CN114118871A

CN114118871A - Building safety monitoring method and system based on artificial intelligence

Info

Publication number: CN114118871A
Application number: CN202111498722.0A
Authority: CN
Inventors: 曹广游
Original assignee: Changshu Jiucheng Intelligent Technology Co ltd
Current assignee: Changshu Jiucheng Intelligent Technology Co ltd
Priority date: 2021-12-09
Filing date: 2021-12-09
Publication date: 2022-03-01

Abstract

The building safety monitoring method and system based on artificial intelligence combine the primary screening of monitoring equipment to obtain a key attribute content set, and then intelligently mine the key attribute content set through an AI model. Therefore, the construction behavior monitoring data can be used for primarily screening the key attribute content set through the monitoring equipment, so that the influence of the non-key attribute content set on the AI model is avoided, and the integrity and the reliability of building safety monitoring are improved.

Description

Building safety monitoring method and system based on artificial intelligence

Technical Field

The application relates to the technical field of video processing, in particular to a building safety monitoring method and system based on artificial intelligence.

Background

In the actual operation process, need detect through artificial mode to building safety inspection, so, not only the time cost of wasting, still extravagant cost of labor. Therefore, a technical solution is needed to improve the problem of inaccurate building safety monitoring.

Disclosure of Invention

In view of this, the present application provides a building safety monitoring method and system based on artificial intelligence.

In a first aspect, a building safety monitoring method based on artificial intelligence is provided, which is applied to a building safety monitoring system based on artificial intelligence, and the method at least includes:

acquiring construction behavior monitoring data to be excavated;

acquiring a key attribute content set mined by monitoring equipment in the construction behavior monitoring data;

determining a first label of the monitoring equipment for the key attribute content set, and mining an identification indication of the key attribute content set in the construction behavior monitoring data according to an AI model;

a first monitoring of the set of key attribute content from the AI model is determined.

In a separately implemented embodiment, the method further comprises:

determining the first monitoring condition as a monitoring condition of construction behavior monitoring data, and uploading the monitoring condition of the construction behavior monitoring data;

or acquiring a second label of the monitoring equipment for the first monitoring condition, and excavating identification indication related to the first monitoring condition according to the AI model;

determining a second intelligent monitoring condition associated with the first monitoring condition and originating from the AI model;

and determining the second intelligent monitoring condition as a monitoring condition of the construction behavior monitoring data, and uploading the monitoring condition of the construction behavior monitoring data.

In a separately implemented embodiment, the method further comprises:

acquiring the monitoring condition of the construction behavior monitoring data to be verified, wherein the monitoring condition of the construction behavior monitoring data to be verified represents a key feature set in the construction behavior monitoring data to be verified;

determining a third label of the monitoring equipment for the construction behavior monitoring data to be verified, and excavating identification indications of key feature sets in the construction behavior monitoring data to be verified according to the AI model;

determining a third monitoring condition for the set of key features from the AI model;

and determining the third monitoring condition as the checking condition of the construction behavior monitoring data, and uploading the checking condition of the construction behavior monitoring data.

In an independently implemented embodiment, the obtaining a content set of key attributes mined by the monitoring device in the construction behavior monitoring data includes: obtaining a key attribute content set mined by a first monitoring equipment model in the construction behavior monitoring data;

the determining that the monitoring equipment finds the identification indication of the key feature set in the construction behavior monitoring data to be verified according to the AI model to the third label of the construction behavior monitoring data to be verified includes: and determining a third label of the second monitoring equipment model for the construction behavior monitoring data to be verified, and excavating identification indications of the key feature set in the construction behavior monitoring data to be verified according to the AI model.

In an independently implemented embodiment, after obtaining the monitoring condition of the construction behavior monitoring data to be verified, the method further comprises: optimizing a key feature set in the construction behavior monitoring data to be verified by combining with an intelligent training model of second monitoring equipment;

before the determining the third monitoring condition as the checking condition of the construction behavior monitoring data and uploading the checking condition of the construction behavior monitoring data, the method further includes: and optimizing the third monitoring condition by combining the intelligent training model of the second monitoring equipment.

In an independently implemented embodiment, on the premise that the determining and monitoring device finds, according to an AI model, the step of identifying and indicating the set of key features in the construction behavior monitoring data to be verified for the first label of the set of key attribute contents, the method further includes:

and determining the key attribute content set as the monitoring condition of the construction behavior monitoring data, and uploading the monitoring condition of the construction behavior monitoring data.

In an independently implemented embodiment, the obtaining a content set of key attributes mined by the monitoring device in the construction behavior monitoring data includes:

and acquiring a key attribute content set mined by monitoring equipment in the locally crossed feature vectors in the construction behavior monitoring data, wherein the key attribute content set covers the global reference feature vector and does not cover the global non-reference feature vector.

In a second aspect, an artificial intelligence based building security monitoring system is provided, comprising a processor and a memory, which are in communication with each other, wherein the processor is configured to read a computer program from the memory and execute the computer program to implement the method described above.

The building safety monitoring method and system based on artificial intelligence provided by the embodiment of the application are characterized in that a key attribute content set is obtained by combining with primary screening of monitoring equipment, and then intelligent mining is carried out on the key attribute content set through an AI model. Therefore, the construction behavior monitoring data can be used for primarily screening the key attribute content set through the monitoring equipment, so that the influence of the non-key attribute content set on the AI model is avoided, and the integrity and the reliability of building safety monitoring are improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a flowchart of a building security monitoring method based on artificial intelligence according to an embodiment of the present disclosure.

Fig. 2 is a block diagram of an artificial intelligence based building safety monitoring apparatus according to an embodiment of the present disclosure.

Fig. 3 is an architecture diagram of an artificial intelligence based building safety monitoring system according to an embodiment of the present application.

Detailed Description

In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.

Referring to fig. 1, a building safety monitoring method based on artificial intelligence is shown, which may include the following steps 100-400.

And step 100, obtaining construction behavior monitoring data to be excavated.

And 200, acquiring a key attribute content set mined by the monitoring equipment in the construction behavior monitoring data.

Step 300, determining a first label of the monitoring equipment to the key attribute content set, and mining the identification indication of the key attribute content set in the construction behavior monitoring data according to the AI model.

Step 400, determining a first monitoring condition for the set of key attribute content originating from the AI model.

It is understood that in executing the content described in the above steps 100-400, the key attribute content set is obtained in conjunction with the preliminary screening of the monitoring device, and then the key attribute content set is intelligently mined through the AI model. Therefore, the construction behavior monitoring data can be used for primarily screening the key attribute content set through the monitoring equipment, so that the influence of the non-key attribute content set on the AI model is avoided, and the integrity and the reliability of building safety monitoring are improved.

Based on the above basis, the following descriptions of step s 11-step s14 can also be included.

And step s11, determining the first monitoring condition as a monitoring condition of the construction behavior monitoring data, and uploading the monitoring condition of the construction behavior monitoring data.

Step s12, or obtaining a second label of the monitoring device for the first monitoring situation, and mining an identification indication associated with the first monitoring situation according to the AI model.

Step s13 determines a second intelligent monitoring situation associated with the first monitoring situation originating from the AI model.

And step s14, determining the second intelligent monitoring condition as the monitoring condition of the construction behavior monitoring data, and uploading the monitoring condition of the construction behavior monitoring data.

It is understood that when the contents described in the above-described step s11 to step s14 are executed, by accurately determining the construction behavior monitoring data, the reliability of the monitoring situation of uploading the construction behavior monitoring data can be improved.

Based on the above basis, the following descriptions of step s 21-step s24 can also be included.

And step s21, obtaining the monitoring condition of the construction behavior monitoring data to be verified, wherein the monitoring condition of the construction behavior monitoring data to be verified represents a key feature set in the construction behavior monitoring data to be verified.

Step s22, when determining that the monitoring device is applied to the third label of the construction behavior monitoring data to be verified, mining the identification indication of the key feature set in the construction behavior monitoring data to be verified according to the AI model.

At step s23, a third monitored condition for the set of key features is determined that originates from the AI model.

And step s24, determining the third monitoring condition as the checking condition of the construction behavior monitoring data, and uploading the checking condition of the construction behavior monitoring data.

It can be understood that, when the contents described in the above-described steps s21 to s24 are executed, the accuracy of the verification situation of the uploading construction behavior monitoring data can be improved by continuously mining the construction behavior monitoring data to be verified.

In the embodiment of the present disclosure, when obtaining the key attribute content set mined by the monitoring device in the construction behavior monitoring data, there is a problem of mining error, so that it is difficult to accurately mine the key attribute content set, and in order to improve the above technical problem, the step of obtaining the key attribute content set mined by the monitoring device in the construction behavior monitoring data, which is described in step 200, may specifically include the content described in step 210 below.

And step 210, obtaining a key attribute content set mined by the first monitoring equipment model in the construction behavior monitoring data.

It can be understood that, when the content described in the above step 210 is executed, the key attribute content set mined by the monitoring device in the construction behavior monitoring data is obtained, so that the problem of mining errors is improved, and the key attribute content set can be accurately mined.

In the embodiment of the present disclosure, in the determining that the third tag of the construction behavior monitoring data to be verified by the monitoring device is inaccurate, so that it is difficult to accurately obtain the third tag, in order to improve the above technical problem, the step of determining the third tag of the construction behavior monitoring data to be verified by the monitoring device and mining the identification indication of the key feature set in the construction behavior monitoring data to be verified according to the AI model described in step s23 may specifically include the content described in the following step s 231.

And s231, determining a third label of the second monitoring equipment model to the construction behavior monitoring data to be verified, and excavating identification indications of key feature sets in the construction behavior monitoring data to be verified according to the AI model.

It can be understood that, when the content described in the above step s231 is executed, when the third tag of the construction behavior monitoring data to be verified is determined by the monitoring device, the problem of inaccurate verification is solved, so that the third tag can be accurately obtained.

Based on the above, after obtaining the monitoring condition of the construction behavior monitoring data to be verified, the following contents described in step s31 may be further included.

And step s31, optimizing the key feature set in the construction behavior monitoring data to be verified by combining with an intelligent training model of second monitoring equipment.

It can be understood that when the above description of step s31 is performed, the key feature set can be optimized to the greatest extent, and the workload of the subsequent work can be reduced.

Based on the above basis, before determining the third monitoring condition as the checking condition of the construction behavior monitoring data and uploading the checking condition of the construction behavior monitoring data, the following contents described in step s51 may be further included.

And step s51, optimizing the third monitoring condition by combining the intelligent training model of the second monitoring equipment.

It can be understood that, when the content described in the above step s51 is executed, the accuracy of optimization is improved through multi-dimensional optimization, so that the workload of subsequent work can be effectively reduced.

Based on the above basis, on the premise that the step of mining the identification indication of the key feature set in the construction behavior monitoring data to be verified according to the AI model for the first label of the key attribute content set by the determination monitoring device, the following content described in step a1 may also be included.

Step a1, determining the key attribute content set as the monitoring condition of the construction behavior monitoring data, and uploading the monitoring condition of the construction behavior monitoring data.

It can be understood that, when the content described in the step a1 is executed, the key attribute content set is analyzed, so as to improve the monitoring accuracy of uploading the construction behavior monitoring data.

In this embodiment, when obtaining the key attribute content set mined by the monitoring device in the construction behavior monitoring data, there is a problem that the construction behavior monitoring data is repeated, so that the feature vector mining is disturbed, and in order to improve the above technical problem, the step of obtaining the key attribute content set mined by the monitoring device in the construction behavior monitoring data described in step 200 may specifically include the content described in step d 1.

And d1, obtaining a key attribute content set mined by the monitoring equipment in the locally crossed feature vectors in the construction behavior monitoring data, wherein the key attribute content set covers the global reference feature vector and does not cover the global non-reference feature vector.

It can be understood that when the content described in the above step d1 is executed, the condition that the construction behavior monitoring data is duplicated is improved when the key attribute content set mined in the construction behavior monitoring data by the monitoring equipment is obtained, so as to avoid the problem of disturbance of feature vector mining.

On the basis, please refer to fig. 2 in combination, which provides an artificial intelligence based building safety monitoring apparatus 200, applied to an artificial intelligence based building safety monitoring system, the apparatus includes:

a data obtaining module 210, configured to obtain construction behavior monitoring data to be mined;

a content obtaining module 220, configured to obtain a key attribute content set mined by the monitoring device in the construction behavior monitoring data;

an indication mining module 230, configured to determine a first tag of the monitoring device to the key attribute content set, and mine an identification indication of the key attribute content set in the construction behavior monitoring data according to an AI model;

a situation determination module 240 is configured to determine a first monitoring situation for the set of key attribute contents originating from the AI model.

On the basis of the above, please refer to fig. 3, which shows an artificial intelligence based building security monitoring system 300, which includes a processor 310 and a memory 320, which are communicated with each other, wherein the processor 310 is used for reading a computer program from the memory 320 and executing the computer program to implement the above method.

On the basis of the above, there is also provided a computer-readable storage medium on which a computer program is stored, which when executed implements the above-described method.

In summary, based on the above scheme, a key attribute content set is obtained by combining with the primary screening of the monitoring device, and then the key attribute content set is intelligently mined through an AI model. Therefore, the construction behavior monitoring data can be used for primarily screening the key attribute content set through the monitoring equipment, so that the influence of the non-key attribute content set on the AI model is avoided, and the integrity and the reliability of building safety monitoring are improved.

It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. An artificial intelligence-based building safety monitoring method is applied to an artificial intelligence-based building safety monitoring system, and the method at least comprises the following steps:

acquiring construction behavior monitoring data to be excavated;

2. The artificial intelligence based building security monitoring method of claim 1, wherein the method further comprises:

3. The artificial intelligence based building security monitoring method of claim 1, wherein the method further comprises:

4. The artificial intelligence based building safety monitoring method of claim 3, wherein the obtaining a key attribute content set mined by the monitoring device in the construction behavior monitoring data comprises: obtaining a key attribute content set mined by a first monitoring equipment model in the construction behavior monitoring data;

5. The artificial intelligence based building safety monitoring method according to claim 3, wherein after obtaining the monitoring condition of the construction behavior monitoring data to be verified, the method further comprises: optimizing a key feature set in the construction behavior monitoring data to be verified by combining with an intelligent training model of second monitoring equipment;

6. The artificial intelligence based construction safety monitoring method according to claim 1, wherein the method further comprises, on the premise that the determination monitoring device excavates the identification indication step of the key feature set in the construction behavior monitoring data to be verified according to an AI model for the first label of the key attribute content set:

7. The artificial intelligence based building security monitoring method of claim 1, wherein the obtaining a set of key attribute contents mined by a monitoring device in the construction behavior monitoring data comprises:

8. An artificial intelligence based building security monitoring system comprising a processor and a memory in communication with each other, the processor being configured to read a computer program from the memory and execute the computer program to implement the method of any one of claims 1 to 7.