KR20220135741A - Method and System for Preventing Safety Accident Through Danger Factor Analysis - Google Patents

Abstract

According to one aspect of the present invention, a method for preventing accidents through danger factor analysis, which analyzes a danger factor for work to prevent accidents, comprises: a step of analyzing first accident information, which is information on an accident case that has occurred during the work, work environment information, which is information on an environment in which the work is performed, and danger information, which is information on a danger factor of the work, to calculate work safety measure information, which is information on safety measures of the work, through at least one between machine learning and deep learning; and a step of determining candidate work information including the calculated work safety measure information as a safety measure through permission of a user.

Description

Method and System for Preventing Safety Accident Through Danger Factor Analysis

The present invention relates to a safety accident prevention method through risk factor analysis and a safety accident prevention system using the same.

Efforts to prevent accidents occurring at manufacturing and construction sites include worker safety education, implementation of a risk assessment system, improvement of related systems, expansion of facilities and human investment, and strengthening of penalties.

In particular, in recent years, with the development of manufacturing and construction technology, thousands of workers per day are distributed vertically and horizontally in a wide space, and the operation of dozens or hundreds of facilities is complicated, making it difficult to manage work, and safety There is a limit to expanding the number of managers. In addition, although the object to be managed is becoming vast and complicated, the method of managing the safety of work using manpower is maintained. Safety management using manpower may cause problems such as lack of experience of managers, lack of awareness of problems, unauthorized dismantling or movement of safety facilities, passage of prohibited areas, and absence of safety facilities.

The present invention is to solve the above-mentioned problems, and its technical feature is to provide a safety accident prevention method through risk factor analysis and a safety accident prevention system using the same.

The safety accident prevention method through risk factor analysis according to an aspect of the present invention for achieving the above object is a method for preventing safety accidents by analyzing risk factors for work, and machine learning and deep learning (Deep Learning) through at least one of the first accident information, which is information about the accident case that occurred during the work, the work environment information which is information about the environment in which the work is performed, and the risk information which is information about the risk factors of the work calculating work safety measures information that is information about the work safety measures by analyzing; and determining the candidate job information including the calculated job safety countermeasure information as a safety countermeasure through the user's permission.

According to an embodiment of the present invention, statistical values and patterns are extracted through machine learning or deep learning based on information on accident cases corresponding to the work, environmental information and risk information of the work. Thus, it is effective to calculate and determine safety measures for work, and to prevent accidents that may occur during work by observing the determined safety measures.

According to an embodiment of the present invention, by analyzing the first accident information, environmental information, and risk information corresponding to the work using machine learning or deep learning, the work safety measure information is calculated and , to determine the safety measures with the user's permission for the calculated work safety measures information. Based on the determined safety measures, a log for TBM (Tool Box Meeting), which is training for accident prevention, is created, and using the generated TBM log, the work personnel and safety personnel of contractors and subcontractors are educated, which may occur during work. It is effective in preventing accidents.

According to an embodiment of the present invention, the effect of classifying the risk factors for the job by evaluating the risks for each job regularly or frequently, and paying attention to the risk factors of the corresponding job during the operation to prevent an accident that may occur there is

According to an embodiment of the present invention, by verifying the statutory safety inspection period of the tool and construction machine to prevent the use of the statutory safety inspection within the statutory safety inspection period, the use of the tool and construction machine, normal machine photos, abnormal machines Risk factors according to worker skill level as safety measures for tools and construction equipment are calculated by analyzing photos, inspection information and accident information of tools and construction equipment through machine learning or deep learning It has the effect of discovering and leveling the level of safety measures.

1 is a diagram schematically showing the configuration of a safety accident prevention system through risk factor analysis according to an embodiment of the present invention.
2 is a diagram schematically showing a flow chart showing a method for managing risk factors of work according to an embodiment of the present invention.
3 is a diagram illustrating an example of a TBM journal generated according to an embodiment of the present invention.
4 is a view schematically showing a flow chart showing a method for managing risk factors of a tool tool and a construction machine according to an embodiment of the present invention.
5 is a diagram illustrating an example of a work plan of a tool and a construction machine generated through a method for managing risk factors of a tool and a construction machine according to an embodiment of the present invention.

Like reference numerals refer to substantially identical elements throughout. In the following description, a detailed description of configurations and functions known in the art and cases not related to the core configuration of the present invention may be omitted. The meaning of the terms described herein should be understood as follows.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be implemented in a variety of different forms, only these embodiments allow the disclosure of the present invention to be complete, and common knowledge in the technical field to which the present invention belongs It is provided to fully inform the possessor of the scope of the invention, and the present invention is only defined by the scope of the claims.

The shapes, sizes, proportions, angles, numbers, etc. disclosed in the drawings for explaining the embodiments of the present invention are illustrative and the present invention is not limited to the illustrated matters. Like reference numerals refer to like elements throughout. In addition, in describing the present invention, if it is determined that a detailed description of a related known technology may unnecessarily obscure the subject matter of the present invention, the detailed description thereof will be omitted.

When 'including', 'having', 'consisting', etc. mentioned in this specification are used, other parts may be added unless 'only' is used. When a component is expressed in the singular, the case in which the plural is included is included unless specifically stated otherwise.

In interpreting the components, it is interpreted as including an error range even if there is no separate explicit description.

In the case of a description of the positional relationship, for example, when the positional relationship of two parts is described as 'on', 'on', 'on', 'beside', etc., 'right' Alternatively, one or more other parts may be positioned between two parts unless 'directly' is used.

In the case of a description of a temporal relationship, for example, 'immediately' or 'directly' when a temporal relationship is described as 'after', 'following', 'after', 'before', etc. It may include cases that are not continuous unless this is used.

Although the first, second, etc. are used to describe various components, these components are not limited by these terms. These terms are only used to distinguish one component from another. Accordingly, the first component mentioned below may be the second component within the spirit of the present invention.

The term “at least one” should be understood to include all possible combinations from one or more related items. For example, the meaning of “at least one of the first, second, and third items” means 2 of the first, second, and third items as well as each of the first, second, or third items. It may mean a combination of all items that can be presented from more than one.

Each feature of the various embodiments of the present invention may be partially or wholly combined or combined with each other, technically various interlocking and driving are possible, and each of the embodiments may be implemented independently of each other or may be implemented together in a related relationship. may be

Hereinafter, a safety accident prevention system through risk factor analysis according to an embodiment of the present invention will be described in detail with reference to FIG. 1 .

1 is a diagram schematically showing the configuration of a safety accident prevention system through risk factor analysis according to an embodiment of the present invention.

Referring to FIG. 1 , the safety accident prevention system 10 according to an embodiment of the present invention includes a risk evaluation unit 110 , a risk analysis unit 120 , a work permit unit 130 , and a TBM log generation unit 140 . , including a tool and construction machine database 210 , a risk database 220 , a work permit database 230 , and a TBM database 240 .

The risk evaluation unit 110 evaluates the risk for each task on a regular basis or from time to time. Specifically, as shown in Table 1, the risk assessment unit 110 classifies the risk factors for each work step, and the classified risk factors for each work step are stored in the risk database 220 .

classification risk factors classification risk factors Mechanical (equipment) factors

협착(감김.끼임) 등

위험한 표면(절단,베임,긁힘)

기계의 낙하, 비례,전복

충돌

넘어짐

추락(개구부 등)

미끄러짐,걸림

붕괴

Stenosis (winding, entrapment), etc.

Hazardous surfaces (cuts, cuts, scratches)

Falling, proportional, overturning of machines

crash

fall

Falls (openings, etc.)

slipping, jamming

collapse chemical (substance) factors

gas

steam

aerosol

liquid/mist

Solid (dust)

reactive substances

radiation

Fire/Explosion Hazard

radiant heat

Environmental Issues and Accident-Inducing Factors biological factors

virus infection

Genetically Modified Material (GMO) Risks

allergy risk

animal

plant

Food hygiene hazards

stenosis (locked) electrical factor

Electric shock

arc

static

Fire/Explosion Hazard
work characteristic factors

noise

Ultrasound, infrasound

vibration

Worker unsafe behavior/mistake

Excessive low pressure or high pressure

Heavy goods handling work

unstable working posture

Inexperience in using work (manipulation) tools

Temperature (hot/cold)

Drinking

taking drugs

choking hazard work environment factors

climate

light

Space and Walkway Hazards

Nearby Workers Condition Hazard
work timeout

Safety culture disregard

burn

Inexperienced storage of work tools

Large amount of fine dust
danger managerial factor

Safety rules and signs not posted

Management Organizational Defects

Regulations, standards not written

Insufficient safety and health management system

Lack of time for education and training

lack of worker guidance

Inadequate health promotion program operation

Insufficient improvement measures and follow-up management

Insufficient on-site management process

In addition, the risk assessment unit 110 receives the frequency of occurrence, risk intensity, risk, and improvement measures for the classified risk factors from the user and stores it in the risk database 220 . At this time, the risk evaluation unit 110 receives the frequency of occurrence, risk intensity, risk, and improvement measures after improvement for the classified risk factors from the user. Risk information may be generated and stored in the risk database 220 by using the frequency of occurrence, risk intensity, risk, and improvement measures for risk factors before and after improvement. The risk analysis unit 120 may perform machine learning or deep learning. (Deep Learning) by analyzing the information on the corresponding job through at least one of the extracted statistical values and patterns to calculate the job safety measure information, which is the safety measure information for the corresponding job. The risk analysis unit 120 stores candidate work information including the calculated work safety countermeasure information in the work permission database 230 . Specifically, the risk analysis unit 120 is the first accident information that is information about the accident case that occurred during the operation through at least one of machine learning (Machine Learning) and deep learning (Deep Learning), the work environment in which the work is performed. Candidate job information including the work safety measure information of the job is calculated by analyzing the risk information which is information about the risk factors evaluated by the work environment information and the risk evaluation unit 110, which is information about the work, and the calculated work safety measure information is stored in the work permission database 230 . In this case, the first accident information may include at least one of serious accident information, recent accident frequency information, and accident result information, and the working environment information is at least one of business site, department, process, equipment, work place, and working temperature information. includes, and the risk information includes at least one of risk factors for each work step, the frequency of occurrence of the risk factors, the intensity of risk, risk information, and improvement measures, and the work safety measures information includes safety measures information for the work and worker compliance It may include at least one of information.

Tools and construction machinery brought into the site must be managed for the statutory safety inspection period in accordance with the 『Occupational Safety and Health Act Article 93』. Accordingly, the risk analysis unit 120 uses the basic information of the tool and the construction machine, which is the basic information of the tool and the construction machine stored in the tool and construction machine database 210, to inspect the legal safety of the tool and the construction machine. Validate the period. If it is determined that the tool and construction machine have been subjected to safety inspection within the statutory safety inspection period, the risk analysis unit 120 converts the tool and construction machine basic information of the tool and construction machine into valid tool and construction machine basic information. It is stored in the construction machine database 210 as information. At this time, basic information on tools and construction machines and effective tools and basic information on construction equipment includes work plan, equipment name, inspection validity period, model name, specifications, operator, etc. based on 『Rule 38 on Industrial Safety and Health Standards』 at least one of the information of

In addition, the risk analysis unit 120 is a normal photo, an abnormal photo, an effective tool and Information on safety measures for tools and construction equipment by analyzing basic information on construction equipment, inspection information, which is information on inspections of the relevant tool and construction equipment, and the second accident information, which is information about accidents that occurred in the relevant tool and construction equipment to calculate Effective tool and construction machine basic information, inspection information, second accident information, and tool tool and construction machine safety measure information are included in the candidate work information, and this candidate work information can be stored in the tool and construction machine database 210 . have. At this time, the effective tool and basic information of construction equipment, as described above, are information such as work plan, equipment name, inspection validity period, model name, specifications, operator, etc. based on 『Rule 38 on Industrial Safety and Health Standards』 includes at least one of, the inspection information includes at least one of the inspection history of each tool and construction machine and information on improvement measures according to the inspection, and the second accident information includes accident summary information and accidents of each tool and construction machine. At least one of result information is included, and the tool and construction machine safety measure information may include at least one of safety measure information and worker compliance information for the corresponding tool tool and construction machine. This will be described in detail later with reference to FIGS. 4 and 5 .

When the work permit unit 130 receives permission from the user for candidate work information including work safety measures information and tool tools and construction machine safety measures information stored in the work permit database 230 by the risk analysis unit 120, The candidate job information is stored in the job permission database 230 as permission job information. At this time, the candidate job information includes job classification, basic information of the corresponding job, first accident information of the corresponding job, risk information, job safety measure information, tool tools and construction equipment basic information of tools and construction machines used in the corresponding job, It includes at least one of inspection information, second accident information, and safety measures information for tools and construction machines. The job classification is information indicating the type of job, and the basic information of the job includes at least one of the job name of the job, the job content, the job process name, the job location, the job department, the job person in charge, and information about the job company, The first accident information may include at least one of serious accident information, recent accident frequency information, and accident result information, and the working environment information includes at least one of workplace, department, process, equipment, work place, and working temperature information. and, the risk information includes at least one of the risk factors for each work stage and the risk frequency, intensity and risk information of the risk factors, and the work safety measure information is calculated as the basic information of the work, the first accident information, and the risk information, Basic information of tools and construction equipment is at least one of a work plan based on 『Rule 38 on Industrial Safety and Health Standards』, equipment names of tools and construction machines, inspection validity period, manufacturer, model name, specifications, and operator information. and, the inspection information includes inspection history of tools and construction machines and information on improvement measures according to the inspection, and the second accident information includes accident summary information and accident result information of tools and construction machines, and and construction machine safety measures information may be information calculated through basic tool and construction machine information, inspection information, and second accident information.

TBM (Tool Box Meeting) is training for accident prevention. Specifically, TBM is training on risk factors and safety measures for work, and the person in charge of the work participates, and such information is recorded in the TBM log.

The TBM log generation unit 140 links the information stored in the risk database 220 and the work permission database 230 to the TBM database 240 . Specifically, the TBM log generation unit 140 converts at least one of the risk information stored in the risk database 220 , the work environment information and the first accident information, and the permitted work information stored in the work permission database 230 to the TBM database 240 . can be stored in Accordingly, the TBM log generation unit 140 generates a TBM log using at least one of risk information, work environment information, first accident information, and permitted work information stored in the TBM database 240 .

According to an embodiment of the present invention, by analyzing the first accident information corresponding to the work, the work environment information, and the risk information of the work through machine learning (Machine Learning) or deep learning (Deep Learning), the first accident information, Statistical values and patterns for work environment information and risk information are extracted, and a log for TBM (Tool Box Meeting), an education for accident prevention, is created based on the safety measures for the extracted work. By using the generated TBM log, it is possible to prevent accidents that may occur during work by educating work managers and safety managers of contractors and subcontractors.

The tool and construction machine database 210 may store basic information on tools and construction machines, basic information on effective tools and construction machines, and information on safety measures for tools and construction machines. At this time, basic information on tools and construction equipment includes basic information on tools and construction machines, and basic information on effective tools and construction equipment includes tools and construction equipment for which the legal safety inspection period has been verified. Basic machine information is included, and the basic information on tools and construction equipment includes work plans based on 『Rule 38 on Industrial Safety and Health Standards』, equipment names of tools and construction machines, inspection validity period, manufacturer, model name, It includes at least one of specifications and operator information, and the safety measures information for tool tools and construction machines is, as described above, in the basic information of tool tools and construction machines of tool tools and construction machines, and inspection history and inspection of tool tools and construction machines. It may be information on safety measures calculated through inspection information including information on improvement measures in accordance with the second accident information including accident summary information and accident result information of tools and construction machines.

The risk database 220 may store at least one of risk information for work, work environment information, and first accident information. At this time, the risk assessment information includes information on risk factors for each work stage, the work environment information includes at least one of workplace, department, process, equipment, work name, work place, and temperature information, and the first accident information is whether a serious accident It may include at least one of information, recent accident frequency information, and accident result information.

The work permission database 230 may store candidate job information and permitted job information. At this time, the candidate job information is job information that has not been approved by the user, the permitted job information is job information that has received permission from the user among the candidate job information, and the candidate job information and permitted job information are job name, job content, and work process, respectively. It may include at least one of a person, place, department/responsible person, work company, risk factors of the work, work safety measures information, and tool and construction machine safety measures information.

The TBM database 240 is linked with the risk database 220 and the work permission database 230 to obtain the first accident information of the risk database 220, the risk information and the work environment information, and the permitted work information of the work permission database 230 can be saved

Hereinafter, with reference to FIGS. 2 to 3, a safety accident prevention method through the analysis of risk factors of work according to an embodiment of the present invention will be described in detail.

Figure 2 is a view schematically showing a flow chart showing a safety accident prevention method through the analysis of risk factors of work according to an embodiment of the present invention, Figure 3 is an example of a TBM log generated according to an embodiment of the present invention It is a drawing showing

Referring to Figure 2, the safety accident prevention method through the risk factor analysis of work according to an embodiment of the present invention is a risk assessment step (S410), work permission registration and approval step (S420) and TBM log generation step (S430) includes

step division Contents example Risk Assessment Risk assessment name BAF Utility Risk Assessment Classification of risk factors by work stage risk factors
Mechanical (equipment) factors >Stenosis (winding, pinched), etc.
Work characteristic factors > noise, etc.
Electrical factors> electric shock, etc.
Administrative factors> Safety rules and signs not posted Hazards of BAF utility replacement work are classified as “electric shock” Risks before/after improvement
Frequency/Strength/Risk Evaluate with points Electric shock: frequency before improvement (2) intensity (3) risk (6), frequency after improvement (1) intensity (1) risk (2) Risk Factor Registration Registration of risk factors according to working conditions Working conditions: process, equipment, work name, work place Process name: BAF
Facility: BAF Utility Facility
Job name: BAF utility replacement job
Venue: Cold Rolling Unit 1 Cold Rolling Mill
→Register the risk factor "electric shock" safety measures and
Register/Apply for work permit Registration of work classification Confined space, general danger, fire, excavation, handling of hazardous substances, electric power outage, height, heavy construction, restricted area access, etc. “General Risk” Basic information registration Name of work, contents of work, name of work process, place
Department / Responsible
work company Job name: BAF utility replacement job
Contents of work: Permission to replace BAF utility work
Process name: BAF
Venue: Cold Rolling Unit 1 Cold Rolling Mill
Department/Responsibilities: Maintenance in charge of cold rolling department
Worker: Ghana Maintenance Registration of safety measures and worker compliance matters ※Registration of safety measures according to risk factors
Ex) Installation of stenosis safety sign
Wear noise earplugs and protective gear
Electricity check before electric shock work Safety Measures: Prevent electric shock by using a detector before shutting off (check the energization) TBM generation Risk / countermeasure information of risk DB is automatically linked to TBM Name of work/place/content, name of partner participating in TBM,
Connection of participants, risk factors and improvement measures, etc. Partners participating in TBM: Ghana Maintenance
Those who attended/trained TBM: Class A, Deputy B

In the risk assessment step (S410), the risk assessment unit 110 periodically or from time to time, as shown in Table 2, evaluates the risk for each task. For example, risk factors are classified according to major classifications such as mechanical (facility) factors, biological factors, electrical factors, work characteristic factors, and the risk of each risk factor can be evaluated. Specifically, the risk evaluation unit 110 evaluates the occurrence frequency, risk intensity, and risk level for each risk factor of the job. The risk assessment unit 110 may generate risk information by using the frequency of occurrence, the intensity of risk, and the degree of risk for the risk factors evaluated for each work step and store the information in the risk database 220 . In the work permit registration and approval step (S420), the risk analysis unit 120 calculates safety measures for risk factors for the work, and the work permit unit 130 safety measures calculated by the risk analysis unit 120 can obtain permission from the user.

In the work permit registration and approval step (S420), the risk analysis unit 120 analyzes the first accident information, work environment information, and risk information through at least one of machine learning and deep learning. Statistical values and patterns for the first accident information, work environment information, and risk information are extracted, and work safety measures information of the corresponding work is calculated using the extracted statistical values and patterns (S421). At this time, the first accident information includes at least one of whether or not a major accident is a serious accident and recent accident frequency information, and the working environment information includes at least one of workplace, department, process, equipment, work place, and working temperature information, and the risk information is It may include at least one of risk factors for each work stage, the frequency of occurrence of the risk factors, risk intensity, and risk level information. For example, as shown in Table 2, the risk analysis unit 120 uses the BAF utility facility of the BAF process, and the "BAF utility replacement operation is carried out at the "Cold Rolling Unit 1 Cold Rolling Mill" and has a risk factor of "electric shock". For ", through analysis using at least one of machine learning and deep learning, it is possible to calculate work safety measures information of "using the detector before breaking (check the energization)" for the corresponding job.

After that, in the work permit registration and approval step (S420), the risk analysis unit 120 converts the candidate work information including the work safety measure information calculated for the work and the tool and construction machine safety measure information to the work permit database ( 230) (S422). Specifically, the risk analysis unit 120 includes first accident information, which is information about an accident case that occurred during the work, work environment information, which is information about the work environment in which the work is performed, and the risk factors evaluated by the risk evaluation unit 110 . Candidate job information including job safety countermeasure information calculated using the risk information that is information on the job permission database 230 is stored. In this case, the candidate work information may include tool tools and construction machine safety measures information of tools and construction machines used in the corresponding work.

Candidate job information stored in the work permission database 230 may be permitted by the user (S422). The work permitting unit 130 stores the permitted candidate work information as permitted work information in the work permit database 230 . Specifically, at this time, the permitted operation information is "closed space", "general danger", "firearm", "excavation", "handling of hazardous materials", "electrical outage", " Classification of at least one of the work of "Aerial height", "Heavy work", "Access to restricted area", and "Other" It includes basic information of the corresponding job, including information, and job safety countermeasure information including safety measures and worker observances of risk factors for the job calculated by the risk analysis unit 120 .

For example, as shown in Table 2, the work permitting unit 130 sets the job classification of the BAF utility replacement job described above as “general risk”, “BAF utility replacement job” as the job name, and “BAF utility replacement job” as the job description. Change work permit”, “BAF” as the process name, “Cold Rolling Unit 1 Cold Rolling Factory” as the location, “Cold Rolling Unit Maintenance Manager” as the department/in charge, and “Gana Maintenance” as the working company in the work permit database ( 230), and as a safety measure for the work, "Prevention of electric shock accidents by using a detector before shutting off (check the energization)" as a worker's observance, and "wearing safety gloves and insulating protective equipment when working on site" as a safety measure for the work in the work permit database (230) ) can be stored in

In addition, although not shown in Table 2, the permitted work information may include tool tools and construction machine safety measures information of tool tools and construction machines used in the corresponding work.

In the TBM log generation step (S430), the TBM log generation unit 140 links the risk information, the work environment information and the permitted work information to the TBM database 240, and TBM log using the information stored in the TBM database 240 create Specifically, the TBM log generation unit 140 interlocks the risk information stored in the risk database 220 , the work environment information and the first accident information, and the permitted work information stored in the work permission database 230 to the TBM database 240 , , the risk information stored in the risk database 220 , the work environment information and the first accident information and the permitted work information stored in the work permission database 230 are stored in the TBM database 240 . For example, as shown in Table 2, the TBM log generation unit 140 sets "BAF utility replacement job" as the job name, "Cold Rolling Unit 1 Cold Rolling Mill" as the location, and "Ghana Maintenance" as the TBM participating partner. , “Chief A, Deputy Director B” as a participant in TBM/training, “Electric shock” as a risk factor, “Prevention of electric shock by using a detector before shutting off (check energization)” as a safety measure, and “On-site work” as a precaution for workers Wearing safety gloves and insulating protective equipment" may be stored in the TBM database 240 .

In addition, as shown in FIG. 3 , the TBM journal generation unit 140 uses information stored in the TBM database 240 to generate a TBM journal. The TBM log may include at least one of the name of the job, the location, the partner participating in the TBM, the person who attended/trained the TBM, the risk factors, the safety measures, and the worker's observances. Accordingly, when the corresponding work is in progress, it is possible to prevent accidents that may occur as the corresponding work is in progress by sharing the work safety measure information calculated for the work through the TBM with the personnel in charge.

Hereinafter, with reference to FIGS. 4 to 5 , a method for managing risk factors of a tool tool and a construction machine according to an embodiment of the present invention will be described in detail.

4 is a diagram schematically showing a flow chart showing a method for managing risk factors of a tool tool and a construction machine according to an embodiment of the present invention, and FIG. 5 is a risk of a tool tool and a construction machine according to an embodiment of the present invention It is a drawing showing an example of the work plan of the tool and construction machine generated through the factor management method.

Referring to FIG. 4 , the safety accident prevention method through risk factor analysis of tool tools and construction machines according to an embodiment of the present invention includes a tool tool and construction machine information input step (S210), a legal safety inspection period verification step (S220). ), a machine-specific risk factor and countermeasure information input step (S230) and a machine-specific risk factor inquiry step (S240). The safety accident prevention method through the analysis of risk factors for tools and construction machinery may be executed before or simultaneously with the execution of the safety accident prevention method through the risk factor analysis of the above-described work.

step division Contents example Enter tool and construction equipment information Classification of tools and construction machinery Construction Equipment>Tower Crane
Construction Equipment>Truck
Construction Equipment> Forklift
Tools > Driver
Tools>Hammer
Tools> Grinder
Vehicle-based unloading and transporting machines> In-house transport vehicles, etc. Construction Equipment> Forklift
Tools and Construction Equipment Information Equipment name, inspection validity period, manufacturer, model name, specification, operator Equipment Name: Forklift, Inspection Validity: 3 Years
Manufacturer: Hyundai, Model name: 180D9
Specifications: Loading capacity 18,000kg, Driver: Il Kim Automatic verification of legal safety inspection cycles Construction Equipment Target and Expiration Date Excavator (1 year), loader (2 years), forklift (2 years), dump truck (1 year), etc. Forklift (2 years) Construction Equipment Target and Valid Period Aerial workbench (2 years), local exhaust system (3 years), lift (2 years), etc. N/A Machine-related risk factors/countermeasures input Risk factors and safety measures for imported machinery risk factors
Mechanical Factors > Mechanical and Equipment Design Defects
Mechanical factor > Poor protection device
Mechanical Factors > Utilities Defects
Mechanical Factors > Defects in the means of transport using equipment
Substance/environmental factors > Poor working space
Substances and environmental factors > Gas, vapor, mist and poor handling of chemicals
Human Factors >Worker's working posture and movement defects
Administrative factors> Safety rules and non-posting of signs, etc.

※Registration of safety measures according to risk factors
Ex) Defective means of transport → Front, rear, left, right of the workshop
secure sight (Risk Factor) Defects in the means of transport:
Collision between vehicle and worker, vehicle overturning due to ground immobility and subsidence
(Safety measures) Securing a clear view when reversing through rearview mirrors and CCTV, etc.

First, in the tool and construction machine information input step (S210), the risk analysis unit 120 stores the tool and construction machine basic information of the tool and construction machine for which the statutory safety inspection period has not been verified in the tool and construction machine database. It can be stored in (210). At this time, as shown in Table 3 and Figure 5, the basic information of tools and construction machines includes the work plan based on 『Rule 38 on Industrial Safety and Health Standards』, equipment names of tools and construction machines, and inspection validity. It may include at least one of information such as period, manufacturer, model name, specification, and operator, and may include inspection information and second accident information. In the statutory safety inspection period verification step (S220), the risk analysis unit 120 uses the tool and construction machine basic information stored in the tool tool and construction machine database 210 for the legal safety inspection period of the tool and construction machine. is verified (S221). Tools and construction machinery brought into the site must be managed for the validity period of inspection in accordance with the 『Occupational Safety and Health Act Article 93』. For example, as shown in Table 3, an excavator has an inspection validity of 1 year, a loader has an inspection validity of 2 years, a forklift has an inspection validity of 2 years, and a dump truck has an inspection validity of 1 year. The Aerial Platform has an inspection validity of 2 years, the local exhaust system has an inspection validity of 3 years, and the lift has an inspection validity of 2 years. Accordingly, the risk analysis unit 120 verifies the statutory safety inspection period by using the tool and construction machine basic information stored in the tool and construction machine database 210 based on the legal safety inspection period for each machine. At this time, if it is determined that the legal safety inspection period of the machine has expired, a warning may be generated and may not be stored as valid tool tool and construction machine basic information (S222). On the other hand, if it is determined that the statutory safety inspection period of the relevant tool and construction machine has not expired, that is, if it is determined that the relevant tool and construction machine have undergone a safety inspection within the statutory safety inspection period, the tool and Construction machine basic information is stored as effective tool and construction machine basic information. That is, only the tool and construction machine information about the tool and the construction machine for which the legal safety inspection period is valid may be stored in the tool and construction machine database 210 as valid tool and construction machine basic information.

According to an embodiment of the present invention, by verifying the legal safety inspection period of the tool and the construction machine, it is possible to prevent the use of the tool and the construction machine for which the legal safety inspection period has expired, thereby preventing an accident.

In the machine-specific risk factor and countermeasure information input step (S230), the risk analysis unit 120 uses the risk factors for the tool tool and the construction machine to calculate safety measure information for the tool tool and the construction machine, and the calculated tool and The construction equipment safety measures information is stored in the tool and construction equipment database 210 . Specifically, the risk analysis unit 120 through at least one of machine learning (Machine Learning) and deep learning (Deep Learning) a normal machine picture, abnormal machine picture, effective tool and construction machine basic information, tool and construction machine By analyzing the inspection information and the second accident information of the tool and construction machine safety measures information is calculated and stored in the tool tool and construction machine database (210). The inspection information of the tool and construction machine includes at least one of the inspection history of the tool and the construction machine and information on improvement measures according to the inspection, and the second accident information of the tool and the construction machine is an overview of the accident of the tool and the construction machine. It may include at least one of information and accident result information.

For example, in order to calculate safety measures information for tools and construction machinery for a freight vehicle, the risk analysis unit 120 performs at least one of machine learning and deep learning of the corresponding freight vehicle. By analyzing normal machine pictures, abnormal machine pictures, effective tool and construction machine basic information including basic tool and construction machine information, inspection information, and second accident information, “collision between vehicle and worker” and “ground of the truck” Information on safety measures for tools and construction machinery such as “Securing a clear view when reversing through rearview mirrors and CCTV” and “Preventing floating settlement of the ground by placing guides” for risk factors such as can be calculated. The calculated tool tool and construction machine safety measure information is stored in the tool tool and construction machine database 210, the calculated tool tool and construction machine safety measure information is included in the candidate work information, and the candidate work information is the work permission database ( 230) can be stored.

According to an embodiment of the present invention, effective tooling and construction including normal machine photos, abnormal machine photos, tool tools and construction machine basic information through at least one of machine learning (Machine Learning) and deep learning (Deep Learning) By analyzing basic machine information, inspection information of tools and construction equipment, and second accident information, standardized safety measures for tools and construction equipment are calculated, and risk factors according to the skill level of workers are discovered and used for the work. It is possible to level the level of safety measures for tool tools and construction machinery.

In the hazard factor history inquiry step for each tool and construction machine ( S240 ), the history of the hazard factors for each machine and information on safety measures for the tool and construction machine stored in the tool and construction machine database 210 are inquired.

According to an embodiment of the present invention, by calculating the risk factors and safety measures for each tool and construction machine used for work, and when the work is performed using the tool and construction machine, the safety measures of the machine are observed In this way, accidents that may occur due to the risk factors of the relevant tool and construction equipment can be prevented.

Those skilled in the art to which the present invention pertains will understand that the above-described present invention may be embodied in other specific forms without changing the technical spirit or essential characteristics thereof.

Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The scope of the present invention is indicated by the following claims rather than the above detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

All disclosed methods and procedures described herein may be implemented, at least in part, using one or more computer programs or components. These components may be configured as a series of serial or non-volatile memory via any conventional computer-readable medium or machine-readable medium, including volatile and non-volatile memory such as RAM, ROM, flash memory, magnetic or optical disks, optical memory, or other storage media. They may be provided as computer instructions. The instructions may be provided as software or firmware, and may be implemented, in whole or in part, in a hardware configuration such as ASICs, FPGAs, DSPs, or any other similar device. The instructions may be configured for execution by one or more processors or other hardware components, which when executing the series of computer instructions perform all or part of the methods and procedures disclosed herein or make it possible to perform

10: Safety accident prevention system 110: Risk assessment department
120: risk analysis unit 130: work permit unit
140: TBM log generation unit 210: tool and construction machinery database
220: risk database 230: work permit database
240: TBM database

Claims (18)

As a method to prevent safety accidents by analyzing risk factors for work,
First accident information that is information about an accident case that occurred during the work through at least one of machine learning and deep learning, work environment information that is information about the environment in which the work is performed, and work environment information of the work Analyzing risk information, which is information on risk factors, and calculating work safety measure information, which is information on safety measures for the work;
A safety accident prevention method through risk factor analysis, comprising: a step in which candidate job information including the calculated job safety countermeasure information is determined as a safety countermeasure through the user's permission. According to claim 1,
In the step of calculating the work safety measure information,
The first accident information, the work environment information, and the risk information are analyzed using at least one of machine learning and deep learning to extract at least one of statistical values and patterns, and the extracted statistics Safety accident prevention method through risk factor analysis, characterized in that the information on work safety measures is calculated based on at least one of values and patterns. According to claim 1,
The first accident information includes at least one of serious accident information and recent accident frequency information, and the working environment information includes at least one of workplace, department, process, equipment, work place, and working temperature information, and the risk The information is a safety accident prevention method through risk factor analysis, characterized in that it includes at least one of risk factors for each stage of work and the frequency of occurrence of the risk factors, risk intensity, and risk level information. According to claim 1,
generating a TBM log using permitted work information including candidate work information determined as the safety measure; further comprising,
The TBM log is a safety accident prevention method through risk factor analysis, characterized in that it includes at least one of the work name, place, TBM participating partner, TBM attendance / training completion, risk factors, safety measures, and worker compliance. According to claim 1,
In the step of calculating the work safety measure information,
The candidate job information is stored in a work permission database,
In a step in which permitted work information including the calculated work safety measure information is determined as a safety measure,
The permitted work information is a safety accident prevention method through risk factor analysis, characterized in that stored in the work permit database. According to claim 1,
verifying the statutory safety inspection period using the tool tools and construction machine basic information of the tool tools and construction machines used for the work; and
Through at least one of Machine Learning and Deep Learning, normal photos of tools and construction machines, abnormal photos, and effective tool and construction machine basic information including the tool and construction machine basic information; A method for preventing safety accidents through risk factor analysis, comprising: analyzing the inspection information and the second accident information to calculate safety measures information for tools and construction machinery. 7. The method of claim 6,
The basic information of the tool and the construction machine includes at least one of an equipment name, an inspection validity period, a model name, a specification, and operator information of the tool and the construction machine, and the inspection information includes an inspection history and inspection of each tool and construction machine. Safety accident prevention through risk factor analysis, characterized in that it includes at least one of information on improvement measures according to Way. 7. The method of claim 6,
In the step of verifying the legal safety inspection period of the tool and construction machine,
When it is determined that the tool and construction machine have undergone a safety inspection within the statutory safety inspection period, the tool and construction machine basic information is stored in the tool and construction machine database as valid tool and construction machine basic information. A safety accident prevention method through risk factor analysis characterized. 9. The method of claim 8,
In the step of calculating the work safety measure information,
The calculated tool and construction machine safety measures information is stored in the tool and construction machine database,
Candidate work information including the work safety measure information and the tool and construction machine safety measures information is a safety accident prevention method through risk factor analysis, characterized in that stored in the work permit database. As a system for preventing safety accidents by analyzing risk factors for work,
a risk assessment unit that generates risk information by evaluating the risks for each task and by each task;
First accident information that is information about an accident case that occurred during the work through at least one of machine learning and deep learning, work environment information that is information about the environment in which the work is performed, and the risk information a risk analysis unit for calculating job safety countermeasure information that is information on safety countermeasures for the corresponding job by analyzing the , and generating candidate job information including the job safety countermeasure information; and
Safety accident prevention system through risk factor analysis, characterized in that it comprises; 11. The method of claim 10,
The risk analysis unit,
The first accident information, the work environment information, and the risk information are analyzed through at least one of the machine learning (Machine Learning) and the deep learning (Deep Learning) to extract at least one of statistical values and patterns for the work, and , a safety accident prevention system through risk factor analysis, characterized in that based on at least one of the extracted statistical values and patterns, work safety measures information for the corresponding work is calculated. 11. The method of claim 10,
The risk information is a safety accident prevention system through risk factor analysis, characterized in that it includes at least one of the risk factors for each job and each step of the job, the risk frequency, intensity, and degree of risk of the corresponding risk factors, and safety measures corresponding to the corresponding risk factors . 11. The method of claim 10,
The risk information is stored in a risk database, and the candidate work information is a safety accident prevention system through risk factor analysis, characterized in that stored in the work permission database. 11. The method of claim 10,
The first accident information includes at least one of serious accident information and recent accident frequency information, and the work environment information includes at least one of workplace, department, process, equipment, working place, and working temperature information. Safety accident prevention system through risk factor analysis. 11. The method of claim 10,
a TBM journal generation unit that links the permission job information to a TBM (Tool Box Meeting) database, and automatically generates a TBM journal by using the permission job information stored in the TBM database; further comprising,
The TBM log is a safety accident prevention system through risk factor analysis, characterized in that it includes at least one of the work name, place, TBM participating partner, TBM attendance / training completion, risk factors, safety measures, and worker compliance. 11. The method of claim 10,
The risk analysis unit,
Validating the statutory safety inspection period of the tool and construction machine using basic information on the tool and construction machine including basic information on the tool and construction machine used for the above work,
If it is determined that the tool and construction machine have undergone a safety inspection within the statutory safety inspection period, the tool and construction machine basic information of the tool and construction machine is valid tool and construction machine basic information. Safety accident prevention system through risk factor analysis, characterized in that it is stored in the construction machine database. 17. The method of claim 16,
The risk analysis unit,
Through at least one of Machine Learning and Deep Learning, normal photos of tools and construction machines, abnormal photos, basic information of effective tools and construction machines, inspection information, and second accident information are analyzed. Calculate safety measures information for tools and construction equipment, and store the calculated information on safety measures for tools and construction equipment in the tool and construction equipment database;
The candidate work information is a safety accident prevention system through risk factor analysis, characterized in that it includes the calculated tool and construction machine safety measures information. 18. The method of claim 17,
The inspection information includes at least one of an inspection history of each tool and construction machine and information on improvement measures according to the inspection, and the second accident information includes at least one of accident summary information and accident result information of each tool and construction machine. Safety accident prevention system through risk factor analysis, characterized in that it comprises a.

Images