KR20160116144A

KR20160116144A - Industrial safety menagement system and mehtod for building the same

Info

Publication number: KR20160116144A
Application number: KR1020150042228A
Authority: KR
Inventors: 김진수; 박완선; 박수찬
Original assignee: (주)다울
Priority date: 2015-03-26
Filing date: 2015-03-26
Publication date: 2016-10-07
Also published as: KR101727580B1

Abstract

The present invention relates to an industrial safety management system and a method for constructing the industrial safety management system. The industrial safety management system includes an environmental information collecting device capable of collectively collecting the environment of the workplace and a situation pattern recognizing environment threats And a risk detection prediction device for predicting the occurrence of a disaster according to the workplace, and a method for constructing the same, which is characterized by including a recognition device, an experience simulator device for confirming a worker state and an alternative form in a dangerous situation, .

Description

INDUSTRIAL SAFETY MANAGEMENT SYSTEM AND METHOD FOR BUILDING THE SAME [0002]

The present invention relates to an industrial safety management system and a method for constructing the industrial safety management system. The system collects and analyzes regular and unstructured data such as environmental information on the worksite, type of industrial accident and the status of workers, Training, and management of industrial safety through simulated training.

Industrial safety refers to all activities that safeguard and maintain industrial facilities and protect workers' bodies and health from all disasters or accidents that occur during industrial activities, , Machinery, materials, etc., and to ensure safety by eliminating potential hazards caused by destruction.

Industrial accidents are physical and mental damages of workers arising from work reasons such as work environment or work behavior in the labor process. When the cause of industrial accidents is seen on the workers side, fatigue of worker, , And the lack of work skills of workers. From the user side, it can be said that it is mainly caused by insufficient safety measures and preventive measures against industrial accidents.

These industrial safety hazards include chemical hazards such as fire, explosion, poisoning, occupational disease, air pollution, water pollution, radiation hazards, physical hazards such as frostbite, hearing loss, Elements and facility hazards such as collapses, subsidence and fallout.

As a result of these risks, the accident rate was about 0.41% and the number of casualties was 67,204 as of last year.

According to the disaster type, the accident victims were 17.6%, 16.4%, 15.3%, 8.4%, 8.1%, and 8.1% %. In addition, 35.2% of accident deaths by accident type, 11.8% by intervention, 9.4% by collision, and 8.3% by laying down and reversing are frequent cases.

In this way, direct damages in the event of a safety accident in the enterprise leads to recovery investment in human and material losses, which reduces economic growth and demand, which has a negative impact on economy and society as a whole. Indirect injury also causes socioeconomic dysfunction, decreased supply, and reduced production capacity.

Moreover, any society or corporation can not be completely free from disaster, and the incidental response can maximize the damage and affect the survival of the enterprise.

Because of the convergence nature of the safety and disaster prevention industry, the identity as technology and industry is vague, and systematic upbringing support has been insufficient. In particular, there is a need for a policy to upgrade the safety and disaster prevention industry to contribute to finding new promising products and spreading them, because the related companies are small and the industry scale is small.

Therefore, as the risks and disasters due to environmental changes become more complex and disaster types become more diverse, research is required to respond to such changes.

It can be seen that industrial accidents are overwhelmingly high in accident and death rates among SMEs, which are smaller than large corporations, and can be reduced by systematic management and investment.

(Patent Document 1) Published Japanese Patent Application No. 10-2008-105433 (Patent Document 2) Patent Registration No. 10-1490373 (Patent Document 3) Published Japanese Patent Application No. 10-2012-96977 (Patent Document 4) Patent Registration No. 10-1370775

Korea's safety and disaster prevention technology and industry level are much bigger than those of advanced countries and there is a lack of infrastructure. Therefore, the government is in need of continuous attention and investment, and there is sufficient potential from the viewpoint of IT technology and manpower. It shows a lot of technological gap compared to advanced countries.

In addition, opportunities for growth of the safety and disaster prevention industry are increasing, and it is necessary to develop technologies and products with global competitiveness.

Disclosure of the Invention The present invention has been conceived to solve the above-described problems, and it is an object of the present invention to provide a system and method for collecting and analyzing formal and unstructured data such as environmental information, It provides an industrial safety management system that can be used to educate, train, and manage industrial safety through possible simulations and how to build it.

An environmental information collecting device capable of collectively collecting the environment of the workplace according to the present invention; a situation pattern recognizing device for recognizing a situation threatening the safety of workers based on environmental gathering information and big data; An experience simulator device for confirming an alternative form, and a risk detection prediction device for predicting the occurrence of a disaster according to the workplace.

The environment information collecting device collects and collects measured values of the image capturing unit, the integrated sensor unit, and the wearable sensor unit, which are an image capturing unit that captures the inside of the workplace, an integrated sensor unit that is installed inside the workplace, And an environment information control unit for controlling the image capturing unit, the integrated sensor unit, and the wearable sensor unit to determine the use time and the operation time of the environment, and to indicate the replacement time.

The situation pattern recognition apparatus includes a trajectory tracing unit for tracing a movement trajectory of an operator (object), an environment analyzing unit for analyzing and managing environment information of a workplace, and an information analyzing unit for analyzing information of a big data base do.

The trajectory tracing unit includes a background modeling module for modeling the background of the workplace using the result of the environment information collecting device, a worker classifying module for classifying the workpiece by worker, assigning object numbers to the worker, And a trajectory tracking module for tracing and classifying a trajectory of an operator to which an object number is assigned.

The environment analyzing unit includes an information building module for building an environment information database in the workplace through an environment information collecting device, an integrated management module for analyzing the constructed information, and dividing and managing the dangerous area and the safe area according to the analysis result .

The information analyzing unit includes a filtering module for collecting and filtering the big data, a big data analyzing module for analyzing and mining the big data, and a big data predicting module for predicting and analyzing the data using the analyzed big data do.

The experience simulator apparatus includes an experience simulator unit for providing a work environment for a worker or a workplace to be worked in as a virtual space, a risk element storage unit for storing risk elements according to movement trajectories of workers, and a risk simulator unit The risk simulator includes a risk component implementation unit, a reaction confirmation unit for collecting and storing the operator's reaction to the implemented risk component, and a training unit for implementing the risk countermeasure scheme in the experience simulator unit.

The risk factor storage unit stores the degree of risk of a chemical risk element, a physical risk factor, an electrical risk factor, and a facility risk factor corresponding to the workplace by the workplace location through the analyzed environment information and the big data information.

The risk element implementing unit implements a risk factor in a virtual space according to an occurrence possibility of the region according to an operator, that is, an administrator, and the response confirming unit recognizes a response of the operator to the dangerous situation occurring in the worker experiencing the experience simulator unit, And provides the result to the risk detection prediction apparatus.

The risk detection prediction apparatus includes a risk prediction unit for predicting a degree of occurrence of a risk in a workplace using a workplace analysis result of the situation pattern recognition apparatus and an operator's condition and response (alternative form) to a risk factor according to the experience simulator apparatus, The risk prediction section, and the experience simulation results are stored and provided to the situation pattern recognizing device, and analyzed by the big data, using the danger level and the movement trajectory of the worker. And an information storage unit.

In addition, a wearable sensor unit is attached to an operator according to the present invention to collect environment information in a workplace by installing an image pickup unit and an integrated sensor unit in a worker's movement and a worksite, A step of recognizing an area where a risk factor exists, a step of experiencing the recognized workplace environment information using a virtual experience simulator, a step of implementing a risk factor in a virtual experience simulator, A step of estimating the degree of risk in the workplace through the risk detection prediction using the collected worker's response and the environmental information of the workplace, and a step of notifying the operator of the occurrence of the risk using the prediction result and the movement trajectory of the worker A step of installing an industrial safety management system It provides.

These hazards refer to chemical hazards, physical hazards, electrical hazards, and facility hazards that may occur in the workplace, and may include fire, explosion, poisoning, contamination, cutting, , Electric shock, collapse, settlement, and fallout.

As described above, the present invention can develop a safety risk prediction model by collecting and analyzing formal and / or non-standard data such as environmental information on a worksite, an industrial accident type, and a worker's state, and fusing a behavior pattern of an operator. Also, it is possible to inspire the safety awareness of industrial disaster through virtual reality based simulator experience.

1 is a block diagram for explaining an industrial safety management system according to an embodiment of the present invention;
2 is a block diagram of an environment information collecting apparatus according to an embodiment.
3 is a block diagram of an integrated sensor unit;
4 is a block diagram of an apparatus for recognizing a situation pattern according to an embodiment.
5 is a block diagram of a trajectory tracking unit;
6 is a block diagram of an information analysis unit;
7 is a block diagram of an experience simulator device in accordance with one embodiment.
8 is a block diagram of a risk detection prediction apparatus according to an embodiment;
FIG. 9 is a flowchart illustrating an industrial safety management system building method according to an embodiment of the present invention. FIG.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. It will be apparent to those skilled in the art that the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, It is provided to let you know. Wherein like reference numerals refer to like elements throughout.

It is to be clarified that the division of components in this specification is merely a division by main function which each component is responsible for. That is, two or more constituent parts to be described below may be combined into one constituent part, or one constituent part may be divided into two or more functions according to functions that are more subdivided. In addition, each of the constituent units described below may additionally perform some or all of the functions of other constituent units in addition to the main functions of the constituent units themselves, and that some of the main functions, And may be carried out in a dedicated manner. Accordingly, the presence or absence of each component described in this specification should be interpreted as a function. For this reason, it is clarified that the industrial safety management system and method of the present invention can be different within the scope of achieving the object of the present invention.

1 is a block diagram illustrating an industrial safety management system according to an embodiment of the present invention. FIG. 2 is a block diagram of an environment information collecting apparatus according to an embodiment, and FIG. 3 is a block diagram of an integrated sensor unit. 4 is a block diagram of an apparatus for recognizing a situation pattern according to an embodiment, FIG. 5 is a block diagram of a trajectory tracking unit, and FIG. 6 is a block diagram of an information analysis unit. 7 is a block diagram of an experience simulator device according to one embodiment. 8 is a block diagram of a danger detection prediction apparatus according to an embodiment of the present invention.

As shown in FIGS. 1 to 8, the industrial safety management system according to the present embodiment includes an environment information collecting device 100 that can collectively collect environment of a workplace, a situation where a safety of a worker based on a big data is threatened An experience simulator device 300 for confirming a worker state and an alternative form in a dangerous situation, and a risk prediction prediction device 400 for predicting occurrence of a workplace-specific disaster .

The environmental information collecting apparatus 100 includes an image photographing unit 110 for photographing the interior of the workplace, an integrated sensor unit 120 installed inside the workplace, a wearable sensor unit 130 for the operator to operate, An environmental information transmission unit 140 for collectively collecting and transmitting measured values of the integrated sensor unit 120 and the wearable sensor unit 130 and a control unit 130 for controlling the operation of the image sensing unit 110, the integrated sensor unit 120, And an environment information control unit 150 for controlling the control unit 130, determining the use time and the presence or absence of the use time, and notifying the replacement time.

The image capturing unit 110 includes a plurality of cameras or CCTV installed inside the workplace. At this time, the image capturing unit 110 is rotatable, and the environment information transmitting unit 150 can transmit only the information of the image capturing unit 110 separately.

The integrated sensor unit 120 includes a chemical risk measurement sensor 121 for measuring fire, explosion, poisoning, air pollution, water pollution, and the like, and a physical risk measurement sensor such as vibration, tumbling, pinching, 122, an electrical risk measurement sensor 123 such as a short circuit or an electric shock, and a facility risk measurement sensor 124 such as collapse, settlement, and dropping.

Various types of sensors can be used as such sensors. It can be used for oxygen concentration measurement sensor, temperature sensor, humidity sensor, illuminance sensor, smoke and flame sensor, gas sensor, wind direction sensor, impact sensor, acceleration sensor, leakage sensor, vibration sensor . The above-described measuring sensor can be operated according to the position and location of the sensors.

The wearable sensor unit 130 includes an operator's environment sensor 131 attached to a body of the worker to sense a risk around the worker and a state sensor 132 for detecting the state of the worker.

As the environmental sensor 131, it is possible to use any one of an oxygen concentration sensor, a temperature sensor, a smoke sensor and a gas sensor. Through this, it is possible to detect in real time the change of the environment around the worker while moving.

The state detection sensor 132 may be an impact detection sensor, an acceleration sensor, or a vibration detection sensor. This allows the operator to quickly identify risk factors such as falling over.

The image capturing unit 110, the integrated sensor unit 120, and the wearable sensor unit 130 communicate with the environment information transmitting unit 140 in real time to provide their detection results.

The environment information transmitting unit 140 provides the received sensing and result values to the situation pattern recognition apparatus.

Of course, the present embodiment may include a plurality of environment information transfer units 140. [ Accordingly, an environmental information transmitting unit may be provided in each of the image capturing unit 110, the integrated sensor unit 120, and the wearable sensor unit 130. Accordingly, the resultant value generated in each unit can be immediately provided to the situation pattern recognition apparatus 200. Even if one environment information transmission unit 140 fails, the result can be provided to the situation pattern recognition apparatus 200 by another sensor, so that the manager can quickly recognize the risk factor of the workplace.

The environmental information transmission unit 140 provides the environmental information control unit 150 with operational status values of the image capturing unit 110, the integrated sensor unit 120, and the wearable sensor unit 130. [

The environment information control unit 150 determines whether the image pickup unit 110, the integrated sensor unit 120 and the wearable sensor unit 130 are faulty or not using the operation state values and the driving time, .

The situation pattern recognizing apparatus 200 recognizes the environment of the workplace collected through the environment information collecting apparatus 100 having the above-described configuration, and judges whether the environment is dangerous.

The situation pattern recognition apparatus 200 includes a trajectory tracing unit 210 for tracing a movement trajectory of an operator (object), an environment analysis unit 220 for analyzing and managing environmental information of a workplace, And an information analysis unit 230.

The trajectory tracking unit 210 includes a background modeling module 211 for modeling the background of the workplace using the result of the environment information collecting apparatus 100, a worker classifying module 211 for classifying the worker by an operator, And a trajectory tracking module 230 for tracing and classifying a trajectory of an operator having an object number assigned in the workplace by the background modeling module 211.

Through the trajectory tracking unit 210, the movement trajectory of the worker in the workplace and the risk occurrence factors for each section can be grasped and the operator can be moved to the nearest safety area in the event of a risk factor.

The environment analysis unit 220 includes an information building module 221 for building an environmental information database in the workplace through an environment information collection device, a system for analyzing the constructed information, and dividing the dangerous area and the safe area according to the analysis result And an integrated management module 222 that manages the information.

The information analysis unit 230 includes a filtering module 231 for collecting and filtering the big data, a big data analysis module 232 for analyzing and mining the big data, And a data prediction module 233.

This makes it easier to classify the hazards that often occur in industrial sites and their hazardous areas.

In the present embodiment, the result recognized and analyzed by the state machine 200 is applied to the worksite in the form of a virtual simulation, and the worker working in the workplace can use the simulation to identify the type of coping with a dangerous situation Analyze the method and use the results to predict and detect the risks in the workplace.

The experience simulator apparatus 300 includes an experience simulator unit 310 for providing a work environment in which a worker is to work or to be worked in a virtual space, a risk element storage unit 320 for storing a risk element according to a movement trajectory of the worker, A reaction confirmation unit 340 for collecting and storing the reaction of the operator with respect to the implemented risk element, and a reaction confirmation unit 340 for implementing the countermeasure for each risk element in the experience simulator unit 310. [ And an education and training unit 350 for implementing the training program.

The experience simulator unit 310 is capable of various devices capable of realizing a virtual space, and has an operation control technique and a control technique of start information.

The risk element storage unit 320 stores the degree of risk of chemical hazards, physical hazards, electrical hazards, and facility hazards corresponding to the workplace according to the workplace location through the analyzed environment information and the big data information. Classification can be classified into areas where risks such as fire, explosion, poisoning, pollution, cutting, falling, pinched, falling, bump, laid, electric shock, collapse, do.

The risk element implementing unit 330 implements a risk factor according to the probability of occurrence of the region in the virtual space according to the operator, i.e., the manager. For example, the experience simulator unit implements a drop in a region where a drop is likely to occur.

The reaction confirmation unit 340 determines how the operator reacts to the dangerous situation occurring in the worker experiencing the experience simulator unit and what kind of action is performed, and provides the result to the risk detection prediction apparatus.

The education and training department 350 instructs the operator by displaying the manuals that the operator should respond to the risk elements implemented in the experience simulator section and causing the operator to act accordingly.

The danger detection prediction apparatus 400 calculates the degree of danger in the workplace using the workplace analysis result of the situation pattern recognition apparatus 200 and the worker's state and response (alternative form) to the risk factors according to the experience simulator apparatus 300 A risk notification unit 420 for predicting the risk to the worker using the predicted risk level and the movement trajectory of the worker, the degree of risk prediction by the workplace, and the simulation result of the experience And provides it to the situation pattern recognition apparatus 200 to analyze it as big data.

The risk notification section 420 may be a wearable notification means.

Through this, it is possible to develop a safety risk prediction model by collecting and analyzing regular and unstructured data such as environmental information of the worksite, types of industrial accidents and workers' condition, and fusing the behavior patterns of the workers. Also, it is possible to inspire the safety awareness of industrial disaster through virtual reality based simulator experience.

Through this, it is possible to secure the grounds for policy formulation along with systematic analysis of current status through establishment of related industrial classification and DB related to safety and disaster prevention. In particular, it is possible to confirm the form of coping with the risk factors and correct it.

Monitoring and protection activities that can achieve safety and disaster advancement through the market-oriented mechanism of the company become active, which can ultimately reduce safety accidents and disasters in industrial sites. By adopting cutting-edge u-IT technology, smart safety management system can be installed to minimize the damage through immediate measures in case of safety accidents and safety accidents, and to expand and build various sensors for safety management of factories such as workers, facilities and environment It is possible to implement a smart factory. It is possible to reduce the accidents rate by about 30% for the contact due to stenosis, collision and abnormal temperature through the warning service according to the worker's location (access to the hazard), and the harmful gas It is possible to reduce the safety accident rate by as much as 25%.

The following provides a method for constructing an industrial safety management system having the above-described configuration.

9 is a flowchart illustrating a method for constructing an industrial safety management system according to an embodiment of the present invention.

As shown in FIG. 9, a wearable sensor unit is attached to an operator, and an image capturing unit and an integrated sensor unit are installed in a worker's movement and a workplace to collect environmental information in a workplace (S110).

At this time, it is effective to use the device or sensor installed in the existing work site as it is. It is effective to collect sensor information for a period of 1 to 10 weeks to collect environmental information in the workplace. In this embodiment, it is described based on the construction time, and such sensors are always applied at all times.

Subsequently, an area having a risk factor is recognized using the collected environment information and the big data (S120).

In other words, each workplace divides the area to be generated for each risk element. It is effective to be automatically classified based on the movement trajectory of the operator and the sensing result of the sensor.

Risk factors refer to chemical hazards, physical hazards, electrical hazards, and facility hazards that can occur in the workplace as described above, and specifically include fire, explosion, poisoning, contamination, cutting, Collision, laying, electric shock, collapse, subsidence, and fallout. And it is classified according to such classification and workable area of workplace.

Then, the operator experiences the recognized workplace environment information using the virtual experience simulator, and implements the risk factor in the virtual experience simulator (S130).

The worker boarded the virtual experience simulator and walked the workplace according to his or her movement trajectory. At this time, the aforementioned risk factors are caused to occur suddenly.

Then, the operator's reaction to the implemented risk factors is collected (S140).

The worker will take actions that he or she can take at the moment against unforeseen risk factors. At this time, the virtual experience simulator collects the reaction of the operator. In other words, it creates a situation where a worker who is walking through a narrow passage is falling in front of a worker, and collects the actions taken by workers in this situation.

Next, the degree of risk occurrence in the workplace is predicted through the risk detection prediction using the collected worker's response and the environmental information of the workplace (S150).

A large number of workers experience the virtual experience simulation, collect the behavioral responses of all the workers, identify the degree of response to the risk, and identify the occurrence interval of the selected risk factors.

This enables accurate prediction of the degree of risk in the workplace.

Thereafter, the risk occurrence can be suppressed by informing the operator of the risk occurrence using the prediction result and the movement trajectory of the worker (S160).

Also, it is possible to reduce the risk by educating the countermeasures in case of danger through virtual experience simulator. Further, by adding the virtual experience simulation result to the big data, more sophisticated analysis can be performed.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the following claims.

100: Environmental information collection device 110:
120: integrated sensor unit 130: wearable sensor unit
140: environment information transmission unit 150: environment control unit
200: situation pattern recognition apparatus 210: trajectory tracking unit
220: environment analysis unit 230: information analysis unit
300: experience simulator device 310: experience simulator
320: Hazardous element storage unit 330: Hazardous element implementation unit
340: reaction confirmation unit 350:
400: danger detection prediction device 410: danger prediction part
420: Risk notification section 430: Information storage section

Claims

An environment information collecting device capable of collectively collecting the environment of the workplace;
A situation pattern recognition device for recognizing environment collecting information and a situation that threatens the safety of workers based on big data;
An experience simulator device for identifying a worker condition and an alternative form in a dangerous situation; And
And a risk detection prediction device for predicting occurrence of a disaster according to the workplace.

The method according to claim 1,
The environment information collecting device collects and collects measured values of the image capturing unit, the integrated sensor unit, and the wearable sensor unit, which are an image capturing unit that captures the inside of the workplace, an integrated sensor unit that is installed inside the workplace, And an environment information control unit for controlling the image pickup unit, the integrated sensor unit, and the wearable sensor unit to determine the use time and the operation time of the environment, Management system.

The method according to claim 1,
The situation pattern recognition apparatus includes a trajectory tracing unit for tracing a movement trajectory of an operator (object), an environment analyzing unit for analyzing and managing environment information of a workplace, and an information analyzing unit for analyzing information of a big data base Industrial safety management system.

The method of claim 3,
The trajectory tracing unit includes a background modeling module for modeling the background of the workplace using the result of the environment information collecting device, a worker classifying module for classifying the workpiece by worker, assigning object numbers to the worker, And a trajectory tracking module for tracking and classifying a trajectory of an operator to which an object number is assigned.

The method of claim 3,
The environment analyzing unit includes an information building module for building an environment information database in the workplace through an environment information collecting device, an integrated management module for analyzing the constructed information, and dividing and managing the dangerous area and the safe area according to the analysis result Wherein said safety management system comprises:

The method of claim 3,
The information analyzing unit includes a filtering module for collecting and filtering the big data, a big data analyzing module for analyzing and mining the big data, and a big data predicting module for predicting and analyzing the data using the analyzed big data Industrial safety management system.

The method according to claim 1,
The experience simulator apparatus includes an experience simulator unit for providing a work environment for a worker or a workplace to be worked in as a virtual space, a risk element storage unit for storing risk elements according to movement trajectories of workers, and a risk simulator unit And an education and training unit for implementing a risk-based countermeasure in the experience simulator unit. The system of claim 1, wherein the risk simulator unit comprises:

8. The method of claim 7,
Wherein the risk factor storage unit stores the degree of risk of chemical hazards, physical hazards, electrical hazards, and facility hazards corresponding to the workplace according to the workplace location through the analyzed environment information and the big data information. Safety management system.

8. The method of claim 7,
The risk element implementing unit implements a risk factor in a virtual space according to an occurrence possibility of the region according to an operator, that is, an administrator, and the response confirming unit recognizes a response of the operator to the dangerous situation occurring in the worker experiencing the experience simulator unit, Judges whether or not to perform the action, and provides the result to the danger detection prediction apparatus.

The method according to claim 1,
The risk detection prediction apparatus includes a risk prediction unit for predicting a degree of occurrence of a risk in a workplace using a workplace analysis result of the situation pattern recognition apparatus and an operator's condition and response (alternative form) to a risk factor according to the experience simulator apparatus, The risk prediction section, and the experience simulation results are stored and provided to the situation pattern recognizing device, and analyzed by the big data, using the danger level and the movement trajectory of the worker. And an information storage unit for storing the information of the occupant.

A step of attaching a wearable sensor unit to a worker and installing an image pickup unit and an integrated sensor unit in a worker's movement and a worksite to collect environmental information in a workplace;
Recognizing an area having a risk factor in the workplace using collected environmental information and big data;
A worker experiences a recognized workplace environment information using a virtual experience simulator, and a risk factor is implemented in a virtual experience simulator;
Collecting the operator's response to the implemented risk factors;
Estimating the degree of risk in the workplace through the risk detection prediction using the collected worker response and the environmental information of the workplace; And
And notifying the operator of the occurrence of the risk using the predicted result and the movement trajectory of the worker.

12. The method of claim 11,
These hazards refer to chemical hazards, physical hazards, electrical hazards, and facility hazards that can occur in the workplace, and may include fire, explosion, poisoning, contamination, cutting, tumbling, pinching, falling, , Electric shock, collapse, settlement, and fallout.