CN112116220A - Artificial intelligent early warning and monitoring method for earthquake damage of large-scale building group - Google Patents
Artificial intelligent early warning and monitoring method for earthquake damage of large-scale building group Download PDFInfo
- Publication number
- CN112116220A CN112116220A CN202010900306.8A CN202010900306A CN112116220A CN 112116220 A CN112116220 A CN 112116220A CN 202010900306 A CN202010900306 A CN 202010900306A CN 112116220 A CN112116220 A CN 112116220A
- Authority
- CN
- China
- Prior art keywords
- early warning
- building
- earthquake damage
- intelligent
- sensor
- 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.)
- Withdrawn
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000012544 monitoring process Methods 0.000 title claims abstract description 34
- 238000004458 analytical method Methods 0.000 claims abstract description 27
- 230000005540 biological transmission Effects 0.000 claims abstract description 17
- 238000012795 verification Methods 0.000 claims description 13
- 238000004891 communication Methods 0.000 claims description 11
- 238000005259 measurement Methods 0.000 claims description 8
- 238000013473 artificial intelligence Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 7
- 240000007651 Rubus glaucus Species 0.000 claims description 5
- 235000011034 Rubus glaucus Nutrition 0.000 claims description 5
- 235000009122 Rubus idaeus Nutrition 0.000 claims description 5
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 230000005284 excitation Effects 0.000 claims description 4
- 238000004806 packaging method and process Methods 0.000 claims description 4
- DMBHHRLKUKUOEG-UHFFFAOYSA-N diphenylamine Chemical compound C=1C=CC=CC=1NC1=CC=CC=C1 DMBHHRLKUKUOEG-UHFFFAOYSA-N 0.000 claims 1
- 238000011156 evaluation Methods 0.000 abstract description 5
- 230000009471 action Effects 0.000 description 6
- 230000001133 acceleration Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/10—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using wireless transmission systems
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- General Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Life Sciences & Earth Sciences (AREA)
- Emergency Management (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Molecular Biology (AREA)
- Data Mining & Analysis (AREA)
- Computer Hardware Design (AREA)
- Computational Linguistics (AREA)
- Primary Health Care (AREA)
- Environmental & Geological Engineering (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geology (AREA)
- Biomedical Technology (AREA)
- Computer Networks & Wireless Communication (AREA)
- Artificial Intelligence (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses an artificial intelligent early warning and monitoring method for earthquake damage of large-scale building groups, which comprises the following steps: s1, installing an intelligent sensor; s2, data acquisition and analysis; s3, optimizing the arrangement of the intelligent sensors; s4, transmitting and checking sensor data; s5, building a earthquake damage database; s6, signal acquisition and analysis; the early warning method comprises the following steps: if more than one sensor group judges that the earthquake damage occurs, information transmission is immediately carried out through the 5G module, and short message early warning and broadcasting are carried out through the Beidou. According to the artificial intelligent early warning and monitoring method for earthquake damage of the large-scale building group, the data acquisition frequency changes along with the resistance performance of the building and the earthquake damage early warning, the integrity of signals is guaranteed, the building group can be verified and evaluated totally, the accuracy of monitoring results is high, the observation means is rich, the combination of urban disaster risk monitoring and evaluation functions is realized, and the practicability is improved.
Description
Technical Field
The invention relates to the technical field of natural disaster early warning, in particular to an artificial intelligent early warning and monitoring method for earthquake damage of large-scale building groups.
Background
Along with the acceleration of the urbanization process, the population and wealth of China are rapidly concentrated into cities, urban building groups are continuously enlarged in scale, the structural form is increasingly complex, the functional coupling is increasingly tight, once a strong earthquake occurs, the urban building groups are seriously damaged, serious casualties and property losses can be caused, the national economic development and social stability are seriously threatened, the comprehensive monitoring, early risk identification and early risk prediction and early warning capabilities of multiple disaster species and disaster chains are improved, the monitoring and early warning, the rescue and restoration reconstruction are accurate, and the existing earthquake damage early warning system has the following problems:
1) the observation system has small scale, is usually single building observation, and can not realize the overall verification and evaluation of building groups;
2) the detection system does not have the functions of monitoring and evaluating the urban disaster risk.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides an artificial intelligent early warning and monitoring method for earthquake damage of a large-scale building group, and solves the problems that the observation scale is small, the overall verification and evaluation of the building group cannot be realized, and the urban disaster risk monitoring and evaluation function is not realized.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: an artificial intelligent early warning and monitoring method for earthquake damage of large-scale building groups comprises the following steps:
s1, installing an intelligent sensor: manually selecting a characteristic building, performing characteristic building data acquisition by using methods of field measurement, index standardization and numerical analysis, and installing an intelligent sensor based on raspberry pi packaging on the selected building;
s2, data acquisition and analysis: acquiring sensor data by the intelligent sensor in the code programming control step S1, so that all signal acquisition is high-frequency acquisition, judging the acquired data by the intelligent sensor after 10S, if the signal is not changed, performing low-frequency recording and archiving, and if the signal is changed, performing high-frequency recording and archiving;
s3, optimizing the arrangement of the intelligent sensors: selecting a representative building by adopting an ant colony intelligent algorithm, and optimizing the distribution position of the intelligent sensor;
s4, sensor data transmission and verification: in the step S3, local area network communication and grid-connected communication are realized among the intelligent sensors through 5G and Beidou, and data transmission and mutual verification among the sensors are realized;
s5, building a seismic damage database: acquiring characteristic building data by means of on-site measurement, index standardization and numerical analysis, acquiring natural vibration period feedback and damage conditions of different buildings in real earthquake, and performing supplementary analysis by adopting a nonlinear finite element model so as to establish a building earthquake damage database;
s6, signal acquisition and analysis: the intelligent sensor judges earthquake disasters by comparing information feedback of the proximity sensor, further distinguishes change sources of structural characteristic signals, judges structural working performance by combining a neural network analysis method, analyzes structural working performance by analyzing building characteristic information and realizes a real-time earthquake damage monitoring function.
Preferably, in step S2, the data acquisition frequency in the steady state is lower and is 1Hz to 10Hz, and the data acquisition frequency under the excitation action is higher and is 50Hz to 100 Hz.
Preferably, in step S4, data transmission and mutual verification are implemented between the smart sensors through a local area network or a cloud server.
The invention also discloses an artificial intelligent early warning method for the earthquake damage of the large-scale building group, when one intelligent sensor judges the earthquake damage, the intelligent sensor sends an early warning signal to an adjacent intelligent sensor, the adjacent intelligent sensor carries out signal acquisition and judges whether the earthquake damage occurs, if more than one sensor group judges that the earthquake damage occurs, information transmission is immediately carried out through a 5G module, and short message early warning broadcasting is carried out through Beidou, so that the earthquake damage early warning is realized, and simultaneously, the earthquake damage is evaluated according to the earthquake grade and by combining with the building characteristics.
Preferably, the short message early warning and broadcasting mode comprises network early warning, satellite early warning or radio early warning.
(III) advantageous effects
The invention provides an artificial intelligent early warning and monitoring method for earthquake damage of large-scale building groups. Compared with the prior art, the method has the following beneficial effects: the large-scale building group earthquake damage artificial intelligent early warning and monitoring method specifically comprises the following steps: s1, installing an intelligent sensor: manually selecting a characteristic building, performing characteristic building data acquisition by using methods of field measurement, index standardization and numerical analysis, and installing an intelligent sensor based on raspberry pi packaging on the selected building; s2, data acquisition and analysis: acquiring sensor data by the intelligent sensor in the code programming control step S1, so that all signal acquisition is high-frequency acquisition, judging the acquired data by the intelligent sensor after 10S, if the signal is not changed, performing low-frequency recording and archiving, and if the signal is changed, performing high-frequency recording and archiving; s3, optimizing the arrangement of the intelligent sensors: selecting a representative building by adopting an ant colony intelligent algorithm, and optimizing the distribution position of the intelligent sensor; s4, sensor data transmission and verification: in the step S3, local area network communication and grid-connected communication are realized among the intelligent sensors through 5G and Beidou, and data transmission and mutual verification among the sensors are realized; s5, building a seismic damage database: acquiring characteristic building data by means of on-site measurement, index standardization and numerical analysis, acquiring natural vibration period feedback and damage conditions of different buildings in real earthquake, and performing supplementary analysis by adopting a nonlinear finite element model so as to establish a building earthquake damage database; s6, signal acquisition and analysis: the intelligent sensor judges earthquake disasters by comparing information feedback of the proximity sensor, further distinguishes structural characteristic signal change sources, then judges structural working performance by combining a neural network analysis method, analyzes the structural working performance by analyzing building characteristic information, and realizes a real-time earthquake damage monitoring function and an early warning method: when an intelligent sensor judges that there is earthquake damage, the intelligent sensor sends an early warning signal to adjacent intelligent sensors, the adjacent intelligent sensors carry out signal acquisition and judge whether the earthquake damage occurs, if more than one sensor group judges that the earthquake damage occurs, then, information transmission is carried out immediately through a 5G module, and short message early warning broadcasting is carried out through the Beidou, earthquake damage early warning is realized, earthquake damage assessment is carried out according to earthquake grades and in combination with building characteristics, data acquisition frequency changes along with the resistance performance and the earthquake damage early warning of the building, the integrity of the signal is ensured, the observation scale is large, the building group can be verified and assessed generally, the accuracy of monitoring results is improved, the observation means are rich, the combination of urban disaster risk monitoring and assessment functions is realized, and the practicability is improved.
Drawings
FIG. 1 is a flow chart of a monitoring method of the present invention;
FIG. 2 is a schematic diagram of a neural network analysis method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, an embodiment of the present invention provides a technical solution: an artificial intelligence monitoring method for earthquake damage of large-scale building groups specifically comprises the following steps:
s1, installing an intelligent sensor: manually selecting a characteristic building, acquiring characteristic building data by using a method of on-site measurement, index standardization and numerical analysis, mounting an intelligent sensor based on raspberry group packaging on the selected building, and connecting and mounting functional modules such as an acceleration sensor, a gyroscope, an inclinometer, a Beidou module, a GPS module, a wireless communication module, a 5G module, a wireless charging module and the like through a raspberry group serial port; the system comprises an acceleration sensor, a gyroscope, a clinometer, a GPS module, a 5G module, a wireless charging module and a power supply module, wherein the acceleration sensor is used for monitoring a three-axis vibration signal of a building, the gyroscope is used for monitoring a three-axis rotation deformation signal of the building, the clinometer is used for monitoring a three-axis inclination angle of the building, the Beidou is used for carrying out a short-time broadcasting function (assisting in supplementing other communication functions), the GPS module is mainly used for carrying out sensor positioning, the 5G module;
s2, data acquisition and analysis: acquiring sensor data by the intelligent sensor in the code programming control step S1, so that all signal acquisition is high-frequency acquisition, judging the acquired data by the intelligent sensor after 10S, if the signal is not changed, performing low-frequency recording and archiving, and if the signal is changed, performing high-frequency recording and archiving;
s3, optimizing the arrangement of the intelligent sensors: by adopting an ant colony intelligent algorithm, selecting a representative building, optimizing the distribution position of the intelligent sensor, monitoring the characteristic value of the building in different buildings in a key way, such as the key monitoring of the natural vibration period of a high-rise building, the key monitoring of the deformation of a large-span structure and the like, and carrying out explosion and local disaster early warning on the optimized sensor group;
s4, sensor data transmission and verification: in the step S3, local area network communication and grid-connected communication are realized among the intelligent sensors through 5G and Beidou, and data transmission and mutual verification among the sensors are realized;
s5, building a seismic damage database: acquiring characteristic building data by means of on-site measurement, index standardization and numerical analysis, acquiring natural vibration period feedback and damage conditions of different buildings in real earthquake, and performing supplementary analysis by adopting a nonlinear finite element model so as to establish a building earthquake damage database;
s6, signal acquisition and analysis: the intelligent sensor judges earthquake disasters by comparing information feedback of the proximity sensor, further distinguishes change sources of structural characteristic signals, judges structural working performance by combining a neural network analysis method, analyzes structural working performance by analyzing building characteristic information and realizes a real-time earthquake damage monitoring function.
In the invention, in step S2, the data acquisition frequency is lower at 1 Hz-10 Hz in a steady state, the data acquisition frequency is higher at 50 Hz-100 Hz under the excitation action, and the excitation action comprises structural resistance sudden change, structural vibration sudden change, wind pressure sudden change, earthquake occurrence, earthquake information input and the like.
In the invention, in step S4, data transmission and mutual verification are realized among the intelligent sensors through a local area network or a cloud server, two ways are adopted for communication, the first way is transmission to the adjacent intelligent sensors through the local area network, the way can carry out early warning on the adjacent intelligent sensors, and the adjacent intelligent sensors carry out high-frequency recording and transmit to the next layer of local area network after receiving early warning signals until reaching a terminal server; the second is uploading to a network cloud server: an intelligent sensor sends the earthquake damage signal to a cloud server, and the cloud server sends the signal to all the other intelligent sensors, and all the other intelligent sensors adopt high frequency record and feed back corresponding results to the cloud server.
The invention also discloses an artificial intelligent early warning method for earthquake damage of large-scale building groups, when one intelligent sensor judges that the earthquake damage exists, the intelligent sensor sends an early warning signal to an adjacent intelligent sensor, the adjacent intelligent sensor carries out signal acquisition and judges whether the earthquake damage occurs, if more than one sensor group judges that the earthquake damage occurs, information transmission is immediately carried out through a 5G module, and short message early warning broadcasting is carried out through the Beidou, so that earthquake damage early warning is realized, and simultaneously, earthquake damage evaluation is carried out according to earthquake grades and by combining with building characteristics
In the invention, the short message early warning and broadcasting mode comprises network early warning, satellite early warning or radio early warning.
It is noted that, herein, relational terms such as first and second, and the like may be 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. Also, 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.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. An artificial intelligence monitoring method for earthquake damage of large-scale building groups is characterized by comprising the following steps: the method specifically comprises the following steps:
s1, installing an intelligent sensor: manually selecting a characteristic building, performing characteristic building data acquisition by using methods of field measurement, index standardization and numerical analysis, and installing an intelligent sensor based on raspberry pi packaging on the selected building;
s2, data acquisition and analysis: acquiring sensor data by the intelligent sensor in the code programming control step S1, so that all signal acquisition is high-frequency acquisition, judging the acquired data by the intelligent sensor after 10S, if the signal is not changed, performing low-frequency recording and archiving, and if the signal is changed, performing high-frequency recording and archiving;
s3, optimizing the arrangement of the intelligent sensors: selecting a representative building by adopting an ant colony intelligent algorithm, and optimizing the distribution position of the intelligent sensor;
s4, sensor data transmission and verification: in the step S3, local area network communication and grid-connected communication are realized among the intelligent sensors through 5G and Beidou, and data transmission and mutual verification among the sensors are realized;
s5, building a seismic damage database: acquiring characteristic building data by means of on-site measurement, index standardization and numerical analysis, acquiring natural vibration period feedback and damage conditions of different buildings in real earthquake, and performing supplementary analysis by adopting a nonlinear finite element model so as to establish a building earthquake damage database;
s6, signal acquisition and analysis: the intelligent sensor judges earthquake disasters by comparing information feedback of the proximity sensor, further distinguishes change sources of structural characteristic signals, judges structural working performance by combining a neural network analysis method, analyzes structural working performance by analyzing building characteristic information and realizes a real-time earthquake damage monitoring function.
2. The artificial intelligence monitoring method for earthquake damage of large-scale building groups according to claim 1, wherein the artificial intelligence monitoring method comprises the following steps: in the step S2, the data acquisition frequency is low at 1Hz to 10Hz in a steady state, and the data acquisition frequency is high at 50Hz to 100Hz under the excitation.
3. The artificial intelligence monitoring method for earthquake damage of large-scale building groups according to claim 1, wherein the artificial intelligence monitoring method comprises the following steps: in the step S4, data transmission and mutual verification are realized between the intelligent sensors through a local area network or a cloud server.
4. An artificial intelligent early warning method for earthquake damage of large-scale building groups is characterized by comprising the following steps: when an intelligent sensor judges that there is earthquake damage, this intelligent sensor sends early warning signal to adjacent intelligent sensor, and adjacent intelligent sensor carries out signal acquisition and judges whether the earthquake damage takes place, if exceed a sensor crowd and all judge that the earthquake damage has appeared, then carry out information transmission through 5G module immediately to carry out the brief early warning through the big dipper and report, realize the earthquake damage early warning, carry out the earthquake damage aassessment according to earthquake grade and combination building characteristic simultaneously.
5. The artificial intelligence early warning method for earthquake damage of large-scale building groups according to claim 4, wherein the method comprises the following steps: the short message early warning and broadcasting mode comprises network early warning, satellite early warning or radio early warning.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010900306.8A CN112116220A (en) | 2020-09-01 | 2020-09-01 | Artificial intelligent early warning and monitoring method for earthquake damage of large-scale building group |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010900306.8A CN112116220A (en) | 2020-09-01 | 2020-09-01 | Artificial intelligent early warning and monitoring method for earthquake damage of large-scale building group |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112116220A true CN112116220A (en) | 2020-12-22 |
Family
ID=73805492
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010900306.8A Withdrawn CN112116220A (en) | 2020-09-01 | 2020-09-01 | Artificial intelligent early warning and monitoring method for earthquake damage of large-scale building group |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112116220A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113432650A (en) * | 2021-07-07 | 2021-09-24 | 苏州瑞茨柏工程监测技术有限公司 | Monitoring system of high and large formwork supporting system |
| CN113536433A (en) * | 2021-07-22 | 2021-10-22 | 东南大学 | BIM platform-based dynamic escape route optimization system for evacuation after disaster of building |
-
2020
- 2020-09-01 CN CN202010900306.8A patent/CN112116220A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113432650A (en) * | 2021-07-07 | 2021-09-24 | 苏州瑞茨柏工程监测技术有限公司 | Monitoring system of high and large formwork supporting system |
| CN113536433A (en) * | 2021-07-22 | 2021-10-22 | 东南大学 | BIM platform-based dynamic escape route optimization system for evacuation after disaster of building |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112381309B (en) | Reservoir dam safety monitoring and early warning method, device and system and storage medium | |
| CN107195164B (en) | Mountain flood on-line monitoring identification method for early warning and its early warning system | |
| KR101788104B1 (en) | System and method for real-time early warning for extreme rainfall-induced landslides | |
| CN111536429B (en) | Decision fusion-based oil and gas pipeline early warning system and method | |
| CN107817744A (en) | Communal facility safety monitoring system | |
| CN107450426A (en) | Foundation pit construction deformable statistical analysis method and system | |
| CN108510112A (en) | A kind of mine disaster prediction and warning method that field monitoring is combined with numerical simulation | |
| CN203490806U (en) | Debris flow pre-disaster early warning system based on wireless sensor network | |
| CN202676400U (en) | Wireless remote water leakage detection system | |
| CN106128035A (en) | The geological disaster forecasting method merged based on neutral net and multi-parameter information | |
| CN105425752A (en) | Real-time monitoring system of pipe network and working method of same | |
| CN101255950A (en) | Intelligent warning system for computation and pipe monitoring of oil transportation collection and transmission system | |
| CN101482620A (en) | Real-time seismic intensity reporting method and system | |
| CN112036086A (en) | Dynamic risk early warning system for gas pipeline | |
| CN112116220A (en) | Artificial intelligent early warning and monitoring method for earthquake damage of large-scale building group | |
| CN106646568A (en) | Novel glass curtain wall intelligent monitoring system and method | |
| CN112533167A (en) | Coal rock dynamic disaster wireless monitoring system and method based on 5G communication | |
| CN115965246B (en) | Early warning analysis method for karst collapse disasters | |
| CN116824807B (en) | Multi-disaster early warning and alarming method and system | |
| CN113053063A (en) | Mobile terminal-based disaster online disposal flow implementation method | |
| CN112460492A (en) | Toughness-evaluation-based collaborative toughness-enhanced gas safety control device | |
| CN114374716A (en) | Geological disaster remote monitoring system and monitoring method thereof | |
| CN112017419A (en) | Building safety monitoring and early warning system and method based on multi-source data | |
| CN105427189A (en) | Power grid vulnerability evaluation method under landslide hazard | |
| CN206021512U (en) | A kind of detection forecasting and warning device of heavy rain type diluted debris flow |
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 | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20201222 |