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
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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.
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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.
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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 |
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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 |
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Application publication date: 20201222 |