CN116030597A

CN116030597A - Geological disaster risk early warning processing method and system and electronic equipment

Info

Publication number: CN116030597A
Application number: CN202211342970.0A
Authority: CN
Inventors: 郑小华; 张宇; 田蒙
Original assignee: Chengdu Smart Enterprise Development Research Institute Co ltd
Current assignee: Chengdu Smart Enterprise Development Research Institute Co ltd
Priority date: 2022-10-31
Filing date: 2022-10-31
Publication date: 2023-04-28

Abstract

According to the geological disaster risk early warning processing method, the geological disaster risk early warning processing system and the electronic equipment, a target area is divided into a plurality of subareas, corresponding similar areas are determined, when precipitation exists, the ground runoff prediction data of each subarea in the non-raining area are determined according to the runoff quantity of the similar areas in the raining area, and then the address disaster generation probability when the rain cloud moves to the non-raining area is determined. Therefore, accurate early warning of the ultra-short period can be realized, and accuracy and instantaneity of early warning of geological disasters are improved.

Description

Geological disaster risk early warning processing method and system and electronic equipment

Technical Field

The application relates to the field of geological disaster risk early warning processing, in particular to a geological disaster risk early warning processing method, a geological disaster risk early warning processing system and electronic equipment.

Background

The geological disasters are always the objects of the key monitoring of natural disasters, the types of the geological disasters are various, such as landslide, debris flow, collapse, ground collapse and the like, and the traditional detection mode mainly obtains the information of geological changes through manual inspection or through the mode of sensor and monitoring equipment information feedback, so that the possibility of occurrence of the geological disasters is estimated.

However, because the change of the geological disaster object is complex, especially the disasters such as landslide, debris flow, mountain torrent and the like are influenced by vegetation coverage rate, soil softness, moisture content, gradient and the like on one hand, and are greatly influenced by short-time strong rainfall and other climatic conditions on the other hand, the information of single geological change obtained from the target monitoring site is difficult to accurately provide enough long early warning time, and the ultra-short-term accurate early warning cannot be realized.

Disclosure of Invention

In order to overcome the above-mentioned shortcomings in the prior art, an object of the present application is to provide a geological disaster risk early warning processing method, which includes:

dividing the target area into a plurality of sub-areas;

for each subarea, determining a similar area similar to the topographic features of the subarea according to the topographic feature data of the subarea;

acquiring the ground runoff data of each subarea acquired by a plurality of intelligent detection devices in a rainy area, and determining the movement direction of the rain cloud;

taking each sub-area in the non-raining area in the rainy cloud movement direction as a target sub-area, and searching whether a target similar area corresponding to the target sub-area exists in the rainy area;

Aiming at a first target subarea with the target similar area in the target subareas, taking the runoff quantity of the target similar area corresponding to the first target subarea as the surface runoff prediction data of the first target subarea;

aiming at a second target subarea in which the target similar area does not exist in the target subareas, calculating the ground runoff prediction data of the second target subarea according to the historical ground runoff data of the second target subarea;

according to the ground runoff prediction data of each target subarea, determining the generation probability of the address disaster when the rain cloud moves to the area without raining, and outputting a corresponding alarm notification according to the generation probability of the address disaster.

In one possible implementation manner, the step of dividing the target area into a plurality of sub-areas includes:

acquiring surface trend data of a target area;

and carrying out surface triangular mesh division processing on the target area according to the surface trend data to obtain a triangular mesh model of the target area, wherein the triangular mesh model comprises a subarea formed by a plurality of triangular patches.

In one possible implementation manner, the step of performing surface triangular mesh division processing on the target area according to the surface trend data includes:

Converting the formation of the target area into a curved surface according to the surface trend data;

determining convex peak points and concave valley points in the curved surface;

and connecting each peak point with the adjacent valley points, and connecting each valley point with other adjacent valley points to form a plurality of triangular patches.

In one possible implementation manner, the step of determining, for each sub-region, a similar region similar to the topographic feature of the sub-region according to the topographic feature data of the sub-region includes:

determining the topography vector of each subarea according to the topography characteristic data of each subarea; the terrain vector comprises data items for representing the area of the subarea, the slope direction of the subarea slope, the vegetation coverage rate of the subarea and the soil looseness of the subarea;

for each subarea, calculating the similarity between other subareas except for the preset distance of the subarea and the topographic vector of the subarea;

and determining other subareas, the similarity of which with the topographic vector of which is greater than a preset threshold value, as the similar areas of the subareas.

In one possible implementation manner, the step of calculating, for a second target sub-area in which the target similar area does not exist in the target sub-area, the ground runoff prediction data of the second target sub-area according to the historical ground runoff data of the second target sub-area includes:

Acquiring historical ground runoff data according to the first target subarea and the second target subarea;

determining the runoff data association proportion of the first target subarea and the second target subarea according to the historical ground runoff data of the first target subarea and the second target subarea;

acquiring the surface runoff prediction data of each first target subarea;

and calculating the surface runoff prediction data of the second target subarea according to the runoff data association proportion and the surface runoff prediction data of the first target subarea.

In a possible implementation manner, the step of determining an address disaster generation probability when the rain cloud moves to the non-raining area according to the ground runoff prediction data of each target sub-area includes:

determining total runoff prediction data of the non-raining areas according to the ground runoff prediction data of each target subarea;

acquiring GNSS surface displacement monitoring data of the non-raining area;

and determining the address disaster generation probability when the rain cloud moves to the non-raining area according to the total runoff prediction data of the non-raining area and the GNSS surface displacement monitoring data.

In one possible implementation manner, the step of determining an address disaster generation probability when a rain cloud moves to the non-raining area according to the total runoff prediction data of the non-raining area and the GNSS surface displacement monitoring data includes:

acquiring multi-model detection data acquired by each intelligent detection device in the target area, wherein the intelligent detection devices comprise a motion sensor, a thermometer and a meteorological sensor; the method comprises the steps of carrying out a first treatment on the surface of the

And determining the probability of address disasters generated when the rain cloud moves to the non-raining area according to the multi-model detection data, the total runoff prediction data and the GNSS surface displacement monitoring data of the non-raining area.

Another object of the present application is to provide a geological disaster risk early warning processing system, the system comprising:

the region dividing module is used for dividing the target region into a plurality of sub-regions;

the similarity calculation module is used for determining a similar region similar to the topographic features of the subareas according to the topographic feature data of the subareas for each subarea;

the data acquisition module is used for acquiring the surface runoff data of each subarea acquired by the plurality of intelligent detection devices in the rainy area and determining the movement direction of the rain cloud;

The regional query module is used for taking each sub-region in the non-raining region in the rainy cloud movement direction as a target sub-region and searching whether a target similar region corresponding to the target sub-region exists in the rainy region;

the first prediction module is used for regarding the first target subarea with the target similar area in the target subareas, and taking the runoff quantity of the target similar area corresponding to the first target subarea as the surface runoff prediction data of the first target subarea;

the second prediction module is used for calculating the ground runoff prediction data of a second target subarea according to the historical ground runoff data of the second target subarea aiming at the second target subarea in which the target similar area does not exist;

the alarm notification module is used for determining the address disaster generation probability when the rain cloud moves to the non-raining area according to the ground runoff prediction data of each target subarea, and outputting a corresponding alarm notification according to the address disaster generation probability.

Another object of the present application is to provide an electronic device, including a processor and a machine-readable storage medium, where the machine-readable storage medium stores machine-executable instructions, which when executed by the processor, implement the geological disaster risk early warning processing method provided by the present application.

Another object of the present application is to provide a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions, which when executed by one or more processors, implement the geological disaster risk early warning processing method provided by the present application.

Compared with the prior art, the application has the following beneficial effects:

according to the geological disaster risk early warning processing method, the geological disaster risk early warning processing system and the electronic equipment, the target area is divided into the plurality of subareas, the corresponding similar areas are determined, when precipitation exists, the ground runoff prediction data of each subarea in the non-raining area are determined according to the runoff quantity of the similar areas in the raining area, and then the address disaster generation probability when the rain cloud moves to the non-raining area is determined. Therefore, accurate early warning of the ultra-short period can be realized, and accuracy and instantaneity of early warning of geological disasters are improved.

Drawings

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

Fig. 1 is a schematic diagram of a geological disaster risk early warning processing method provided in an embodiment of the present application;

fig. 2 is a schematic diagram of division of subareas according to an embodiment of the present application;

FIG. 3 is a schematic diagram of the similarity region determination according to the embodiment of the present application;

FIG. 4 is a second schematic diagram of the similarity region determination provided in the embodiments of the present application;

fig. 5 is a schematic diagram of an electronic device according to an embodiment of the present application;

fig. 6 is a schematic functional block diagram of a geological disaster risk early warning processing system according to an embodiment of the present application.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.

Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.

In the description of the present application, it should be noted that the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.

In the description of the present application, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art in a specific context.

Referring to fig. 1, fig. 1 is a flowchart of a geological disaster risk early warning processing method provided in this embodiment, and the method includes various steps described in detail below.

Step S110, dividing the target area into a plurality of sub-areas.

In a possible implementation manner, surface trend data of a target area may be acquired in step S110, and then surface triangular mesh division processing is performed on the target area according to the surface trend data, so as to obtain a triangular mesh model of the target area, where the triangular mesh model includes a sub-area formed by a plurality of triangular patches.

For example, referring to fig. 2, the topography of the target area may be converted into a curved surface according to the surface trend data, raised peak points and recessed valley points in the curved surface may be determined, each peak point may be connected to a valley point adjacent thereto, and each valley point may be connected to other valley points adjacent thereto, so as to form a plurality of triangular patches. Alternatively, to reduce the amount of computation, a portion of the smaller bumps may be omitted and only bumps greater than a predetermined value are analyzed. By adopting the mode, the slope division of the topographic map can be realized by using computer programming, and the time cost of manual division is greatly reduced.

In this embodiment, the target area may be a single area for early warning management, or an area set in other manners, which is not specifically limited herein. Preferably, in the present embodiment, the coverage area of the target area may be greater than tens of kilometers.

Step S120, for each sub-area, determining a similar area similar to the topographic feature of the sub-area according to the topographic feature data of the sub-area.

In a possible implementation manner, in step S120, a terrain vector of each sub-region may be determined according to the terrain feature data of the sub-region, where the terrain vector includes data items that characterize the sub-region area, the sub-region slope direction, the sub-region slope inclination, the sub-region vegetation coverage rate, and the sub-region soil looseness.

The topography vector may be normalized for facilitating subsequent computation. In order to facilitate similarity calculation, the terrain vector can be normalized, the slope is oriented at 0 degrees in the east, 90 degrees in the north, 180 degrees in the west and 270 degrees in the south, and the slope is normalized to 0-1 in the angle; normalizing slope inclination to 0-1 at 0-90 degrees; and normalizing the vegetation coverage and the soil looseness to 0-1 according to the change range of the target area.

Then, for each subarea, calculating the similarity between the subareas and the terrain vector of the subarea except for the preset distance of the subarea. And determining other subareas, the similarity of which with the topographic vector of which is greater than a preset threshold value, as the similar areas of the subareas.

Because the rain cloud needs a certain time to reach the area to be predicted for prediction, if the area to be predicted is too close to the rainy area, the prediction time is too short, the meaning of prediction is not great, and therefore the area exceeding a certain distance needs to be predicted. Based on the principle, for each subarea, calculating the similarity between all other subareas except the preset distance and the subarea by using the center of the subarea. For example, referring to fig. 3, the center of the circle shown in fig. 3 is the sub-area, the radius is the preset record, and in this embodiment, only the similarity between the sub-areas other than the circle and the sub-area at the center of the circle is calculated. In the case of the second embodiment, as shown in fig. 4, the region a is raining, and the direction of the wind is oriented toward the region B, and only the distance between the region a and the region B reaches a certain value, it is predicted that the distance is significant.

In this embodiment, the similarity may use any vector similarity calculation method, which is not limited herein. Saving sub-region pairs with similarity greater than the preset threshold, for example, 345 th sub-region and 872 th sub-region with similarity greater than 0.8, and saving (345, 872,0.8) to a database; and carrying out similar similarity calculation on each sub-region in the target region to obtain all sub-region pairs with similarity larger than the preset threshold value.

Step S130, acquiring the surface runoff data of all the subareas acquired by the plurality of intelligent detection devices in the rainy area, and determining the movement direction of the rain cloud.

In this embodiment, the surface runoff data of each sub-region in the rainy region may be obtained by a plurality of the intelligent detection devices set in the target region. And, weather data may be acquired by the smart detection device or from other devices to determine the rain cloud movement direction.

And step S140, taking each sub-area in the non-raining area in the rainy cloud movement direction as a target sub-area, and searching whether a target similar area corresponding to the target sub-area exists in the rainy area.

In this embodiment, the target sub-area in the non-raining area may have a plurality of similar areas where the similarity of the topographic vectors is greater than a preset threshold, and in step S140, the raining area may be searched for whether there is a target similar area corresponding to the target sub-area.

Step S150, regarding a first target sub-area in which the target similar area exists in the target sub-areas, taking the runoff amount of the target similar area corresponding to the first target sub-area as the surface runoff prediction data of the first target sub-area.

Because the target similar region has higher geographic feature similarity with the target sub-region, when the rain cloud passes through, the surface runoffs of the target sub-region and the target similar region should also have higher similarity, so that the measured runoff quantity of the target similar region corresponding to the target similar region can be used as the surface runoff prediction data of the first target sub-region when the rain cloud passes through the first target sub-region.

For example, as shown in fig. 4, region a (i.e., the rainy region) is raining, and the rain-laden cloud is traveling toward region B (i.e., the non-rainy region), a similar region is found from region a for each sub-region in region B. If the area in the area B exists in the area A, the area A and the area B can generate similar ponding effect if the ponding cloud runs to the area B, so that the measured real-time runoff in the area A can be assigned to the similar area in the area B for subsequent prediction.

Step S160, aiming at a second target subarea which does not exist in the target similar area, calculating the ground runoff prediction data of the second target subarea according to the historical ground runoff data of the second target subarea.

In a possible implementation manner, in step S160, historical surface runoff data according to the first target sub-area and the second target sub-area may be obtained, and a runoff data association proportion of the first target sub-area and the second target sub-area may be determined according to the historical surface runoff data of the first target sub-area and the second target sub-area.

And then obtaining the surface runoff prediction data of each first target subarea, and calculating the surface runoff prediction data of the second target subarea according to the runoff data association proportion and the surface runoff prediction data of the first target subarea.

For example, for areas B where no similar area to the area being rained is found, runoff calculation may be performed using the data of the sub-areas where the corresponding rained area has been found, as well as the history data. Illustratively, the area 1 has a corresponding similar rainy subarea, the runoff of the area 1 is determined to be m according to the rainy subarea, the area 2 does not have a corresponding similar rainy subarea, but the runoff of the area 2 is 0.8 times that of the area 1 according to the historical data, and the runoff data of the area 2 is set to be 0.8m.

Step S170, determining the generation probability of the address disaster when the rain cloud moves to the non-raining area according to the ground runoff prediction data of each target subarea, and outputting a corresponding alarm notification according to the generation probability of the address disaster.

The method comprises the steps that all subareas in the area B are subjected to surface runoff assignment, runoffs generated by each slope are collected at the lowest point of a triangle, then runoffs in all minimum values (namely, each small valley) are calculated according to the surface runoffs in each subarea, the runoffs in each small valley are collected in valleys with lower coordinates, the surface runoffs generated by slopes with different sizes in the whole area to be predicted can be obtained, the runoffs are mainly generated by overlarge short-time runoffs, the larger the runoffs are, the larger the probability of occurrence of the runoffs and the debris flow is, and therefore the ground runoffs when the rain accumulation clouds run to the area without rain can be determined according to the calculation result of the runoffs, the probability of occurrence of the runoffs, the debris flow and other disasters after the rain accumulation in the area without rain is predicted, when the probability is very large, ultra-short-term early warning is carried out, and the specific probability size can be determined according to historical runoffs and disaster data.

Based on the above design, since the rain accumulation cloud is moving at a certain speed, typically 30 to 60 km/h, when one area is raining, another area may take tens or even tens of minutes to be raining, and by using this time difference, the area to be rained can be predicted by real-time data of the raining area, so as to improve the accuracy.

Further, in the present embodiment, the early warning is performed once the monitoring data reaches the early warning condition. The early warning flow is as follows: monitoring early warning, triggering system early warning, short message, weChat public number early warning, early warning treatment linkage, starting a plan and dispatching command. After the system obtains an early warning result through model analysis, triggering an early warning process of the system, pushing early warning information to different responsible hands according to early warning registration, and adopting a plurality of early warning notification modes to ensure that the early warning information can be notified to related personnel; early warning treatment linkage: automatically positioning the early warning position, displaying surrounding geographic position information and affected village, personnel and road information, linking surrounding intelligent equipment, displaying real-time monitoring information and corresponding historical variation trend, video monitoring and the like, and linking treatment plans and the like.

Further, after triggering the early warning, the processing can be performed according to a preset emergency plan. For example, the protocol is first structured: the original text plan is decomposed and digitized to form an intelligent plan. Carrying out structuring processing on the plan according to plan basic information, command relationship diagram, emergency resource configuration and the like, wherein the plan basic information comprises: information such as plan name, plan level (level i (particularly significant), level ii (significant), level iii (large), and level iv (general)), type (natural disasters, accident disasters), event scenario, and the like; command relationship diagram: the job name and the contact way of the personnel associated with the plan are matched in advance, so that the plan can be conveniently and timely scheduled when being started; emergency resource configuration: related information such as emergency resources, expert resources, rescue teams, refuge sites, medical sanitation, communication institutions and the like is associated in advance according to the plan. Secondly, designing a plan flow: and carrying out plan flow design according to the flows of early warning generation, early warning positioning, plan identification and plan execution. The logic business of the plan execution flow is as follows: first, the key flow in the plan is extracted through the early warning type, the early warning grade and the early warning positioning related intelligent plan content, and the intelligent plan brief information is displayed. Secondly, focusing industrial television video, focusing and matching peripheral monitoring video through early warning GPS positioning, and returning field pictures in real time. Third, the important content of the matched plans comprises main plans, current processes, associated equipment, associated emergency resources, associated emergency expert teams and the like. Fourth, emergency treatment is performed according to the plans and the site contents. Including emergency treatment stage tracking, emergency treatment content display, emergency material matching display, emergency specialist team, video consultation, etc. Finally, summarizing plans, namely analyzing the automatic focusing time and focusing point of the industrial television and the video matching degree of the industrial television, which are located in the manual operation click view, through extracting steps in the early warning treatment process and the treatment process, such as the automatic focusing time and the focusing point of the industrial television after emergency occurs; circulation tracking of resources after emergency resource scheduling; tracking personnel scheduling; recording, recording frequency and the like in video consultation, extracting key contents of each stage in the intelligent plan, analyzing the matching degree of the intelligent plan and the plan, generating a summary report, and supporting optimization of the intelligent plan based on the summary report.

In a possible implementation manner, in step S170, total runoff prediction data of the non-rained region may be determined according to the surface runoff prediction data of each target sub-region.

Meanwhile, GNSS surface displacement monitoring data of the non-raining area can be obtained, and the address disaster generation probability when the rain cloud moves to the non-raining area is determined according to the total runoff prediction data of the non-raining area and the GNSS surface displacement monitoring data.

Wherein, single device data analysis model (GNSS) monitors data based on history and real time of a single device. Firstly, analyzing the displacement deformation of the GNSS, and calculating the displacement from X, Y and Z directions. The displacement level is determined, such as attention to 30mm/d, early warning level of 50mm/d and warning level of 80mm/d. The displacement calculation is performed by making a difference between the current GNSS coordinates and the yesterday coordinates. And secondly, analyzing the deformation rate, and calculating deformation change rates in the X, Y and Z directions. And finally, analyzing the deformation direction angle, calculating the deformation angle through the change of the X, Y and Z directions, and determining whether the disaster early warning level can be achieved according to the displacement, the deformation rate and the deformation angle required by geological disasters such as landslide, collapse and the like.

Further, multi-model detection data acquired by each intelligent detection device in the target area can be acquired, wherein the intelligent detection devices comprise a motion sensor, a thermometer and a meteorological sensor; and determining the probability of address disasters generated when the rain cloud moves to the non-raining area according to the multi-model detection data, the total runoff prediction data and the GNSS surface displacement monitoring data of the non-raining area.

After binary data returned by each intelligent device is received, the data are analyzed, meaning of various fields in the data is obtained, and classification is carried out according to different types of the data. And then checking the classified data to remove obvious error data.

Specifically, the intelligent device according to the present embodiment includes, but is not limited to, a common monitoring device in current geological disaster detection, such as a displacement sensor, a thermometer, a meteorological sensor (measuring wind speed, temperature, rainfall, etc.), a flowmeter, etc. The intelligent equipment is installed at the target detection place and is generally distributed at the positions of mountain peaks, valley bottoms, sensitive slopes and the like. The intelligent device can acquire various geological, geographic and meteorological data at the installation position, and the data is transmitted to a background server through a wired or wireless network in real time after the data is acquired for subsequent analysis and processing. Meanwhile, the background server records all historical data, and calls and analyzes the historical data when necessary.

Because the detection data are more, the intelligent device usually returns binary data to reduce the data transmission amount, the binary data are required to be analyzed after being received to acquire the meanings of various fields in the data, the data are classified according to different types of the data, and the data of each type are conveniently called and stored in a data table or a database.

The reported data of the intelligent equipment is not completely reliable due to the aging of the sensor equipment, the difference of the installation positions, the local weather and the like, so that the data are further analyzed after the data are analyzed, and the data problem caused by equipment errors is determined.

Specifically, since the single-device data analysis model can only analyze from local sites, if a geological change with a very small range, such as rock fracture, etc., occurs, there is a very high probability of false alarm, and further, the embodiment further provides a device region comprehensive analysis model, which analyzes data, such as daily change rates, displacement amounts, etc., deformation angles, trends, etc., of a plurality of devices according to design data analysis results of a plurality of intelligent devices (such as GNSS) in a part of geological disaster regions, analyzes the change condition of a region, and determines whether a disaster early warning level can be reached according to whether the whole region reaches the required displacement, deformation rate, deformation angle, etc., of landslide, collapse, etc.

Further, the formation of geological disasters may also be affected by weather, vegetation, and the like. In order to consider weather and other factors, the implementation further provides a multi-type equipment analysis model; the data of different types of equipment are obtained, and through analyzing the monitored displacement, weather history contrast and weather change processes, various change conditions such as displacement change speed, displacement direction and the like, more comprehensive trend prediction is carried out on geological disasters, and the false alarm rate is reduced.

Furthermore, the single-equipment data analysis model, the equipment region comprehensive analysis model and the multi-type equipment analysis model are suitable for long-term monitoring, and the geological disaster burstiness is strong; the mountain torrent, the debris flow and the like can save a large number of life and property as long as early warning can be performed for more than ten minutes, and in order to perform ultra-short-term early warning, the embodiment further provides a regional joint model.

An important factor causing outbreaks of mountain floods, debris flows and the like is short-time heavy rainfall, and although rainfall, wind direction and the like can be predicted through weather forecast, on one hand, the weather forecast has a certain error in the direction, and the weather forecast is forecast in a large range, so that the weather forecast is difficult to be used for short-time accurate prediction in a special area; on the other hand, because the topography, the vegetation and the like of the monitoring area are different, the surface runoffs formed under the same rainfall are not identical, and the probability of forming the torrential flood and the debris flow is not identical, the detailed modeling of different differences by using the same parameters is difficult, and the accurate prediction is difficult.

The present embodiment also provides an electronic device, which may be, but is not limited to, a server, a personal computer (personal computer, PC), or the like. Referring to fig. 5, a block diagram of the electronic device 100 is shown. The electronic device 100 includes a geological disaster risk early warning processing system 110, a machine readable storage medium 120, a processor 130.

The machine-readable storage medium 120, the processor 130, and the communication unit 140 are electrically connected directly or indirectly to each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The geological disaster risk early warning processing system 110 includes at least one software function module that may be stored in the machine readable storage medium 120 in the form of software or firmware (firmware) or cured in an Operating System (OS) of the electronic device 100. The processor 130 is configured to execute executable modules stored in the machine-readable storage medium 120, such as software functional modules and computer programs included in the geological disaster risk early warning processing system 110.

The machine-readable storage medium 120 may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc. The machine-readable storage medium 120 is configured to store a program, and the processor 130 executes the program after receiving an execution instruction, so as to implement the geological disaster risk early warning processing method provided in this embodiment.

The processor 130 may be an integrated circuit chip with signal processing capabilities. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

Referring to fig. 6, the present embodiment further provides a geological disaster risk early warning processing system 110, where the geological disaster risk early warning processing system 110 includes at least one functional module that can be stored in a machine-readable storage medium 120 in a software form. Functionally divided, the geological disaster risk early warning processing system 110 may include a region division module 111, a similarity calculation module 112, a data acquisition module 113, a region query module 114, a first prediction module 115, a second prediction module 116, and an alarm notification module 117.

The region dividing module 111 is configured to divide the target region into a plurality of sub-regions.

In this embodiment, the area dividing module 111 may be used to perform step S110 shown in fig. 1, and a specific description of the area dividing module 111 may refer to a description of the step S110.

The similarity calculation module 112 is configured to determine, for each sub-region, a similar region similar to the topographic feature of the sub-region according to the topographic feature data of the sub-region.

In this embodiment, the similar calculation module 112 may be used to perform step S120 shown in fig. 1, and a specific description of the similar calculation module 112 may refer to the description of step S120.

The data acquisition module 113 is configured to acquire surface runoff data of each sub-region acquired by a plurality of intelligent detection devices in a rainy region, and determine a movement direction of a rain cloud.

In this embodiment, the data acquisition module 113 may be used to perform step S130 shown in fig. 1, and a specific description of the data acquisition module 113 may refer to a description of step S130.

The region query module 114 is configured to use each sub-region in the non-raining region in the rainy cloud movement direction as a target sub-region, and find whether a target similar region corresponding to the target sub-region exists in the rainy region.

In this embodiment, the area query module 114 may be configured to execute step S140 shown in fig. 1, and a specific description of the area query module 114 may refer to a description of step S140.

The first prediction module 115 is configured to, for a first target sub-area in which the target similar area exists in the target sub-areas, take, as surface runoff prediction data of the first target sub-area, a runoff amount of the target similar area corresponding to the first target sub-area.

In this embodiment, the first prediction module 115 may be used to perform step S150 shown in fig. 1, and a specific description of the first prediction module 115 may refer to a description of the step S150.

The second prediction module 116 is configured to calculate, for a second target sub-region in which the target similar region does not exist in the target sub-region, surface runoff prediction data of the second target sub-region according to historical surface runoff data of the second target sub-region.

In this embodiment, the second prediction module 116 may be used to perform step S160 shown in fig. 1, and a specific description of the second prediction module 116 may refer to the description of step S160.

The alarm notification module 117 is configured to determine an address disaster generation probability when the rain cloud moves to the non-raining area according to the ground runoff prediction data of each target sub-area, and output a corresponding alarm notification according to the address disaster generation probability.

In this embodiment, the alarm notification module 117 may be configured to perform step S170 shown in fig. 1, and a specific description of the alarm notification module 117 may refer to a description of step S170.

In summary, according to the geological disaster risk early warning processing method, the geological disaster risk early warning processing system and the electronic equipment provided by the embodiment of the application, the target area is divided into the plurality of subareas, the corresponding similar areas are determined, when precipitation exists, the ground runoff prediction data of each subarea in the non-raining area is determined according to the runoff amount of the similar areas in the non-raining area, and then the address disaster generation probability when the rain cloud moves to the non-raining area is determined. Therefore, accurate early warning of the ultra-short period can be realized, and accuracy and instantaneity of early warning of geological disasters are improved.

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

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

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The foregoing is merely various embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A geological disaster risk early warning processing method, which is characterized by comprising the following steps:

dividing the target area into a plurality of sub-areas;

2. The method of claim 1, wherein the step of dividing the target area into a plurality of sub-areas comprises:

acquiring surface trend data of a target area;

3. The method of claim 2, wherein the step of performing surface triangulation processing on the target area according to the surface trend data comprises:

Determining convex peak points and concave valley points in the curved surface;

4. The method according to claim 1, wherein the step of determining, for each of the sub-areas, a similar area similar to the topographical features of the sub-area from the topographical feature data of the sub-area, comprises:

5. The method of claim 1, wherein the step of calculating the surface runoff prediction data for a second target sub-region for which the target similar region is not present, from historical surface runoff data for the second target sub-region, comprises:

acquiring the surface runoff prediction data of each first target subarea;

6. The method according to claim 1, wherein the step of determining an address disaster generation probability when the rain cloud moves to the non-rained region based on the surface runoff prediction data of each target sub-region comprises:

acquiring GNSS surface displacement monitoring data of the non-raining area;

7. The method of claim 6, wherein the step of determining an address hazard occurrence probability when a rain cloud moves to the non-rained region from the total runoff prediction data of the non-rained region and the GNSS surface displacement monitoring data comprises:

8. A geological disaster risk early warning processing system, the system comprising:

9. An electronic device comprising a processor and a machine-readable storage medium storing machine-executable instructions which, when executed by the processor, implement the method of any one of claims 1-7.

10. A machine-readable storage medium storing machine-executable instructions which, when executed by one or more processors, implement the method of any one of claims 1-7.