CN112785798B - Behavior analysis method for constructors of power substation engineering construction project - Google Patents
Behavior analysis method for constructors of power substation engineering construction project Download PDFInfo
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- 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
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- 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
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- 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
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- 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
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- 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
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- 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
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- 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
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- 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
Abstract
The application discloses a behavior analysis method for constructors of electric power substation engineering construction projects, which is based on an intelligent video system and a chromatographic partition management technology of a machine learning method, and adopts a computer network communication technology, a video monitoring technology and a safe color area management technology to analyze and process construction site monitoring data. Setting the functional division and the safety attribute of the regional grid working surface, carrying out color identification on the region, carrying out real-time monitoring on the state change of the environment and facility equipment, setting a threshold early warning, binding and interacting personnel with the identified region and the equipment, and carrying out data capturing and classification on related information. The application can monitor in real time, judge abnormal conditions and give out alarms in the fastest speed and the largest way, thereby effectively carrying out early warning in advance.
Description
Technical Field
The application relates to a behavior analysis method for project constructors, and belongs to the technical field of intelligent monitoring.
Background
The existing power construction site is monitored by adopting a video monitoring system and is basically in a passive manual monitoring state, but due to the wide range of the transformer substation construction site, the number of the laid monitoring devices is large, and the picture monitoring is performed in a manual mode, so that omission and mistakes can be possibly caused. .
Disclosure of Invention
The application aims to: in order to overcome the defects in the prior art, the application provides a behavior analysis method for constructors of power substation engineering construction projects, which adopts technologies such as image processing, pattern recognition, computer vision and the like, filters useless information of a video picture by adding an intelligent video analysis module into a monitoring system and by means of powerful processing capacity of a computer, analyzes and extracts video key information, rapidly and accurately positions accident sites, judges abnormal conditions and gives out alarms in a fastest and maximum way, thereby effectively performing full-automatic all-weather and real-time monitoring intelligent systems for pre-warning, in-process and evidence collection after the accident. The application can monitor in real time, judge abnormal conditions and give out alarms in the fastest speed and the largest way, thereby effectively carrying out early warning in advance.
The technical scheme is as follows: in order to achieve the above purpose, the application adopts the following technical scheme:
a behavior analysis method for constructors of power substation engineering construction projects is based on an intelligent video system and a chromatographic partition management technology of a machine learning method, and analysis processing is carried out on construction site monitoring data by adopting a computer network communication technology, a video digital compression processing technology, a video monitoring technology and a safe color area management technology. The function division and the safety attribute of the regional grid working surface are set, and the method is a brand new construction management mode. The color of the area is identified, the state changes of the environment and facility equipment are monitored in real time, a threshold early warning is set, then personnel are bound and interacted with the identified areas and the equipment, and related information is subjected to data capturing and classification. And analyzing the logic relation and development rule among different information, performing simulation modeling on the behavior trend of the identification object, acquiring dangerous behavior precursors of personnel 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 functional division and the safety attribute of the regional grid working surface, carrying out color identification on the region, arranging beacons, carrying out real-time monitoring on the state changes of the environment and facility equipment, setting threshold early warning, binding and interacting personnel with the identified region and the identified equipment, and carrying out data capturing and classification on related information.
Step 2, AI intelligent video monitoring system based on thing networking: arranging a camera on the regional grid working surface, 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, rapidly and accurately positioning an accident scene by the AI intelligent video monitoring system, judging abnormal conditions by image processing and pattern recognition of the AI intelligent video monitoring system, and sending an alarm at the fastest speed.
Step 3, intelligent safety helmet based on thing networking: an intelligent terminal with NB-IoT, bluetooth and GPS/Beidou intelligent chips is integrated on the safety helmet.
And 4, after the worker takes the safety helmet, the safety helmet sends data to the server at regular time, and in a fixed time interval, the worker passes through a place with a beacon, namely, the distance between the Bluetooth module in the safety helmet and the Bluetooth module in the beacon meets the threshold requirement, and the intelligent terminal reports personal information of the worker and the combined information of the beacon code. If the beacon information is not transmitted within a fixed time interval, the intelligent terminal reports personal information of workers and GPRS positioning information.
Meanwhile, the camera collects the video of the worker at the moment, the shot video is sent to the AI intelligent video monitoring system, and the AI intelligent video monitoring system judges whether abnormal conditions exist according to the video.
Preferably: the chromatographic partition management system collects enough field data at the initial moment, and after screening, the rule model is generated by fitting training through a logistic regression algorithm, and different rules exist in the rule model according to different application scenes. And when the data reported by the intelligent terminal is received, if the data meets the screening requirement, carrying out similarity analysis on the data and each rule in the rule model, and if the data are similar, predicting the future behaviors of workers according to the rules. If the data are dissimilar, the data are stored in a warehouse and serve as the original data to carry out new rule training. The treatment results were as follows: and predicting the future behaviors of the workers by data similar to the rule matching, and giving an alarm if the predicted future behaviors are dangerous behaviors or aggregate behaviors.
Preferably: AI intelligent video monitoring system realizes that constructor abnormal behavior monitoring has loiter detection: actively triggering an alarm when abnormal loitering personnel exist in the key area; and (3) fall monitoring: through falling monitoring, staff abnormality is rapidly found; off-duty monitoring: monitoring whether an operator on duty works in an on duty room; aggregation monitoring: actively triggering an alarm when the field crowd is dense; wearing monitoring: triggering an alarm when detecting that the safety helmet or the working clothes personnel are not worn; climbing detection: and actively triggering an alarm when a person climbs abnormally on site.
Preferably: when the Bluetooth module of the safety helmet accords with the dangerous area beacon, a voice module arranged in the safety helmet can play the warning voice recorded in advance.
Compared with the prior art, the application has the following beneficial effects:
according to the intelligent safety helmet design based on the 5G narrowband Internet of things technology, a construction site personnel management system based on the intelligent safety helmet is established, the Internet of things technology and related video analysis technology are used for carrying out statistical analysis on behavior information data of personnel, real-time monitoring on site and dynamic analysis on the behavior of the personnel are achieved, AI intelligent video monitoring and auxiliary accurate determination phase are combined for carrying out safety management on a transformer substation site, the intelligent safety helmet oriented to a construction environment is designed and applied, constructor information is transmitted to a cloud server in real time in a 5G Internet of things transmission mode, the activity track of workers is obtained, the activity trend of the workers is predicted, and danger is avoided.
Drawings
FIG. 1 is a flow chart of behavioral analysis.
Detailed Description
The present application is further illustrated in the accompanying drawings and detailed description which are to be understood as being merely illustrative of the application and not limiting of its scope, and various equivalent modifications to the application will fall within the scope of the application as defined in the appended claims after reading the application.
A behavior analysis method for power substation engineering construction project constructors comprises the following steps:
step 1, a chromatographic partition management system based on the Internet of things: setting the functional division and the safety attribute of the regional grid working surface, carrying out color identification on the region, arranging beacons, carrying out real-time monitoring on the state changes of the environment and facility equipment, setting threshold early warning, binding and interacting personnel with the identified region and the identified equipment, and carrying out data capturing and classification on related information.
The chromatographic partition management technology is a region management technology based on security color, and is a management mode for the position, behavior and efficiency of people on a construction site. The elements in the construction site range are comprehensively considered and scientifically classified, and the function division and the safety attribute of the regional grid working surface are set, so that the construction site is a brand-new construction management mode. The color of the area is identified, the state changes of the environment and facility equipment are monitored in real time, a threshold early warning is set, then personnel are bound and interacted with the identified areas and the equipment, and related information is subjected to data capturing and classification. And analyzing the logic relation and development rule among different information, performing simulation modeling on the behavior trend of the identification object, acquiring dangerous behavior precursors of personnel and giving early warning.
The behavioral analysis workflow is as in figure 1. Such as: when a worker takes the helmet, the helmet sends data to the server at regular time, and in a fixed time interval (such as 3 minutes), the worker passes through a place with a beacon, namely, the distance between a Bluetooth module in the helmet and a Bluetooth module in the beacon meets the threshold requirement, and the intelligent terminal reports personal information of the worker and the combined information of the beacon code; if the beacon information is not transmitted within a fixed time interval, the intelligent terminal reports personal information of workers and GPRS positioning information. The system eliminates some obviously incorrect data according to the prefabricated site conditions, such as: GPRS location drift, points with low accuracy or points located outside the construction range. The specific data processing rules are as follows: at the initial moment, enough field data are collected, after screening, fitting training is carried out through a logistic regression algorithm to generate a rule model, the rule model has different rules according to different application scenes, for example, for carpenters, a behavior rule can be obtained after data fitting training: "carpenters will appear in the materials area at 7 am". When new data are received, if the new data meet the screening requirements, carrying out similarity analysis on the new data and each rule in the rule model, for example, analyzing whether the position of a worker in a certain period is similar to the rule, and if so, predicting the future behaviors of the worker according to the rule; if the data are dissimilar, the data are stored in a warehouse and serve as the original data to carry out new rule training. It should be noted that different regions and different seasons may have a certain time shift to the behavior rules, for example, xinjiang and Nanjing have a certain time difference, which results in a difference between the working hours of workers. Therefore, after the new data is matched and similar to the rule, a multidimensional feature transformation operation is performed to balance the influence of regional and seasonal differences on the behaviors of workers, and the reasons and deviation values of the time differences are recorded. The treatment results were as follows: data similar to rule matching is used for predicting the future behaviors of workers, if dangerous behaviors or aggregate behaviors are predicted, an alarm is sent out, for example, the workers are predicted to enter dangerous areas, when the Bluetooth module of the safety helmet accords with the dangerous area beacon, the built-in voice module can play the pre-recorded alarm voice with 'care', the front dangerous-! At the same time, the management interface pops up an alarm flashing icon of the corresponding position of the worker on the construction site so as to draw the attention of the supervisory personnel.
Step 2, AI intelligent video monitoring system based on thing networking: arranging a camera on the regional grid working surface, 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, rapidly and accurately positioning an accident scene by the AI intelligent video monitoring system, judging abnormal conditions by image processing and pattern recognition of the AI intelligent video monitoring system, and sending an alarm at the fastest speed.
Based on chromatographic partition management technology, the security management strategy of the substation construction site combines AI intelligent video monitoring with auxiliary accurate positioning. Compared with the three-dimensional construction mode of most building projects, the transformer substation belongs to the planarization construction, the camera is arranged on the construction site to effectively record the personnel activity condition in the monitoring range, the intelligent image processing algorithm can be used for automatically detecting, identifying and tracking the constructors, and meanwhile, the real-time environment monitoring of the construction site can be realized by combining with the sensors such as sound, optics and the like. However, accurate positioning of the spatial position, such as the floor where the constructor is located, cannot be performed only through 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 for the worker operation specification in the safety management strategy. Positioning information of personnel is collected through the intelligent safety helmet, and is transmitted to the cloud platform by utilizing the 5G internet of things technology, so that effective supervision of constructors is realized. On the other hand, chromatographic partition management is adopted for a construction area, elements in the construction area range are comprehensively considered, scientific classification is carried out, the area grid is set to serve as the functional division and the safety attribute of the page, a personnel accurate positioning system is combined, simulation modeling of the behavior trend of construction personnel is realized, the dangerous behavior aura of personnel is obtained, and early warning is timely given.
By adopting technologies such as image processing, pattern recognition and computer vision, an intelligent video analysis module is added in a monitoring system, useless information of a video picture is filtered by means of strong processing capacity of a computer, video key information is analyzed and extracted, an accident scene can be rapidly and accurately positioned through AI intelligent monitoring, abnormal conditions are judged by the image processing technology and pattern recognition of the AI, and an alarm is sent out at the fastest speed, so that the intelligent system for full-automatic all-weather real-time monitoring of pre-warning, in-process and evidence collection is effectively carried out. Based on the system, the abnormal behavior monitoring of constructors can be realized, and the main monitoring scenes are as follows: (1) loitering detection: actively triggering an alarm when abnormal loitering personnel exist in the key area; (2) fall monitoring: through falling monitoring, staff abnormality is rapidly found; (3) off-Shift monitoring: monitoring whether the operator works in the operator's room; (4) aggregation monitoring: actively triggering an alarm when the field crowd is dense; (5) wear monitoring: triggering an alarm when detecting that the safety helmet or the working clothes personnel are not worn; (6) climbing detection: and actively triggering an alarm when a person climbs abnormally on site.
Step 3, intelligent safety helmet based on 5G narrowband internet of things: on the basis of the safety protection function of the traditional safety helmet, the intelligent terminal of the intelligent chip integrating technologies such as NB-IoT, bluetooth, GPS/Beidou and the like is used for realizing accurate positioning management of constructors based on site chromatographic partitioning, a construction site personnel management system based on the intelligent safety helmet is established, and the behavior information data of people are statistically analyzed by utilizing the Internet of things technology and related video analysis technology, so that real-time monitoring on site and dynamic analysis of the behaviors of the people are realized.
And 4, after the worker takes the safety helmet, the safety helmet sends data to the server at regular time, and in a fixed time interval, the worker passes through a place with a beacon, namely, the distance between the Bluetooth module in the safety helmet and the Bluetooth module in the beacon meets the threshold requirement, and the intelligent terminal reports personal information of the worker and the combined information of the beacon code. If the beacon information is not transmitted within a fixed time interval, the intelligent terminal reports personal information of workers and GPRS positioning information.
Meanwhile, the camera collects the video of the worker at the moment, the shot video is sent to the AI intelligent video monitoring system, and the AI intelligent video monitoring system judges whether abnormal conditions exist according to the video.
Preferably:
preferably: the AI intelligent video monitoring system realizes that constructor abnormal behavior monitoring has loiter detection, fall monitoring, off-duty monitoring, aggregation monitoring, wearing monitoring and climbing detection.
Preferably: when the Bluetooth module of the safety helmet accords with the dangerous area beacon, a voice module arranged in the safety helmet can play the warning voice recorded in advance.
In order to achieve accurate positioning and track tracking of constructors, positioning information NB-IoT of the constructors is transmitted to a cloud platform and then analyzed and processed. The key steps are as follows: and (3) data acquisition: when a worker takes the safety helmet, the safety helmet sends data to the server at regular time, and in a fixed time interval (such as 3 minutes), the worker passes through a place with a beacon, 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 takes the reported corresponding beacon code as the worker position information; if the beacon information is not sent within a fixed time interval, the intelligent terminal reports the GPRS positioning information. Data screening: the system eliminates some obviously incorrect data according to the prefabricated site conditions, such as: GPRS location drift, points with lower accuracy or points located outside the project.
Based on chromatographic partition management technology, the security management strategy of the transformer substation construction site combining AI intelligent video monitoring and auxiliary accurate positioning is used for focusing on the behavior dynamics of workers in the construction process in real time, so that unsafe behavior in the power transmission and transformation construction process is fundamentally avoided. And the effectiveness and practicability of the proposed method can be proved by verifying the proposed security management strategy through a certain transformer substation field project. Based on chromatographic partition management technology, the security management strategy research of the transformer substation construction site combining AI intelligent video monitoring and auxiliary accurate positioning realizes the functions of accurate positioning, track tracking, automatic alarming and the like of personnel in the transformer substation engineering construction process, improves the informatization level of the transformer substation construction process, provides effective reference information for the engineering monitoring, and provides powerful guarantee for the security construction and engineering quality of engineering. The construction environment of the transformer substation is high in risk, but the requirements on supervision of most of constructional engineering, especially mechanical equipment, are low, so that the strategy provided herein 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 so as 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 foregoing is only a preferred embodiment of the application, it being 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 present application, and such modifications and adaptations are intended to be comprehended within the scope of the application.
Claims (4)
1. The behavior analysis method for the constructors of the power substation engineering construction project is characterized by comprising the following steps of:
step 1, a chromatographic partition management system based on the Internet of things: setting functional division and safety attributes of the regional grid working surface, carrying out color identification on the region, arranging beacons, carrying out real-time monitoring on the state changes of the environment and facility equipment, setting threshold early warning, binding and interacting personnel with the identified region and the identified equipment, and carrying out data capturing and classification on related information;
step 2, AI intelligent video monitoring system based on thing networking: arranging a camera on the regional grid working surface, transmitting a 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 into the monitoring system, rapidly and accurately positioning an accident scene by the AI intelligent video monitoring system, judging abnormal conditions by image processing and pattern recognition of the AI intelligent video monitoring system, and sending an alarm at the fastest speed;
step 3, intelligent safety helmet based on thing networking: an intelligent terminal with NB-IoT, bluetooth and GPS/Beidou intelligent chips is integrated on the safety helmet;
step 4, after the worker takes the safety helmet, the safety helmet sends data to the server at regular time, and in a fixed time interval, the worker passes through the place with the beacon, namely the distance between the Bluetooth module in the safety helmet and the Bluetooth module in the beacon meets the threshold requirement, and the intelligent terminal reports personal information of the worker and the combined information of the beacon code; if the beacon information is not sent within a fixed time interval, the intelligent terminal reports personal information of workers and GPRS positioning information;
meanwhile, the camera collects the video of the worker at the moment, the shot video is sent to the AI intelligent video monitoring system, and the AI intelligent video monitoring system judges whether abnormal conditions exist according to the video.
2. The behavior analysis method for power substation engineering construction project constructors according to claim 1, wherein the behavior analysis method comprises the following steps: the chromatographic partition management system collects enough field data at the initial moment, and after screening, fitting training is carried out through a logistic regression algorithm to generate a rule model, wherein the rule model has different rules according to different application scenes; when the data reported by the intelligent terminal is received, if the screening requirement is met, carrying out similarity analysis on the data and each rule in the rule model, and if the data are similar, predicting the future behaviors of workers according to the rules; if the data are dissimilar, the data are stored in a warehouse and serve as original data to carry out new rule training; the treatment results were as follows: and predicting the future behaviors of the workers by data similar to the rule matching, and giving an alarm if the predicted future behaviors are dangerous behaviors or aggregate behaviors.
3. The behavior analysis method for power substation engineering construction project constructors according to claim 1, wherein the behavior analysis method comprises the following steps: the AI intelligent video monitoring system realizes that constructor abnormal behavior monitoring has loiter detection, fall monitoring, off-duty monitoring, aggregation monitoring, wearing monitoring and climbing detection.
4. The behavior analysis method for power substation engineering construction project constructors according to claim 1, wherein the behavior analysis method comprises the following steps: when the Bluetooth module of the safety helmet accords with the dangerous area beacon, a voice module arranged in the safety helmet can play the warning voice recorded in advance.
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| CN111445524A (en) * | 2020-03-31 | 2020-07-24 | 清华大学 | Scene understanding-based construction site worker unsafe behavior identification method |
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