CN113566894A - Geological disaster monitoring method, system, terminal and medium based on Internet of things - Google Patents
Geological disaster monitoring method, system, terminal and medium based on Internet of things Download PDFInfo
- Publication number
- CN113566894A CN113566894A CN202110929986.0A CN202110929986A CN113566894A CN 113566894 A CN113566894 A CN 113566894A CN 202110929986 A CN202110929986 A CN 202110929986A CN 113566894 A CN113566894 A CN 113566894A
- Authority
- CN
- China
- Prior art keywords
- rainfall
- geological disaster
- target area
- accumulated
- disaster monitoring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Alarm Systems (AREA)
Abstract
The invention discloses a geological disaster monitoring method, a system, a terminal and a medium based on the Internet of things, relating to the technical field of geological disaster monitoring, and the key points of the technical scheme are as follows: generating a geological disaster monitoring signal when the effective accumulated rainfall exceeds the preset accumulated rainfall; acquiring remote sensing image information of a target area and calling shallow surface type information; identifying and extracting the vegetation type and the vegetation coverage rate of the target area from the remote sensing image information, and performing simulation analysis to obtain the basic drainage quantity, the porosity and the washout coefficient of the shallow surface layer of the target area; measuring the seepage depth information of the shallow surface layer in real time, calculating to obtain total seepage rainfall, and calculating to obtain total discharge rainfall; and outputting a monitoring early warning signal when the damage accumulation factor is larger than a preset factor threshold. The invention comprehensively considers a plurality of factors such as rainfall information, vegetation coverage, topographic and geomorphic information, geological information and the like, calculates the effective accumulated rainfall in unit time and can effectively improve the accuracy of geological disaster monitoring.
Description
Technical Field
The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster monitoring method, a geological disaster monitoring system, a geological disaster monitoring terminal and a geological disaster monitoring medium based on the Internet of things.
Background
Geological disasters refer to geological phenomena which are formed under the action of natural or human factors and damage and lose human lives, properties and environments. Such as collapse, landslide, debris flow, water and soil loss, land desertification and swampiness, soil salinization, earthquake, volcano, geothermal damage and the like, wherein geological disasters such as collapse, landslide, debris flow, water and soil loss and the like are common, and therefore, monitoring of such geological disasters is very necessary.
At present, the monitoring of landslide, water and soil loss and other geological disasters is realized mainly by comparing and analyzing real-time monitoring rainfall information and preset rainfall information. However, the occurrence of geological disasters is not only influenced by rainfall, but also by various factors such as vegetation coverage, landform and geological conditions, and the monitoring is realized by simply comparing and analyzing rainfall information monitored in real time and preset rainfall information, so that the condition of low monitoring accuracy inevitably exists.
Therefore, how to research and design a geological disaster monitoring method, system, terminal and medium based on the internet of things, which can overcome the defects, is a problem which is urgently needed to be solved at present.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a geological disaster monitoring method, system, terminal and medium based on the Internet of things.
The technical purpose of the invention is realized by the following technical scheme:
in a first aspect, a geological disaster monitoring method based on the internet of things is provided, which comprises the following steps:
acquiring actual accumulated rainfall of a target area in real time, calculating according to the actual accumulated rainfall and accumulated duration to obtain effective accumulated rainfall, and generating a geological disaster monitoring signal when the effective accumulated rainfall exceeds a preset accumulated rainfall;
acquiring remote sensing image information of a target area in real time according to a geological disaster monitoring signal and calling shallow surface type information of the target area;
identifying and extracting the vegetation type and the vegetation coverage rate of the target area from the remote sensing image information, and obtaining the basic drainage, the porosity and the washout coefficient of the shallow surface layer of the target area by combining the shallow surface layer type information simulation analysis;
measuring the water seepage depth information of a shallow surface layer in real time according to the geological disaster monitoring signal, calculating according to the water seepage depth information and the porosity to obtain total seepage rainfall, and calculating according to the effective accumulated rainfall, the basic drainage and the total seepage rainfall to obtain total drainage rainfall;
and calculating to obtain a damage accumulation factor according to the total discharged rainfall and the scouring coefficient, and outputting a monitoring early warning signal when the damage accumulation factor is greater than a preset factor threshold value.
Further, the calculation formula of the effective accumulated rainfall is as follows:
wherein Y represents an effective accumulated rainfall; t represents an accumulated time period; s (i) represents the rain value measured at the ith time.
Further, the preset accumulated rainfall is inversely related to dynamic change along with the change of the accumulated duration, and when the preset accumulated rainfall is lower than the standard accumulated rainfall, the accumulated duration is initialized to zero, and the calculation formula of the preset accumulated rainfall specifically includes:
wherein B represents a preset accumulated rainfall; a is a constant; y is0Indicating a standard cumulative rainfall.
Further, the simulation analysis process of the shallow surface layer of the target area specifically includes:
establishing a three-dimensional simulation model according to the topographic and geomorphic information, the vegetation type, the vegetation coverage rate and the superficial layer type information of the target area;
obtaining the maximum effective accumulated rainfall when no soil loss exists through loading rainfall simulation analysis as the basic displacement;
obtaining porosity through comprehensive analysis of shallow surface type information, vegetation type and vegetation coverage;
and obtaining the ratio of the total soil loss mass to the total rainfall collection mass when soil loss exists through loading rainfall simulation analysis as a scouring coefficient.
Further, the formula for calculating the total discharged rainfall is specifically as follows:
P＝(Y-D)t-HΦ
wherein P represents a total discharged rainfall; d represents the base displacement; h represents water penetration depth information; Φ represents porosity.
Further, the formula for calculating the damage accumulation factor is specifically as follows:
wherein Z represents a destruction accumulation factor; f represents vegetation coverage; alpha represents the flush coefficient; l1An influence coefficient representing a shallow surface type; l2Expressing the influence factors of the vegetation types, the more root nature is obtained, the larger value is; k represents the influence factor of the landform of the target area, and the larger the gradient is, the larger the value is.
Further, the real-time measuring process of the water penetration depth information is as follows:
vertically inserting a measuring rod with a plurality of negative probes and a positive probe arranged at intervals into the shallow surface layer, wherein the positive probe is arranged at the tail end of the measuring rod;
sequentially and uniquely starting a plurality of negative probes along the direction from the surface of the shallow surface layer to the inside, and measuring an electric signal between the corresponding negative probe and the positive probe until the electric signal is 0;
and converting the finally obtained electric signal into a water level value to obtain water penetration depth information.
In a second aspect, a geological disaster monitoring system based on the internet of things is provided, which includes:
the data acquisition module is used for acquiring the actual accumulated rainfall of the target area in real time, calculating to obtain the effective accumulated rainfall according to the actual accumulated rainfall and the accumulated duration, and generating a geological disaster monitoring signal when the effective accumulated rainfall exceeds the preset accumulated rainfall;
the information acquisition module is used for acquiring remote sensing image information of the target area in real time according to the geological disaster monitoring signal and calling shallow surface type information of the target area;
the simulation analysis module is used for identifying and extracting the vegetation type and the vegetation coverage rate of the target area from the remote sensing image information, and obtaining the basic drainage quantity, the porosity and the scouring coefficient of the shallow surface layer of the target area by combining the shallow surface layer type information simulation analysis;
the rainfall calculation module is used for measuring the seepage depth information of the shallow surface layer in real time according to the geological disaster monitoring signal, calculating to obtain total seepage rainfall according to the seepage depth information and the porosity, and calculating to obtain total drainage rainfall according to the effective accumulated rainfall, the basic drainage and the total seepage rainfall;
and the monitoring and early warning module is used for calculating a damage accumulation factor according to the total discharged rainfall and the scouring coefficient, and outputting a monitoring and early warning signal when the damage accumulation factor is greater than a preset factor threshold value.
In a third aspect, a computer terminal is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the method for monitoring geological disasters based on the internet of things according to any one of the first aspect is implemented.
In a fourth aspect, a computer readable medium is provided, on which a computer program is stored, wherein the computer program is executed by a processor, and the method for monitoring geological disasters based on the internet of things according to any one of the first aspect can be implemented.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention comprehensively considers rainfall information, vegetation coverage, topographic and geomorphic information, geological information and other factors, and calculates the effective accumulated rainfall in unit time, thereby effectively improving the accuracy of geological disaster monitoring;
2. according to the invention, the preset accumulated rainfall is dynamically set, so that the geological disaster monitoring program is more reasonably started, and the operation cost in the geological disaster monitoring process is effectively reduced;
3. the invention considers the basic drainage performance of different target areas in the geological disaster monitoring process, analyzes the water and soil loss performance of the target areas and effectively reduces the error of geological disaster monitoring.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a flow chart in an embodiment of the invention;
FIG. 2 is a schematic view of the structure of a measuring rod in an embodiment of the present invention;
fig. 3 is a block diagram of a system in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1: the geological disaster monitoring method based on the Internet of things as shown in FIG. 1 comprises the following steps:
s1: acquiring actual accumulated rainfall of a target area in real time, calculating according to the actual accumulated rainfall and accumulated duration to obtain effective accumulated rainfall, and generating a geological disaster monitoring signal when the effective accumulated rainfall exceeds a preset accumulated rainfall;
s2: acquiring remote sensing image information of a target area in real time according to a geological disaster monitoring signal and calling shallow surface type information of the target area;
s3: identifying and extracting the vegetation type and the vegetation coverage rate of the target area from the remote sensing image information, and obtaining the basic drainage, the porosity and the washout coefficient of the shallow surface layer of the target area by combining the shallow surface layer type information simulation analysis;
s4: measuring the water seepage depth information of a shallow surface layer in real time according to the geological disaster monitoring signal, calculating according to the water seepage depth information and the porosity to obtain total seepage rainfall, and calculating according to the effective accumulated rainfall, the basic drainage and the total seepage rainfall to obtain total drainage rainfall;
s5: and calculating to obtain a damage accumulation factor according to the total discharged rainfall and the scouring coefficient, and outputting a monitoring early warning signal when the damage accumulation factor is greater than a preset factor threshold value.
The invention comprehensively considers rainfall information, vegetation coverage, topographic and geomorphic information, geological information and other factors, and calculates the effective accumulated rainfall in unit time, thereby effectively improving the accuracy of geological disaster monitoring;
in step S1, the calculation formula of the effective accumulated rainfall is:
wherein Y represents an effective accumulated rainfall; t represents an accumulated time period; s (i) represents the rain value measured at the ith time.
In step S1, the preset accumulated rainfall varies inversely with the variation of the accumulated duration, and when the preset accumulated rainfall is lower than the standard accumulated rainfall, the accumulated duration is initialized to zero, and the calculation formula of the preset accumulated rainfall is specifically:
wherein B represents a preset accumulationRainfall; a is a constant; y is0Indicating a standard cumulative rainfall. According to the invention, the preset accumulated rainfall is dynamically set, so that the geological disaster monitoring program is more reasonably started, and the operation cost in the geological disaster monitoring process is effectively reduced.
In step S3, the simulation analysis process of the shallow surface layer of the target region specifically includes:
s301: establishing a three-dimensional simulation model according to the topographic and geomorphic information, the vegetation type, the vegetation coverage rate and the superficial layer type information of the target area;
s302: obtaining the maximum effective accumulated rainfall when no soil loss exists through loading rainfall simulation analysis as the basic displacement;
s303: obtaining porosity through comprehensive analysis of shallow surface type information, vegetation type and vegetation coverage;
s304: and obtaining the ratio of the total soil loss mass to the total rainfall collection mass when soil loss exists as a washing coefficient through loading rainfall simulation analysis.
In step S4, the formula for calculating the total discharged rainfall is specifically:
P＝(Y-D)t-HΦ
wherein P represents a total discharged rainfall; d represents the base displacement; h represents water penetration depth information; Φ represents porosity.
In step S4, as shown in fig. 2, the real-time measuring process of the water penetration depth information includes:
s401: vertically inserting a measuring rod which is provided with a plurality of negative probes and a positive probe into the shallow surface layer, wherein the negative probes and the positive probe are arranged at intervals, and the positive probe is arranged at the tail end of the measuring rod;
s402: sequentially and uniquely starting a plurality of negative probes along the direction from the surface of the shallow surface layer to the inside, and measuring an electric signal between the corresponding negative probe and the positive probe until the electric signal is 0;
s403: and converting the finally obtained electric signal into a water level value to obtain water penetration depth information.
In step S5, the formula for calculating the destruction accumulation factor is specifically:
wherein Z represents a destruction accumulation factor; f represents vegetation coverage; alpha represents the flush coefficient; l1An influence coefficient representing a shallow surface type; l2Expressing the influence factors of the vegetation types, the more root nature is obtained, the larger value is; k represents the influence factor of the landform of the target area, and the larger the gradient is, the larger the value is. The invention considers the basic drainage performance of different target areas in the geological disaster monitoring process, analyzes the water and soil loss performance of the target areas and effectively reduces the error of geological disaster monitoring.
Example 2: the geological disaster monitoring system based on the internet of things is shown in fig. 3 and comprises a data acquisition module, an information acquisition module, a simulation analysis module, a rainfall calculation module and a monitoring and early warning module.
The data acquisition module is used for acquiring the actual accumulated rainfall of the target area in real time, calculating to obtain the effective accumulated rainfall according to the actual accumulated rainfall and the accumulated duration, and generating a geological disaster monitoring signal when the effective accumulated rainfall exceeds the preset accumulated rainfall. And the information acquisition module is used for acquiring remote sensing image information of the target area in real time according to the geological disaster monitoring signal and calling shallow surface type information of the target area. And the simulation analysis module is used for identifying and extracting the vegetation type and the vegetation coverage rate of the target area from the remote sensing image information, and obtaining the basic drainage quantity, the porosity and the scouring coefficient of the shallow surface layer of the target area by combining the shallow surface layer type information simulation analysis. And the rainfall calculation module is used for measuring the seepage depth information of the shallow surface layer in real time according to the geological disaster monitoring signal, calculating to obtain total seepage rainfall according to the seepage depth information and the porosity, and calculating to obtain total drainage rainfall according to the effective accumulated rainfall, the basic drainage and the total seepage rainfall. And the monitoring and early warning module is used for calculating a damage accumulation factor according to the total discharged rainfall and the scouring coefficient, and outputting a monitoring and early warning signal when the damage accumulation factor is greater than a preset factor threshold value.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. The geological disaster monitoring method based on the Internet of things is characterized by comprising the following steps:
acquiring actual accumulated rainfall of a target area in real time, calculating according to the actual accumulated rainfall and accumulated duration to obtain effective accumulated rainfall, and generating a geological disaster monitoring signal when the effective accumulated rainfall exceeds a preset accumulated rainfall;
acquiring remote sensing image information of a target area in real time according to a geological disaster monitoring signal and calling shallow surface type information of the target area;
identifying and extracting the vegetation type and the vegetation coverage rate of the target area from the remote sensing image information, and obtaining the basic drainage, the porosity and the washout coefficient of the shallow surface layer of the target area by combining the shallow surface layer type information simulation analysis;
measuring the water seepage depth information of a shallow surface layer in real time according to the geological disaster monitoring signal, calculating according to the water seepage depth information and the porosity to obtain total seepage rainfall, and calculating according to the effective accumulated rainfall, the basic drainage and the total seepage rainfall to obtain total drainage rainfall;
and calculating to obtain a damage accumulation factor according to the total discharged rainfall and the scouring coefficient, and outputting a monitoring early warning signal when the damage accumulation factor is greater than a preset factor threshold value.
2. The internet of things-based geological disaster monitoring method according to claim 1, wherein the effective accumulated rainfall is calculated by the formula:
wherein Y represents an effective accumulated rainfall; t represents an accumulated time period; s (i) represents the rain value measured at the ith time.
3. The method for monitoring geological disasters based on the internet of things as claimed in claim 1, wherein the preset accumulated rainfall is in an inversely related dynamic change along with the change of the accumulated duration, and when the preset accumulated rainfall is lower than the standard accumulated rainfall, the accumulated duration is initialized to zero, and the calculation formula of the preset accumulated rainfall is specifically as follows:
wherein B represents a preset accumulated rainfall; a is a constant; y is0Indicating a standard cumulative rainfall.
4. The method for monitoring the geological disaster based on the internet of things as claimed in claim 1, wherein the simulation analysis process of the shallow surface layer of the target area is specifically as follows:
establishing a three-dimensional simulation model according to the topographic and geomorphic information, the vegetation type, the vegetation coverage rate and the superficial layer type information of the target area;
obtaining the maximum effective accumulated rainfall when no soil loss exists through loading rainfall simulation analysis as the basic displacement;
obtaining porosity through comprehensive analysis of shallow surface type information, vegetation type and vegetation coverage;
and obtaining the ratio of the total soil loss mass to the total rainfall collection mass when soil loss exists through loading rainfall simulation analysis as a scouring coefficient.
5. The internet of things-based geological disaster monitoring method as claimed in claim 1, wherein the calculation formula of the total discharged rainfall is specifically as follows:
P＝(Y-D)t-HΦ
wherein P represents a total discharged rainfall; d represents the base displacement; h represents water penetration depth information; Φ represents porosity.
6. The internet of things-based geological disaster monitoring method as claimed in claim 1, wherein the formula for calculating the damage accumulation factor is specifically as follows:
wherein Z represents a destruction accumulation factor; f represents vegetation coverage; alpha represents the flush coefficient; l1An influence coefficient representing a shallow surface type; l2Expressing the influence factors of the vegetation types, the more root nature is obtained, the larger value is; k represents the influence factor of the landform of the target area, and the larger the gradient is, the larger the value is.
7. The Internet of things-based geological disaster monitoring method according to any one of claims 1-6, wherein the real-time measuring process of the water penetration depth information is as follows:
vertically inserting a measuring rod with a plurality of negative probes and a positive probe arranged at intervals into the shallow surface layer, wherein the positive probe is arranged at the tail end of the measuring rod;
sequentially and uniquely starting a plurality of negative probes along the direction from the surface of the shallow surface layer to the inside, and measuring an electric signal between the corresponding negative probe and the positive probe until the electric signal is 0;
and converting the finally obtained electric signal into a water level value to obtain water penetration depth information.
8. Geological disaster monitoring system based on thing networking, characterized by includes:
the data acquisition module is used for acquiring the actual accumulated rainfall of the target area in real time, calculating to obtain the effective accumulated rainfall according to the actual accumulated rainfall and the accumulated duration, and generating a geological disaster monitoring signal when the effective accumulated rainfall exceeds the preset accumulated rainfall;
the information acquisition module is used for acquiring remote sensing image information of the target area in real time according to the geological disaster monitoring signal and calling shallow surface type information of the target area;
the simulation analysis module is used for identifying and extracting the vegetation type and the vegetation coverage rate of the target area from the remote sensing image information, and obtaining the basic drainage quantity, the porosity and the scouring coefficient of the shallow surface layer of the target area by combining the shallow surface layer type information simulation analysis;
the rainfall calculation module is used for measuring the seepage depth information of the shallow surface layer in real time according to the geological disaster monitoring signal, calculating to obtain total seepage rainfall according to the seepage depth information and the porosity, and calculating to obtain total drainage rainfall according to the effective accumulated rainfall, the basic drainage and the total seepage rainfall;
and the monitoring and early warning module is used for calculating a damage accumulation factor according to the total discharged rainfall and the scouring coefficient, and outputting a monitoring and early warning signal when the damage accumulation factor is greater than a preset factor threshold value.
9. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for internet of things based geological disaster monitoring according to any of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, the computer program being executable by a processor to implement the method for internet of things based monitoring of geological disasters according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110929986.0A CN113566894A (en) | 2021-08-13 | 2021-08-13 | Geological disaster monitoring method, system, terminal and medium based on Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110929986.0A CN113566894A (en) | 2021-08-13 | 2021-08-13 | Geological disaster monitoring method, system, terminal and medium based on Internet of things |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113566894A true CN113566894A (en) | 2021-10-29 |
Family
ID=78171622
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110929986.0A Pending CN113566894A (en) | 2021-08-13 | 2021-08-13 | Geological disaster monitoring method, system, terminal and medium based on Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113566894A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115081757A (en) * | 2022-08-20 | 2022-09-20 | 山东高速股份有限公司 | Automatic road disease detection method based on robot technology |
| CN116257805A (en) * | 2023-05-16 | 2023-06-13 | 中国地质大学（武汉） | Traffic prediction model construction method and traffic prediction method |
| CN116933535A (en) * | 2023-07-24 | 2023-10-24 | 广东省有色矿山地质灾害防治中心 | Geological disaster displacement monitoring method, device, equipment and storage medium |
| CN116990491A (en) * | 2023-09-26 | 2023-11-03 | 中国标准化研究院 | Automatic soil information monitoring system based on Internet of things |
-
2021
- 2021-08-13 CN CN202110929986.0A patent/CN113566894A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115081757A (en) * | 2022-08-20 | 2022-09-20 | 山东高速股份有限公司 | Automatic road disease detection method based on robot technology |
| CN116257805A (en) * | 2023-05-16 | 2023-06-13 | 中国地质大学（武汉） | Traffic prediction model construction method and traffic prediction method |
| CN116257805B (en) * | 2023-05-16 | 2023-08-15 | 中国地质大学（武汉） | Traffic prediction model construction method and traffic prediction method |
| CN116933535A (en) * | 2023-07-24 | 2023-10-24 | 广东省有色矿山地质灾害防治中心 | Geological disaster displacement monitoring method, device, equipment and storage medium |
| CN116933535B (en) * | 2023-07-24 | 2024-03-19 | 广东省有色矿山地质灾害防治中心 | Geological disaster displacement monitoring method, device, equipment and storage medium |
| CN116990491A (en) * | 2023-09-26 | 2023-11-03 | 中国标准化研究院 | Automatic soil information monitoring system based on Internet of things |
| CN116990491B (en) * | 2023-09-26 | 2023-12-26 | 中国标准化研究院 | Automatic soil information monitoring system based on Internet of things |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113566894A (en) | Geological disaster monitoring method, system, terminal and medium based on Internet of things | |
| Wise | Assessing the quality for hydrological applications of digital elevation models derived from contours | |
| CN113538861B (en) | Geological disaster information management system based on mineral geological exploration | |
| CN115167212B (en) | Dynamic construction control system and method for foundation pit based on monitoring platform | |
| CN114333249A (en) | Landslide early warning method and device | |
| CN116757097A (en) | Digital twin hydraulic engineering operation and maintenance monitoring system and method | |
| Zandbergen | Accuracy considerations in the analysis of depressions in medium-resolution Lidar DEMs | |
| CN115100819B (en) | Landslide hazard early warning method and device based on big data analysis and electronic equipment | |
| CN108920429A (en) | A kind of abnormal data analysis method of Water level trend monitoring | |
| CN114565313A (en) | Geological disaster hidden danger identification and analysis evaluation method and system | |
| CN108303512B (en) | Method for in-situ testing soil-water characteristic curve | |
| CN115909664A (en) | BIM-based river channel safety early warning method, device and equipment | |
| CN114937133A (en) | Point cloud data-based visual monitoring method and system for embankment | |
| CN112099032A (en) | Ice crack morphology analysis method and device based on laser height measurement satellite data | |
| CN107452059A (en) | A kind of three-dimensional interpolation modeling method, equipment and its storage device | |
| CN116165718A (en) | Method and device for measuring seepage safety of earth and rockfill dam, electronic equipment and medium | |
| CN110796310A (en) | Method and system for predicting susceptibility to regional geological disasters | |
| CN111141768A (en) | Method and device for acquiring rock wettability change in crude oil displacement | |
| CN116296130A (en) | Early warning system for foundation pit leakage detection | |
| CN110610187A (en) | Soil slope collapse accident diagnosis method based on Bayesian network | |
| JP6707749B1 (en) | Reservoir failure prediction system, reservoir failure prediction method, and program | |
| JP2020041405A (en) | Evaluation method for frozen ground and evaluation apparatus for frozen ground | |
| CN109472083B (en) | Civil engineering structure damage detection method based on statistical process control | |
| CN115311574B (en) | Building monitoring method, equipment and medium | |
| CN118009987A (en) | Method and system for measuring earth and stone square quantity of pipe ditch, storage medium and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |