CN114004138A - Building monitoring method and system based on big data artificial intelligence and storage medium - Google Patents
Building monitoring method and system based on big data artificial intelligence and storage medium Download PDFInfo
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- 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
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Abstract
The application provides a building monitoring method, a building monitoring system and a storage medium based on big data artificial intelligence, which are used for acquiring various sensing data of a building to be detected, and acquiring durability influence factor data of the building to be detected through data preprocessing according to the various sensing data; inputting the durability influence factor data of the building to be tested into the trained building analysis model to obtain the health state of the building to be tested; and obtaining a trained building analysis model based on deep learning of the big data sample. The method and the device have the advantages that the big data are combined with the machine learning technology to analyze the health state of the industrial building, various sensor data are obtained through various sensors, various industrial building durability influence factors are determined through correlation analysis, and the health state of the industrial building is determined through analysis of the machine learning model, so that real-time health assessment of the industrial building under the severe environment is realized, and later-period timely maintenance is convenient for the industrial building.
Description
Technical Field
The application belongs to the technical field of building safety monitoring, and particularly relates to a building monitoring method and system based on big data artificial intelligence and a storage medium.
Background
The existing health condition analysis aiming at the building structure mostly analyzes and monitors the health condition of the building through the collection of stress-strain performance. However, the method has single analysis data, cannot be applied to industrial buildings in severe environments, and cannot perform comprehensive health condition analysis on industrial buildings with more complex reinforced concrete structures;
a reinforced concrete structure refers to a structure made of concrete reinforced with steel bars; at present, with the continuous development of industrialization, a large number of industrial buildings are produced, and different from buildings with other purposes, the industrial buildings are often produced and operated in a severe environment with high temperature and high humidity and are contacted with various corrosive gases, liquids and the like, so that the structural performance of the industrial buildings is easy to have safety problems of structural damage, aging, even partial function loss and the like; the industrial building data has the characteristics of large scale, high identification difficulty, various types, low value and the like.
With the rise of big data, the big data is also called huge data, which refers to a massive and diversified information asset, and a novel processing mode with stronger decision-making power, insight and flow optimization capability is often needed for the information asset.
Therefore, how to combine the big data technology to analyze the health condition of the industrial building with the reinforced concrete structure in real time becomes a research focus, and a building health monitoring scheme based on big data artificial intelligence analysis is urgently needed.
Disclosure of Invention
The invention provides a building monitoring method, a building monitoring system and a storage medium based on big data artificial intelligence, and aims to solve the problems that in the prior art, when the health condition of a building structure is analyzed, the analysis data is single, the building monitoring method and the building monitoring system cannot be applied to severe environments or more complex industrial buildings, and further cannot perform comprehensive analysis on the health condition of the building.
According to a first aspect of the embodiments of the present application, there is provided a building monitoring method based on big data artificial intelligence, including the following steps:
acquiring a plurality of kinds of sensing data of a building to be detected,
obtaining durability influence factor data of the building to be detected through data preprocessing according to various sensing data;
inputting the durability influence factor data of the building to be tested into the trained building analysis model to obtain the health state of the building to be tested; and obtaining a trained building analysis model based on deep learning of the big data sample.
In some embodiments of the present application, obtaining the trained building analysis model based on deep learning of the big data sample specifically includes:
acquiring durability influence factor data and damage degree data of the industrial building as a sample set; wherein, the data of the influence factors on the durability of the industrial building and the data of the damage degree of the industrial building have correlation;
and inputting the sample set into an artificial neural network for training to obtain a trained building analysis model.
In some embodiments of the present application, determining a correlation between the industrial building durability influencing factor data and the industrial building damage degree data by a pearson correlation coefficient algorithm; the pearson correlation coefficient r is expressed as:
wherein r is a Pearson correlation coefficient between the influence factors of the durability of the industrial building and the damage degree of the industrial building; x is an influence factor of the durability of the industrial building; n is the number of samples; p is the damage degree of the industrial building;
wherein if the correlation coefficient r is greater than 0.5 and less than 1, it indicates strong correlation;
if the correlation coefficient r is greater than 0 and less than 0.5, weak correlation is represented;
if the correlation coefficient r is less than 0, no correlation is indicated.
In some embodiments of the present application, acquiring multiple kinds of sensing data of a building to be detected specifically includes:
acquiring various sensing data through a temperature sensor, a humidity sensor, a CO2 sensor, a PH value sensor, an optical detector and/or a thermal infrared image detector;
and remotely transmitting various sensing data through a network.
In some embodiments of the present application, obtaining durability influencing factor data of a building to be tested by data preprocessing according to a plurality of sensing data specifically includes:
receiving a plurality of sensing data transmitted remotely;
carrying out data cleaning, conversion, denoising and normalization processing on various sensing data to obtain industrial building durability influence factor data;
and storing the durability influence factor data of the industrial building and the damage degree data of the industrial building to obtain a historical database.
In some embodiments of the present application, the health status of the building to be tested specifically includes:
a stage: on the basis of not taking relevant repair measures for improving the durability of the structure, the structure can meet the use requirement of the target service life;
b, stage: according to specific conditions, relevant measures for improving the durability of the structure can be taken or maintenance is not carried out, and the structure can meet the use requirement of the next target service life;
c, stage: the structure fails to meet the conditions for the next target age and requires extensive repair to improve its durability.
According to a second aspect of the embodiments of the present application, there is provided a building monitoring system based on big data artificial intelligence, specifically including:
a sensor module: used for acquiring various sensing data of a building to be measured,
a data preprocessing module: the system is used for obtaining durability influence factor data of a building to be detected through data preprocessing according to various sensing data;
intelligent analysis and grade decision module: the system is used for inputting the durability influence factor data of the building to be tested into the trained building analysis model to obtain the health state of the building to be tested; and obtaining a trained building analysis model based on deep learning of the big data sample.
In some embodiments of the present application, the system further includes a deep learning module, configured to obtain a trained building analysis model based on deep learning of the big data sample; the method specifically comprises the following steps:
acquiring durability influence factor data and damage degree data of the industrial building as a sample set; wherein, the data of the influence factors on the durability of the industrial building and the data of the damage degree of the industrial building have correlation;
and inputting the sample set into an artificial neural network for training to obtain a trained building analysis model.
In some embodiments of the present application, the system further includes a wireless transmission module, a big data platform, and a system management module;
the wireless transmission module is connected with the sensor module and the data preprocessing module and remotely transmits various sensing data of the sensor module to the data preprocessing module through a network;
the big data platform is connected with the data preprocessing module and the deep learning module and used for storing industrial building durability influence factor data and industrial building damage degree data of the data preprocessing module to obtain a historical database; the deep learning module is used for transmitting the historical database data to the deep learning module;
the system management module is connected with the intelligent analysis and grade judgment module and the big data platform and used for visually displaying and managing the building health monitoring data.
According to a third aspect of embodiments of the present application, there is provided a computer-readable storage medium having a computer program stored thereon; the computer program is executed by a processor to implement a building monitoring method.
By adopting the building monitoring method, the building monitoring system and the storage medium based on big data artificial intelligence in the embodiment of the application, various sensing data of the building to be detected are obtained, and durability influence factor data of the building to be detected are obtained through data preprocessing according to the various sensing data; inputting the durability influence factor data of the building to be tested into the trained building analysis model to obtain the health state of the building to be tested; and obtaining a trained building analysis model based on deep learning of the big data sample. The method and the device have the advantages that the big data are combined with the machine learning technology to analyze the health state of the industrial building, various sensor data are obtained through various sensors, various industrial building durability influence factors are determined through correlation analysis, and the health state of the industrial building is determined through analysis of the machine learning model, so that real-time health assessment of the industrial building under the severe environment is realized, and later-period timely maintenance is convenient for the industrial building.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 shows a schematic structural diagram of a building monitoring system based on big data artificial intelligence according to an embodiment of the application.
FIG. 2 illustrates a schematic structural diagram of a big-data artificial intelligence based building monitoring system according to another embodiment of the present application;
FIG. 3 is a flow chart illustrating steps of a building monitoring method based on big data artificial intelligence according to an embodiment of the application;
a schematic structural diagram of a building monitoring device according to an embodiment of the application is shown in fig. 4.
Detailed Description
In the process of implementing the present application, the inventor finds that with the continuous development of industrialization, a large number of industrial buildings are produced, and unlike buildings for other applications, the industrial buildings are often produced and operated in a severe environment with high temperature and high humidity, and are in contact with various corrosive gases, liquids and the like, so that the structural performance of the industrial buildings is easy to have safety problems such as structural damage, aging and even partial function loss; the industrial building data has the characteristics of large scale, high identification difficulty, various types, low value and the like.
However, in the health analysis of building structures, the health of buildings is often monitored by analyzing the collection of stress-strain performance. However, the method has single analysis data, and cannot be applied to industrial buildings in severe environments and further cannot perform comprehensive health condition analysis on industrial buildings with more complex reinforced concrete structures.
Therefore, the application provides a big data artificial intelligence analysis system, which comprises a wireless remote monitoring subsystem, a wireless transmission module, a data preprocessing module, an external data interface end, a big data platform, a deep learning module, an intelligent analysis and grade judgment module, a system management module and a maintenance platform.
The method and the device have the advantages that the big data are combined with the machine learning technology to analyze the health state of the industrial building, various sensor data are obtained through various sensors, various industrial building durability influence factors are determined through correlation analysis, and the health state of the industrial building is determined through analysis of the machine learning model, so that real-time health assessment of the industrial building under the severe environment is realized, and later-period timely maintenance is convenient for the industrial building.
Specifically, the specific process of the deep learning module is as follows: firstly, extracting a large amount of strongly-related and weakly-related industrial building durability influence factor data and industrial building damage degree data from a historical database, and taking the data as a sample set; then, dividing the sample set into a training set of 70% and a testing set of 30%; then, building an artificial neural network, inputting the sample set into the artificial neural network as input data for training, and forming an intelligent analysis model; and finally, inputting 30% of test sets into the intelligent analysis model for testing, outputting the model if the accuracy rate reaches 95%, and otherwise, resampling until the model meets expectations.
Specifically, the specific processing procedure of the intelligent analysis and grade judgment module is as follows: firstly, acquiring various sensing data, and extracting strongly relevant and weakly relevant industrial building durability influence factor data; secondly, inputting strongly relevant and weakly relevant industrial building durability influence factor data into an intelligent analysis model to obtain the damage degree of the industrial building, namely the health state of the industrial building; and finally, grading according to the health state of the industrial building: a stage: on the basis of not taking relevant repair measures for improving the durability of the structure, the structure can meet the use requirement of the target service life; b, stage: according to specific conditions, relevant measures for improving the durability of the structure can be taken or maintenance is not carried out, and the structure can meet the use requirement of the next target service life; c, stage: the structure fails to meet the conditions for the next target age and requires extensive repair to improve its durability.
Compared with the prior art, the invention has the beneficial effects that:
1. the big data artificial intelligence analysis system analyzes the health state of the industrial building by adopting a big data + machine learning technology, acquires various sensing data through various sensors compared with the existing health state analysis method of the industrial building, determines various industrial building durability influence factors through correlation analysis, and finally analyzes and determines the health state of the industrial building through a machine learning model, so that real-time health assessment of the industrial building under severe environment is facilitated, and later-period timely maintenance of the industrial building is facilitated.
2. The big data artificial intelligence analysis system adopts a multi-hop wireless ad hoc network to transmit data of various types of sensing data, thereby being beneficial to guaranteeing timeliness and integrity of data transmission, further being beneficial to guaranteeing long-term stable operation of the big data artificial intelligence analysis system, and being suitable for industrial building scenes in severe environment.
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example 1
Fig. 1 shows a schematic structural diagram of a building monitoring system based on big data artificial intelligence according to an embodiment of the application.
As shown in fig. 1, the building monitoring system of the present embodiment includes a sensor module 10, a data preprocessing module 20, and an intelligent analysis and grade determination module 30.
In particular, the method comprises the following steps of,
the sensor module 10 is used for acquiring various sensing data of a building to be detected. The data preprocessing module 20 is configured to obtain durability influencing factor data of the building to be detected through data preprocessing according to the multiple kinds of sensing data. The intelligent analysis and grade judgment module 30 is used for inputting the durability influence factor data of the building to be tested into the trained building analysis model to obtain the health state of the building to be tested; and obtaining a trained building analysis model based on deep learning of the big data sample.
In a preferred embodiment, the building monitoring system further includes a deep learning module 40, and the deep learning module 40 is configured to obtain a trained building analysis model based on deep learning of the big data sample.
The deep learning module 40 specifically includes: acquiring the data of the industrial building durability influencing factors and the data of the industrial building damage degree of the data preprocessing module 20 as a sample set; wherein, the data of the influence factors on the durability of the industrial building and the data of the damage degree of the industrial building have correlation; and finally, inputting the sample set into an artificial neural network for training to obtain a trained building analysis model.
Wherein a correlation between the industrial building durability influencing factor and the industrial building damage degree needs to be determined before model training.
Specifically, the method further comprises the steps of determining the correlation between the data of the influence factors on the durability of the industrial building and the data of the damage degree of the industrial building through a Pearson correlation coefficient algorithm; the pearson correlation coefficient r is expressed as:
wherein r is a Pearson correlation coefficient between the influence factors of the durability of the industrial building and the damage degree of the industrial building; x is an influence factor of the durability of the industrial building; n is the number of samples; p is the damage degree of the industrial building;
wherein if the correlation coefficient r is greater than 0.5 and less than 1, it indicates strong correlation;
if the correlation coefficient r is greater than 0 and less than 0.5, weak correlation is represented;
if the correlation coefficient r is less than 0, no correlation is indicated.
And finally, selecting a large amount of strongly-related and weakly-related industrial building durability influence factor data and industrial building damage degree data, and taking the data as a sample set.
FIG. 2 shows a schematic structural diagram of a building monitoring system based on big data artificial intelligence according to another embodiment of the present application.
As shown in fig. 2, in some embodiments of the present application, a wireless transmission module 50, a big data platform 60, and a system management module 70 are further included.
As shown in fig. 2, the wireless transmission module 50 is connected to the sensor module 10, that is, the sensor module is disposed at the wireless remote monitoring subsystem in fig. 2, and the wireless transmission module 50 is further connected to the data preprocessing module 20. The wireless transmission module 50 remotely transmits various sensing data of the sensor module 10 to the data preprocessing module 20 through a network.
The big data platform 60 is connected to the data preprocessing module 20 and the deep learning module 40. In this embodiment, as shown in fig. 2, the big data platform 60 is connected to the data preprocessing module 20 through an external data interface, and stores the data of the durability influencing factors of the industrial building and the data of the damage degree of the industrial building to obtain a historical database.
The big data platform 60 is also used to transmit the formed historical database data to the deep learning module 40 for machine learning.
The system management module 70 is connected to the intelligent analysis and grade determination module 30 and the big data platform 60, and is configured to visually display and manage the building health monitoring data.
The system management module 70 may further be connected to a maintenance platform, and configured to obtain a maintenance plan, perform field maintenance according to the maintenance plan, and feed back the maintenance plan to the system management module.
As will be further described herein, the term "fluid" is used,
in the building monitoring system, the wireless remote monitoring subsystem provided with the sensor module 10 includes, but is not limited to, a temperature sensor, a humidity sensor, a CO2 sensor, a PH sensor, an optical detector and a thermographic infrared detector.
The wireless remote monitoring subsystem for field acquisition adopts a multi-hop wireless ad hoc network to transmit data, and the timeliness and integrity of data transmission are guaranteed.
Wherein the historical database is formed by collecting the whole process of the service life of the industrial building for a long time.
Then, the wireless transmission module 50 transmits various kinds of sensing data over a long distance by using a 5G communication network.
The data preprocessing module 20 receives various sensing data transmitted remotely; and carrying out data cleaning, conversion, denoising and normalization processing on the various sensing data to obtain the durability influence factor data of the industrial building.
The big data platform 60 receives the data of the industrial building durability influencing factors through an external data interface end, and stores the data in combination with corresponding industrial building damage degree data to obtain a historical database.
Then, the deep learning module 40 is combined with the historical database of big data to perform intelligent building analysis model training, and the training process is specifically as follows:
s1: firstly, extracting a large amount of strongly-related and weakly-related industrial building durability influence factor data and industrial building damage degree data from a historical database, and taking the data as a sample set;
s2: then, dividing the sample set of step S1 into a training set of 70% and a testing set of 30%;
s3: building an artificial neural network, inputting the sample set of the step S2 into the artificial neural network as input data for training to obtain a building analysis model;
s4: and finally, inputting 30% of the test set into a building analysis model for testing, outputting the model if the accuracy rate reaches 95%, and otherwise, resampling until the model meets the expectation.
When the trained building analysis model is applied to specifically monitor the health of the industrial building to be monitored, the specific monitoring process is as follows:
firstly, acquiring various sensing data of a building to be detected, and extracting strongly relevant and weakly relevant industrial building durability influence factor data;
secondly, inputting strongly relevant and weakly relevant industrial building durability influence factor data into an intelligent analysis model to obtain the damage degree of the industrial building, namely the health state of the industrial building;
and finally, grading according to the health state of the industrial building:
a stage: on the basis of not taking relevant repair measures for improving the durability of the structure, the structure can meet the use requirement of the target service life;
b, stage: according to specific conditions, relevant measures for improving the durability of the structure can be taken or maintenance is not carried out, and the structure can meet the use requirement of the next target service life;
c, stage: the structure fails to meet the conditions for the next target age and requires extensive repair to improve its durability.
By adopting the building monitoring system based on big data artificial intelligence in the embodiment of the application, the sensor module 10 acquires various sensing data of a building to be detected, and the data preprocessing module 20 acquires durability influence factor data of the building to be detected through data preprocessing according to the various sensing data; the intelligent analysis and grade judgment module 30 inputs the durability influence factor data of the building to be tested into the trained building analysis model to obtain the health state of the building to be tested; and obtaining a trained building analysis model based on deep learning of the big data sample. The method and the device have the advantages that the big data are combined with the machine learning technology to analyze the health state of the industrial building, various sensor data are obtained through various sensors, various industrial building durability influence factors are determined through correlation analysis, and the health state of the industrial building is determined through analysis of the machine learning model, so that real-time health assessment of the industrial building under the severe environment is realized, and later-period timely maintenance is convenient for the industrial building.
Example 2
For details that are not disclosed in the building monitoring method of this embodiment, please refer to specific implementation contents of the building monitoring system in other embodiments.
FIG. 3 is a flow chart illustrating steps of a building monitoring method based on big data artificial intelligence according to an embodiment of the application.
As shown in fig. 3, the building monitoring method provided in this embodiment specifically includes the following steps:
s101: and acquiring various sensing data of the building to be detected.
Specifically, various sensing data are acquired through a temperature sensor, a humidity sensor, a CO2 sensor, a PH value sensor, an optical detector and/or an infrared thermography detector; and then remotely transmitting the various sensing data through the network.
S102: and obtaining durability influence factor data of the building to be detected through data preprocessing according to various sensing data.
Specifically, receiving a variety of sensing data remotely transmitted in S101; carrying out data cleaning, conversion, denoising and normalization processing on various sensing data to obtain industrial building durability influence factor data; and storing the durability influence factor data of the industrial building and the damage degree data of the industrial building to obtain a historical database.
S103: inputting the durability influence factor data of the building to be tested into the trained building analysis model to obtain the health state of the building to be tested; and obtaining a trained building analysis model based on deep learning of the big data sample.
The method for obtaining the trained building analysis model based on deep learning of the big data sample specifically comprises the following steps:
firstly, acquiring data of durability influencing factors of an industrial building and data of damage degree of the industrial building as a sample set; wherein, the data of the influence factors on the durability of the industrial building and the data of the damage degree of the industrial building have correlation;
and then, inputting the sample set into an artificial neural network for training to obtain a trained building analysis model.
Before model training, correlation between the influence factors of the durability of the industrial building and the damage degree of the industrial building needs to be determined.
Specifically, determining the correlation between the data of the influence factors on the durability of the industrial building and the data of the damage degree of the industrial building through a Pearson correlation coefficient algorithm; the pearson correlation coefficient r is expressed as:
wherein r is a Pearson correlation coefficient between the influence factors of the durability of the industrial building and the damage degree of the industrial building; x is an influence factor of the durability of the industrial building; n is the number of samples; p is the damage degree of the industrial building;
wherein if the correlation coefficient r is greater than 0.5 and less than 1, it indicates strong correlation;
if the correlation coefficient r is greater than 0 and less than 0.5, weak correlation is represented;
if the correlation coefficient r is less than 0, no correlation is indicated.
And finally, selecting a large amount of strongly-related and weakly-related industrial building durability influence factor data and industrial building damage degree data, and taking the data as a sample set.
Further, the health status of the building to be tested specifically includes:
a stage: on the basis of not taking relevant repair measures for improving the durability of the structure, the structure can meet the use requirement of the target service life;
b, stage: according to specific conditions, relevant measures for improving the durability of the structure can be taken or maintenance is not carried out, and the structure can meet the use requirement of the next target service life;
c, stage: the structure fails to meet the conditions for the next target age and requires extensive repair to improve its durability.
By adopting the building monitoring method based on big data artificial intelligence in the embodiment of the application, various sensing data of the building to be detected are obtained, and durability influence factor data of the building to be detected are obtained through data preprocessing according to the various sensing data; inputting the durability influence factor data of the building to be tested into the trained building analysis model to obtain the health state of the building to be tested; and obtaining a trained building analysis model based on deep learning of the big data sample. The method and the device have the advantages that the big data are combined with the machine learning technology to analyze the health state of the industrial building, various sensor data are obtained through various sensors, various industrial building durability influence factors are determined through correlation analysis, and the health state of the industrial building is determined through analysis of the machine learning model, so that real-time health assessment of the industrial building under the severe environment is realized, and later-period timely maintenance is convenient for the industrial building.
Example 3
For details that are not disclosed in the building monitoring device of this embodiment, please refer to specific implementation contents of the building monitoring method or system in other embodiments.
A schematic structural diagram of a building monitoring device 400 according to an embodiment of the application is shown in fig. 4.
As shown in fig. 4, a construction monitoring device 400 includes:
the memory 402: for storing executable instructions; and
a processor 401 is coupled to the memory 402 to execute executable instructions to perform the motion vector prediction method.
Those skilled in the art will appreciate that the schematic diagram 4 is merely an example of the building monitoring device 400 and does not constitute a limitation on the building monitoring device 400 and may include more or fewer components than shown, or some components in combination, or different components, e.g., the building monitoring device 400 may also include input-output devices, network access devices, buses, etc.
The Processor 401 (CPU) may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor 401 may be any conventional processor or the like, the processor 401 being the control center for the building monitoring device 400 and the various interfaces and lines connecting the various parts of the overall building monitoring device 400.
The memory 402 may be used to store computer readable instructions and the processor 401 may implement the various functions of the building monitoring device 400 by executing or executing computer readable instructions or modules stored in the memory 402 and invoking data stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the stored data area may store data created from use of the construction monitoring device 400, and the like. In addition, the Memory 402 may include a hard disk, a Memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Memory Card (Flash Card), at least one disk storage device, a Flash Memory device, a Read-Only Memory (ROM), a Random Access Memory (RAM), or other non-volatile/volatile storage devices.
The modules integrated by the construction monitoring device 400 may be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by hardware related to computer readable instructions, which may be stored in a computer readable storage medium, and when the computer readable instructions are executed by a processor, the steps of the method embodiments may be implemented.
Example 4
The present embodiment provides a computer-readable storage medium having stored thereon a computer program; the computer program is executed by a processor to implement the building monitoring method in other embodiments.
By adopting the building monitoring method, the building monitoring system and the storage medium based on big data artificial intelligence in the embodiment of the application, various sensing data of the building to be detected are obtained, and durability influence factor data of the building to be detected are obtained through data preprocessing according to the various sensing data; inputting the durability influence factor data of the building to be tested into the trained building analysis model to obtain the health state of the building to be tested; and obtaining a trained building analysis model based on deep learning of the big data sample. The method and the device have the advantages that the big data are combined with the machine learning technology to analyze the health state of the industrial building, various sensor data are obtained through various sensors, various industrial building durability influence factors are determined through correlation analysis, and the health state of the industrial building is determined through analysis of the machine learning model, so that real-time health assessment of the industrial building under the severe environment is realized, and later-period timely maintenance is convenient for the industrial building.
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 terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A building monitoring method based on big data artificial intelligence is characterized by comprising the following steps:
acquiring a plurality of kinds of sensing data of a building to be detected,
obtaining durability influence factor data of the building to be detected through data preprocessing according to the multiple kinds of sensing data;
inputting the durability influence factor data of the building to be tested into the trained building analysis model to obtain the health state of the building to be tested; and obtaining the trained building analysis model based on deep learning of a big data sample.
2. The building monitoring method according to claim 1, wherein the obtaining of the trained building analysis model based on deep learning of big data samples specifically comprises:
acquiring durability influence factor data and damage degree data of the industrial building as a sample set; wherein, the data of the influence factors on the durability of the industrial building and the data of the damage degree of the industrial building have correlation;
and inputting the sample set into an artificial neural network for training to obtain a trained building analysis model.
3. The building monitoring method of claim 2, further comprising determining a correlation between the industrial building durability influencing factor data and the industrial building damage degree data by a pearson correlation coefficient algorithm; the Pearson correlation coefficient r formula is as follows:
wherein r is a Pearson correlation coefficient between the influence factors of the durability of the industrial building and the damage degree of the industrial building; x is an influence factor of the durability of the industrial building; n is the number of samples; p is the damage degree of the industrial building;
wherein if the correlation coefficient r is greater than 0.5 and less than 1, it represents a strong correlation;
if the correlation coefficient r is greater than 0 and less than 0.5, weak correlation is represented;
and if the correlation coefficient r is less than 0, no correlation is indicated.
4. The building monitoring method according to claim 1, wherein the acquiring of the multiple kinds of sensing data of the building to be monitored specifically comprises:
by temperature sensor, humidity sensor, CO2The sensor, the PH value sensor, the optical detector and/or the infrared thermal image detector acquire various sensing data;
and remotely transmitting the various sensing data through a network.
5. The building monitoring method according to claim 4, wherein the obtaining durability influencing factor data of the building to be tested through data preprocessing according to the various sensing data specifically comprises:
receiving the remotely transmitted multiple sensing data;
carrying out data cleaning, conversion, denoising and normalization processing on the multiple kinds of sensing data to obtain industrial building durability influence factor data;
and storing the durability influence factor data of the industrial building and the damage degree data of the industrial building to obtain a historical database.
6. The building monitoring method according to claim 1, wherein the health status of the building to be monitored specifically comprises:
a stage: on the basis of not taking relevant repair measures for improving the durability of the structure, the structure can meet the use requirement of the target service life;
b, stage: by taking relevant measures for improving the durability of the structure or not carrying out maintenance, the structure can meet the use requirement of the next target service life;
c, stage: the structure cannot meet the conditions for the next target age.
7. The utility model provides a building monitoring system based on big data artificial intelligence which characterized in that specifically includes:
a sensor module: used for acquiring various sensing data of a building to be measured,
a data preprocessing module: the system is used for obtaining durability influence factor data of the building to be detected through data preprocessing according to the multiple kinds of sensing data;
intelligent analysis and grade decision module: the system is used for inputting the durability influence factor data of the building to be tested into the trained building analysis model to obtain the health state of the building to be tested; and obtaining the trained building analysis model based on deep learning of a big data sample.
8. The building monitoring system of claim 7, further comprising a deep learning module for obtaining the trained building analysis model based on deep learning of big data samples; the method specifically comprises the following steps:
acquiring durability influence factor data and damage degree data of the industrial building as a sample set; wherein there is a correlation between the industrial building durability influencing factor data and the industrial building damage level data;
and inputting the sample set into an artificial neural network for training to obtain a trained building analysis model.
9. The building monitoring system of claim 8, further comprising a wireless transmission module, a big data platform, and a system management module;
the wireless transmission module is connected with the sensor module and the data preprocessing module and remotely transmits various sensing data of the sensor module to the data preprocessing module through a network;
the big data platform is connected with the data preprocessing module and the deep learning module and used for storing industrial building durability influence factor data and industrial building damage degree data of the data preprocessing module to obtain a historical database; the historical database data are transmitted to a deep learning module;
the system management module is connected with the intelligent analysis and grade judgment module and the big data platform and used for visually displaying and managing the building health monitoring data.
10. A computer-readable storage medium, having stored thereon a computer program; the computer program is executed by a processor to implement the big data artificial intelligence based building monitoring method according to any one of claims 1-6.
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| CN115063040A (en) * | 2022-07-28 | 2022-09-16 | 湖南工商大学 | Method and system for collaborative evaluation and prediction of house building structure health |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115063040A (en) * | 2022-07-28 | 2022-09-16 | 湖南工商大学 | Method and system for collaborative evaluation and prediction of house building structure health |
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