CN116227917A

CN116227917A - Method and device for processing flood prevention risk of building, electronic equipment and storage medium

Info

Publication number: CN116227917A
Application number: CN202211675421.5A
Authority: CN
Inventors: 况卢娟; 徐京; 林锋; 林淮; 黎松军
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2022-12-26
Filing date: 2022-12-26
Publication date: 2023-06-06

Abstract

The embodiment of the invention provides a method, a device, electronic equipment and a storage medium for processing flood prevention risks of a building, wherein the method ensures the reliability and the accuracy of a prediction result by carrying out flood prevention risk prediction through monitoring data acquired by hydrologic stations, rainfall stations and the like around the building and geographical environment data of the building, predicts the future moment, constructs a corresponding risk prediction curve, and facilitates the presentation of flood prevention risks of a target building in a certain future time, so that corresponding personnel can timely make corresponding flood prevention measures, and the safety of the target building is ensured.

Description

Method and device for processing flood prevention risk of building, electronic equipment and storage medium

Technical Field

The present invention relates to the field of risk prediction technologies, and in particular, to a method for processing a flood prevention risk of a building, a device for processing a flood prevention risk of a building, an electronic device, and a computer readable storage medium.

Background

The communication building is a very important node in the wired and wireless communication network of operators, and various communication devices are arranged in the communication building. In sudden rainstorm or typhoon seasons, flood or waterlogging is often accompanied, so that communication buildings are flood-covered, and communication around the buildings is interrupted. For this situation, a set of effective risk pre-judging method for flood control of the communication machine building is urgently needed to be researched, so that decision makers can pre-judge in advance when sudden disasters occur, pre-estimate risk grades, and pre-implement flood control emergency works such as flood control material preparation, flood control team organization, scheduling circuit and the like, so that various losses caused by flood control of the communication machine building are avoided or reduced.

Disclosure of Invention

The embodiment of the invention provides a method and a device for processing flood prevention risks of a building, electronic equipment and a computer readable storage medium, which are used for solving or partially solving the problem that the flood prevention risks of the building cannot be effectively predicted.

The embodiment of the invention discloses a method for processing flood prevention risks of a building, which comprises the following steps:

acquiring target data corresponding to a target building, wherein the target data at least comprises water level real-time monitoring data and hypervigilance real-time monitoring data acquired by a hydrological station, weather forecast data corresponding to an area where the target building is located acquired by a rainfall station and geographic environment data corresponding to the target building;

if the water level real-time monitoring data meets the warning condition, carrying out flood-receiving risk prediction on the target building according to the water level real-time monitoring data, the hyper-warning real-time monitoring data, the weather forecast data and the geographic environment data to obtain a current flood-receiving risk value corresponding to the target building and a current risk level corresponding to the current flood-receiving risk value;

if the risk level is a middle risk level or a high risk level, predicting the risk of the target building at a future moment according to the current flood risk value, and obtaining a future flood risk value corresponding to the target building and a predicted risk level of the future flood risk value;

And correlating the current flood receiving risk value with the future flood receiving risk value according to the time interval corresponding to the real-time water level monitoring data and the hyper-alert real-time monitoring data, and generating a flood receiving risk prediction curve aiming at the target building.

Optionally, the water level real-time monitoring data includes monitoring a water level, and if the water level real-time monitoring data meets an alert condition, carrying out flood-receiving risk prediction on the target building according to the water level real-time monitoring data, the hyper-alert real-time monitoring data, the weather forecast data and the geographical environment data to obtain a current flood-receiving risk value corresponding to the target building and a current risk level corresponding to the current flood-receiving risk value, including:

if the monitored water level accumulation exceeds a first preset threshold value within 24 hours, carrying out flood-receiving risk prediction on the target building according to the water level real-time monitoring data, the hyper-warning real-time monitoring data, the weather forecast data and the geographic environment data, and obtaining a current flood-receiving risk value corresponding to the target building and a current risk level corresponding to the current flood-receiving risk value.

Optionally, the predicting the flood-receiving risk of the target building according to the water level real-time monitoring data, the hyper-alert real-time monitoring data, the weather forecast data and the geographical environment data, to obtain a current flood-receiving risk value corresponding to the target building and a risk level corresponding to the current flood-receiving risk value, includes:

carrying out water level risk prediction on the hydrologic station according to the water level real-time monitoring data to generate a corresponding hydrologic water level risk value;

performing hypervigilance risk prediction on the hydrologic station according to the hypervigilance real-time monitoring data to generate a corresponding hydrologic hypervigilance risk value;

according to the weather forecast data, carrying out rainfall prediction on the area where the target building is located, and generating a corresponding rainfall risk value;

calculating risk coefficients of the target building according to the geographic environment data, and generating corresponding risk coefficients;

and carrying out flood-receiving risk prediction on the target building by adopting the hydrological water level risk value, the hydrological ultra-warning risk value, the rainfall risk value and the risk coefficient to obtain a current flood-receiving risk value corresponding to the target building.

Optionally, the water level real-time monitoring data further includes a reference water level, and the performing water level risk prediction on the hydrologic station according to the water level real-time monitoring data, generating a corresponding hydrologic water level risk value includes:

Calculating the water level change of the hydrologic station by adopting the monitored water level and the reference water level, and obtaining a water level rising change coefficient corresponding to the hydrologic station;

acquiring a first distance between the target building and a water channel of the hydrologic station, and determining a water channel distance risk value between the target building and the hydrologic station corresponding to the first distance;

and carrying out water level risk prediction on the hydrologic station by adopting the water level rising change coefficient and the water channel distance risk value to generate a corresponding hydrologic water level risk value.

Optionally, the hypervigilance real-time monitoring data includes a level exceeding a vigilance water level and a vigilance water level, and the hypervigilance risk prediction is performed on the hydrologic station according to the hypervigilance real-time monitoring data, so as to generate a corresponding hydrologic hypervigilance risk value, including:

calculating the water level change of the hydrologic station by adopting the water level exceeding the warning water level and the warning water level to obtain a hydrologic super-warning risk coefficient corresponding to the hydrologic station;

acquiring a second distance between the target building and a water channel of the super-alert hydrologic station, and determining a super-alert water channel distance risk value between the target building and the super-alert hydrologic station corresponding to the second distance;

And performing hyperwarning risk prediction on the hydrologic station by adopting the hydrologic super warning risk coefficient and the super warning water channel distance risk value to generate a corresponding hydrologic super warning risk value.

Optionally, the weather forecast data includes a rainfall value, and the performing rainfall prediction on the area where the target building is located according to the weather forecast data, to generate a corresponding rainfall risk value, includes:

acquiring a rainfall risk coefficient corresponding to the rainfall value and an area rainfall risk value corresponding to the target building, wherein the area rainfall risk value is used for representing that the target building has the same area characteristics in a preset range;

and carrying out rainfall prediction on the area where the target building is located by adopting the rainfall risk coefficient and the area rainfall risk value, and generating a corresponding rainfall risk value.

Optionally, the geographical environment data includes a ground level of a river water surface relative to the target building and a low-lying identifier of the target building, and the calculating the risk coefficient of the target building according to the geographical environment data generates a corresponding risk coefficient, including:

if the ground height is greater than or equal to 0 m, acquiring a first water level risk probability coefficient aiming at the target building; if the ground height is smaller than 0 meter, acquiring a second water level risk probability coefficient aiming at the target building;

If the low-lying mark represents that the target building is a low-lying building, acquiring a first rainfall risk probability coefficient aiming at the target building; and if the low-lying mark represents that the target building is not the low-lying building, acquiring a second rainfall risk probability coefficient aiming at the target building.

Optionally, the risk coefficient includes a water level risk coefficient and a rainfall risk coefficient, and the adoption of the hydrologic water level risk value, the hydrologic ultra-police risk value, the rainfall risk value and the risk coefficient performs flood-receiving risk prediction on the target building to obtain a current flood-receiving risk value corresponding to the target building, and includes:

and carrying out flood risk prediction on the target building by adopting the hydrological water level risk value, the hydrological super-warning risk value, the rainfall risk value, the water level risk probability coefficient and the rainfall risk probability coefficient to obtain a current flood risk value corresponding to the target building.

Optionally, the predicting the risk of the target building at the future time according to the current flood-receiving risk value, obtaining a future flood-receiving risk value corresponding to the target building and a predicted risk level of the future flood-receiving risk value, includes:

And carrying out prediction calculation on the hydrologic water level risk value, the hydrologic ultra-warning risk value, the rainfall risk value, the water level risk probability coefficient and the rainfall risk probability coefficient by a primary smoothing index method to obtain a future flood receiving risk value corresponding to the target building and a predicted risk level of the future flood receiving risk value.

Optionally, the method further comprises:

and acquiring building attribute information, historical flood receiving data and municipal risk data corresponding to the target building, and inputting the building attribute information, the historical flood receiving data and the municipal risk data into a flood receiving risk prediction model to obtain a building flood prevention risk value corresponding to the target building.

The embodiment of the invention also discloses a device for processing the flood prevention risk of the building, which comprises the following steps:

the data acquisition module is used for acquiring target data corresponding to a target building, wherein the target data at least comprises water level real-time monitoring data and hypervigilance real-time monitoring data acquired by a hydrologic station, weather forecast data corresponding to an area where the target building is located acquired by a rainfall station and geographic environment data corresponding to the target building;

the current flood-receiving risk calculation module is used for predicting flood-receiving risk of the target building according to the water level real-time monitoring data, the hyper-alert real-time monitoring data, the weather forecast data and the geographic environment data if the water level real-time monitoring data meets the alert condition, so as to obtain a current flood-receiving risk value corresponding to the target building and a current risk level corresponding to the current flood-receiving risk value;

The future flood-receiving risk prediction module is used for predicting the risk of the target building at a future moment according to the current flood-receiving risk value if the risk level is a medium risk level or a high risk level, and obtaining a future flood-receiving risk value corresponding to the target building and a predicted risk level of the future flood-receiving risk value;

and the curve generation module is used for correlating the current flood receiving risk value with the future flood receiving risk value according to the time interval corresponding to the real-time water level monitoring data and the hyper-warning real-time monitoring data to generate a flood receiving risk prediction curve aiming at the target building.

Optionally, the water level real-time monitoring data includes monitoring a water level, and the current flood risk calculating module is specifically configured to:

Optionally, the current flood exposure risk calculation module is specifically configured to:

Optionally, the water level real-time monitoring data further includes a reference water level, and the current flood risk calculating module is specifically configured to:

Optionally, the overstepping real-time monitoring data includes exceeding a warning water level and a warning water level, and the current flood risk calculating module is specifically configured to:

Optionally, the weather forecast data includes a rainfall value, and the current flood risk calculating module is specifically configured to:

Optionally, the geographical environment data includes a ground level of a river water surface relative to the target building and a low-lying identification of the target building, and the current flood risk calculating module is specifically configured to:

Optionally, the risk coefficient includes a water level risk probability coefficient and a rainfall risk probability coefficient, and the current flood risk calculating module is specifically configured to:

Optionally, the future flood exposure risk prediction module is specifically configured to:

Optionally, the method further comprises:

and the flood prevention risk calculation module is used for acquiring building attribute information, historical flood receiving data and municipal risk data corresponding to the target building, inputting the building attribute information, the historical flood receiving data and the municipal risk data into a flood receiving risk prediction model, and acquiring a building flood prevention risk value corresponding to the target building.

The embodiment of the invention also discloses electronic equipment, which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;

the memory is used for storing a computer program;

the processor is configured to implement the method according to the embodiment of the present invention when executing the program stored in the memory.

Embodiments of the present invention also disclose a computer-readable storage medium having instructions stored thereon, which when executed by one or more processors, cause the processors to perform the method according to the embodiments of the present invention.

The embodiment of the invention has the following advantages:

in the embodiment of the invention, for flood prevention of a building, the target building can be subjected to flood prevention risk prediction according to the water level real-time monitoring data, the super-alert real-time monitoring data, the weather forecast data and the geographical environment data by acquiring target data corresponding to the target building, wherein the target data at least comprises water level real-time monitoring data acquired by a hydrological station, weather forecast data corresponding to an area where the target building is located and geographical environment data corresponding to the target building, acquired by a rainfall station, and if the water level real-time monitoring data meets an alert condition, the target building is subjected to current flood prevention risk prediction according to the water level real-time monitoring data, the super-alert real-time monitoring data, the weather forecast data and the geographical environment data, so that a current flood prevention risk value corresponding to the target building and a current risk level corresponding to the current flood prevention risk value are acquired; if the risk level is a middle-risk level or a high-risk level, predicting the risk of the target building at a future moment according to the current flood-risk value to obtain a future flood-risk value corresponding to the target building and a predicted risk level of the future flood-risk value, and then correlating the current flood-risk value with the future flood-risk value according to a time interval corresponding to the real-time water level monitoring data and the overstrain real-time monitoring data to generate a flood-risk prediction curve for the target building, so that the reliability and the accuracy of a prediction result are ensured by carrying out flood-risk prediction on monitoring data collected by hydrologic stations, rainfall stations and the like around the building and geographic environment data of the building, and meanwhile, predicting the future moment and constructing a corresponding risk prediction curve to facilitate the flood-risk presentation of the target building at a certain future time.

Drawings

Fig. 1 is a step flowchart of a method for handling flood prevention risks of a building according to an embodiment of the present invention;

FIG. 2 is a schematic illustration of the exclusion of risk identification provided in an embodiment of the invention;

FIG. 3 is a schematic view of the calculation of flood risk provided in an embodiment of the present invention;

FIG. 4 is a schematic view of the calculation of flood risk provided in an embodiment of the present invention;

FIG. 5 is a schematic illustration of flood risk calculation and prediction provided in an embodiment of the present invention;

FIG. 6 is a schematic diagram of flood control risk calculation provided in an embodiment of the present invention;

FIG. 7 is a flow chart of model evaluation provided in an embodiment of the present invention;

FIG. 8 is a schematic flow chart of the test and analysis provided in the embodiment of the present invention;

FIG. 9 is a schematic flow chart of flood control assessment provided in an embodiment of the present invention;

fig. 10 is a block diagram of a construction flood prevention risk treatment device according to an embodiment of the present invention;

fig. 11 is a block diagram of an electronic device provided in an embodiment of the invention.

Detailed Description

In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.

As an example, a communication building is a very important node in a wired and wireless communication network of an operator, in which various types of communication devices are deployed. In sudden rainstorm or typhoon seasons, flood or waterlogging is often accompanied, so that communication buildings are flood-covered, and communication around the buildings is interrupted. For this situation, a set of effective risk pre-judging method for flood control of the communication machine building is urgently needed to be researched, so that decision makers can pre-judge in advance when sudden disasters occur, pre-estimate risk grades, and pre-implement flood control emergency works such as flood control material preparation, flood control team organization, scheduling circuit and the like, so that various losses caused by flood control of the communication machine building are avoided or reduced.

In view of the above, one of the key points of the invention is that for flood prevention of a building, the target data can be obtained by acquiring target data corresponding to the target building, wherein the target data at least comprises water level real-time monitoring data acquired by a hydrological station, super-alert real-time monitoring data, weather forecast data corresponding to an area where the target building is located acquired by a rainfall station and geographic environment data corresponding to the target building, and if the water level real-time monitoring data meets alert conditions, flood-receiving risk prediction is performed on the target building according to the water level real-time monitoring data, the super-alert real-time monitoring data, the weather forecast data and the geographic environment data, so as to obtain a current flood-receiving risk value corresponding to the target building and a current risk level corresponding to the current flood-receiving risk value; if the risk level is a middle-risk level or a high-risk level, predicting the risk of the target building at a future moment according to the current flood-risk value to obtain a future flood-risk value corresponding to the target building and a predicted risk level of the future flood-risk value, and then correlating the current flood-risk value with the future flood-risk value according to a time interval corresponding to the real-time water level monitoring data and the overstrain real-time monitoring data to generate a flood-risk prediction curve for the target building, so that the reliability and the accuracy of a prediction result are ensured by carrying out flood-risk prediction on monitoring data collected by hydrologic stations, rainfall stations and the like around the building and geographic environment data of the building, and meanwhile, predicting the future moment and constructing a corresponding risk prediction curve to facilitate the flood-risk presentation of the target building at a certain future time.

In order to enable those skilled in the art to better understand the technical solutions of the embodiments of the present invention, the following explains and describes some technical features related to the embodiments of the present invention:

the target building can be a communication building in which various communication devices are arranged.

The risk of flood control of the machine building refers to the possibility and degree of influence of flood disasters on a certain communication machine building in the current and future in the evolution process of the flood disaster event. The probability of machine building flood reception is related to factors such as rainfall, rainfall increase and decrease change, lifting condition of water level of nearby rivers and reservoirs, water level of nearby rivers and reservoirs exceeding warning water level, machine building geographical environment and the like, and has the characteristic of dynamics.

The machine building flood prevention risk means: when a certain communication building is in a flood disaster, the possibility and degree that the building cannot resist the flood disaster and form loss are provided. The flood prevention risk of the machine building is related to factors such as the height of a water retaining wall of the machine building, whether flood drainage facilities are complete, whether flood prevention materials are sufficient, the number of sustainable operation hours of a battery/oil engine, the highest position of a latest 5 calendar Shi Shuijin, municipal risk and the like, and most of the factors are inherent factors of the machine building.

The important machine building flood risk prediction system mainly comprises a prediction model and a prediction element, wherein the prediction model is responsible for the establishment, optimization and output of the model, the model is imported into the machine building flood risk prediction system, the flood risk value and the flood risk level in the current and future medium and short periods of the machine building are calculated according to the latest monitoring data and historical data by the model, the flood risk prediction result is finally output, and the auxiliary decision data of the support platform are applied.

Referring to fig. 1, a step flowchart of a method for processing a flood prevention risk of a building provided in an embodiment of the present invention may specifically include the following steps:

step 101, obtaining target data corresponding to a target building, wherein the target data at least comprises water level real-time monitoring data and hypervigilance real-time monitoring data acquired by a hydrological station, weather forecast data corresponding to an area where the target building is located acquired by a rainfall station and geographic environment data corresponding to the target building;

for the communication machine room, various communication devices are distributed, and the operation of the communication devices is easy to bring great safety problems in waterlogging, so that the safety of the communication machine room can be effectively ensured by carrying out flood prevention risk prediction on the communication machine room. In this regard, the target data corresponding to the target building may be obtained, where the target data may include water level real-time monitoring data and hypervigilance real-time monitoring data collected by the hydrologic station, weather forecast data corresponding to an area where the target building is located collected by the rainfall station, and geographic environment data corresponding to the target building.

The water level real-time monitoring data can comprise water level data such as river channel water level, reservoir water level and the like; the super-alert real-time monitoring data can comprise water level data such as river channel super-alert water level, water level super-alert water level and the like; weather forecast data, which can include rainfall and other data; the geographical environment data can be data related to the target building, so that flood risk prediction is performed by combining the attribute of the building, the water level related data monitored by the hydrologic station and the rainfall related data, and the reliability and the accuracy of a prediction result are ensured.

102, if the water level real-time monitoring data meets an alert condition, carrying out flood-receiving risk prediction on the target building according to the water level real-time monitoring data, the hyper-alert real-time monitoring data, the weather forecast data and the geographic environment data, and obtaining a current flood-receiving risk value corresponding to the target building and a current risk level corresponding to the current flood-receiving risk value;

in the embodiment of the invention, before flood risk prediction is performed on a target building, whether a condition for flood risk prediction is triggered can be judged first, specifically, water level real-time monitoring data collected by a hydrological station can comprise monitoring water levels, if the accumulation of the monitoring water levels exceeds a first preset threshold value within 24 hours, flood risk prediction is performed on the target building according to the water level real-time monitoring data, the overstrain real-time monitoring data, weather forecast data and geographic environment data, a current flood risk value corresponding to the target building and a current risk level corresponding to the current flood risk value are obtained, so that interference caused by normal fluctuation of the monitoring value of the hydrological station to the flood risk prediction of the target building can be effectively reduced by judging whether the condition for flood risk prediction is triggered first, and accuracy and reliability of the flood risk prediction result are ensured.

Optionally, according to the target building flood-receiving risk related factors in the target building flood-receiving risk related definition, the target building flood-receiving risk prediction model is built by collecting water level related data of hydrologic stations upstream of a river reservoir in m kilometers nearby the target building, collecting latest 24-hour rainfall related data of rainfall stations in n kilometers nearby the target building, combining geographical environment information such as the distance between the target building and the river reservoir and whether the target building is low or not. Meanwhile, the similarity of regional rainfall characteristics is considered, n takes a value of 5 km as an initial condition for starting a model, and adjustment and optimization are carried out subsequently according to the flood risk conditions of each target building. The target building without the rainfall stations at the periphery can be subjected to substitution processing by collecting relevant rainfall forecast data in authoritative weather forecast of an area where the target building is located, and the rainfall substitution rule is 24 hours: the extra heavy storm corresponds to 250 mm, the heavy storm corresponds to 100 mm, the heavy storm corresponds to 50 mm, the heavy storm corresponds to 25 mm, the medium rain corresponds to 10 mm, the light rain corresponds to 5 mm, and the no rain corresponds to 0 mm.

In a specific implementation, the water level risk prediction can be performed on the hydrologic station according to the water level real-time monitoring data respectively to generate a corresponding hydrologic water level risk value, the hypervigilance risk prediction is performed on the hydrologic station according to the hypervigilance real-time monitoring data to generate a corresponding hydrologic hypervigilance risk value, the rainfall prediction is performed on the area where the target building is located according to the weather forecast data to generate a corresponding rainfall risk value, the risk coefficient calculation is performed on the target building according to the geographic environment data to generate a corresponding risk coefficient, and then the hydrologic water level risk value, the hydrologic hypervigilance risk value, the rainfall risk value and the risk coefficient are adopted to perform flood risk prediction on the target building to obtain a current flood risk value corresponding to the target building, so that the flood risk prediction is performed on the monitoring data collected by the hydrologic station, the measuring station and the like around the building and the geographic environment data of the building, thereby ensuring the reliability and accuracy of the prediction result.

For the hydrologic water level risk value, the water level change of the hydrologic station can be calculated by adopting the monitored water level and the reference water level, the water level rising change coefficient corresponding to the hydrologic station is obtained, the first distance between the target building and the water channel of the hydrologic station is obtained, the water channel distance risk value between the target building corresponding to the first distance and the hydrologic station is determined, then the water level risk prediction is carried out on the hydrologic station by adopting the water level rising change coefficient and the water channel distance risk value, and the corresponding hydrologic water level risk value is generated.

For the hydrologic super-warning risk value, the super-warning real-time monitoring data comprise the exceeding of the warning water level and the warning water level, the water level change of the hydrologic station can be calculated by adopting the exceeding of the warning water level and the warning water level, the hydrologic super-warning risk coefficient corresponding to the hydrologic station is obtained, then a second distance between a target building and a water channel of the super-warning hydrologic station is obtained, a super-warning water channel distance risk value between the target building corresponding to the second distance and the super-warning hydrologic station is determined, and then the hydrologic station is subjected to super-warning risk prediction by adopting the hydrologic super-warning risk coefficient and the super-warning water channel distance risk value, so that the corresponding hydrologic super-warning risk value is generated.

For the rainfall risk value, the weather forecast data can include the rainfall value, and then the rainfall risk coefficient corresponding to the rainfall value and the regional rainfall risk value corresponding to the target building can be obtained first, wherein the regional rainfall risk value is used for representing that the target building has the same regional characteristics in the preset range, and then the rainfall prediction is carried out on the region where the target building is located by adopting the rainfall risk coefficient and the regional rainfall risk value, so that the corresponding rainfall risk value is generated.

For the risk coefficient, the risk coefficient may include a water level risk coefficient and a rainfall risk coefficient, wherein the geographic environment data includes a ground height of a river water surface relative to a target building and a low-lying mark of the target building, and if the ground height is greater than or equal to 0 m, a first water level risk coefficient for the target building is obtained; and if the ground height is smaller than 0 meter, acquiring a second water level risk probability coefficient aiming at the target building. If the low-lying mark represents that the target building is a low-lying building, acquiring a first rainfall risk probability coefficient aiming at the target building; and if the low-lying logo represents that the target building is not the low-lying building, acquiring a second rainfall risk probability coefficient aiming at the target building.

After the different parameter values are obtained, the hydrologic water level risk value, hydrologic super-warning risk value, rainfall risk value, water level risk probability coefficient and rainfall risk probability coefficient can be used for carrying out flood-receiving risk prediction on the target building, and the current flood-receiving risk value corresponding to the target building is obtained, so that flood-receiving risk prediction is carried out through monitoring data collected by hydrologic stations, rainfall stations and the like around the building and geographical environment data of the building, and reliability and accuracy of a prediction result are guaranteed.

In an example, referring to fig. 2, a schematic diagram for eliminating risk identification provided in the embodiment of the present invention is shown, and referring to fig. 3, a schematic diagram for calculating flood risk provided in the embodiment of the present invention is shown, specifically, the current flood risk of the machine building is related to the hydrological water level near the machine building, the rainfall in the area 24 hours, the geographic environment of the machine building, and other factors. And obtaining the current flood risk value and the current risk level of the building through calculation. The calculation process correlation factor is described as follows:

1) A (water level rise coefficient): when the water level of the most recent m times of monitoring data rises, the ratio of the difference between the most recent 2 times of monitoring water level and the reference water level (|Yt-Yt-m+1|)/(|Yt-1-Yt-m+1|), yt represents the water level value at the time t, yt-1 represents the water level value at the time t-1, yt-m+1 represents the water level value at the time m before the time t, and the risk value calculation is performed when the accumulated fluctuation of the monitoring water level for the most recent 24 hours exceeds n cm in consideration of the influence of normal up-down fluctuation of the water level on the risk value calculation. m takes a value 3 as a model starting value, and is optimized and adjusted according to the water level rising condition; taking regional climate seasonal variation factors into consideration, and taking the value of n as the average value of daily water level fluctuation of the non-oversleeching water level of the water level monitoring station in the last 3 months (1 quarter).

2) B, D, F (distance risk value): distance k kilometers between the machine buildings B and D and the water channel of the relevant water level monitoring station (k < = 5): k < = 1, 10 minutes; k >1 and k < = 2,8 minutes; k >2 and k < = 3,6 minutes; k >3 and k < = 4,4 minutes; k >4,2 minutes. Considering that rainfall within k kilometers of a building has the same regional characteristics, calculating according to a risk value of 10 minutes, namely F=10; and taking the k value of 5 as a model starting value, and carrying out optimization adjustment according to the risk of the building and the flood receiving condition.

For A, the numerical value is 0-1; for B, it corresponds to 0-10 minutes; for C, it may be a positive real number; for D, it corresponds to 0-10 minutes. For a and B, the corresponding calculation model may be l=max (a×b), and optionally, real-time monitoring data of a hydrologic station upstream of the river reservoir within k kilometers may be obtained. k takes a value of 5; taking the maximum value of each hydrologic risk. Risk value > = 8 is high risk, risk value > = 5 and <8 is medium risk, the others are low risk. For C and D, the corresponding calculation model may be m=max ((1+C) ×d), and optionally, real-time monitoring data of the river reservoir upstream hyperwarning hydrologic station within k km range may be obtained. k takes a value of 5; taking the maximum value of each hydrologic overtaking alarm risk. Risk value > = 8 is high risk, risk value > = 5 and <8 is medium risk, the others are low risk.

Further, for F, which corresponds to 10 points, consider that rainfall in the k kilometer range of the building has the same regional characteristics, calculated as a risk value of 10 points, i.e., f=10. k takes a value of 5.

3) C (hydrologic hyper-warning risk coefficient): the ratio of the difference between the water level beyond the warning level and the warning level to the difference between the highest water level within the last n years and the warning level is taken as the value of the coefficient C, c= (P-P warning)/(P highest-P warning), P represents the overstepping water level value, P warning represents the warning water level value, and P highest represents the highest water level value within the n years. When the system initially has no history data and cannot obtain the history data, the highest water level is the same as the current water level, i.e. ppax=p. n takes a value of 5, referring to the highest water level of the last 5 years.

4) E (last 24 hours rainfall risk factor): rainfall value > = 100 mm (heavy storm), coefficient E takes a value of 1; the rainfall value is between 0 and 100 mm, and the ratio of the rainfall value to 100 mm is taken as the value of the coefficient E.

For E, which takes a value of 0-1, the calculation model between F and F may be n=max (e×f). Alternatively, real-time monitoring data for rainfall stations in the k kilometer range may be acquired. k takes a value of 5; taking the maximum value of each rainfall risk. Risk value > = 8 is high risk, risk value > = 5 is and risk in <8, others are low risk; and replacing the rainfall forecast data in the authoritative weather forecast of the area where the machine building is located for the machine building without the rainfall stations around. 24 hour rainfall substitution rules: the extra heavy storm corresponds to 250 mm, the heavy storm corresponds to 100 mm, the heavy storm corresponds to 50 mm, the heavy storm corresponds to 25 mm, the medium rain corresponds to 10 mm, the light rain corresponds to 5 mm, and the no rain corresponds to 0 mm.

5) I (water level risk probability coefficient): the height difference (m) (G) > =0 m of the river water surface relative to the machine building floor, and the coefficient I takes a value of 1, so that the machine building is most likely to be flood-catching after the river overflows; in other cases, the coefficient J takes a value of 0.5, which indicates that the machine building may or may not be subjected to flood after the river overflows.

6) J (rainfall risk probability coefficient): whether the machine building is a low-lying machine building (H) is judged, and the coefficient J takes a value of 1 to indicate that the low-lying machine building is most likely to be flood-controlled under the rainfall condition; in other cases, the value of the coefficient J is 0.5, which indicates that the machine building may or may not be subjected to flood under the rainfall condition.

For I, J, it takes a value of 0-1; for H, it is "yes" or "no", i.e. low-lying as well as non-low-lying; for G, which may be a real number, the corresponding calculation model may be (z=max (i×l+β×m), j×n)

7) Building flood risk calculation model (z=max (I (α+l+β×m), j×n)): and respectively adjusting the influence weights of the hydrologic risk and the rainfall risk through the risk probability coefficients of the machine building on the hydrologic station and the rainfall station. The water level rising and overstepping risks have different influence degrees in the water level risk composition, namely alpha and beta, alpha+beta=1, and according to the twenty-eight law, alpha takes a value of 0.2 and beta takes a value of 0.8. For the calculation result, the maximum value of each water level risk and rainfall risk is taken, the risk value > = 8 is high risk, the risk value > = 5 is medium risk, and the other is low risk.

Optionally, the influence weights of the hydrologic risk and the rainfall risk can be respectively adjusted through the risk probability coefficients of the machine building on the hydrologic station and the rainfall station; the water level rising and overstepping risks have different influence degrees in the water level risk composition, namely alpha and beta, alpha+beta=1, and according to the twenty-eight law, alpha takes a value of 0.2 and beta takes a value of 0.8.

8) And (5) early warning is carried out on the medium-risk machine building, and warning is carried out on the high-risk machine building.

Step 103, if the risk level is a middle risk level or a high risk level, predicting the risk of the target building at a future moment according to the current flood risk value, and obtaining a future flood risk value corresponding to the target building and a predicted risk level of the future flood risk value;

in a specific implementation, when the risk level is low risk, flood risk prediction of a target building at a future moment is not needed; when the risk level is medium risk or high risk, the hydrologic water level risk value, hydrologic ultra-warning risk value, rainfall risk value, water level risk probability coefficient and rainfall risk probability coefficient can be predicted and calculated through a one-time smooth index method, and a future flood receiving risk value prediction risk level corresponding to the target building are obtained.

In an example, referring to fig. 4, a schematic diagram of calculation of flood risk provided in the embodiment of the present invention is shown, and on the basis of a current flood risk calculation process of a building, prediction calculation is performed on risk values of hydrologic water level risk, hydrologic overstrain risk and rainfall risk at a future time t by a primary smooth exponential method, so as to obtain a predicted flood risk value and a risk level at the future time t of the building. The process-related calculation factors are described as follows:

1) At (water level rise change coefficient At time t): when the water level of the monitoring data rises m times recently at the time t, the ratio of the difference between the monitoring water level and the reference water level 2 times recently at the time t (|Yt-Yt-m+1|)/(|Yt-1-Yt-m+1|), yt represents the water level value at the time t, yt-1 represents the water level value at the time t-1, and Yt-m+1 represents the water level value at the time m before the time t. m takes a value of 3 as a model starting value, and is optimized and adjusted according to the water level rising condition.

2) B, G, L (distance risk value): distance k kilometers between the buildings B and G and the water channel of the related water level station (k < = 5): k < = 1, 10 minutes; k >1 and k < = 2,8 minutes; k >2 and k < = 3,6 minutes; k >3 and k < = 4,4 minutes; k >4,2 minutes. Considering that rainfall within k kilometers of a building has the same regional characteristics, calculating according to a risk value of 10 minutes, namely F=10; and taking the k value of 5 as a model starting value, and carrying out optimization adjustment according to the risk of the building and the flood receiving condition.

3) Dt (water level change rate): the ratio of the amount of change between the most recent m times of water level monitor values (C) at time t is taken as the value of the change rate Dt, dt= (Ct-Ct-m+1)/(Ct-1-Ct-m+1), the water level rises to a positive number, the water level falls to a negative number, dt represents the water level change rate at time t, ct represents the water level monitor value at time t, ct-1 represents the water level monitor value at time t-1, and Ct-m+1 represents the water level monitor value at time m before time t. And taking the value of m as a model starting value, and performing optimization adjustment according to historical data.

4) Et (water level prediction factor at time t, et=α×dt-1+ (1- α) ×et-1): alpha represents a smooth index, and 0-1 is taken; e represents a predicted value, and D represents an actual value; et represents a predicted value at time t, dt-1 represents an actual value at time t-1, and Et-1 represents a predicted value at time t-1; initially, E0 takes the mean of the most recent m water level change rates (D). Alpha takes a value of 0.5 and m takes a value of 3 as model starting values, and is optimized and adjusted according to the accurate prediction condition.

5) Ft (hydrologic hyper-warning risk coefficient at time t): the ratio of the difference between the warning water level and the warning water level exceeding the time t and the difference between the highest water level and the warning water level within the last n years is taken as the value of a coefficient Ft, ft= (Ht-H warning)/(Hmax-H warning), ht represents the overstepping water level value at the time t, hwarning represents the warning water level value, and Hmax represents the highest water level value within the n years. When the system initially has no history data and cannot obtain the history data, the highest water level is the same as the current water level, i.e. mhmax=ht. n takes the value of 5 and takes the highest water level in the last 5 years as a reference.

6) It (water level overstep change rate): the ratio of the variation between the most recent m times of water level overstep monitoring values (H) at the time t, it= (Ht-Ht-m+1)/(Ht-1-Ht-m+1), the water level rises to a positive number, the water level falls to a negative number, it represents the water level variation rate at the time t, ht represents the water level monitoring value at the time t, ht-1 represents the water level monitoring value at the time t-1, and Ht-m+1 represents the water level monitoring value at the time m before the time t. And taking the value of m as a model starting value, and performing optimization adjustment according to historical data.

7) Jt (water level hyper-alert prediction coefficient at time t, jt=α×it-1+ (1- α) ×Jt-1): alpha represents a smooth index, and 0-1 is taken; j represents a predicted value, and I represents an actual value; jt represents a predicted value at time t, it-1 represents an actual value at time t-1, and Jt-1 represents a predicted value at time t-1; initially, J0 takes the mean of the most recent m water level hypervigilance rates of change (I). Alpha takes a value of 0.5 and m takes a value of 3 as model starting values, and is optimized and adjusted according to the accurate prediction condition.

8) Kt (last 24 hours rainfall risk factor at time t): rainfall > = 100 mm (heavy storm), coefficient Kt takes a value of 1; the rainfall is between 0 and 100 mm, and the ratio of the rainfall value to 100 mm is taken as the value of the coefficient Kt.

9) Nt (rainfall change rate): the ratio between the variation of the rainfall monitoring value (M) at the time of t for the last M times of 24 hours is Nt= (Mt-Mt-m+1)/(Mt-1-Mt-m+1), the rainfall is positive when rising, the rainfall is negative when falling, nt represents the rainfall variation rate at the time of t, mt represents the rainfall monitoring value at the time of t, mt-1 represents the rainfall monitoring value at the time of t-1, and Mt-m+1 represents the rainfall monitoring value at the time of the previous M at the time of t. And taking the value of m as a model starting value, and performing optimization adjustment according to historical data.

10 Ot (t moment rainfall prediction coefficient, ot=α×nt-1+ (1- α) ×ot-1): alpha represents a smooth index, and 0-1 is taken; o represents a predicted value, and N represents an actual value; ot represents a predicted value at time t, nt-1 represents an actual value at time t-1, and Ot-1 represents a predicted value at time t-1; initially, O0 takes the mean of the last m rain change rates (N). Alpha takes a value of 0.5 and m takes a value of 3 as model starting values, and is optimized and adjusted according to the accurate prediction condition.

11 R (water level risk probability coefficient): the height difference (m) (P) of the river water surface relative to the floor of the machine building is equal to 0 m, and the coefficient R takes a value of 1, so that the machine building is most likely to be flood-catching after the river overflows; in other cases, the value of the coefficient R is 0.5, which indicates that the machine building may or may not be flood-controlled after the river overflows.

12 S (rainfall risk probability coefficient): whether the machine building is a low-lying machine building (Q) is judged, and the coefficient S takes a value of 1 to indicate that the low-lying machine building is most likely to be flood-controlled under the rainfall condition; and in other cases, the coefficient S takes a value of 0.5, which indicates that the machine building is likely to be flood-controlled or not flood-controlled under the rainfall condition.

13 Time t machine floor flood risk calculation model (zt=max (R (α×ut+β×vt), s×xt)): zt represents a machine building flood risk value at time t, ut represents a hydrological water level risk value at time t, vt represents a hydrological super-warning risk value at time t, and Xt represents a rainfall risk value at time t. And respectively adjusting the influence weights of the hydrologic risk and the rainfall risk through the risk probability coefficients of the machine building on the hydrologic station and the rainfall station. The water level rising and overstepping risks have different influence degrees in the water level risk composition, namely alpha and beta, alpha+beta=1, and according to the twenty-eight law, alpha takes a value of 0.2 and beta takes a value of 0.8. And (3) taking the maximum value of each water level risk and rainfall risk through flood risk prediction calculation, wherein the risk value > =8 is high risk, the risk value > =5 is medium risk, and the other risks are low risk.

And 104, correlating the current flood receiving risk value with the future flood receiving risk value according to the time interval corresponding to the real-time water level monitoring data and the hyper-alert real-time monitoring data, and generating a flood receiving risk prediction curve aiming at the target building.

In the embodiment of the invention, after the current flood-receiving risk value and the corresponding future flood-receiving risk value corresponding to the target building are obtained, the current flood-receiving risk value and the future flood-receiving risk value can be correlated according to the time interval corresponding to the real-time water level monitoring data and the hyper-alert real-time monitoring data to generate the flood-receiving risk prediction curve aiming at the target building, so that the future moment is predicted, the corresponding risk prediction curve is constructed, the flood-receiving risk of the target building at a certain future time is conveniently presented, corresponding flood-preventing measures can be timely made by corresponding personnel, and the safety of the target building is ensured.

Referring to fig. 5, a schematic diagram of flood risk calculation and prediction provided in an embodiment of the present invention is shown, which specifically may include: 1) And calculating the monitoring data and the historical data of the current relevant hydrologic station and the rainfall station according to the current flood risk value calculation model of the machine building to obtain the current flood risk value and the current flood risk level of the machine building. 2) And calculating the predicted value and the risk level of the flood-receiving risk of the machine building at different moments such as middle-short period t+1, t+2, t+n and the like in the future of the machine building according to the calculation process of the short-term flood-receiving risk value of the machine building in the future of the machine building by combining the history data of the machine building on the basis of the current flood-receiving risk value of the machine building. 3) Predicting a time interval, and collecting time periods of water level and rainfall real-time monitoring data (or weather forecast data of an area where a building is located) along (for example: every 15 minutes), the flood receiving risk prediction values of a plurality of time points of the machine building are connected together to form a machine building flood receiving risk prediction curve which is unfolded according to time sequence. 4) And when the collected water condition or rain condition monitoring data is updated each time, the prediction result of the machine building flood-receiving risk value is synchronously updated along with the calculation of the flood-receiving risk value, and the machine building flood-receiving risk prediction curve is corrected in real time.

The flood risk corresponding to the target building can be predicted through the process, and the target building is required to have a certain flood risk when facing the corresponding flood risk, so that the building flood risk value corresponding to the target building can be obtained by acquiring the building attribute information, the historical flood risk data and the municipal risk data corresponding to the target building and inputting the building attribute information, the historical flood risk data and the municipal risk data into the flood risk prediction model.

In a specific implementation, according to the related factors of the machine building flood prevention risk in the machine building flood prevention risk definition, a machine building flood prevention risk prediction model is established through factors such as a machine building geographic environment, flood prevention facilities, historical flood receiving data and the like, so that references are provided for establishment and implementation of a machine building flood prevention safety reinforcement scheme, configuration of machine building flood prevention materials and personnel, establishment of a machine building flood prevention emergency plan and the like. Referring to fig. 6, a schematic diagram of flood control risk calculation provided in the embodiment of the present invention is shown, where a machine building flood control risk prediction model removes a subjective and dynamic management method and process such as a flood control emergency plan, flood control personnel configuration, and the like, and comprehensively evaluates objective flood control capability of a machine building from factors quantitatively calculated such as flood control related inherent attributes such as machine building geographic characteristics, historical flood receiving data, surrounding municipal risk data, and the like, so as to obtain a machine building flood control risk value and a risk level, and provide references for machine building flood control capability reinforcement, flood control management method, emergency plan, and the like. The process-related calculation factors are described as follows:

1) Building a building flood prevention risk calculation model (Z= (A+B+C+D+E+F+G)/70) from building attributes, historical flood receiving data and municipal risk data.

2) The flood prevention risk value and the risk grade of the computer building are obtained through calculation: risk value > = 8 is high risk, risk value > = 5 is medium risk, and others are low risk.

3) And (5) early warning is carried out on the medium-risk machine building, and warning is carried out on the high-risk machine building.

The correlation calculation model, the corresponding relation of each correlation factor and the detailed description related to the process can be shown in the following table:

in addition, the model for predicting the flood-receiving and flood-preventing risks of the machine building relates to factors such as the water level, rainfall, geographical environment of the machine building and the like of a river reservoir around the machine building, and the risks have dynamic characteristics. Therefore, the machine building flood-receiving and flood-preventing risk prediction model also needs to be continuously optimized and upgraded along with the changes of corresponding risk factors affecting the machine building, the improvement of the prediction accuracy of the model, the setting of personalized parameters of the machine building and the like. Referring to fig. 7, a schematic flow chart of model evaluation provided in the embodiment of the present invention is shown, and for the building flood control and flood control risk model establishment and optimization processes, follow: and (3) identifying relevant factors, analyzing the factor relevance, establishing or updating a model, and checking and evaluating historical data.

It should be noted that the embodiments of the present invention include, but are not limited to, the foregoing examples, and it is to be understood that, under the guidance of the idea of the embodiments of the present invention, the embodiments of the present invention may be set in other ways, which are not limited thereto.

In order to enable those skilled in the art to better understand the technical solutions according to the embodiments of the present invention, the following description is given by way of example:

the machine building flood-receiving and flood-preventing risk prediction model related in the embodiment of the invention can be applied to the following business, and provides references for machine building reinforcement, flood prevention and rescue and emergency dispatch:

1) Predicting, early warning and alarming the flood risk of the machine building;

2) Testing and analyzing the flood risk pressure of the building;

3) Analyzing flood prevention risks of the machine building and reinforcing the machine building;

4) Predicting the flood risk of the machine building, and early warning and alarming.

And (3) forming a machine building flood risk prediction curve by using a machine building flood risk prediction model and calculating the risk value and the risk level in the current and future medium and short periods of the machine building in real time. And early warning is carried out on the machine building with the middle risk of the current risk level, and warning is carried out on the machine building with the high risk of the current risk level.

In addition, the method can be used for testing and analyzing the stress of the flood risk of the machine building, and referring to fig. 8, a flow chart of the testing and analyzing provided by the embodiment of the invention is shown, and the simulation test is carried out on the stress conditions of the flood risk of the machine building under no scene such as heavy rainfall, flood discharge of a river reservoir, river overflow and the like. According to the characteristics of different rainfall, water level of a river reservoir and the like in different scenes, one or more groups of water level, super warning water level and 24-hour rainfall monitoring data values at different time points are simulated according to time sequences, the flood-receiving risk value and the risk level of the machine building under each group of simulated monitoring data at each time point according to time sequence progress are calculated through a machine building flood-receiving risk prediction model, the calculation result is analyzed to obtain medium risk and high risk of the machine building at which time point, and the pressure time points of the medium risk and the high risk of the machine building are obtained, so that references are provided for time limit requirements of machine building flood prevention and rescue buffering. Specifically, the method comprises the steps of calculating the machine building flood risk value and the risk level under the condition of simulating the water level, the overwarning water level and the 24-hour rainfall according to the scene simulation time sequence (the water level is progressed from low to high along the time), the overwarning water level data (the overwarning water level is progressed from low to high along the time), the 24-hour rainfall data (the rainfall is progressed from small to large along the time) according to the scene simulation time sequence, calculating the machine building flood risk value and the risk level under the condition of simulating the water level, the overwarning water level and the 24-hour rainfall at each time sequence point according to the machine building flood risk prediction model, analyzing the flood risk value and the risk level at each time sequence point of the machine building, obtaining the time when the medium risk and the high risk of the machine building appear, outputting a machine building flood risk pressure test report, judging whether to continue the flood risk pressure test under other scenes, and returning to corresponding steps if yes, otherwise, ending.

And the system can also be used for early warning, alarming and reinforcing the machine building flood prevention risk, the relevant factors of the machine building flood prevention stability are reflected through a machine building flood prevention risk prediction model, the machine building with medium risk is early warned through calculation of the machine building flood prevention risk value and the risk level, and the machine building with high risk is warned. Referring to fig. 9, a schematic flow chart of flood control assessment provided in the embodiment of the invention is shown, and a machine building manager can adjust relevant flood control factor parameters according to a machine building flood control risk prediction model, evaluate and obtain a machine building flood control reinforcement and correction scheme with relatively reasonable input-output ratio, and perform reinforcement and correction on a machine building according to the machine building reinforcement and correction scheme so as to improve the flood control capability of the machine building. Meanwhile, the flood prevention weak factors of the machine building are marked, and important attention and prevention are paid in flood prevention and emergency plans. Specifically, a flood prevention risk value can be calculated according to the machine building attribute data, flood receiving history data and municipal data, the corresponding flood prevention risk level is evaluated, then whether the risk is acceptable is judged, if not, flood prevention reinforcement is realized by reinforcing relevant flood prevention materials of the machine building, personnel configuration and the like, and if so, the corresponding evaluation can be ended.

It should be noted that, for simplicity of description, the method embodiments are shown as a series of acts, but it should be understood by those skilled in the art that the embodiments are not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred embodiments, and that the acts are not necessarily required by the embodiments of the invention.

Referring to fig. 10, a block diagram of a device for handling flood prevention risks of a building according to an embodiment of the present invention may specifically include the following modules:

the data acquisition module 1001 is configured to acquire target data corresponding to a target building, where the target data at least includes water level real-time monitoring data and hypervigilance real-time monitoring data acquired by a hydrologic station, weather forecast data corresponding to an area where the target building is located acquired by a rainfall station, and geographic environment data corresponding to the target building;

the current flood-receiving risk calculation module 1002 is configured to predict flood-receiving risk of the target building according to the water level real-time monitoring data, the overstrain real-time monitoring data, the weather forecast data and the geographic environment data if the water level real-time monitoring data meets an alert condition, so as to obtain a current flood-receiving risk value corresponding to the target building and a current risk level corresponding to the current flood-receiving risk value;

The future flood-receiving risk prediction module 1003 is configured to predict, according to the current flood-receiving risk value, the risk of the target building at a future time if the risk level is a middle risk level or a high risk level, and obtain a future flood-receiving risk value corresponding to the target building and a predicted risk level of the future flood-receiving risk value;

the curve generating module 1004 is configured to correlate the current flood receiving risk value with the future flood receiving risk value according to a time interval corresponding to the real-time water level monitoring data and the hyper-alert real-time monitoring data, and generate a flood receiving risk prediction curve for the target building.

In an alternative embodiment, the water level real-time monitoring data includes monitoring a water level, and the current flood risk calculating module 1002 is specifically configured to:

In an alternative embodiment, the current flood risk calculation module 1002 is specifically configured to:

In an alternative embodiment, the water level real-time monitoring data further includes a reference water level, and the current flood risk calculating module 1002 is specifically configured to:

In an alternative embodiment, the overstep real-time monitoring data includes an overstep water level and an alert water level, and the current flood risk calculation module 1002 is specifically configured to:

In an alternative embodiment, the weather forecast data includes a rain value, and the current flood risk calculation module 1002 is specifically configured to:

In an alternative embodiment, the geographical environment data includes a ground level of a river water surface relative to the target building and a depression identification of the target building, and the current flood risk calculation module 1002 is specifically configured to:

In an alternative embodiment, the risk coefficient includes a water level risk probability coefficient and a rainfall risk probability coefficient, and the current flood risk calculating module 1002 is specifically configured to:

In an alternative embodiment, the future flood risk prediction module 1003 is specifically configured to:

In an alternative embodiment, further comprising:

For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.

In addition, the embodiment of the invention also provides electronic equipment, which comprises: the processor, the memory, the computer program stored on the memory and capable of running on the processor, the computer program realizes each process of the above-mentioned building flood prevention risk processing method embodiment when being executed by the processor, and can achieve the same technical effect, in order to avoid repetition, the description is omitted here.

The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, realizes the processes of the above embodiment of the method for processing building flood prevention risks, and can achieve the same technical effects, and in order to avoid repetition, the description is omitted here. Wherein the computer readable storage medium is selected from Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.

Fig. 11 is a schematic diagram of a hardware structure of an electronic device implementing various embodiments of the present invention.

The electronic device 1100 includes, but is not limited to: radio frequency unit 1101, network module 1102, audio output unit 1103, input unit 1104, sensor 1105, display unit 1106, user input unit 1107, interface unit 1108, memory 1109, processor 1110, and power supply 1111. It will be appreciated by those skilled in the art that the structure of the electronic device according to the embodiments of the present invention is not limited to the electronic device, and the electronic device may include more or less components than those illustrated, or may combine some components, or may have different arrangements of components. In the embodiment of the invention, the electronic equipment comprises, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted terminal, a wearable device, a pedometer and the like.

It should be understood that, in the embodiment of the present invention, the radio frequency unit 1101 may be used for receiving and transmitting signals during the process of receiving and transmitting information or communication, specifically, receiving downlink data from a base station and then processing the received downlink data by the processor 1110; and, the uplink data is transmitted to the base station. Typically, the radio frequency unit 1101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 1101 may also communicate with networks and other devices through a wireless communication system.

The electronic device provides wireless broadband internet access to the user through the network module 1102, such as helping the user to send and receive e-mail, browse web pages, and access streaming media, etc.

The audio output unit 1103 may convert audio data received by the radio frequency unit 1101 or the network module 1102 or stored in the memory 1109 into an audio signal and output as sound. Also, the audio output unit 1103 may also provide audio output (e.g., a call signal reception sound, a message reception sound, etc.) related to a specific function performed by the electronic device 1100. The audio output unit 1103 includes a speaker, a buzzer, a receiver, and the like.

The input unit 1104 is used for receiving an audio or video signal. The input unit 1104 may include a graphics processor (Graphics Processing Unit, GPU) 11041 and a microphone 11042, the graphics processor 11041 processing image data of still pictures or video obtained by an image capturing device (such as a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 1106. The image frames processed by the graphics processor 11041 may be stored in memory 1109 (or other storage medium) or transmitted via the radio frequency unit 1101 or the network module 1102. The microphone 11042 may receive sound and can process such sound into audio data. The processed audio data may be converted into a format output that can be transmitted to the mobile communication base station via the radio frequency unit 1101 in the case of a telephone call mode.

The electronic device 1100 also includes at least one sensor 1105, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 11061 according to the brightness of ambient light, and the proximity sensor can turn off the display panel 11061 and/or the backlight when the electronic device 1100 moves to the ear. As one of the motion sensors, the accelerometer sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and direction when stationary, and can be used for recognizing the gesture of the electronic equipment (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; the sensor 1105 may further include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which are not described herein.

The display unit 1106 is used to display information input by a user or information provided to the user. The display unit 1106 may include a display panel 11061, and the display panel 11061 may be configured in the form of a liquid crystal display (Liquid Crystal Display, LCD), an Organic Light-Emitting Diode (OLED), or the like.

The user input unit 1107 may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the electronic device. Specifically, the user input unit 1107 includes a touch panel 11071 and other input devices 11072. The touch panel 11071, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on the touch panel 11071 or thereabout using any suitable object or accessory such as a finger, stylus, etc.). The touch panel 11071 may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends the touch point coordinates to the processor 1110, and receives and executes commands sent from the processor 1110. In addition, the touch panel 11071 may be implemented in various types of resistive, capacitive, infrared, surface acoustic wave, and the like. The user input unit 1107 may include other input devices 11072 in addition to the touch panel 11071. In particular, other input devices 11072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail herein.

Further, the touch panel 11071 may be overlaid on the display panel 11061, and when the touch panel 11071 detects a touch operation thereon or thereabout, the touch panel is transferred to the processor 1110 to determine a type of touch event, and then the processor 1110 provides a corresponding visual output on the display panel 11061 according to the type of touch event. It will be appreciated that in one embodiment, the touch panel 11071 and the display panel 11061 are implemented as two separate components to implement the input and output functions of the electronic device, but in some embodiments, the touch panel 11071 may be integrated with the display panel 11061 to implement the input and output functions of the electronic device, which is not limited herein.

The interface unit 1108 is an interface for connecting an external device to the electronic apparatus 1100. For example, the external devices may include a wired or wireless headset port, an external power (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 1108 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the electronic apparatus 1100 or may be used to transmit data between the electronic apparatus 1100 and an external device.

The memory 1109 may be used to store software programs as well as various data. The memory 1109 may mainly include a storage program area that may store an operating system, application programs required for at least one function (such as a sound playing function, an image playing function, etc.), and a storage data area; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, memory 1109 may include high-speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

The processor 1110 is a control center of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing software programs and/or modules stored in the memory 1109, and invoking data stored in the memory 1109, thereby performing overall monitoring of the electronic device. Processor 1110 may include one or more processing units; preferably, the processor 1110 may integrate an application processor that primarily handles operating systems, user interfaces, applications, etc., with a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 1110.

The electronic device 1100 may also include a power supply 1111 (e.g., a battery) for powering the various components, and the power supply 1111 may preferably be logically coupled to the processor 1110 by a power management system that performs functions such as managing charging, discharging, and power consumption.

In addition, the electronic device 1100 includes some functional modules that are not shown, and are not described herein.

It should be noted that, in this document, 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.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.

The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units 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 invention 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 invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.

The foregoing is merely illustrative of the present invention, and the present invention 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 invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.

Claims

1. A method for managing flood prevention risks of a building, comprising:

2. The method according to claim 1, wherein the water level real-time monitoring data includes monitoring a water level, and if the water level real-time monitoring data meets an alert condition, performing flood risk prediction on the target building according to the water level real-time monitoring data, the hyper-alert real-time monitoring data, the weather forecast data and the geographical environment data to obtain a current flood risk value corresponding to the target building and a current risk level corresponding to the current flood risk value, including:

3. The method according to claim 2, wherein the predicting the flood risk of the target building according to the water level real-time monitoring data, the hypervigilance real-time monitoring data, the weather forecast data and the geographical environment data, obtaining a current flood risk value corresponding to the target building and a risk level corresponding to the current flood risk value, includes:

4. A method according to claim 3, wherein the water level real-time monitoring data further comprises a reference water level, the performing water level risk prediction on the hydrologic station according to the water level real-time monitoring data, and generating a corresponding hydrologic water level risk value comprises:

5. A method according to claim 3, wherein the hyperalert real-time monitoring data includes an excess alert level and an alert level, and wherein the performing the hyperalert risk prediction on the hydrologic station according to the hyperalert real-time monitoring data generates a corresponding hydrologic hyperalert risk value, comprising:

6. The method of claim 3, wherein the weather forecast data includes a rainfall value, and the performing rainfall prediction on the area where the target building is located according to the weather forecast data, and generating a corresponding rainfall risk value includes:

7. A method according to claim 3, wherein the geographical environment data includes a ground level of a river water surface relative to the target building and a depression identification of the target building, and wherein the calculating the risk factor for the target building based on the geographical environment data generates the corresponding risk factor comprises:

8. The method according to claim 7, wherein the risk factors include a water level risk probability factor and a rainfall risk probability factor, the predicting flood risk of the target building using the hydrological water level risk value, the hydrological super-warning risk value, the rainfall risk value and the risk factor to obtain a current flood risk value corresponding to the target building includes:

9. The method according to claim 8, wherein the predicting risk of the target building at the future time according to the current flood risk value, obtaining a future flood risk value corresponding to the target building and a predicted risk level of the future flood risk value, includes:

10. The method as recited in claim 2, further comprising:

11. A device for handling flood control risks in a building, comprising:

12. An electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

The memory is used for storing a computer program;

the processor being configured to implement the method of any of claims 1-10 when executing a program stored on a memory.

13. A computer-readable storage medium having instructions stored thereon, which when executed by one or more processors, cause the processors to perform the method of any of claims 1-10.