CN115457467A

CN115457467A - Building quality hidden danger positioning method and system based on data mining

Info

Publication number: CN115457467A
Application number: CN202211097302.6A
Authority: CN
Inventors: 郭和玉; 杨艳; 刘毅; 李兴祥; 王跃
Original assignee: Individual
Current assignee: Individual
Priority date: 2022-09-08
Filing date: 2022-09-08
Publication date: 2022-12-09

Abstract

The invention provides a method and a system for positioning hidden danger of building quality based on data mining, and relates to the technical field of buildings. In the invention, building monitoring videos acquired by each video monitoring terminal device are acquired, and building quality information of a target building object is determined based on a plurality of acquired building monitoring videos; determining whether building quality hidden danger positioning processing needs to be carried out on the target building object or not based on the building quality information of the target building object; when the positioning processing of the hidden construction quality danger of the target building object is needed, analyzing and processing the building monitoring video frames included in the building monitoring video aiming at each building monitoring video in the plurality of building monitoring videos to obtain a video analyzing result for representing whether the hidden construction quality danger exists in the building part corresponding to the building monitoring video. Based on the method, the problem of low efficiency of positioning hidden danger of building quality in the prior art can be solved.

Description

Building quality hidden danger positioning method and system based on data mining

Technical Field

The invention relates to the technical field of buildings, in particular to a method and a system for positioning hidden danger of building quality based on data mining.

Background

In the field of building technology, when the determined building quality does not meet the quality requirement, the existing hidden danger of building quality may need to be further positioned so as to facilitate the treatment such as maintenance. However, in the prior art, a building is generally detected based on a quality hidden trouble detector to obtain a corresponding positioning result of the building quality hidden trouble, that is, a building portion of the building, which has a building quality problem, so that the efficiency of positioning the building quality hidden trouble is not high.

Disclosure of Invention

In view of the above, the present invention provides a method and a system for positioning hidden danger of building quality based on data mining, so as to improve the problem of low efficiency of positioning hidden danger of building quality in the prior art.

In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:

a building quality hidden danger positioning method based on data mining is applied to a building quality monitoring server and comprises the following steps:

the method comprises the steps of obtaining a building monitoring video acquired by each of a plurality of video monitoring terminal devices in communication connection, obtaining a plurality of building monitoring videos corresponding to the plurality of video monitoring terminal devices, and determining building quality information of a target building object based on the plurality of building monitoring videos, wherein the plurality of video monitoring terminal devices are respectively used for monitoring a plurality of building parts of the target building object to obtain corresponding building monitoring videos, and each building monitoring video in the plurality of building monitoring videos comprises a plurality of frames of building monitoring video frames;

determining whether building quality hidden danger positioning processing needs to be carried out on the target building object or not based on the building quality information of the target building object;

when the building quality hidden danger positioning processing needs to be carried out on the target building object, analyzing and processing building monitoring video frames included in the building monitoring videos aiming at each building monitoring video in the plurality of building monitoring videos to obtain a video analyzing result used for representing whether building quality hidden dangers exist in the building portion corresponding to the building monitoring video.

In some preferred embodiments, in the method for locating a building quality risk based on data mining, the step of determining whether building quality risk locating processing needs to be performed on the target building object based on the building quality information of the target building object includes:

determining a relative size relationship between the building quality information of the target building object and pre-configured building quality threshold information;

and if the building quality information of the target building object is less than or equal to the building quality threshold information, determining that the building quality hidden danger positioning processing needs to be carried out on the target building object, and if the building quality information of the target building object is greater than the building quality threshold information, determining that the building quality hidden danger positioning processing does not need to be carried out on the target building object.

In some preferred embodiments, in the data mining-based building quality hidden danger positioning method, when the target building object needs to be located, for each of the plurality of building monitoring videos, a building monitoring video frame included in the building monitoring video is parsed to obtain a video parsing result for representing whether a building portion corresponding to the building monitoring video has a building quality hidden danger, including:

when the building quality hidden danger positioning processing is required to be carried out on the target building object, for each building monitoring video in the building monitoring videos, respectively calculating the similarity between every two adjacent building monitoring video frames included in the building monitoring video, obtaining the similarity of a first video frame corresponding to every two adjacent building monitoring video frames, and determining the relative size relation between the similarity of the first video frame and a preset first similarity threshold value;

for each building monitoring video in the building monitoring videos, removing one building monitoring video frame in two adjacent building monitoring video frames of which the similarity of a corresponding first video frame in the building monitoring video is greater than or equal to the first similarity threshold value to obtain at least one representative building monitoring video frame corresponding to the building monitoring video;

and determining a video analysis result for representing whether the building part corresponding to the building monitoring video has the hidden danger of building quality or not based on at least one representative building monitoring video frame corresponding to the building monitoring video for each building monitoring video in the building monitoring videos.

In some preferred embodiments, in the method for locating a building quality risk based on data mining, the step of determining, for each of the plurality of building monitoring videos, a video parsing result for characterizing whether a building quality risk exists in a building portion corresponding to the building monitoring video based on a frame of the building monitoring video represented by at least one frame corresponding to the building monitoring video includes:

counting the number of at least one frame representing building monitoring video frame corresponding to the building monitoring video aiming at each building monitoring video in the plurality of building monitoring videos to obtain the statistical number of video frames corresponding to the building monitoring video, and determining the relative size relation between the statistical number of the video frames and a preset video frame statistical number threshold value;

for each building monitoring video in the building monitoring videos, if the statistical number of the video frames is greater than or equal to the statistical number threshold of the video frames, respectively calculating the similarity between every two adjacent building monitoring video frames in the multi-frame building monitoring video representative frames corresponding to the building monitoring video, and obtaining the similarity of the representative video frames corresponding to every two adjacent building monitoring video representative frames;

respectively determining the relative size relationship between the similarity of representative video frames corresponding to each two adjacent representative building video frames corresponding to the building monitoring video and a pre-configured second similarity threshold value aiming at each building monitoring video in the building monitoring videos, and determining the similarity of each representative video frame smaller than the second similarity threshold value as the similarity of a target representative video frame;

and determining a video analysis result for representing whether the building part corresponding to the building monitoring video has the hidden danger of building quality or not based on the similarity of the target representative video frame corresponding to the building monitoring video for each building monitoring video in the building monitoring videos.

In some preferred embodiments, in the method for locating a building quality risk based on data mining, the step of determining, for each of the plurality of building monitoring videos, a video parsing result for characterizing whether a building portion corresponding to the building monitoring video has a building quality risk based on a similarity of target representative video frames corresponding to the building monitoring video includes:

counting the number of similarity of target representative video frames corresponding to the building monitoring video aiming at each building monitoring video in the building monitoring videos to obtain the number of similarity statistics corresponding to the building monitoring video, and determining the relative size relationship between the number of similarity statistics corresponding to the building monitoring video and a preset threshold value of the number of similarity statistics;

and determining a video analysis result for representing whether the building part corresponding to the building monitoring video has the building quality hidden danger or not based on the relative size relationship between the similarity statistic quantity corresponding to the building monitoring video and the similarity statistic quantity threshold value aiming at each building monitoring video in the building monitoring videos, wherein if the similarity statistic quantity is larger than or equal to the similarity statistic quantity threshold value, the building part corresponding to the building monitoring video is determined to have the building quality hidden danger, and if the similarity statistic quantity is smaller than the similarity statistic quantity threshold value, the building part corresponding to the building monitoring video is determined not to have the building quality hidden danger.

In some preferred embodiments, in the method for locating a building quality risk based on data mining, the step of determining, for each of the plurality of building monitoring videos, a video parsing result for characterizing whether a building quality risk exists in a building portion corresponding to the building monitoring video based on a frame of the building monitoring video represented by at least one frame corresponding to the building monitoring video further includes:

for each building monitoring video in the building monitoring videos, if the video frame counting number is smaller than the video frame counting number threshold, performing linear detection on the representative building monitoring video frame aiming at each frame representative building monitoring video frame corresponding to the building monitoring video to obtain a first linear set corresponding to the representative building monitoring video frame, and performing curve detection on the representative building monitoring video frame to obtain a first curve set corresponding to the representative building monitoring video frame;

acquiring a standard video frame corresponding to a building part corresponding to each building monitoring video in the building monitoring videos, wherein the standard video frame is obtained by performing video monitoring on the corresponding building part without hidden building quality danger;

for each building monitoring video in the building monitoring videos, performing linear detection on a standard video frame corresponding to the building monitoring video to obtain a corresponding second linear set, and performing curve detection on the standard video frame to obtain a corresponding second curve set;

for each frame of the representative building monitoring video frame, determining the contact ratio between a first straight line set and a second straight line set corresponding to the representative building monitoring video frame to obtain the contact ratio of the first set corresponding to the representative building monitoring video frame, and determining the contact ratio between a first curve set and a second curve set corresponding to the representative building monitoring video frame to obtain the contact ratio of the second set corresponding to the representative building monitoring video frame;

and determining a video analysis result for representing whether the building part corresponding to the building monitoring video has the hidden danger of building quality or not based on the corresponding first set contact ratio and the corresponding second set contact ratio of each frame representative building monitoring video frame corresponding to the building monitoring video for each building monitoring video in the plurality of building monitoring videos.

In some preferred embodiments, in the method for positioning hidden building quality danger based on data mining, the step of obtaining a building monitoring video acquired by each of a plurality of video monitoring terminal devices in communication connection to obtain a plurality of building monitoring videos corresponding to the plurality of video monitoring terminal devices, and determining building quality information of a target building object based on the plurality of building monitoring videos includes:

the method comprises the steps of obtaining a building monitoring video acquired by each of a plurality of video monitoring terminal devices in communication connection to obtain a plurality of building monitoring videos corresponding to the plurality of video monitoring terminal devices, wherein the plurality of video monitoring terminal devices are respectively used for monitoring a plurality of building parts of a target building object to obtain corresponding building monitoring videos;

calculating the video similarity between each building monitoring video and a pre-configured building reference video aiming at each building monitoring video in the building monitoring videos;

and determining the building quality information of the target building object based on the video similarity corresponding to each building monitoring video in the plurality of building monitoring videos.

The embodiment of the invention also provides a building quality hidden danger positioning system based on data mining, which is applied to a building quality monitoring server, and the building quality hidden danger positioning system based on data mining comprises:

the building quality determining module is used for acquiring a building monitoring video acquired by each of a plurality of video monitoring terminal devices in communication connection, acquiring a plurality of building monitoring videos corresponding to the plurality of video monitoring terminal devices, and determining building quality information of a target building object based on the plurality of building monitoring videos, wherein the plurality of video monitoring terminal devices are respectively used for monitoring a plurality of building parts of the target building object to acquire corresponding building monitoring videos, and each building monitoring video in the plurality of building monitoring videos comprises a plurality of building monitoring video frames;

the hidden danger positioning processing determining module is used for determining whether building quality hidden danger positioning processing needs to be carried out on the target building object or not based on the building quality information of the target building object;

and the building quality hidden danger determining module is used for analyzing and processing building monitoring video frames included in the building monitoring videos aiming at each building monitoring video in the plurality of building monitoring videos when the building quality hidden danger positioning processing needs to be carried out on the target building object, so as to obtain a video analysis result for representing whether the building quality hidden danger exists in the building part corresponding to the building monitoring video.

In some preferred embodiments, in the data mining-based building quality risk localization system, the risk localization processing and determining module is specifically configured to:

In some preferred embodiments, in the data mining-based building quality risk localization system, the building quality risk determination module is specifically configured to:

when the building quality hidden danger positioning processing is required to be carried out on the target building object, respectively calculating the similarity between every two adjacent building monitoring video frames included in the building monitoring video aiming at each building monitoring video in the plurality of building monitoring videos to obtain the similarity of a first video frame corresponding to every two adjacent building monitoring video frames, and determining the relative magnitude relation between the similarity of the first video frame and a preset first similarity threshold value;

for each building monitoring video in the building monitoring videos, removing one building monitoring video frame in two adjacent building monitoring video frames of which the similarity of the corresponding first video frame in the building monitoring video is greater than or equal to the first similarity threshold value to obtain at least one representative building monitoring video frame corresponding to the building monitoring video;

According to the building quality hidden danger positioning method and system based on data mining provided by the embodiment of the invention, the building monitoring videos acquired by each video monitoring terminal device can be acquired, the building quality information of the target building object is determined based on the plurality of acquired building monitoring videos, and then whether building quality hidden danger positioning processing needs to be carried out on the target building object is determined based on the building quality information of the target building object, so that when the building quality hidden danger positioning processing needs to be carried out on the target building object, a building monitoring video frame included in each building monitoring video in the building monitoring videos is analyzed and processed to obtain a video analysis result for representing whether building quality hidden dangers exist in a building part corresponding to the building monitoring video.

In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

Fig. 1 is a block diagram of a building quality monitoring server according to an embodiment of the present invention.

Fig. 2 is a schematic flow chart illustrating steps included in the method for locating a hidden construction quality risk based on data mining according to an embodiment of the present invention.

Fig. 3 is a schematic diagram of modules included in a data mining-based building quality risk localization system according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.

Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.

As shown in fig. 1, an embodiment of the present invention provides a building quality monitoring server. Wherein the building quality monitoring server may include a memory and a processor.

In detail, the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, they may be electrically connected to each other via one or more communication buses or signal lines. The memory can have stored therein at least one software function (computer program) which can be present in the form of software or firmware. The processor may be configured to execute the executable computer program stored in the memory, so as to implement the method for locating a building quality risk based on data mining provided by the embodiment of the present invention (described later).

For example, in some possible embodiments, the Memory may be, but is not limited to, random Access Memory (RAM), read Only Memory (ROM), programmable Read-Only Memory (PROM), erasable Read-Only Memory (EPROM), electrically Erasable Read-Only Memory (EEPROM), and the like.

For example, in some possible embodiments, the Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), a System on Chip (SoC), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.

Also, the configuration shown in fig. 1 is merely illustrative, and the building quality monitoring server may include more or fewer components than shown in fig. 1, or have a different configuration than that shown in fig. 1, and may include a communication adapter (such as a network adapter) for information interaction with other devices, for example. A communications adapter may be coupled to the bus and may be configured to enable communications with a computing or communications network and/or other computing systems. In various illustrative embodiments, any type of networking configuration may be implemented using a communications adapter, such as wired, wireless, pre-configured, peer-to-peer, LAN, WAN, or the like.

With reference to fig. 2, an embodiment of the present invention further provides a method for positioning hidden danger of building quality based on data mining, which can be applied to the building quality monitoring server. The method steps defined by the flow related to the data mining-based building quality hidden danger positioning method can be realized by the building quality monitoring server. The specific process shown in FIG. 2 will be described in detail below.

Step S100, building monitoring videos acquired by each of a plurality of video monitoring terminal devices in communication connection are acquired, a plurality of building monitoring videos corresponding to the plurality of video monitoring terminal devices are acquired, and building quality information of a target building object is determined based on the plurality of building monitoring videos.

In the embodiment of the present invention, the building quality monitoring server may obtain a building monitoring video acquired by each of a plurality of video monitoring terminal devices in communication connection, obtain a plurality of building monitoring videos corresponding to the plurality of video monitoring terminal devices, and determine building quality information of a target building object based on the plurality of building monitoring videos. The plurality of video monitoring terminal devices are respectively used for monitoring a plurality of building portions of the target building object to obtain corresponding building monitoring videos, and each building monitoring video in the plurality of building monitoring videos comprises a plurality of building monitoring video frames.

And step S200, determining whether building quality hidden danger positioning processing needs to be carried out on the target building object or not based on the building quality information of the target building object.

In the embodiment of the present invention, the building quality monitoring server may determine whether building quality hidden danger positioning processing needs to be performed on the target building object based on the building quality information of the target building object.

Step S300, when the building quality hidden danger positioning processing needs to be carried out on the target building object, analyzing building monitoring video frames included in the building monitoring videos aiming at each building monitoring video in the building monitoring videos to obtain a video analysis result for representing whether building quality hidden dangers exist in the building portion corresponding to the building monitoring video.

In the embodiment of the present invention, when the building quality hidden danger positioning processing needs to be performed on the target building object, the building quality monitoring server may analyze, for each building monitoring video of the plurality of building monitoring videos, a building monitoring video frame included in the building monitoring video to obtain a video analysis result for representing whether a building portion corresponding to the building monitoring video has a building quality hidden danger.

The building quality hidden danger positioning method based on data mining comprises the steps of firstly obtaining the building monitoring videos acquired by each video monitoring terminal device, determining the building quality information of a target building object based on a plurality of obtained building monitoring videos, and then determining whether building quality hidden danger positioning processing needs to be carried out on the target building object based on the building quality information of the target building object, so that when building quality hidden danger positioning processing needs to be carried out on the target building object, analyzing the building monitoring video frames included in the building monitoring videos aiming at each building monitoring video in the building monitoring videos to obtain video analysis results for representing whether building quality hidden dangers exist in building parts corresponding to the building monitoring videos.

For example, in some possible embodiments, step S100 may include the following contents, such as the contents included in step S110, step S120, and step S130.

Step S110, obtaining a building monitoring video acquired by each of a plurality of video monitoring terminal devices in communication connection, and obtaining a plurality of building monitoring videos corresponding to the plurality of video monitoring terminal devices.

In the embodiment of the present invention, the building quality monitoring server may obtain a building monitoring video acquired by each of a plurality of video monitoring terminal devices in communication connection, and obtain a plurality of building monitoring videos corresponding to the plurality of video monitoring terminal devices. The video monitoring terminal devices are respectively used for monitoring a plurality of building parts of a target building object to obtain corresponding building monitoring videos, and each building monitoring video in the building monitoring videos comprises a plurality of building monitoring video frames.

Step S120, aiming at each building monitoring video in the building monitoring videos, calculating the video similarity between the building monitoring video and a pre-configured building reference video.

In the embodiment of the present invention, the building quality monitoring server may calculate, for each of the plurality of building monitoring videos, a video similarity between the building monitoring video and a pre-configured building reference video.

Step S130, determining the building quality information of the target building object based on the video similarity corresponding to each building surveillance video in the multiple building surveillance videos.

In the embodiment of the present invention, the building quality monitoring server may determine the building quality information of the target building object based on the video similarity corresponding to each of the plurality of building monitoring videos.

Based on the above steps S110, S120, and S130, the building monitoring video acquired by each video monitoring terminal device may be obtained first to obtain a plurality of corresponding building monitoring videos, and then, for each building monitoring video, the video similarity between the building monitoring video and the pre-configured building reference video may be calculated, so that the building quality information of the target building object may be determined based on the video similarity corresponding to each building monitoring video in the plurality of building monitoring videos, so that the building quality information may be determined by comparing with the building reference video.

For example, in some possible embodiments, step S110 may include the following:

firstly, judging whether a building quality prediction instruction is received or not, and when the building quality prediction instruction is received, analyzing the building quality prediction instruction to obtain building object identity information of a target building object corresponding to the building quality prediction instruction;

secondly, determining terminal equipment identity information of a plurality of video monitoring terminal equipment for monitoring the target building object based on the building object identity information, wherein the terminal equipment identity information and the building object identity information are bound in advance;

then, sending the generated monitoring video acquisition notification information to each video monitoring terminal device corresponding to the terminal device identity information, wherein each video monitoring terminal device is used for sending a building monitoring video obtained by monitoring a corresponding building part by the video monitoring terminal device to a building quality monitoring server after receiving the monitoring video acquisition notification information;

and then, respectively acquiring the building monitoring videos sent by each video monitoring terminal device based on the monitoring video acquisition notification information to obtain a plurality of corresponding building monitoring videos.

For example, in some possible embodiments, the step of sending the generated monitoring video acquisition notification information to each video monitoring terminal device corresponding to the terminal device identity information may include the following steps:

firstly, analyzing the building quality prediction instruction to obtain building quality prediction precision information of a target building object corresponding to the building quality prediction instruction, and determining corresponding monitoring duration information based on the building quality prediction precision information, wherein the monitoring duration information and the building quality prediction precision information have positive correlation;

secondly, generating corresponding monitoring video acquisition notification information based on the monitoring duration information, and sending the monitoring video acquisition notification information to each video monitoring terminal device corresponding to the terminal device identity information, wherein each video monitoring terminal device is used for monitoring a corresponding building part based on the monitoring duration information after receiving the monitoring video acquisition notification information, obtaining a corresponding building monitoring video, and sending the building monitoring video to the building quality monitoring server.

For example, in some possible embodiments, step S120 may include the following:

firstly, acquiring a plurality of pre-configured building reference videos, wherein each building reference video in the plurality of building reference videos is obtained based on monitoring of other building objects, and the building quality of two other building objects corresponding to every two building reference videos is different;

secondly, respectively calculating the video similarity between the building monitoring video and each building reference video in the building reference videos aiming at each building monitoring video in the building monitoring videos to obtain a plurality of video similarities corresponding to the building monitoring video.

For example, in some possible embodiments, the step of calculating, for each of the building monitoring videos, a video similarity between the building monitoring video and each of the building reference videos to obtain a plurality of video similarities corresponding to the building monitoring video may include the following steps:

firstly, respectively determining a building part corresponding to each frame of building reference video frame in the building reference video aiming at each building reference video in the plurality of building reference videos;

secondly, classifying multi-frame building reference video frames included by the building reference video based on whether the corresponding building parts are the same or not in allusion to each building reference video in the building reference videos to obtain a plurality of corresponding building reference video frame classification sets, and determining the building parts corresponding to the building reference video frames included by each building reference video frame classification set in the building reference video frame classification sets as set tag information corresponding to the corresponding building reference video frame classification sets, wherein the number of the building reference video frame classification sets corresponding to the same building reference video is the same as the number of the building monitoring videos;

then, for each building monitoring video in the building monitoring videos, calculating the similarity between the building monitoring video and the building reference video frame classification set having the set label information corresponding to the building portion corresponding to the building monitoring video, and taking the similarity as the video similarity between the building reference video and the corresponding building reference video to obtain a plurality of video similarities corresponding to the building monitoring video.

For example, in some possible embodiments, the step of calculating, for each of the building monitoring videos, a similarity between the building monitoring video and a building reference video frame classification set having set tag information corresponding to a building portion corresponding to the building monitoring video, as a video similarity with the corresponding building reference video, to obtain a plurality of video similarities corresponding to the building monitoring video may include the following steps:

firstly, aiming at each building monitoring video in the plurality of building monitoring videos, determining a building reference video frame classification set with set label information corresponding to a building part corresponding to the building monitoring video as a target building reference video frame classification set corresponding to the building monitoring video;

secondly, for each building monitoring video in the plurality of building monitoring videos, respectively performing video frame screening processing on the building monitoring video based on the number of building reference video frames included in each target building reference video frame classification set corresponding to the building monitoring video to obtain a plurality of building monitoring screening videos corresponding to the building monitoring video, wherein a plurality of building monitoring video frames included in each building monitoring screening video in the plurality of building monitoring screening videos are continuous in time sequence;

then, for each building monitoring screening video corresponding to each building monitoring video in the plurality of building monitoring videos, performing similarity calculation operation on the building monitoring screening video and the corresponding target building reference video frame classification set to obtain corresponding video similarity.

For example, in some possible embodiments, the similarity calculating operation in the above embodiments may include the following:

firstly, calculating the similarity of each building monitoring video frame in the building monitoring screening video and the video frames of other building monitoring video frames in the building monitoring screening video, determining the relative size relationship between the similarity of the video frames and a preset video frame similarity threshold value, and determining other building monitoring video frames corresponding to the similarity of the video frames as the similar video frames corresponding to the building monitoring video frames when the similarity of the video frames is greater than the video frame similarity threshold value;

secondly, for each building monitoring video frame in the building monitoring screening video, sequencing each similar video frame corresponding to the building monitoring video frame based on the precedence relationship of each similar video frame corresponding to the building monitoring video frame in the building monitoring screening video to obtain a similar video frame sequence corresponding to the building monitoring video frame;

then, aiming at each building reference video frame in the target building reference video frame classification set, calculating the video frame similarity between the building reference video frame and each other building reference video frame in the target building reference video frame classification set, determining the relative size relationship between the video frame similarity and the video frame similarity threshold, and determining other building reference video frames corresponding to the video frame similarity as the similar reference video frames corresponding to the building reference video frames when the video frame similarity is greater than the video frame similarity threshold;

then, aiming at each building reference video frame in the target building reference video frame classification set, sequencing each frame of similar video frame corresponding to the building reference video frame based on the precedence relationship of each frame of similar video frame corresponding to the building reference video frame in the target building reference video frame classification set to obtain a sequence of similar video frames corresponding to the building reference video frame;

further, for each building monitoring video frame in the building monitoring screening video, determining a frame of building reference video frame (such as all first frame video frames or all second frame video frames) with the same precedence relationship in the target building reference video frame classification set as a corresponding building reference video frame corresponding to the building monitoring video frame;

further, for each building monitoring video frame in the building monitoring screening video, calculating a similarity between the building monitoring video frame and a corresponding building reference video frame to obtain a second video frame similarity corresponding to the building monitoring video frame, calculating a sequence similarity between a similar video frame sequence corresponding to the building monitoring video frame and a similar video frame sequence corresponding to the corresponding building reference video frame (for example, calculating an average value of the similarities between every two video frames between two similar video frame sequences), obtaining a third video frame similarity corresponding to the building monitoring video frame, and calculating a weighted sum value of the similarities of the second video frame and the third video frame (for example, the weighted coefficient corresponding to the similarity of the second video frame may be greater than the weighted coefficient corresponding to the similarity of the third video frame), obtaining a weighted sum value of the similarities of the video frames corresponding to the building monitoring video frame;

and finally, determining (such as performing mean value calculation and the like) the video similarity corresponding to the building monitoring video corresponding to the building monitoring screening video based on the video frame similarity weighted sum value corresponding to each frame of building monitoring video frame in the building monitoring screening video.

For example, in some possible embodiments, step S130 may include the following:

firstly, for each building monitoring video in the building monitoring videos, based on a plurality of video similarities corresponding to the building monitoring video, performing weighted summation calculation on building quality information of a plurality of other building objects corresponding to a plurality of building reference videos corresponding to the video similarities to obtain first building quality information of a building portion corresponding to the building monitoring video;

secondly, calculating the similarity between every two adjacent building monitoring video frames included in the building monitoring video aiming at each building monitoring video in the plurality of building monitoring videos to obtain the similarity of a first video frame corresponding to every two adjacent building monitoring video frames;

then, respectively determining a relative size relationship between a first video frame similarity corresponding to every two adjacent building monitoring video frames included in the building monitoring video and a pre-configured video frame similarity threshold value for each building monitoring video in the multiple building monitoring videos, determining the first video frame similarity as a target first video frame similarity corresponding to the building monitoring video when the first video frame similarity is less than or equal to the video frame similarity threshold value, calculating an average value of the first video frame similarities of each target to obtain a first similarity average value corresponding to the building monitoring video, and determining second building quality information of a building portion corresponding to the building monitoring video based on the first similarity average value, wherein the first similarity average value and the second building quality information have a positive correlation relationship, and when any one of the building monitoring videos does not have the corresponding target first video frame similarity, assigning the second building quality information of the corresponding building portion to a maximum value;

then, determining a fusion value of first building quality information and second building quality information of a building part corresponding to each building monitoring video in the building monitoring videos to obtain building quality fusion information of the building part corresponding to the building monitoring video;

further, calculating the building surface area of the target building object, calculating the building surface area of the building portion corresponding to the building surveillance video for each building surveillance video in the plurality of building surveillance videos, and calculating the ratio between the building surface area of the building portion corresponding to the building surveillance video and the building surface area of the target building object to obtain the building surface area ratio of the building portion corresponding to the building surveillance video;

finally, based on the building surface area ratio of each building portion in the target building object (i.e. as a weighting coefficient), the building quality fusion information of each building portion is subjected to weighted summation calculation to obtain the building quality information of the target building object.

For example, in some possible embodiments, step S200 may include the following:

firstly, determining the relative size relationship between the building quality information of the target building object and the pre-configured building quality threshold value information;

secondly, if the building quality information of the target building object is less than or equal to the building quality threshold value information, determining that the building quality hidden danger positioning processing needs to be carried out on the target building object, and if the building quality information of the target building object is greater than the building quality threshold value information, determining that the building quality hidden danger positioning processing does not need to be carried out on the target building object.

For example, in some possible embodiments, step S300 may include the following:

firstly, when building quality hidden danger positioning processing needs to be carried out on the target building object, respectively calculating the similarity between every two adjacent building monitoring video frames included in the building monitoring video aiming at each building monitoring video in the plurality of building monitoring videos, obtaining the similarity of a first video frame corresponding to every two adjacent building monitoring video frames, and determining the relative magnitude relation between the similarity of the first video frame and a preset first similarity threshold value;

secondly, for each building monitoring video in the building monitoring videos, screening out one building monitoring video frame in two adjacent building monitoring video frames of which the similarity of a corresponding first video frame in the building monitoring video is greater than or equal to the first similarity threshold value to obtain at least one representative building monitoring video frame corresponding to the building monitoring video;

then, for each building monitoring video in the plurality of building monitoring videos, determining a video analysis result for representing whether a building part corresponding to the building monitoring video has a building quality hidden danger or not based on at least one representative building monitoring video frame corresponding to the building monitoring video.

For example, in some possible embodiments, the step of determining, for each building monitoring video in the building monitoring videos, a video parsing result for characterizing whether a building portion corresponding to the building monitoring video has a building quality risk or not based on that at least one frame corresponding to the building monitoring video represents a building monitoring video frame may include the following steps:

firstly, counting the number of at least one frame representing building monitoring video frame corresponding to the building monitoring video aiming at each building monitoring video in the plurality of building monitoring videos to obtain the video frame counting number corresponding to the building monitoring video, and determining the relative size relation between the video frame counting number and a preset video frame counting number threshold value;

secondly, for each building monitoring video in the building monitoring videos, if the statistical number of the video frames is greater than or equal to the statistical number threshold of the video frames, respectively calculating the similarity between every two adjacent building monitoring video frames in the multi-frame representative building monitoring video frames corresponding to the building monitoring video, and obtaining the similarity of the representative video frames corresponding to every two adjacent building monitoring video frames;

then, respectively determining the relative size relationship between the similarity of the representative video frames corresponding to each two adjacent representative building monitoring video frames corresponding to the building monitoring video and a pre-configured second similarity threshold value aiming at each building monitoring video in the plurality of building monitoring videos, and determining the similarity of each representative video frame smaller than the second similarity threshold value as the similarity of the target representative video frame;

and finally, determining a video analysis result for representing whether the building part corresponding to the building monitoring video has the hidden danger of building quality or not according to the similarity of the target representative video frame corresponding to the building monitoring video for each building monitoring video in the building monitoring videos.

For example, in some possible embodiments, the step of determining, for each of the building monitoring videos, a video parsing result for characterizing whether a building portion corresponding to the building monitoring video has a building quality risk based on a target representative video frame similarity corresponding to the building monitoring video may include the following steps:

firstly, counting the number of similarity of a target representative video frame corresponding to each building monitoring video in the building monitoring videos to obtain the number of similarity statistics corresponding to the building monitoring video, and determining the relative size relationship between the number of similarity statistics corresponding to the building monitoring video and a preset threshold value of the number of similarity statistics;

secondly, for each building monitoring video in the plurality of building monitoring videos, determining a video analysis result for representing whether a building part corresponding to the building monitoring video has building quality hidden danger or not based on the relative size relationship between the similarity statistic quantity corresponding to the building monitoring video and the similarity statistic quantity threshold, wherein if the similarity statistic quantity is greater than or equal to the similarity statistic quantity threshold, it is determined that the building part corresponding to the building monitoring video has the building quality hidden danger, and if the similarity statistic quantity is less than the similarity statistic quantity threshold, it is determined that the building part corresponding to the building monitoring video does not have the building quality hidden danger.

For example, in some possible embodiments, the step of determining, for each of the building monitoring videos, a video parsing result for characterizing whether a building quality risk exists in a building portion corresponding to the building monitoring video based on at least one frame representing a building monitoring video frame corresponding to the building monitoring video may further include the following steps:

firstly, aiming at each building monitoring video in the building monitoring videos, if the video frame counting number is smaller than the video frame counting number threshold value, aiming at each frame representative building monitoring video frame corresponding to the building monitoring video, carrying out straight line detection on the representative building monitoring video frame to obtain a first straight line set corresponding to the representative building monitoring video frame, and carrying out curve detection on the representative building monitoring video frame to obtain a first curve set corresponding to the representative building monitoring video frame;

secondly, acquiring a standard video frame corresponding to a building part corresponding to each building monitoring video in the building monitoring videos, wherein the standard video frame is obtained by performing video monitoring on the corresponding building part without the hidden danger of building quality;

then, aiming at each building monitoring video in the plurality of building monitoring videos, carrying out straight line detection on a standard video frame corresponding to the building monitoring video to obtain a corresponding second straight line set, and carrying out curve detection on the standard video frame to obtain a corresponding second curve set;

then, aiming at each frame of the representative building monitoring video frame, determining the contact ratio between a first straight line set and a second straight line set corresponding to the representative building monitoring video frame to obtain the contact ratio of the first set corresponding to the representative building monitoring video frame, determining the contact ratio between a first curve set and a second curve set corresponding to the representative building monitoring video frame to obtain the contact ratio of the second set corresponding to the representative building monitoring video frame;

finally, for each building monitoring video in the plurality of building monitoring videos, determining a video parsing result for representing whether a building portion corresponding to the building monitoring video has a building quality hidden danger or not based on a first set overlap ratio and a second set overlap ratio corresponding to each frame representative building monitoring video frame corresponding to the building monitoring video (for example, when the first set overlap ratio corresponding to each frame representative building monitoring video frame is greater than or equal to a first preset threshold value, and the second set overlap ratio corresponding to each frame representative building monitoring video frame is greater than or equal to a second preset threshold value, it may be determined that the building portion corresponding to the building monitoring video frame does not have the building quality hidden danger).

With reference to fig. 3, an embodiment of the present invention further provides a building quality hidden danger positioning system based on data mining, which is applicable to the building quality monitoring server. The data mining-based building quality hidden danger positioning system can comprise the following modules:

In some possible embodiments, the hidden danger localization processing determining module is specifically configured to:

In some possible embodiments, the building quality risk determination module is specifically configured to:

In summary, according to the method and system for positioning hidden construction quality danger based on data mining provided by the present invention, a building surveillance video acquired by each video surveillance terminal device may be obtained first, building quality information of a target building object may be determined based on a plurality of obtained building surveillance videos, and then, based on the building quality information of the target building object, it may be determined whether building quality hidden danger positioning processing needs to be performed on the target building object, so that when building quality hidden danger positioning processing needs to be performed on the target building object, a building surveillance video frame included in the building surveillance video may be analyzed for each building surveillance video, and a video analysis result for representing whether a building portion corresponding to the building surveillance video has a hidden construction quality danger is obtained, so that it may be efficiently determined whether each building portion in the target building object has a hidden construction quality danger, that is, positioning is achieved, and thus a problem of low efficiency of building quality hidden danger positioning in the prior art is improved.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A building quality hidden danger positioning method based on data mining is characterized by being applied to a building quality monitoring server and comprising the following steps:

2. The method for locating the building quality risk based on data mining as claimed in claim 1, wherein the step of determining whether the building quality risk locating process needs to be performed on the target building object based on the building quality information of the target building object comprises:

3. The method for positioning hidden building quality danger based on data mining according to claim 1, wherein when the target building object needs to be positioned, for each of the plurality of building surveillance videos, the step of analyzing a building surveillance video frame included in the building surveillance video to obtain a video analysis result for representing whether the building quality hidden danger exists in a building portion corresponding to the building surveillance video includes:

4. The method for locating the potential building quality hazard based on data mining according to claim 3, wherein the step of determining, for each building surveillance video in the plurality of building surveillance videos, a video parsing result for characterizing whether a building quality hazard exists in a building portion corresponding to the building surveillance video based on at least one frame corresponding to the building surveillance video representing a building surveillance video frame comprises:

counting the number of at least one frame representing building monitoring video frames corresponding to the building monitoring video aiming at each building monitoring video in the plurality of building monitoring videos to obtain the statistical number of the video frames corresponding to the building monitoring video, and determining the relative size relation between the statistical number of the video frames and the pre-configured threshold value of the statistical number of the video frames;

respectively determining the relative size relationship between the similarity of the representative video frames corresponding to each two adjacent representative building monitoring video frames corresponding to the building monitoring video and a pre-configured second similarity threshold value aiming at each building monitoring video in the building monitoring videos, and determining the similarity of each representative video frame smaller than the second similarity threshold value as the similarity of the target representative video frame;

5. The method for locating the hidden construction quality problem based on data mining according to claim 4, wherein the step of determining, for each of the plurality of building monitoring videos, a video parsing result for characterizing whether the hidden construction quality problem exists in the building portion corresponding to the building monitoring video based on the similarity of the target representative video frames corresponding to the building monitoring video includes:

6. The method for locating the building quality risk based on data mining as claimed in claim 4, wherein the step of determining, for each of the plurality of building monitoring videos, a video parsing result for characterizing whether the building quality risk exists in the building portion corresponding to the building monitoring video based on at least one frame corresponding to the building monitoring video representing a building monitoring video frame further comprises:

for each building monitoring video in the building monitoring videos, performing straight line detection on a standard video frame corresponding to the building monitoring video to obtain a corresponding second straight line set, and performing curve detection on the standard video frame to obtain a corresponding second curve set;

7. The method for positioning hidden construction quality troubles based on data mining according to any one of claims 1 to 6, wherein the step of obtaining the construction monitoring video acquired by each of a plurality of video monitoring terminal devices in communication connection, obtaining a plurality of construction monitoring videos corresponding to the plurality of video monitoring terminal devices, and determining the construction quality information of the target construction object based on the plurality of construction monitoring videos comprises:

8. The building quality hidden danger positioning system based on data mining is characterized by being applied to a building quality monitoring server and comprising:

9. The data mining-based building quality risk localization system of claim 8, wherein the risk localization process determining module is specifically configured to:

10. The data mining-based building quality risk localization system of claim 8, wherein the building quality risk determination module is specifically configured to: