And acquiring the road surface ponding information of the subareas through the traffic monitoring system, and calculating the danger coefficient of the second subarea. The method enables rapid identification and evaluation of dangerous areas, and provides important basis for flood control decisions. According to the method, the pavement ponding depth of the second subarea in a period of time is predicted according to the ponding depth of the current second subarea and the rainfall prediction result. This provides the possibility to predict and prevent possible flooded areas in advance. By acquiring the position information of the personnel and the vehicles, the method can send the ponding depth, the danger level and the evacuation instruction corresponding to the nearby second subarea to the user. This helps to improve the safety of personnel and vehicles, especially in emergency situations. The method combines various information such as a Geographic Information System (GIS) and a traffic monitoring system, and realizes comprehensive management of the monitored area. This helps to improve the accuracy and effectiveness of the flood control decisions. By acquiring the accurate information of the dangerous area, the method can optimize resource allocation and ensure that flood prevention rescue work is performed more efficiently and timely. By accurately predicting the dangerous area, providing timely early warning and implementing effective evacuation instructions, the method can improve the confidence of the public on flood control work and increase the trust of the public on governments and related institutions. In summary, the flood prevention monitoring method improves the accuracy of rainfall prediction, the rapid identification of dangerous areas, the accurate prediction of the depth of accumulated water on the road surface, the safety of personnel and vehicles, the comprehensive management of monitoring areas, the optimization of resource allocation and the improvement of public confidence by comprehensively applying various technologies and means.

Drawings

Fig. 1 is a schematic diagram of a flood control monitoring method according to the present application.

Detailed Description

In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, and the described embodiments are merely some, rather than all, embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

The embodiment provides a flood prevention monitoring method, which comprises the following steps:

the working principle of the technical scheme is as follows: firstly, presetting a sampling frequency to monitor rainfall information of the subareas, and adjusting the sampling frequency according to the actual rainfall. This step is to ensure accuracy and real-time of the rainfall data. And then respectively predicting the rainfall at different places through a plurality of models (such as a statistical model, a physical model, a neural network model, a deep learning model and the like), and comparing the predicted data with the actual rainfall to obtain a comparison result. And according to the comparison result, a final rainfall prediction model can be selected. This step is to improve the accuracy of rainfall prediction. Next, road and ground information of the monitored area is acquired and updated through a Geographic Information System (GIS). Such information may include the location, length, width, traffic conditions, etc. of the road. With this information, the road information of the monitoring area can be divided into a plurality of sub-areas. The sub-regions include a first sub-region and a second sub-region. And then, acquiring the road surface ponding information of the second subarea through a traffic monitoring system. And calculating the risk coefficient of the second subarea through the road surface ponding information. This step is to evaluate the effect of road surface water on traffic safety. And then, according to the accumulated water depth of the current second subarea and the rainfall prediction result of the corresponding area, predicting the accumulated water depth of the road surface of the second subarea for a period of time. And then according to the pavement ponding depth and the danger coefficient of the second subarea, the comprehensive index of the second subarea can be obtained. Based on this comprehensive index, the risk level of the second region can be obtained. Finally, the position information of the person and the vehicle is acquired. According to the position information of the vehicle and the personnel, the ponding depth and the danger level corresponding to the nearby second subarea and evacuation instructions can be sent to the user. This step is to guide the user for safe evacuation.

The technical scheme has the effects that: according to the method, rainfall prediction data of different places are obtained through multiple models, and actual rainfall is compared with the prediction data to select a final rainfall prediction model. This helps to improve the accuracy of rainfall prediction. And acquiring the road surface ponding information of the subareas through the traffic monitoring system, and calculating the danger coefficient of the second subarea. The method enables rapid identification and evaluation of dangerous areas, and provides important basis for flood control decisions. According to the method, the pavement ponding depth of the second subarea in a period of time is predicted according to the ponding depth of the current second subarea and the rainfall prediction result. This provides the possibility to predict and prevent possible flooded areas in advance. By acquiring the position information of the personnel and the vehicles, the method can send the ponding depth, the danger level and the evacuation instruction corresponding to the nearby second subarea to the user. This helps to improve the safety of personnel and vehicles, especially in emergency situations. The method combines various information such as a Geographic Information System (GIS) and a traffic monitoring system, and realizes comprehensive management of the monitored area. This helps to improve the accuracy and effectiveness of the flood control decisions. By acquiring the accurate information of the dangerous area, the method can optimize resource allocation and ensure that flood prevention rescue work is performed more efficiently and timely. By accurately predicting the dangerous area, providing timely early warning and implementing effective evacuation instructions, the method can improve the confidence of the public to flood control work. In summary, the flood prevention monitoring method improves the accuracy of rainfall prediction, the rapid identification of dangerous areas, the accurate prediction of the depth of accumulated water on the road surface, the safety of personnel and vehicles, the comprehensive management of monitoring areas, the optimization of resource allocation and the improvement of public confidence by comprehensively applying various technologies and means. These advantages have important significance for flood prevention work, and are helpful for reducing the influence of flood disasters and guaranteeing the life and property safety of people.

In this embodiment, the flood prevention monitoring method, S1 includes:

according to different stages, respectively setting sampling frequencies of rainfall monitoring stations, and monitoring rainfall information; and adjusting the sampling frequency according to the actual monitoring result and the prediction information. The method comprises the steps that the preset sampling frequency of a rainy stage is larger than that of a normal stage, and the preset sampling frequency of the normal stage is larger than that of a less-rainy stage;

，

wherein,for the adjusted sampling frequency, +.>For a preset sampling frequency, +.>The actual maximum rainfall in unit time in the current preset time period; />For predicting the maximum rainfall per unit time within the current preset time period +.>The average rainfall in the predicted current preset time period is calculated; />The average rainfall in the historical record is in the same preset time period at the same stage in the historical record;

the rain amount statistical data can be divided into: from historical meteorological data, such as average rainfall on a monthly or quarterly basis, the seasons of relatively more, normal and less rain may be determined. In general, if the rainfall in one season is significantly higher than the other seasons, it can be classified as a rainy season; if the rainfall is moderate and relatively stable, it can be defined as a normal season; if the rainfall is significantly lower, it can be classified as a rainless season.

A rainy stage: in seasons or periods of high weather humidity and rain, it is called rainy season. This generally refers to periods of higher rainfall frequency and intensity, such as spring and summer. At this stage, the rainfall is greater and the risk of surface water is higher.

Normal phase: the normal season refers to a season or period in which the climate conditions are relatively smooth and the rainfall is moderate. This may mean that in autumn or in part winter, the rainfall is relatively low and there is no extreme climate change. At this stage, the risk of road surface water is relatively low.

Stage of less rain: drought periods refer to seasons or periods of dry climates with less rainfall. This generally means that in winter or early spring, etc., the rainfall is very limited and extreme drought conditions may occur. At this stage, the risk of surface water is low and may even be completely absent.

The working principle of the technical scheme is as follows: first, the monitoring area is divided into a plurality of stages according to climate information and historical rainfall information of the monitoring area. These phases include a rainy phase, a normal phase and a rainless phase. This division is to adjust the sampling frequency of the rainfall monitoring station according to different weather conditions, so that the rainfall information is better monitored. Then, according to different stages, the sampling frequency of the rainfall monitoring station is set respectively, and rainfall information is monitored. In the actual monitoring process, if the actual monitoring result and the predicted information have larger difference, the sampling frequency is adjusted in time to improve the accuracy and the reliability of monitoring, wherein the preset sampling frequency of the rainy stage is larger than that of the normal stage, and the preset sampling frequency of the normal stage is larger than that of the rainless stage.

Next, the formula is used to calculateCalculating an adjusted sampling frequencyThe formula considers a plurality of factors including the actual maximum rainfall per unit time in the current preset time period, the predicted maximum rainfall per unit time in the current preset time period, and the predicted average rainfall per unit time in the current preset time period->And average rainfall in the history record in the same preset time period at the same stage in the history record; finally according to the calculated adjusted sampling frequency +.>。

The technical scheme has the effects that: and (3) improving the sampling precision: according to the climate information and the historical rainfall information of the monitoring area, the time period is divided into a rainy stage, a normal stage and a rainless stage, and different preset sampling frequencies are set according to different stages. And the sampling frequency is adjusted through the comparison of actual monitoring and prediction information, so that the sampling precision is improved. And dynamically adjusting the sampling frequency of the rainy stage, the normal stage and the rainless stage according to the actual monitoring result and the prediction information, so as to optimize the sampling scheme. By continuously adjusting the sampling frequency, accurate rainfall data can be ensured to be collected, and flood prevention monitoring level is improved. The sampling frequency can be dynamically adjusted according to a functional formula And the timeliness of the sampling precision is improved. In conclusion, the flood control monitoring method can optimize a sampling scheme, improve sampling technology and accuracy, and provide important data support for flood control decisions, so that loss caused by flood disasters is effectively reduced.

The flood prevention monitoring method in this embodiment, the S2 includes:

obtaining prediction data of rainfall at different places through multiple models; the prediction data comprise average rainfall, total rainfall and maximum rainfall in unit time in a preset time period; establishing a plurality of pre-rainfall prediction models through regional radar data, satellite remote sensing data and high-altitude observation data, wherein the preset time can be half an hour, one hour, two hours and one day;

comparing the actual rainfall with predicted rainfall data to obtain a comparison result; selecting a final rainfall prediction model according to the comparison result;

Wherein the plurality of predictive models includes:

linear regression model: linear regression models are commonly used to study the linear relationship between variables. After a mathematical model is established that represents the relationship between the influencing factors, the model can be used to predict future rainfall.

Neural network model: the neural network model can be trained by using factors such as rainfall, air humidity, air temperature and the like in the earlier stage to obtain output results under different input values so as to predict.

Autoregressive moving average model (ARMA): the ARMA model is a time sequence analysis method, and predicts the future rainfall by searching for information such as periodicity, trend, seasonality and the like in a data set.

Decision tree model: the decision tree model can classify various rainfall, temperature, wind and other factors, constantly judge the rainfall under different parameters, and finally obtain a prediction result.

The working principle of the technical scheme is as follows: firstly, the method obtains the prediction data of rainfall at different places through various models. These predicted data include average rainfall, total rainfall, and maximum rainfall per unit time for a preset period of time. These data may be obtained from regional radar data, satellite telemetry data, high altitude observations, etc. These data are used to build various pre-rainfall prediction models, which may be statistical models, physical models, neural network models, or deep learning models, etc. Next, the actual rainfall data is monitored by the rainfall monitoring station. The monitoring stations can acquire data of sampling points of different monitoring stations arranged according to sampling time sequence and group the sampling points according to a preset time period. The actual rainfall in each time period of each monitoring point is also counted. The actual rainfall includes an actual average rainfall, an actual total rainfall and an actual maximum rainfall in a unit time in a preset time period. And then, comparing the actual rainfall with predicted rainfall data to obtain a comparison result. The result of the comparison may include a comparison of the actual rainfall and the predicted rainfall for each monitoring station, an error of the actual rainfall and the predicted rainfall for each monitoring station, and the like. And finally, selecting a final rainfall prediction model according to the comparison result. The selected final model should be the model that best fits the actual rainfall situation. According to the prediction result of the final model, corresponding flood prevention decisions and measures, such as starting an emergency plan, allocating rescue materials, evacuating people and the like, can be formulated.

The technical scheme has the effects that: and obtaining rainfall prediction data of different places through multiple models, wherein the rainfall prediction data comprise average rainfall, total rainfall and maximum rainfall in unit time in a preset time period. And a plurality of pre-rainfall prediction models are established by using regional radar data, satellite remote sensing data and high-altitude observation data, so that the accuracy of prediction can be improved by comprehensively using a plurality of data sources. The actual rainfall data is monitored through the rainfall monitoring stations, sampling points of different monitoring stations are arranged according to the sampling time sequence, and the sampling points are grouped according to a preset time period. And counting the actual rainfall in each time period of each monitoring point, wherein the actual rainfall comprises the actual average rainfall, the actual total rainfall and the actual maximum rainfall in unit time. And comparing the actual rainfall with predicted rainfall data to obtain a comparison result. And selecting a final rainfall prediction model through comparison results, so that the accuracy of rainfall prediction is improved. Through accurate rainfall prediction data and actual rainfall monitoring data, important support can be provided for flood control decisions. According to the comparison result of the predicted data and the actual data, corresponding emergency measures can be adopted in time, and flood prevention preparation work is done in advance. In conclusion, the flood control monitoring method can improve the accuracy of rainfall prediction, and provide important data support for flood control decisions, so that the loss caused by flood disasters is effectively reduced.

According to the flood prevention monitoring method, actual rainfall is compared with predicted rainfall data to obtain a comparison result; the selecting of the final rainfall prediction model according to the comparison result comprises the following steps:

the comparison result comprises a first difference value、/>、/>；

First difference:

，

second difference:

，

third difference:

，

wherein,the actual average rainfall of a certain prediction model in a certain preset time period of the same monitoring station; />The predicted average rainfall amount of a preset time period for the corresponding prediction model; />The actual total rainfall of the same monitoring station in a certain preset time period is corresponding to the prediction model; />The method comprises the steps of presetting a predicted total rainfall for a time period corresponding to a prediction model; />The actual maximum rainfall of unit time in a certain preset time period of the same monitoring station is corresponding to the prediction model; />Presetting a maximum rainfall in a prediction unit time of a time period for a corresponding prediction model; />The number of the preset time periods is set; />The number of the monitoring stations;obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>A maximum of the values of (2);obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>The minimum of the values of (2);obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model >A maximum of the values of (2);obtaining +.f. corresponding to predictive model multiple monitoring stations for multiple preset time periods>The minimum of the values of (2);obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>A maximum of the values of (2); />Obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>The minimum of the values of (2); />、/>The value range is (0, 1) for the weight;

the comprehensive difference is as follows:，

wherein,、/>、/>coefficients, ranges (0, 1);

The working principle of the technical scheme is as follows: firstly, the method obtains the prediction data of rainfall at different places through various models. These predicted data include average rainfall, total rainfall, and maximum rainfall per unit time for a preset period of time. And establishing various rainfall prediction models by using regional radar data, satellite remote sensing data, high-altitude observation data and the like. The actual rainfall data is then monitored by a rainfall monitoring station. And arranging sampling points of different monitoring stations according to the sampling time sequence, and grouping according to a preset time period. The actual rainfall in each time period at each monitoring point is also counted. The actual rainfall includes an actual average rainfall, an actual total rainfall and an actual maximum rainfall in a unit time in a preset time period. Then, the actual rainfall is compared with predicted data of the rainfall to obtain a comparison result. This comparison process includes three main steps: for each predictive model and each monitoring station, three differences are calculated for each preset time period: the first difference, the second difference and the third difference. Each difference is calculated based on a comparison of the predicted data and the actual data. And calculating a comprehensive difference CZ according to the first difference, the second difference and the third difference. This composite difference is a weighted average of three differences, where the weights may be fixed or may be set based on other factors. And taking the first two models corresponding to the value with the minimum comprehensive difference value as rainfall prediction models. This selection process is based on the consideration of: the smaller the integrated difference value, the higher the matching degree between the prediction model and the actual data, and thus the more likely the prediction model is selected as the final rainfall prediction model. And finally, taking the maximum value of each of the average rainfall, the total rainfall and the maximum rainfall in unit time in a period of time predicted in the two selected rainfall prediction models as a final rainfall prediction result. This is to choose a more conservative prediction result from the two prediction models, so as to ensure the prediction safety. In general, the flood control monitoring method selects a prediction model which best accords with the actual situation by comparing the actual rainfall with the predicted rainfall, and then makes flood control decisions and measures based on the prediction result of the model.

The technical scheme has the effects that: by comparing the actual rainfall with the predicted rainfall and selecting a prediction model which is most in line with the actual situation, the accuracy of rainfall prediction can be remarkably improved. The method utilizes the prediction results of the models, and comprehensively considers a plurality of aspects (average rainfall, total rainfall and maximum rainfall in unit time) of the actual rainfall, so that the accuracy of the prediction models can be more comprehensively evaluated. The method adopts a systematic mode to evaluate and select the prediction model, and determines the final prediction model through the steps of calculating the difference value, weighted average, comprehensive difference value and the like. The method avoids the interference of subjective judgment and experience sense, so that the selection process is more objective and scientific. The method not only considers the average rainfall, but also considers the total rainfall and the maximum rainfall in unit time. These three considerations make the prediction of rainfall more comprehensive and accurate. Especially in flood control works, the prediction of total rainfall and maximum rainfall per unit time is very important, as these data are of great importance for assessing the risk of flood disasters and for formulating emergency plans. The two finally selected rainfall prediction models provide an uncertainty range of the prediction result. By selecting the maximum value predicted in the two models as the final predicted result, more conservative predictions can be obtained, thereby ensuring the safety of the predicted result to a certain extent. Through an automatic data analysis and model selection process, the method can quickly obtain an accurate prediction result, and is beneficial to improving the efficiency and response speed of flood control decision. This can strive for more time for flood control rescue work, reduces the influence of flood disasters. In general, the flood prevention monitoring method improves the accuracy of rainfall prediction and provides more comprehensive prediction results by comprehensively considering the actual rainfall and the prediction results of a plurality of prediction models. The method can provide powerful support for flood control decisions.

The flood prevention monitoring method in this embodiment, the S3 includes:

dividing the first subarea into a plurality of second subareas through position information; dividing a ground road into a plurality of sub-road sections according to the region and the intersection, wherein each sub-road section is a second sub-region; dividing a ground parking lot, an underground parking lot and an underground passage into a second subarea according to the positions;

marking the second sub-region with a plurality of pothole areas; detecting the fluctuation condition of the road surface by using elevation data or laser radar data; if there is a significant depression somewhere, beyond the set depth and area thresholds, it is marked as a pothole.

The working principle of the technical scheme is as follows: road and ground information of the monitored area is acquired and updated through a geographic information system. This step ensures that the acquired road and ground information is up-to-date and accurate. Then, the road information of the monitoring area is divided into a plurality of first sub-areas. These first sub-areas may include ground roads, ground parking lots, underground tunnels, and the like. This partitioning is to facilitate subsequent in-depth analysis and processing. Next, the first sub-area is further divided into a plurality of second sub-areas by the position information. In this step, the ground road is divided into a plurality of sub-segments, each corresponding to a second sub-segment, by region and intersection. Meanwhile, the ground parking lot, the underground parking lot and the underground passage are also divided into second subareas according to the positions. This division is to provide a finer understanding of the details of each zone for better flood control monitoring. Then, a plurality of pothole areas are marked in each second sub-area. In this step, elevation data or lidar data is used to detect the presence of an elevation on the road surface. If a place has a significant depression and both depth and area exceed set thresholds, then the place will be marked as a pothole area. These potholes may have an impact on traffic and may also be potential areas for water accumulation, thus requiring special attention. And finally, according to the marked pothole areas, corresponding flood prevention measures such as early warning, traffic guiding, pothole filling and the like can be adopted. The measures can effectively reduce the influence of rainfall in the flood season on road traffic. In general, the flood prevention monitoring method divides road information into a plurality of subareas by acquiring and updating road and ground information, detects and marks the hollow areas, and finally adopts corresponding flood prevention measures, thereby effectively reducing the influence of rainfall in flood season on road traffic.

The technical scheme has the effects that: the road and ground information of the monitoring area can be rapidly and accurately acquired and updated through the geographic information system, and the freshness and accuracy of the information are ensured. The road information is divided into a plurality of first sub-areas, and then the sub-areas are divided into a plurality of second sub-areas by the position information. This refined management approach makes the processing and analysis of the road information more systematic and accurate. Each second sub-area is marked with a plurality of pothole areas which may have an impact on traffic safety and flood control. By using elevation data or lidar data, the level of the road surface can be accurately detected and accurately marked for those places that have significant depressions and exceed set depth and area thresholds. The method can improve the comprehensiveness and accuracy of road monitoring. By marking the hollow areas, the method can provide support for flood control decisions. For example, when decision makers need to assess the possible risk of flood season, they can make more accurate decisions by the marked pothole areas. In addition, these potholes can also be used to predict places where drainage may be needed, thereby helping decision makers to better prepare and plan flood control work. Accurately marking the pothole area can help flood control staff to better know the condition of the road and determine the area needing important monitoring and processing. The efficiency and effect of flood control work can be improved, and therefore safety and smoothness of road traffic are better protected. In general, the flood control monitoring method divides road information into a plurality of subareas by acquiring and updating road and ground information, detects and marks the hollow areas, and finally adopts corresponding flood control measures, thereby effectively reducing the influence of rainfall in flood season on road traffic and improving the efficiency and effect of flood control work.

In this embodiment, the flood prevention monitoring method, S4 includes:

，

a risk factor for the second zone;

presetting the average value of the accumulated water depth of the pavement of all second subareas under the rainfall threshold;

the number of the hollow areas in the second subarea;

establishing a corresponding function relation between the accumulated water depth and the rainfall of each sub-area road surface through historical data;

，

wherein the method comprises the steps ofFor the water accumulation depth of the second subarea, +.>For rainfall, add->The functional relationship may be linear, exponential or logarithmic, and is not specifically limited herein according to the actual situation.

The working principle of the technical scheme is as follows: firstly, road surface water accumulation information of each second subarea is acquired through a traffic monitoring system. Such water accumulation information includes water accumulation depth and the like. Such information may be obtained by way of sensors or other devices mounted on the road, such as cameras or radar.

Then, according to the road surface ponding information, the risk coefficient of the second subarea can be calculated. This risk factorIs calculated by the mathematical formulaThe formula takes into account a number of factors including the depth of water accumulation in the second sub-zone at a predetermined rainfall threshold>The average value of the accumulated water depth of the pavement of all the second subareas under the preset rainfall threshold value is +.>Depth and area of each pothole area, determination of depth threshold and area threshold of pothole area, and coefficient +.>. This formula may reflect the extent to which different factors affect the risk factor. Next, through the history data, the corresponding function relation of the accumulated water depth and the rainfall of each subarea pavement can be established>. This functional relationship may be determined from historical data, may be linear, exponential or logarithmic, and the particular form may be selected according to the circumstances. By means of the functional relation, the depth of the accumulated water on the road surface under the condition of the same rainfall in the future can be predicted. Finally, according to the calculated risk factor +.>And the predicted pavement ponding depth, corresponding flood prevention measures can be adopted. For example, if the risk coefficient of a certain second sub-area is higher and the predicted surface water accumulation depth is larger, early warning or other flood prevention measures can be performed in advance to avoid serious water accumulation problems of the road. In general, the flood prevention monitoring method establishes a functional relation between the road surface water accumulation and the rainfall by acquiring and analyzing the road surface water accumulation information, calculates and obtains a danger coefficient and predicts the future road surface water accumulation condition, and finally adopts corresponding flood prevention measures to ensure the safety and smoothness of road traffic.

In this embodiment, the flood prevention monitoring method, S5 includes:

predicting the pavement water accumulation depth of the second subarea in a next period of time according to the water accumulation depth of the current second subarea and the rainfall prediction result of the corresponding second subarea;

，

obtaining a second sub-region comprehensive index according to the pavement ponding depth and the second sub-region danger coefficient;

，

wherein,the second regional pavement water accumulation depth can be the current second regional water accumulation depth or the predicted second regional pavement water accumulation depth after predicting a certain period of time; />Is a dangerous depth threshold; />Is constant, range (0, 1),>a risk factor for the second zone;

acquiring a second sub-region danger level according to the second sub-region comprehensive index;

is of a first grade;

a second level;

a third grade;

fourth grade;

wherein,the method comprises the steps of presetting a comprehensive index threshold value; the composite index threshold may be set in conjunction with the data distribution, knowledge base.

The working principle of the technical scheme is as follows: firstly, road surface water accumulation information of each second subarea is acquired through a traffic monitoring system. Such water accumulation information includes water accumulation depth and the like. This information may be obtained by way of a sensor or other device mounted on the road, such as a camera or radar. Then, according to the road surface water accumulation information, the risk coefficient of the second subarea can be calculated. This risk factor->Is calculated by a mathematical formula which takes into account a number of factors including a predetermined rainfall thresholdThe accumulated water depth of the lower second subarea, the average value of the accumulated water depths of all the road surfaces of the second subarea under the preset rainfall threshold value and the number of the hollow areas of the second subarea +.>Depth and area of each hollow region, and coefficient +.>. This formula may reflect the extent to which different factors affect the risk factor. Next, based on the water accumulation depth of the current second sub-area and the rainfall prediction result of the corresponding second sub-area, the pavement water accumulation depth +_ of the second sub-area for a period of time next can be predicted>. This prediction can be used to pre-warn or plan countermeasures in advance. Then, according to the pavement ponding depth and the danger coefficient of the second subarea, the comprehensive index of the second subarea can be obtained >. This comprehensive index->Is calculated by a mathematical formula which considers a plurality of factors such as the depth of the accumulated water on the road surface, the danger coefficient and the like. According to the comprehensive index->The risk level of the second area may be determined. Finally, according to the second region comprehensive index +.>And adopting corresponding flood prevention measures. For example, if the comprehensive index->If the threshold value is exceeded, early warning or other flood prevention measures can be performed in advance so as to avoid serious ponding of the road. Overall, such flood controlThe monitoring method predicts the future road surface water accumulation condition by acquiring and analyzing the road surface water accumulation information, judges the dangerous grade according to the comprehensive index and takes corresponding flood prevention measures so as to ensure the safety and smoothness of road traffic.

The technical scheme has the effects that: the rainfall condition can be monitored more accurately by acquiring the road surface ponding information of each second subarea, and potential flood conditions can be early warned and dealt with in time; by predicting the pavement ponding depth of the second subarea for a period of time, the possible ponding problem can be mastered in advance, so that measures are taken in time to prevent and deal with. By risk factor And a second subregion synthesis indicator->The flood control system can evaluate and master the flood control conditions of the road more comprehensively, so that targeted flood control measures can be adopted better. According to different dangerous grades, different countermeasures can be adopted, so that the fine management of flood control work is realized, and the pertinence and the effectiveness of the flood control work are improved. By establishing the corresponding function relation between the pavement ponding depth and the rainfall of each subarea, the future pavement ponding situation can be predicted more accurately, and the accuracy and the reliability of flood prevention monitoring are improved. The flood prevention monitoring method can automatically acquire data and analyze through the traffic monitoring system, reduces manual intervention, and improves working efficiency and monitoring accuracy. The method can forecast flood disasters in advance, and provides more sufficient time and accurate information for rescue. Meanwhile, according to the dangerous grade of the area, corresponding measures can be taken to avoid or reduce disaster loss in time. The method can be continuously improved and optimized through long-term accumulation and analysis of monitoring data, so that flood prevention capacity is improved. Meanwhile, the method can be integrated with other flood control monitoring methods and systems to form a complete flood control monitoring system, and the integral flood control and disaster resistance capacity is improved. In a word, the flood prevention monitoring method can effectively predict and evaluate the risk degree of flood disasters, provide scientific basis for rescue and flood prevention measures, and reduce the risk degree Less disaster loss and improved flood control capability.

In this embodiment, the flood prevention monitoring method, S6 includes:

acquiring real-time position information of personnel and vehicles; the method can be realized in various modes, for example, the position information is reported by a user, or tracking is carried out by positioning technologies such as GPS;

generating a corresponding evacuation instruction according to the estimated ponding depth and the risk level; the evacuation instructions comprise information such as a suggested evacuation route, suggested evacuation time, an area to be avoided and the like;

sending the evacuation instruction to the user in various modes; for example, by means of mobile phone short messages, application program pushing and the like; meanwhile, the instructions can be issued in public places such as television stations, broadcasting stations and the like so as to expand the information spreading range;

The working principle of the technical scheme is as follows: real-time location information of personnel and vehicles is obtained in a variety of ways. The location information may be reported by the user, for example, by a mobile phone application, or tracked by a positioning technique such as GPS. According to the real-time position information of the vehicle and the personnel, the water accumulation depth and the danger level of the area can be estimated. This assessment may be based on a pre-set algorithm or model, taking into account a variety of factors including rainfall, road surface water depth, movement trajectories of vehicles and personnel, and the like. Based on the estimated water depth and the risk level, a corresponding evacuation instruction may be generated. These instructions include information on suggested evacuation routes, suggested evacuation times, areas to be avoided, etc., intended to guide personnel and vehicles to evacuate safely and effectively. The evacuation instructions are sent to the user in a number of ways. The modes comprise mobile phone short messages, application program pushing and the like, so that a user can acquire evacuation instructions in time. Meanwhile, the instructions can be issued in public places such as television stations, broadcasting stations and the like so as to expand the information spreading range. The location information of personnel and vehicles is continuously tracked, and the rainfall, water accumulation depth and danger level of the area are continuously tracked. This may be accomplished by periodically sending location update information to the user, or by a real-time monitoring system in the background. According to the latest data, the evacuation instruction can be regenerated and timely sent to the user. This ensures the accuracy and timeliness of evacuation instructions to cope with new situations that may arise.

The technical scheme has the effects that: by acquiring and analyzing the position information of personnel and vehicles and related environmental data, possible flood conditions can be timely pre-warned and dealt with, and therefore the efficiency and reliability of flood prevention work are improved. By timely sending and effective guiding of the evacuation instructions, people and vehicles can be evacuated rapidly and safely, so that casualties and property loss are reduced. By issuing the evacuation instructions in various modes, the public can know the flood control conditions and the flood control knowledge, and the flood control consciousness and the coping capacity of the public are improved. The acquisition of the real-time position information of the personnel and the vehicle can be realized in various modes, for example, the position information is reported by a user or the tracking is carried out by positioning technologies such as GPS; according to the real-time position information of the vehicle and the personnel, the ponding depth and the danger level of the area can be estimated; according to the estimated ponding depth and the risk level, a corresponding evacuation instruction can be generated; the location information of personnel and vehicles is continuously tracked, and the rainfall, water accumulation depth and danger level of the area are continuously tracked. By establishing a more accurate model and algorithm, the flood conditions can be evaluated more scientifically and accurately, and evacuation instructions can be generated, so that the scientificity and the refinement level of flood control work are improved. In general, the flood control monitoring method can effectively early warn and cope with flood conditions in time, improve the efficiency and reliability of flood control work, reduce casualties and property loss, promote information sharing and collaboration, and strengthen the scientificity and refinement level of flood control work.

The embodiment provides a flood prevention monitoring system, the system includes:

The working principle of the technical scheme is as follows: firstly, presetting a sampling frequency to monitor rainfall information of a subarea, and adjusting the sampling frequency according to the actual rainfall. This step is to ensure accuracy and real-time of the rainfall data. And then respectively predicting the rainfall at different places through a plurality of models (such as a statistical model, a physical model, a neural network model, a deep learning model and the like), and comparing the predicted data with the actual rainfall to obtain a comparison result. And according to the comparison result, a final rainfall prediction model can be selected. This step is to improve the accuracy of rainfall prediction. Next, road and ground information of the monitored area is acquired and updated through a Geographic Information System (GIS). Such information may include the location, length, width, traffic conditions, etc. of the road. With this information, the road information of the monitoring area can be divided into a plurality of sub-areas. The sub-regions include a first sub-region and a second sub-region. And then, acquiring the road surface ponding information of the subareas through a traffic monitoring system. And calculating the risk coefficient of the second subarea through the road surface ponding information. This step is to evaluate the effect of road surface water on traffic safety. And then, according to the accumulated water depth of the current second subarea and the rainfall prediction result of the corresponding area, predicting the accumulated water depth of the road surface of the second subarea for a period of time. And then according to the pavement ponding depth and the danger coefficient of the second subarea, the comprehensive index of the second subarea can be obtained. Based on this comprehensive index, the risk level of the second region can be obtained. Finally, the position information of the person and the vehicle is acquired. According to the position information of the vehicle and the personnel, the ponding depth and the danger level corresponding to the nearby second subarea and evacuation instructions can be sent to the user. This step is to guide the user for safe evacuation.

The technical scheme has the effects that: according to the method, rainfall prediction data of different places are obtained through multiple models, and actual rainfall is compared with the prediction data to select a final rainfall prediction model. This helps to improve the accuracy of rainfall prediction. And acquiring the road surface ponding information of the subareas through the traffic monitoring system, and calculating the danger coefficient of the second subarea. The method enables rapid identification and evaluation of dangerous areas, and provides important basis for flood control decisions. According to the method, the pavement ponding depth of the second subarea in a period of time is predicted according to the ponding depth of the current second subarea and the rainfall prediction result. This provides the possibility to predict and prevent possible flooded areas in advance. By acquiring the position information of the personnel and the vehicles, the method can send the ponding depth, the danger level and the evacuation instruction corresponding to the nearby second subarea to the user. This helps to improve the safety of personnel and vehicles, especially in emergency situations. The method combines various information such as a Geographic Information System (GIS) and a traffic monitoring system, and realizes comprehensive management of the monitored area. This helps to improve the accuracy and effectiveness of the flood control decisions. By acquiring the accurate information of the dangerous area, the method can optimize resource allocation and ensure that flood prevention rescue work is performed more efficiently and timely. By accurately predicting the dangerous area, providing timely early warning and implementing effective evacuation instructions, the method can improve the confidence of the public on flood control work and increase the trust of the public on governments and related institutions. In summary, the flood prevention monitoring method improves the accuracy of rainfall prediction, the rapid identification of dangerous areas, the accurate prediction of the depth of accumulated water on the road surface, the safety of personnel and vehicles, the comprehensive management of monitoring areas, the optimization of resource allocation and the improvement of public confidence by comprehensively applying various technologies and means. These advantages have important significance for flood prevention work, and are helpful for reducing the influence of flood disasters and guaranteeing the life and property safety of people.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. A flood control monitoring method, the method comprising:

s2, respectively acquiring prediction data of rainfall at different places through multiple models; comparing the actual rainfall with predicted rainfall data to obtain a comparison result; selecting a final rainfall prediction model according to the comparison result to predict rainfall;

S6, obtaining position information of the personnel and the vehicle, and according to the position information of the vehicle and the personnel; and sending the ponding depth and the danger level corresponding to the nearby second subarea to a user and evacuating instructions.

2. The flood control monitoring method according to claim 1, wherein S1 comprises:

3. The flood control monitoring method according to claim 1, wherein S2 comprises:

4. A flood control monitoring method according to claim 3, wherein the comparing the actual rainfall with the predicted rainfall data to obtain a comparison result; the selecting of the final rainfall prediction model according to the comparison result comprises the following steps:

the comparison result comprises a first difference valueSecond difference->Third difference->；

First difference:

，

second difference:

，

third difference:

，

wherein the method comprises the steps ofThe actual average rainfall of a certain prediction model in a certain preset time period of the same monitoring station; />The predicted average rainfall amount of a preset time period for the corresponding prediction model; />The actual total rainfall of the same monitoring station in a certain preset time period is corresponding to the prediction model; />The method comprises the steps of presetting a predicted total rainfall for a time period corresponding to a prediction model; />The actual maximum rainfall of unit time in a certain preset time period of the same monitoring station is corresponding to the prediction model; />Presetting a maximum rainfall in a prediction unit time of a time period for a corresponding prediction model; />The number of the preset time periods is set; />The number of the monitoring stations; />Obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model >A maximum of the values of (2); />Obtained for a plurality of preset time periods of a plurality of monitoring stations corresponding to the predictive model

The minimum of the values of (2); />Obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>A maximum of the values of (2); />Obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>The minimum of the values of (2); />Obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>A maximum of the values of (2); />Obtaining +.f. for a plurality of preset time periods for a plurality of monitoring stations corresponding to the predictive model>The minimum of the values of (2); />、/>The value range is (0, 1) for the weight;

the comprehensive difference is as follows:，

wherein,、/>、/>the value range is (0, 1) for the coefficient;

5. The flood control monitoring method according to claim 1, wherein S3 comprises:

dividing the first subarea into a second subarea through position information;

the second sub-region is marked with a plurality of pothole regions.

6. The flood control monitoring method according to claim 1, wherein S4 comprises:

，

a risk factor for the second zone;

the number of the hollow areas in the second subarea;

for the depth of each hollow area, +.>The area of each hollow area; />Judging a depth threshold value of the hollow area;judging the area threshold value of the hollow area; />Coefficients, ranges (0, 1);

and establishing a corresponding functional relation between the pavement ponding depth and the rainfall of each second subarea through historical data.

7. The flood control monitoring method according to claim 1, wherein S5 comprises:

，

wherein,for the pavement water accumulation depth of the second subarea, < > in->Is a dangerous depth threshold; />Is constant, range (0, 1),>is a risk factor for the second subregion;

8. The flood control monitoring method according to claim 1, wherein S6 comprises:

acquiring real-time position information of personnel and vehicles;

sending the evacuation instruction to the user in various modes;

9. A flood control monitoring system, the system comprising:

model selection module: respectively acquiring prediction data of rainfall at different places through multiple models; comparing the actual rainfall with predicted rainfall data to obtain a comparison result; selecting a final rainfall prediction model according to the comparison result to predict rainfall;