CN113271439B - Construction site safety monitoring configuration processing method and system - Google Patents
Construction site safety monitoring configuration processing method and system Download PDFInfo
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- CN113271439B CN113271439B CN202110520396.2A CN202110520396A CN113271439B CN 113271439 B CN113271439 B CN 113271439B CN 202110520396 A CN202110520396 A CN 202110520396A CN 113271439 B CN113271439 B CN 113271439B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
- H04N21/83—Generation or processing of protective or descriptive data associated with content; Content structuring
- H04N21/845—Structuring of content, e.g. decomposing content into time segments
- H04N21/8455—Structuring of content, e.g. decomposing content into time segments involving pointers to the content, e.g. pointers to the I-frames of the video stream
Abstract
The application discloses a construction site safety monitoring configuration processing method and a system, wherein the method comprises the following steps: receiving videos shot by a plurality of cameras within a preset time period, wherein the plurality of cameras are respectively installed at different positions of a construction site; acquiring identification information of each camera in a plurality of cameras, and using the identification information of each camera as identification information of a video shot by the camera; searching whether a video processing task exists according to the label information of each video; under the condition that a video processing task exists, extracting a video clip meeting the requirement of the video processing task from a first video corresponding to the video processing task as monitoring information; and storing the extracted monitoring information. The problem that the camera of construction site is not fully utilized among the prior art is solved through this application to improve the monitoring effect of construction site camera, adapted to the various control needs of construction site.
Description
Technical Field
The application relates to the field of building construction, in particular to a construction site safety monitoring configuration processing method and system.
Background
All be provided with a plurality of cameras at the construction building site at present, these cameras are the image of shooing usually, when what problem appears, carry out the analysis to these images. Videos of the cameras of the construction site are generally stored for one month, which is mainly due to the limitation of storage space, and the monitoring videos are deleted after one month.
The present invention relates to a method for monitoring a plurality of conditions in a construction site, for example, vehicles entering and exiting the construction site, and discloses a method for monitoring the conditions in the construction site.
Disclosure of Invention
The embodiment of the application provides a construction site safety monitoring configuration processing method and system, and aims to at least solve the problem that a camera of a construction site is not fully utilized in the prior art.
According to one aspect of the application, a construction site safety monitoring configuration processing method is provided, and comprises the following steps: receiving videos shot by a plurality of cameras within a preset time period, wherein the plurality of cameras are respectively installed at different positions of a construction site; acquiring identification information of each camera in the plurality of cameras, and using the identification information of each camera as identification information of a video shot by the camera; searching whether a video processing task exists according to the label information of each video, wherein the video processing task is used for finding specific monitoring information from the video, and the corresponding relation between the video processing task and the label information is configured in advance; under the condition that the video processing task exists, extracting a video clip meeting the requirement of the video processing task from a first video corresponding to the video processing task as the monitoring information; and storing the extracted monitoring information.
Further, storing the extracted monitoring information includes: naming the monitoring information using tag information of the first video and a reception time of the first video; and storing the named monitoring information.
Further, the method also comprises the following steps: and after searching according to the label information of each video, storing the video without the corresponding video processing task under the condition of determining that the video processing task corresponding to the label information does not exist.
Further, the method also comprises the following steps: and traversing all the stored videos without the corresponding video processing task, and deleting the videos with the storage time exceeding the first threshold value.
According to another aspect of the present application, there is also provided a construction site safety monitoring configuration processing system, including: a plurality of cameras, wherein the plurality of cameras are respectively installed at different positions of a construction site; software for receiving videos shot by a plurality of cameras within a predetermined period of time, wherein the plurality of cameras are respectively installed at different positions of a construction site; acquiring identification information of each camera in the plurality of cameras, and using the identification information of each camera as identification information of a video shot by the camera; searching whether a video processing task exists according to the label information of each video, wherein the video processing task is used for finding specific monitoring information from the videos, and the corresponding relation between the video processing task and the label information is configured in advance; under the condition that the video processing task exists, extracting a video clip meeting the requirement of the video processing task from a first video corresponding to the video processing task as the monitoring information; and storing the extracted monitoring information.
Further, the software is configured to name the monitoring information using tag information of the first video and a time of receipt of the first video; and storing the named monitoring information.
Further, the software is configured to store the video without the corresponding video processing task when it is determined that the video processing task corresponding to the tag information does not exist after the tag information of each video is searched.
Further, the software is used for traversing all the stored videos without the corresponding video processing task and deleting the videos with the storage time exceeding the first threshold value.
Further, the function of the software is provided by the server as a service.
Further, the server includes a cloud server.
In the embodiment of the application, the method comprises the steps of receiving videos shot by a plurality of cameras within a preset time period, wherein the plurality of cameras are respectively installed at different positions of a construction site; acquiring identification information of each camera in the plurality of cameras, and using the identification information of each camera as identification information of a video shot by the camera; searching whether a video processing task exists according to the label information of each video, wherein the video processing task is used for finding specific monitoring information from the videos, and the corresponding relation between the video processing task and the label information is configured in advance; under the condition that the video processing task exists, extracting a video clip meeting the requirement of the video processing task from a first video corresponding to the video processing task as the monitoring information; and storing the extracted monitoring information. Through this application, the problem that the camera of construction site is not by make full use of among the prior art has been solved to improve the monitoring effect of construction site camera, adapted to the various control needs of construction site.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 is a flowchart of a construction site security monitoring configuration processing method according to an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than here.
In this embodiment, a method for processing safety monitoring configuration of a construction site is provided, and fig. 1 is a flowchart of a method for processing safety monitoring configuration of a construction site according to an embodiment of the present application, and as shown in fig. 1, the flowchart includes the following steps:
step S102, receiving videos shot by a plurality of cameras within a preset time period, wherein the plurality of cameras are respectively installed at different positions of a construction site;
step S104, acquiring identification information of each camera in the plurality of cameras, and using the identification information of each camera as identification information of a video shot by the camera;
step S106, whether a video processing task exists is searched according to the label information of each video, wherein the video processing task is used for finding out specific monitoring information from the videos, and the corresponding relation between the video processing task and the label information is configured in advance;
step S108, under the condition that the video processing task exists, extracting a video clip meeting the requirement of the video processing task from a first video corresponding to the video processing task as the monitoring information;
and step S110, storing the extracted monitoring information.
As a preferred embodiment, when saving the monitoring information, in order to ensure that the information is saved more safely, the monitoring information may be encrypted and compressed, and the used compression password is the tag information of the first video plus the receiving time of the first video.
As another alternative, the server may store the monitoring information in a blockchain, in which embodiment: the server sends the monitoring information to the endorsement node; the endorsement node is one of the nodes in the block chain, and is used for generating an endorsement signature for each received monitoring information and sending the signed transaction to the server; the server is used for endorsing the monitoring information into a payload and broadcasting the monitoring information to the sequencing service node; the sequencing service node is used for placing sequenced monitoring information into a block and sending the sequenced monitoring information to all nodes in a channel, the sequencing service node is one of the nodes in a block chain, and the sequencing service node provides a shared communication channel and provides broadcast service for messages containing the monitoring information; the block is used to generate a chain of blocks, the block including at least one transaction and a timestamp of the block.
The monitoring information stored in the blockchain may be already encrypted monitoring information.
Through the steps, video monitoring tasks can be configured in advance, for example, the number of vehicles entering and exiting the construction site in one day is monitored, two cameras at the door are required to be used for the secondary video monitoring tasks, the label information of the two cameras at the door is assumed to be 000A and 000B, and the corresponding relation between the video monitoring tasks and the 000A and 000B cameras is configured. After the videos of the two cameras are acquired, the videos are also named 000A and 000B. The videos 000A and 000B are input into a video task of extracting the vehicle, the video task acquires video clips of the existing vehicle, and the video clips are stored. Of course, other video monitoring tasks may be configured, for example, the use of the material may be monitored, and a camera of the material stacking area may be used. Therefore, the camera corresponding to each video monitoring task can be flexibly configured through the steps, and the problem that the camera of the construction site is not fully utilized in the prior art is solved, so that the monitoring effect of the camera of the construction site is improved, and various monitoring requirements of the construction site are met.
Preferably, the storing the extracted monitoring information includes: naming the monitoring information using tag information of the first video and a reception time of the first video; and storing the named monitoring information.
Preferably, the method further comprises the following steps: and after searching according to the label information of each video, storing the video without the corresponding video processing task under the condition of determining that the video processing task corresponding to the label information does not exist.
Preferably, the method further comprises the following steps: and traversing all the stored videos without the corresponding video processing task, and deleting the videos with the storage time exceeding the first threshold value.
In this embodiment, a method for setting spatial configuration of a security device (camera) is further provided, which is used to solve how to arrange a plurality of cameras in a construction site, and the process includes the following steps:
step S21, system parameters of the security device to be installed are obtained, wherein the system parameters at least comprise: the monitoring range of the security device;
step S22, obtaining space parameters of a preset space of the security device to be installed, wherein the space parameters include: abstracting the space into a three-dimensional model, and then obtaining the length of each edge, the position relation of each edge and the included angle between adjacent edges of the three-dimensional model, wherein the preset space is a construction site, and the height of the preset space is the highest value of a building to be constructed in the construction site after the building is constructed;
optionally, when the stereoscopic model is irregular, the stereoscopic model is split into a plurality of regular stereos (for example, cylinders, cones, cubes, and the like), the spatial parameters of each regular stereoscopic are input to step S23, the number and the installation positions of the cameras used in the regular stereoscopic are obtained, and the number and the installation positions of the cameras used in all the regular stereoscopic are collected to obtain the number and the installation positions of the cameras of the stereoscopic model.
Step S23, inputting the system parameters and the space parameters into a pre-trained first model, wherein the first model is a neural network model and is obtained by using a plurality of groups of training data to be trained in advance; each group of training data in the multiple groups of training data comprises input data and output data, the input data represents data input into the first model, the output data represents data output by the first model, the input data for training are system parameters of security devices and space parameters of a space, the output data for training are the number of the security devices to be installed in the space corresponding to the input data and the positions of the security devices to be installed, and the installed security devices in the output data can cover all directions of the space;
wherein the training process comprises: the method comprises the steps that input data of training data are the coverage range of security devices and the size of a space to be installed, the coverage range is abstracted to be a three-dimensional space, the size of the three-dimensional space is calculated, the size of the space is divided by the size of the three-dimensional space to obtain the installation number, a first model lays out a plurality of installation modes in the space according to the installation number, output data are known optimal layout modes, the first model matches the closest layout mode in the optimal layout modes in the output data from the plurality of layout installation modes, a function is built according to the closest layout mode, system parameters of the security devices and the size of the space, and the function is used for generating the position of each security device in the layout modes according to the system parameters of the security devices and the size of the space; the first model finds out a function corresponding to each group of training data according to the multiple groups of training data, and the first model obtains a common calculation mode and a parameter range of the multiple functions according to the multiple functions; the first model is trained after obtaining a calculation mode and a parameter range, and the model can be used; the first model is built by using a TensorFlow machine learning model framework; of course, other open-source framework building methods can be used, and after knowing the above calculation process, a person skilled in the art can write out corresponding codes according to the corresponding framework.
Step S24, obtaining output data from the first model, wherein the output data comprises the number of the security devices installed in the preset space and the position of each security device in the stereoscopic model;
and step S25, marking the position of each security device in the three-dimensional model in a real image of the preset space, and displaying the marked image to an installer.
The installation position of the security device is determined by using a machine learning mode through the steps, so that the problem of potential safety hazards caused by the fact that the installation position of the security device is determined by the experience of installation personnel in the related technology is solved, and the safety is improved to a certain extent.
The embodiment may also be used to check whether the position of the security device installed by the installer is correct, and after displaying the marked image to the installer, the method may further include: after the installer finishes installing at the mark position of the preset space, receiving the click of the installer on the mark, and prompting the installer to upload the installed picture; receiving the field pictures uploaded by the installer after the camera is installed; and comparing the field picture with the real image of the preset space to determine the correctness of the installation position of the field picture.
In order to enable an installer to recognize which security devices are installed and which security devices are not installed, after the installation position of the field picture is determined to be correct, the mark of the security device on the real image of the predetermined space is modified to be installed, and installation completion time is displayed, wherein the installation completion time is the time when the installer uploads the field picture.
There are many ways to determine whether the installation position is correct, and a screenshot comparison way is adopted in this embodiment. Namely, the comparison between the live picture and the real image of the predetermined space comprises the following steps: acquiring a position where the security device is installed in the real image of the predetermined space; capturing an image of the periphery of the security device in a preset range according to the acquired position and the preset range; and comparing the live picture with the captured image.
The comparison can also be performed in a machine learning manner. Comparing the live picture to the captured image includes: inputting the live picture and the captured image into a second model for comparison, wherein the second model is a neural network model and is obtained by using a second plurality of groups of training data for training in advance; each of the second plurality of sets of training data includes input data and output data, the input data represents data input into the second model, the output data represents data output by the second model, the input data for training is two pictures, and the output data for training indicates whether the two pictures are label information of the same building part.
The security device related to in the embodiment can be any security device. Such as a camera.
In this embodiment, a system for monitoring, configuring and processing safety of a construction site is further provided, which includes: a plurality of cameras, wherein the plurality of cameras are respectively installed at different positions of a construction site; software for receiving videos shot by a plurality of cameras within a predetermined period of time, wherein the plurality of cameras are respectively installed at different positions of a construction site; acquiring identification information of each camera in the plurality of cameras, and using the identification information of each camera as identification information of a video shot by the camera; searching whether a video processing task exists according to the label information of each video, wherein the video processing task is used for finding specific monitoring information from the videos, and the corresponding relation between the video processing task and the label information is configured in advance; under the condition that the video processing task exists, extracting a video clip meeting the requirement of the video processing task from a first video corresponding to the video processing task as the monitoring information; and storing the extracted monitoring information.
Preferably, the software is configured to name the monitoring information using tag information of the first video and a reception time of the first video; and storing the named monitoring information.
Preferably, the software is configured to store the video without the corresponding video processing task when it is determined that the video processing task corresponding to the tag information does not exist after the tag information of each video is searched.
Preferably, the software is configured to traverse all videos that have been saved without a corresponding video processing task, and delete videos whose saving time exceeds a first threshold.
Preferably, the software functions as a service provided by a server; the server comprises a cloud server.
The functions of the software have been described in the description of the method, and are not described in detail herein.
In this embodiment, an electronic device is provided, comprising a memory in which a computer program is stored and a processor configured to run the computer program to perform the method in the above embodiments.
These computer programs may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks, and corresponding steps may be implemented by different modules.
The programs described above may be run on a processor or may also be stored in memory (or referred to as computer-readable media), which includes both non-transitory and non-transitory, removable and non-removable media, that implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
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 to which the present application pertains. 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 (4)
1. A construction site safety monitoring configuration processing method is characterized by comprising the following steps:
receiving videos shot by a plurality of cameras within a preset time period, wherein the plurality of cameras are respectively installed at different positions of a construction site; wherein installing the plurality of cameras at different locations of the construction site comprises: acquiring system parameters of a camera to be installed, wherein the system parameters at least comprise: the monitoring range of the camera; acquiring spatial parameters of a preset space of the camera to be installed, wherein the spatial parameters comprise: abstracting the space into a three-dimensional model, and then obtaining the length of each edge, the position relation of each edge and the included angle between adjacent edges of the three-dimensional model, wherein the preset space is a construction site, and the height of the preset space is the highest value of a building to be constructed in the construction site after the building is constructed; inputting the system parameters and the space parameters into a pre-trained first model, wherein the first model is a neural network model and is obtained by using a plurality of groups of training data to be trained in advance; each training data set in the multiple training data sets comprises input data and output data, the input data represents data input into the first model, the output data represents data output by the first model, input data for training are system parameters of cameras and space parameters of a space, output data for training are the number of the cameras needing to be installed in the space and the positions of the cameras needing to be installed, which correspond to the input data, and the installed cameras in the output data can cover all directions of the space; obtaining output data from the first model, wherein the output data comprises the number of cameras installed in the predetermined space and the position of each camera in the stereoscopic model; marking the position of each camera in the three-dimensional model in a real image of the preset space, and displaying the marked image to an installer;
acquiring identification information of each camera in the plurality of cameras, and using the identification information of each camera as identification information of a video shot by the camera;
searching whether a video processing task exists according to the label information of each video, wherein the video processing task is used for finding specific monitoring information from the video, and the corresponding relation between the video processing task and the label information is configured in advance;
under the condition that the video processing task exists, extracting a video clip meeting the requirement of the video processing task from a first video corresponding to the video processing task as the monitoring information;
and storing the extracted monitoring information.
2. The method of claim 1, wherein storing the extracted monitoring information comprises:
naming the monitoring information using tag information of the first video and a reception time of the first video;
and storing the named monitoring information.
3. The method of claim 2, further comprising:
and after searching according to the label information of each video, storing the video without the corresponding video processing task under the condition of determining that the video processing task corresponding to the label information does not exist.
4. The method of claim 3, further comprising:
and traversing all the stored videos without the corresponding video processing task, and deleting the videos with the storage time exceeding the first threshold value.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105828052A (en) * | 2016-06-02 | 2016-08-03 | 中国联合网络通信集团有限公司 | Video monitoring method and monitoring system based on Storm technology |
| CN109194913A (en) * | 2018-08-21 | 2019-01-11 | 平安科技（深圳）有限公司 | Processing method, device, equipment and the medium of monitor video data |
| EP3474533A1 (en) * | 2017-10-20 | 2019-04-24 | Checkout Technologies srl | Device for detecting the interaction of users with products arranged on a stand with one or more shelves of a store |
| CN110225309A (en) * | 2019-06-19 | 2019-09-10 | 中信梧桐港供应链管理有限公司 | For the video storage method of chief storekeeper, system and cloud server |
| CN111182269A (en) * | 2020-01-03 | 2020-05-19 | 重庆特斯联智慧科技股份有限公司 | Security camera equipment with intelligent illegal warning function and method |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004117868A (en) * | 2002-09-26 | 2004-04-15 | Babcock Hitachi Kk | Device for monitoring training |
| JP2004236020A (en) * | 2003-01-30 | 2004-08-19 | Eisuke Fujii | Photographing device, photographing system, remote monitoring system and program |
| WO2008092255A1 (en) * | 2007-01-30 | 2008-08-07 | March Networks Corporation | Method and system for task-based video analytics processing |
| US9230250B1 (en) * | 2012-08-31 | 2016-01-05 | Amazon Technologies, Inc. | Selective high-resolution video monitoring in a materials handling facility |
| US10248891B2 (en) * | 2017-06-20 | 2019-04-02 | At&T Intellectual Property I, L.P. | Image prediction |
| US11163998B2 (en) * | 2018-10-22 | 2021-11-02 | Woven Planet North America, Inc. | Systems and methods for automated image labeling for images captured from vehicles |
-
2021
- 2021-05-13 CN CN202110520396.2A patent/CN113271439B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105828052A (en) * | 2016-06-02 | 2016-08-03 | 中国联合网络通信集团有限公司 | Video monitoring method and monitoring system based on Storm technology |
| EP3474533A1 (en) * | 2017-10-20 | 2019-04-24 | Checkout Technologies srl | Device for detecting the interaction of users with products arranged on a stand with one or more shelves of a store |
| CN109194913A (en) * | 2018-08-21 | 2019-01-11 | 平安科技（深圳）有限公司 | Processing method, device, equipment and the medium of monitor video data |
| CN110225309A (en) * | 2019-06-19 | 2019-09-10 | 中信梧桐港供应链管理有限公司 | For the video storage method of chief storekeeper, system and cloud server |
| CN111182269A (en) * | 2020-01-03 | 2020-05-19 | 重庆特斯联智慧科技股份有限公司 | Security camera equipment with intelligent illegal warning function and method |
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