CN112785798A - Behavior analysis method for construction project constructors of electric power substation engineering - Google Patents
Behavior analysis method for construction project constructors of electric power substation engineering Download PDFInfo
- Publication number
- CN112785798A CN112785798A CN202011407948.0A CN202011407948A CN112785798A CN 112785798 A CN112785798 A CN 112785798A CN 202011407948 A CN202011407948 A CN 202011407948A CN 112785798 A CN112785798 A CN 112785798A
- Authority
- CN
- China
- Prior art keywords
- data
- intelligent
- behavior
- monitoring
- safety helmet
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
- G08B13/19613—Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/14—Receivers specially adapted for specific applications
- G01S19/17—Emergency applications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
- G08B21/0407—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis
- G08B21/043—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis detecting an emergency event, e.g. a fall
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y10/00—Economic sectors
- G16Y10/35—Utilities, e.g. electricity, gas or water
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/10—Detection; Monitoring
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/50—Safety; Security of things, users, data or systems
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/60—Positioning; Navigation
Landscapes
- Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Health & Medical Sciences (AREA)
- Emergency Management (AREA)
- Psychiatry (AREA)
- Gerontology & Geriatric Medicine (AREA)
- General Health & Medical Sciences (AREA)
- Social Psychology (AREA)
- Psychology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computer Networks & Wireless Communication (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Computer Security & Cryptography (AREA)
- Alarm Systems (AREA)
Abstract
The invention discloses a behavior analysis method for construction project constructors of an electric power substation project, which is based on an intelligent video system and a chromatography partition management technology of a machine learning method and adopts a computer network communication technology, a video monitoring technology and a safety color region management technology to analyze and process construction site monitoring data. Setting the function division and safety attribute of the operation surface of the regional grid, carrying out color identification on the region, carrying out real-time monitoring on the state change of environment and facility equipment, setting threshold early warning, binding and interacting personnel with the identified region and equipment, and carrying out data capture and classification on related information. The invention can monitor in real time, judge abnormal conditions and send out an alarm in a fastest speed and maximum mode, thereby effectively carrying out advance early warning.
Description
Technical Field
The invention relates to a behavior analysis method for project constructors, and belongs to the technical field of intelligent monitoring.
Background
The existing electric power construction site is supervised by adopting a video monitoring system and basically still in a passive human monitoring state, but due to the wide range of the construction site of a transformer substation, the quantity of the arranged monitoring equipment is large, and the picture monitoring is carried out by adopting a manual mode, so that omission and errors can be possibly caused. .
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a behavior analysis method for construction project constructors of an electric power substation project, which adopts the technologies of image processing, mode recognition, computer vision and the like, filters useless information of a video picture by means of strong processing capacity of a computer by adding an intelligent video analysis module in a monitoring system, analyzes and extracts video key information, quickly and accurately positions an accident site, judges abnormal conditions and sends an alarm in the fastest speed and the maximum mode, thereby effectively carrying out full-automatic, all-weather and real-time monitoring on early warning, in-accident processing and after-accident evidence obtaining. The invention can monitor in real time, judge abnormal conditions and send out an alarm in a fastest speed and maximum mode, thereby effectively carrying out advance early warning.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a behavior analysis method for construction project constructors of an electric power substation project is based on an intelligent video system and a chromatography partition management technology of a machine learning method, and a computer network communication technology, a video digital compression processing technology, a video monitoring technology and a safety color area management technology are adopted to analyze and process construction site monitoring data. The method is a brand new construction management mode by setting the function division and the safety attribute of the regional grid operation surface. The method comprises the steps of identifying the color of a region, monitoring the state change of environment and facility equipment in real time, setting threshold early warning, binding and interacting personnel with the identified region and equipment, and capturing and classifying relevant information. And analyzing the logic relation and the development rule among different information, performing simulation modeling on the behavior trend of the identification object, acquiring the personnel dangerous behavior aura and giving early warning. The method specifically comprises the following steps:
step 1, a chromatographic partition management system based on the Internet of things: setting the function division and safety attribute of the operation surface of the regional grid, carrying out color identification on the region, arranging beacons, carrying out real-time monitoring on the state change of environment and facility equipment, setting threshold early warning, binding and interacting personnel with the identified region and equipment, and carrying out data capture and classification on related information.
Step 2, an AI intelligent video monitoring system based on the Internet of things: the method comprises the steps that a camera is arranged on a regional grid operation surface, the camera sends shot videos to an AI intelligent video monitoring system, an intelligent video analysis module is added in the monitoring system to analyze and extract key video information, an accident site is quickly and accurately positioned through the AI intelligent video monitoring system, abnormal conditions are judged through image processing and mode recognition of the AI intelligent video monitoring system, and an alarm is sent out at the highest speed.
Step 3, intelligent safety helmet based on the internet of things: an intelligent terminal with NB-IoT, Bluetooth and GPS/Beidou intelligent chips is integrated on the safety helmet.
And 4, after the worker wears the safety helmet, the safety helmet sends data to the server in a timing mode, the worker passes through a place with the beacon within a fixed time interval, namely the distance between the Bluetooth module in the safety helmet and the Bluetooth module in the beacon meets the threshold value requirement, and the intelligent terminal reports the personal information of the worker and the combined information of the beacon codes. And if the beacon information is not sent within a fixed time interval, the intelligent terminal reports the personal information of the worker and the GPRS positioning information.
Meanwhile, the camera collects videos of workers at the moment, the shot videos are sent to the AI intelligent video monitoring system, and the AI intelligent video monitoring system judges whether abnormal conditions exist according to the videos.
Preferably: the chromatographic zone management system collects enough field data at an initial moment, and after screening, the chromatographic zone management system performs fitting training through a logistic regression algorithm to generate a rule model, wherein the rule model has different rules according to different application scenes. And when the data reported by the intelligent terminal is received, if the data meets the screening requirement, performing similarity analysis on the data and each rule in the rule model, and if the data is similar, predicting the future behavior of the worker according to the rules. And if the data are not similar to the original data, storing the data in a warehouse and performing new rule training as the original data. The treatment results were as follows: and predicting the future behavior of the worker by using the data similar to the rule matching, and giving an alarm if the future behavior is predicted to be dangerous behavior or aggregative behavior.
Preferably: the AI intelligent video monitoring system realizes that the abnormal behavior monitoring of constructors has loitering detection: actively triggering alarm when the key area has abnormal wandering personnel; and (3) fall monitoring: through falling monitoring, the abnormity of the staff is rapidly discovered; off-duty monitoring: supervising whether the person on duty works in the duty room; aggregation monitoring: when the field crowd is dense, an alarm is actively triggered; wearing and monitoring: when detecting that the personnel do not wear the safety helmet or the working clothes, triggering an alarm; climbing detection: and when people climb abnormally on the spot, an alarm is triggered actively.
Preferably: when the safety helmet Bluetooth module and the dangerous area beacon accord with the threshold value, the built-in voice module of the safety helmet can play warning voice recorded in advance.
Compared with the prior art, the invention has the following beneficial effects:
according to the intelligent safety helmet design based on the 5G narrow-band Internet of things technology, a construction site personnel management system based on the intelligent safety helmet is established, the Internet of things technology and the related video analysis technology are utilized, the behavior information data of people are subjected to statistical analysis, the real-time monitoring on site and the dynamic behavior analysis of the people are realized, the AI intelligent video monitoring and the auxiliary accurate positioning are combined for transformer substation construction site safety management, the intelligent safety helmet facing to the construction environment is applied, the constructor information is transmitted to the cloud server in real time in the 5G Internet of things transmission mode, the activity track of workers is obtained, the activity trend of the workers is predicted, and the occurrence of danger is avoided.
Drawings
Fig. 1 is a flow chart of behavior analysis.
Detailed Description
The present invention is further illustrated by the following description in conjunction with the accompanying drawings and the specific embodiments, it is to be understood that these examples are given solely for the purpose of illustration and are not intended as a definition of the limits of the invention, since various equivalent modifications will occur to those skilled in the art upon reading the present invention and fall within the limits of the appended claims.
A behavior analysis method for construction project constructors of an electric power substation project comprises the following steps:
step 1, a chromatographic partition management system based on the Internet of things: setting the function division and safety attribute of the operation surface of the regional grid, carrying out color identification on the region, arranging beacons, carrying out real-time monitoring on the state change of environment and facility equipment, setting threshold early warning, binding and interacting personnel with the identified region and equipment, and carrying out data capture and classification on related information.
The chromatographic partition management technology is a safety color-based area management technology and is a management mode for the position, behavior and efficiency of workers on a construction site. The method is a brand new construction management mode which comprehensively considers and scientifically classifies elements in a construction site range, sets function division and safety attributes of an area grid operation surface and is a brand new construction management mode. The method comprises the steps of identifying the color of a region, monitoring the state change of environment and facility equipment in real time, setting threshold early warning, binding and interacting personnel with the identified region and equipment, and capturing and classifying relevant information. And analyzing the logic relation and the development rule among different information, performing simulation modeling on the behavior trend of the identification object, acquiring the personnel dangerous behavior aura and giving early warning.
The behavior analysis workflow is as in fig. 1. Such as: when a worker wears the safety helmet, the safety helmet sends data to the server in a timing mode, the worker passes through a place with a beacon within a fixed time interval (such as 3 minutes), namely the distance between a Bluetooth module in the safety helmet and a Bluetooth module in the beacon meets the threshold value requirement, and the intelligent terminal reports personal information of the worker and combined information of beacon codes; and if the beacon information is not sent within a fixed time interval, the intelligent terminal reports the personal information of the worker and the GPRS positioning information. The system culls some apparently incorrect data based on pre-fabricated field conditions, such as: and the GPRS positioning drift, a point with lower precision or a point positioned outside the construction range. The specific data processing rules are as follows: at an initial moment, enough field data are collected, after screening, a logistic regression algorithm is used for fitting and training to generate a rule model, and the rule model has different rules according to different application scenes, for example, for carpenters, a behavior rule may be obtained after data fitting and training: "carpentry will appear in the material area at 7 am". When new data is received, if the new data meets the screening requirement, similarity analysis is carried out on the new data and each rule in the rule model, for example, whether the position of a certain worker in a certain time period is similar to the rule or not is analyzed, and if the position of the certain worker in the certain time period is similar to the rule, the future behavior of the worker is predicted according to the rule; and if the data are not similar to the original data, storing the data in a warehouse and performing new rule training as the original data. It should be noted that different regions and seasons will produce certain time deviation to the behavior rules, for example, there is a certain time difference between Xinjiang and Nanjing, which results in different working hours for workers. Therefore, after the new data is matched with the rules, a multidimensional feature conversion operation is carried out to balance the influence of regional and seasonal differences on the behavior of workers, and simultaneously, the reason for generating the time difference and the deviation value are recorded. The treatment results were as follows: and predicting the future behavior of the worker by using data similar to the rule matching, and giving an alarm if the worker predicts dangerous behavior or aggregative behavior, for example, predicting that the worker is likely to enter a dangerous area, and when the Bluetooth module of the safety helmet and a beacon of the dangerous area accord with a threshold value, playing an alarm voice recorded in advance by a built-in voice module to play a caution and a danger in front! Meanwhile, the management interface pops up an alarm flashing icon of a worker at the corresponding position on the workplace so as to attract the attention of the supervisory personnel.
Step 2, an AI intelligent video monitoring system based on the Internet of things: the method comprises the steps that a camera is arranged on a regional grid operation surface, the camera sends shot videos to an AI intelligent video monitoring system, an intelligent video analysis module is added in the monitoring system to analyze and extract key video information, an accident site is quickly and accurately positioned through the AI intelligent video monitoring system, abnormal conditions are judged through image processing and mode recognition of the AI intelligent video monitoring system, and an alarm is sent out at the highest speed.
And based on a chromatography partition management technology, the safety management strategy of the transformer substation construction site combines AI intelligent video monitoring and auxiliary accurate positioning. For the three-dimensional construction mode of most building projects, the transformer substation belongs to planar construction, the arrangement of the cameras on the construction site can effectively record the activity condition of personnel in a monitoring range, the automatic detection, identification and tracking of constructors can be realized by using an intelligent image processing algorithm, and meanwhile, the real-time environment monitoring of a construction site can be realized by combining sensors such as sound and optics. However, the accurate positioning of the spatial position, such as the floor where the constructor is located, cannot be performed only by video monitoring, and effective alarm interaction cannot be performed, so that the auxiliary personnel accurate positioning system based on the intelligent safety helmet is a direct supervision and management aiming at the worker operation specification in the safety management strategy proposed herein. The positioning information of the personnel is collected through the intelligent safety helmet, and the positioning information is transmitted to the cloud platform through the 5G Internet of things technology, so that the effective supervision of the constructors is realized. On the other hand, the construction area is subjected to chromatography partition management, elements in a construction site range are comprehensively considered and scientifically classified, area grids are set to be used as functional division and safety attributes of pages, a staff accurate positioning system is combined, simulation modeling of behavior trends of constructors is achieved, staff dangerous behavior precursors are obtained, and early warning is given in time.
The intelligent system adopts the technologies of image processing, pattern recognition, computer vision and the like, filters useless information of video pictures by adding an intelligent video analysis module in a monitoring system with the help of strong processing capacity of a computer, analyzes and extracts key video information, can quickly and accurately locate accident sites through AI intelligent monitoring, judges abnormal conditions through the image processing technology and the pattern recognition of AI and sends an alarm at the highest speed, thereby effectively carrying out full-automatic, all-weather and real-time monitoring on early warning, handling in the accident and obtaining evidence after the accident. Based on this system can realize constructor abnormal behavior monitoring, and the main monitoring scene has: (1) loitering detection: actively triggering alarm when the key area has abnormal wandering personnel; (2) and (3) fall monitoring: through falling monitoring, the abnormity of the staff is rapidly discovered; (3) off-duty monitoring: supervising whether the person on duty works in the duty room; (4) aggregation monitoring: when the field crowd is dense, an alarm is actively triggered; (5) wearing and monitoring: when detecting that the personnel do not wear the safety helmet or the working clothes, triggering an alarm; (6) climbing detection: and when people climb abnormally on the spot, an alarm is triggered actively.
Step 3, the intelligent safety helmet based on the 5G narrow-band Internet of things: on the basis of the safety protection function of the traditional safety helmet, the intelligent terminal of an intelligent chip integrating technologies such as NB-IoT, Bluetooth, GPS/Beidou and the like is used for realizing accurate positioning management of constructors after division based on site chromatography, establishing a construction site personnel management system based on the intelligent safety helmet, and performing statistical analysis on behavior information data of people by using an Internet of things technology and a related video analysis technology to realize real-time monitoring on site and dynamic analysis on behaviors of the personnel.
And 4, after the worker wears the safety helmet, the safety helmet sends data to the server in a timing mode, the worker passes through a place with the beacon within a fixed time interval, namely the distance between the Bluetooth module in the safety helmet and the Bluetooth module in the beacon meets the threshold value requirement, and the intelligent terminal reports the personal information of the worker and the combined information of the beacon codes. And if the beacon information is not sent within a fixed time interval, the intelligent terminal reports the personal information of the worker and the GPRS positioning information.
Meanwhile, the camera collects videos of workers at the moment, the shot videos are sent to the AI intelligent video monitoring system, and the AI intelligent video monitoring system judges whether abnormal conditions exist according to the videos.
Preferably:
preferably: the AI intelligent video monitoring system realizes that monitoring of abnormal behaviors of constructors includes wandering detection, falling monitoring, off-duty monitoring, gathering monitoring, wearing monitoring and climbing detection.
Preferably: when the safety helmet Bluetooth module and the dangerous area beacon accord with the threshold value, the built-in voice module of the safety helmet can play warning voice recorded in advance.
In order to realize accurate positioning and track tracking of the constructors, positioning information NB-IoT of the constructors is transmitted to a cloud platform and then is analyzed and processed. The key steps are as follows: data acquisition: when a worker wears the safety helmet, the safety helmet sends data to the server in a timing mode, the worker passes through a place with a beacon within a fixed time interval (such as 3 minutes), namely the distance between a Bluetooth module in the safety helmet and a Bluetooth module in the beacon meets the threshold value requirement, and the intelligent terminal reports a corresponding beacon code as worker position information; and if the beacon information is not sent within a fixed time interval, the intelligent terminal reports GPRS positioning information. And (3) screening data: the system culls some apparently incorrect data based on pre-fabricated field conditions, such as: GPRS has a drift in positioning, a point of lower accuracy or a point positioned outside the item.
Based on a chromatography partition management technology, a substation construction site safety management strategy combining AI intelligent video monitoring and auxiliary accurate positioning pays attention to the behavior dynamics of workers in the construction process in real time, and the unsafe behaviors in the power transmission and transformation construction process are fundamentally avoided. And the provided safety management strategy is verified through a field project of a certain transformer substation, so that the effectiveness and the practicability of the provided method can be proved. Based on the chromatographic zoning management technology, the substation construction site safety management strategy research combining AI intelligent video monitoring and auxiliary accurate positioning realizes the functions of accurate positioning of personnel, track tracking, automatic alarm and the like in the construction process of the substation engineering, improves the informatization level of the construction process of the substation, provides effective reference information for the monitoring of the substation engineering, and provides powerful guarantee for the safety construction and the engineering quality of the engineering. The construction environment of the transformer substation is high in risk, but the requirements on supervision of mechanical equipment are lower compared with most of construction projects, and therefore the strategy provided by the invention can achieve an effective personnel management target aiming at the environment of the transformer substation. In the future, for more complex building scenes, richer sensing modules can be integrated in the safety helmet terminal to collect more comprehensive personnel related data, and effective auxiliary means are provided for meeting the safety supervision of different construction environments. Under the condition of construction of constructors, unsafe behaviors in the power transmission and transformation construction process can be effectively and fundamentally avoided through intelligent chips of technologies such as 5G, NB-IoT, Bluetooth, GPS/Beidou and the like.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (4)
1. A behavior analysis method for construction project constructors of an electric power substation engineering is characterized by comprising the following steps:
step 1, a chromatographic partition management system based on the Internet of things: setting function division and safety attributes of a regional grid operation surface, carrying out color identification on regions, arranging beacons, carrying out real-time monitoring on state changes of environment and facility equipment, setting threshold early warning, carrying out binding interaction on personnel and the identified regions and equipment, and carrying out data capture and classification on related information;
step 2, an AI intelligent video monitoring system based on the Internet of things: arranging a camera on the operation surface of the regional grid, sending the shot video to an AI intelligent video monitoring system by the camera, analyzing and extracting video key information by adding an intelligent video analysis module in the monitoring system, quickly and accurately positioning an accident scene by the AI intelligent video monitoring system, judging abnormal conditions by image processing and mode recognition of the AI intelligent video monitoring system, and sending an alarm at the highest speed;
step 3, intelligent safety helmet based on the internet of things: an intelligent terminal with NB-IoT, Bluetooth and GPS/Beidou intelligent chips is integrated on the safety helmet;
step 4, after the worker wears the safety helmet, the safety helmet sends data to the server in a timing mode, the worker passes through a place with a beacon within a fixed time interval, namely the distance between a Bluetooth module in the safety helmet and a Bluetooth module in the beacon meets the threshold value requirement, and the intelligent terminal reports personal information of the worker and combined information of beacon codes; if the beacon information is not sent within a fixed time interval, the intelligent terminal reports the personal information of workers and GPRS positioning information;
meanwhile, the camera collects videos of workers at the moment, the shot videos are sent to the AI intelligent video monitoring system, and the AI intelligent video monitoring system judges whether abnormal conditions exist according to the videos.
2. The behavior analysis method for the constructors of the electric power substation engineering construction project according to claim 1, characterized in that: the method comprises the following steps that a chromatography partition management system collects enough field data at an initial moment, after screening, fitting training is carried out through a logistic regression algorithm to generate a rule model, and the rule model has different rules according to different application scenes; when data reported by the intelligent terminal are received, if the data meet the screening requirements, similarity analysis is carried out on the data and each rule in the rule model, and if the data are similar, future behaviors of workers are predicted according to the rules; if not, storing the data in a warehouse and taking the data as original data to carry out new rule training; the treatment results were as follows: and predicting the future behavior of the worker by using the data similar to the rule matching, and giving an alarm if the future behavior is predicted to be dangerous behavior or aggregative behavior.
3. The behavior analysis method for the constructors of the electric power substation engineering construction project according to claim 1, characterized in that: the AI intelligent video monitoring system realizes that monitoring of abnormal behaviors of constructors includes wandering detection, falling monitoring, off-duty monitoring, gathering monitoring, wearing monitoring and climbing detection.
4. The behavior analysis method for the constructors of the electric power substation engineering construction project according to claim 1, characterized in that: when the safety helmet Bluetooth module and the dangerous area beacon accord with the threshold value, the built-in voice module of the safety helmet can play warning voice recorded in advance.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011407948.0A CN112785798B (en) | 2020-12-04 | 2020-12-04 | Behavior analysis method for constructors of power substation engineering construction project |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011407948.0A CN112785798B (en) | 2020-12-04 | 2020-12-04 | Behavior analysis method for constructors of power substation engineering construction project |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112785798A true CN112785798A (en) | 2021-05-11 |
| CN112785798B CN112785798B (en) | 2023-09-26 |
Family
ID=75750709
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011407948.0A Active CN112785798B (en) | 2020-12-04 | 2020-12-04 | Behavior analysis method for constructors of power substation engineering construction project |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112785798B (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113538844A (en) * | 2021-07-07 | 2021-10-22 | 中科院成都信息技术股份有限公司 | Intelligent video analysis system and method |
| CN113569670A (en) * | 2021-07-15 | 2021-10-29 | 尤利卡（江苏）集成电气有限公司 | Transformer substation monitoring system |
| CN114145531A (en) * | 2021-07-19 | 2022-03-08 | 国网江苏省电力有限公司江阴市供电分公司 | Distribution network uninterrupted operation safety standard monitoring method |
| CN114782988A (en) * | 2022-03-29 | 2022-07-22 | 西安交通大学 | Construction environment-oriented multi-stage safety early warning method |
| CN114845313A (en) * | 2022-04-01 | 2022-08-02 | 国网江苏省电力有限公司建设分公司 | 5G data collaborative system based on cloud model and oriented to electric power ultrahigh river-crossing iron tower engineering |
| CN114898513A (en) * | 2022-03-25 | 2022-08-12 | 中铁工程服务有限公司 | Gantry crane environment construction early warning system and method based on electroencephalogram and wireless direction finding |
| CN115331385A (en) * | 2022-07-21 | 2022-11-11 | 国家能源集团谏壁发电厂 | Monitoring method, device, equipment and storage medium for power plant coal conveying system |
| CN115619204A (en) * | 2022-12-20 | 2023-01-17 | 天津建设发展集团有限公司 | Engineering field monitoring method and system based on wireless Internet of things |
| CN116151802A (en) * | 2023-03-02 | 2023-05-23 | 广东省源天工程有限公司 | Comprehensive management and control system for hydraulic and electric engineering construction and equipment operation and maintenance |
| CN116432989A (en) * | 2023-06-13 | 2023-07-14 | 中天建设集团有限公司 | Intelligent construction-based construction site safety control system |
| CN116665419A (en) * | 2023-05-09 | 2023-08-29 | 三峡高科信息技术有限责任公司 | Intelligent fault early warning system and method based on AI analysis in power production operation |
| CN117952570A (en) * | 2024-03-27 | 2024-04-30 | 中建安装集团有限公司 | Engineering construction data management system and method based on Internet of things |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108174165A (en) * | 2018-01-17 | 2018-06-15 | 重庆览辉信息技术有限公司 | Electric power safety operation and O＆M intelligent monitoring system and method |
| US20180357583A1 (en) * | 2017-06-12 | 2018-12-13 | Optical Operations LLC | Operational monitoring system |
| CN110889941A (en) * | 2019-10-23 | 2020-03-17 | 万翼科技有限公司 | Constructor safety monitoring system and method |
| CN111163420A (en) * | 2020-02-18 | 2020-05-15 | 浙江省建工集团有限责任公司 | Intelligent factory area positioning and identifying system |
| CN111178465A (en) * | 2019-12-20 | 2020-05-19 | 国网山东省电力公司莱芜供电公司 | Transformer substation construction operator safety control system based on information passive storage and active positioning |
| CN111445524A (en) * | 2020-03-31 | 2020-07-24 | 清华大学 | Scene understanding-based construction site worker unsafe behavior identification method |
-
2020
- 2020-12-04 CN CN202011407948.0A patent/CN112785798B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180357583A1 (en) * | 2017-06-12 | 2018-12-13 | Optical Operations LLC | Operational monitoring system |
| CN108174165A (en) * | 2018-01-17 | 2018-06-15 | 重庆览辉信息技术有限公司 | Electric power safety operation and O＆M intelligent monitoring system and method |
| CN110889941A (en) * | 2019-10-23 | 2020-03-17 | 万翼科技有限公司 | Constructor safety monitoring system and method |
| CN111178465A (en) * | 2019-12-20 | 2020-05-19 | 国网山东省电力公司莱芜供电公司 | Transformer substation construction operator safety control system based on information passive storage and active positioning |
| CN111163420A (en) * | 2020-02-18 | 2020-05-15 | 浙江省建工集团有限责任公司 | Intelligent factory area positioning and identifying system |
| CN111445524A (en) * | 2020-03-31 | 2020-07-24 | 清华大学 | Scene understanding-based construction site worker unsafe behavior identification method |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113538844A (en) * | 2021-07-07 | 2021-10-22 | 中科院成都信息技术股份有限公司 | Intelligent video analysis system and method |
| CN113569670B (en) * | 2021-07-15 | 2023-09-01 | 尤利卡（江苏）集成电气有限公司 | Substation monitoring system |
| CN113569670A (en) * | 2021-07-15 | 2021-10-29 | 尤利卡（江苏）集成电气有限公司 | Transformer substation monitoring system |
| CN114145531A (en) * | 2021-07-19 | 2022-03-08 | 国网江苏省电力有限公司江阴市供电分公司 | Distribution network uninterrupted operation safety standard monitoring method |
| CN114898513A (en) * | 2022-03-25 | 2022-08-12 | 中铁工程服务有限公司 | Gantry crane environment construction early warning system and method based on electroencephalogram and wireless direction finding |
| CN114782988A (en) * | 2022-03-29 | 2022-07-22 | 西安交通大学 | Construction environment-oriented multi-stage safety early warning method |
| CN114782988B (en) * | 2022-03-29 | 2024-04-02 | 西安交通大学 | Multistage safety early warning method oriented to construction environment |
| CN114845313A (en) * | 2022-04-01 | 2022-08-02 | 国网江苏省电力有限公司建设分公司 | 5G data collaborative system based on cloud model and oriented to electric power ultrahigh river-crossing iron tower engineering |
| CN115331385A (en) * | 2022-07-21 | 2022-11-11 | 国家能源集团谏壁发电厂 | Monitoring method, device, equipment and storage medium for power plant coal conveying system |
| CN115619204A (en) * | 2022-12-20 | 2023-01-17 | 天津建设发展集团有限公司 | Engineering field monitoring method and system based on wireless Internet of things |
| CN116151802A (en) * | 2023-03-02 | 2023-05-23 | 广东省源天工程有限公司 | Comprehensive management and control system for hydraulic and electric engineering construction and equipment operation and maintenance |
| CN116151802B (en) * | 2023-03-02 | 2024-04-09 | 广东省源天工程有限公司 | Comprehensive management and control system for hydraulic and electric engineering construction and equipment operation and maintenance |
| CN116665419A (en) * | 2023-05-09 | 2023-08-29 | 三峡高科信息技术有限责任公司 | Intelligent fault early warning system and method based on AI analysis in power production operation |
| CN116665419B (en) * | 2023-05-09 | 2024-01-16 | 三峡高科信息技术有限责任公司 | Intelligent fault early warning system and method based on AI analysis in power production operation |
| CN116432989B (en) * | 2023-06-13 | 2023-09-22 | 中天建设集团有限公司 | Intelligent construction-based construction site safety control system |
| CN116432989A (en) * | 2023-06-13 | 2023-07-14 | 中天建设集团有限公司 | Intelligent construction-based construction site safety control system |
| CN117952570A (en) * | 2024-03-27 | 2024-04-30 | 中建安装集团有限公司 | Engineering construction data management system and method based on Internet of things |
| CN117952570B (en) * | 2024-03-27 | 2024-05-28 | 中建安装集团有限公司 | Engineering construction data management system and method based on Internet of things |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112785798B (en) | 2023-09-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112785798B (en) | Behavior analysis method for constructors of power substation engineering construction project | |
| CN111598040B (en) | Construction worker identity recognition and safety helmet wearing detection method and system | |
| US10565439B2 (en) | Method and system for tracking workers at worksites | |
| CN108319926A (en) | A kind of the safety cap wearing detecting system and detection method of building-site | |
| CN112396658B (en) | Indoor personnel positioning method and system based on video | |
| CN112235537B (en) | Transformer substation field operation safety early warning method | |
| CN113807240A (en) | Intelligent transformer substation personnel dressing monitoring method based on uncooperative face recognition | |
| CN111325954B (en) | Personnel loss early warning method, device, system and server | |
| CN112800910A (en) | Communication machine room maintenance operation efficiency evaluation method and system | |
| CN113012374A (en) | Intelligent park security system and control method thereof | |
| KR101989376B1 (en) | Integrated track circuit total monitoring system | |
| CN110543866A (en) | Safety management system and method for capital construction engineering constructors | |
| CN112613449A (en) | Safety helmet wearing detection and identification method and system based on video face image | |
| CN114333273A (en) | Intelligent analysis system and method for nuclear power construction site | |
| CN115471865A (en) | Operation site digital safety control method, device, equipment and storage medium | |
| CN112381435A (en) | Gridding directional pushing management method for dynamic risk in hydropower station operation process | |
| CN115100724A (en) | Multi-camera personnel state identification method | |
| CN115880722A (en) | Intelligent identification method, system and medium worn by power distribution operating personnel | |
| CN111723725A (en) | Multi-dimensional analysis system based on video AI | |
| CN117612107A (en) | Method for detecting abnormal personnel in place | |
| CN112330742A (en) | Method and device for recording activity routes of key personnel in public area | |
| CN117081917A (en) | Intelligent gateway based on AI (advanced technology attachment) internet of things sewage treatment operation and maintenance management | |
| CN109815921A (en) | The prediction technique and device of the class of activity in hydrogenation stations | |
| CN115953815A (en) | Monitoring method and device for infrastructure site | |
| CN115019259A (en) | AI image recognition supervision method and system for intelligent mine |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |