KR101102488B1 - Method for estimating industrial disaster using space analysis and system for thereof - Google Patents

Abstract

According to the present invention, a list of complex sensor devices to be applied to spatial analysis is selected from a list of complex sensor devices installed at different locations within one workspace to sense detection elements corresponding to each installation location. Setting the spatial analysis target group for the target, receiving respective sensing information from the composite sensor devices in the workspace, and extracting the sensing information of the composite sensor devices corresponding to the spatial analysis target group and combining them with each other. To provide a method and system for predicting occupational safety and disasters in the manufacturing industry using spatial analysis comprising the step of generating data for disaster prediction in the workspace.

According to the industrial safety accident prediction method using the disclosed spatial analysis, the combined sensor devices for sensing detection elements corresponding to the installation location are installed at different locations in the work space, and the data obtained by combining the sensing information of the installed composite sensor devices. By using as the spatial analysis data for the workspace can improve the accuracy and reliability of disaster prediction. In addition, each of the multiple sensor device is equipped with only the sensors corresponding to the installation site, it is possible to reduce the battery power consumption, reduce the replacement cost of the sensor, and there is an advantage that can reduce the maintenance cost accordingly.

Description

Method for estimating industrial disaster using space analysis and system for knowledge}

The present invention relates to a method and system for predicting industrial safety accidents in the manufacturing industry using spatial analysis, and more particularly, to industrial safety accidents using spatial analysis to predict various disasters that may occur in the work space during ship construction work. It relates to a prediction method and system.

In general, a very large ship is large in size, so that overall management and monitoring of each work space in the ship is not easy, and it is difficult to effectively respond to various safety accidents.

Conventionally, there is a method of integrally monitoring a disaster by disposing a complex sensor device equipped with a plurality of sensors at a predetermined place in a work space. By the way, the installation position of the sensors for the optimal detection ability on the workspace is different from each other according to the detection element properties of each sensor. Therefore, in the case of collecting the detection elements by fixedly installing a complex sensor device in which several sensors are concentrated in one place as described above, optimal detection cannot be achieved, and it is difficult to accurately identify an accident even in the event of a serious disaster. It is difficult to respond quickly and can increase the damage of human and physical property.

The present invention is more reliable disaster by performing the spatial analysis by extracting and combining the sensing information from the complex sensor devices installed in different locations in the same one workspace to sense the detection element corresponding to each installation location It is an object of the present invention to provide a method and system for predicting occupational safety accidents in the manufacturing industry using spatial analysis that enables judgment to be performed.

According to the present invention, a list of complex sensor devices to be applied to spatial analysis is selected from a list of complex sensor devices installed at different locations within one workspace to sense detection elements corresponding to each installation location. Setting the spatial analysis target group for the target, receiving respective sensing information from the composite sensor devices in the workspace, and extracting the sensing information of the composite sensor devices corresponding to the spatial analysis target group and combining them with each other. To provide a method for predicting industrial safety accidents in the manufacturing industry using spatial analysis comprising the step of generating data for disaster prediction in the workspace.

Herein, the complex sensor devices are driven by their own battery units, and as the detection element, a plurality of sensors that detect oxygen, temperature, humidity, illuminance, VOC, infrared rays, smoke, flame, human body, and gas, respectively, are removable. It may be provided with a plurality of sockets to. In this case, only sensors selected from the plurality of sensors may be mounted in corresponding sockets so as to sense only detection elements corresponding to the respective installation positions.

In addition, the complex sensor devices, the type of the sensor to be mounted may be determined according to the sensing angle of the sensor, the sensing distance, the intermediate obstacle, or the characteristics of the detection element determined according to each installation position in the workspace.

In addition, the industrial safety accident prediction method using the spatial analysis may further include storing the sensing information transmitted in a predetermined period from the complex sensor devices in a DB for each complex sensor device. In the generating of the data for the disaster prediction, the latest sensing information of each of the complex sensor devices corresponding to the spatial analysis target group may be extracted from the DB and combined with each other.

The latest sensing information may include all recent sensing information collected in a period within a set time from the present, from among the sensing information transmitted in a set period from the complex sensor devices and collected in the DB.

In addition, the industrial safety accident prediction method of the manufacturing industry using the spatial analysis, by analyzing the data for the disaster prediction step of predicting a disaster in the fire, explosion, suffocation in the workspace, and disaster prediction for the workspace The method may further include the step of notifying the result.

The generating of the data for disaster prediction by combining the sensing information may include oxygen, temperature, humidity, illuminance, VOC, and gas among detection elements collected from the complex sensor devices corresponding to the spatial analysis target group. The detection element related to the detection may use the maximum or minimum information of the corresponding sensing information, and the detection element related to the detection of the infrared, smoke, flame, and the human body may use spatial analysis using the sensing value of the corresponding sensing information.

The setting of the spatial analysis target group may include displaying a form of the workspace, individual locations of all the complex sensor devices installed in the workspace, and a list of the installed complex sensor devices on a graphic. And providing a list of the composite sensor devices to be applied to the spatial analysis from the list of the composite sensor devices and setting the group as a spatial analysis target group for the workspace.

In addition, the present invention, the list of the composite sensor devices that are installed at different locations in one workspace to sense the detection element corresponding to each installation location, the list of the composite sensor devices to be applied to the spatial analysis is selected A setting unit configured to set a spatial analysis target group for a workspace, a receiver receiving respective sensing information from the complex sensor devices in the workspace, and sensing information of the composite sensor devices corresponding to the spatial analysis target group And in combination with each other to provide an industrial safety accident prediction system of the manufacturing industry using a spatial analysis including a combination for generating data for disaster prediction in the workspace.

According to the industrial safety accident prediction method and system using the spatial analysis according to the present invention, the composite sensor devices for sensing the detection element corresponding to the installation location in different locations in the work space and installed so By using the data combining the sensing information as spatial analysis data for the workspace, it is possible to improve the accuracy and reliability of disaster prediction. In addition, each of the multiple sensor device is equipped with only the sensors corresponding to the installation site, it is possible to reduce the battery power consumption, reduce the replacement cost of the sensor, and there is an advantage that can reduce the maintenance cost accordingly.

Industrial safety accident prediction method and system using the spatial analysis according to the present invention is applied to industrial sites in the manufacturing industry in the coffee field. That is, it can be applied to any industrial site that requires monitoring or prevention of disasters.

As an example, such a disaster prediction method and system can be usefully applied to disaster prediction for the internal workspaces (workshops) of a large ship under construction. In more detail, there are a number of workspaces (zones) inside the ship, and the work that requires attention, such as welding, painting, assembly, etc. required for the ship construction is performed by the workers. These workspaces usually tend to be enclosed, making it difficult for workers to quickly identify when a disaster occurs at a location other than the current location.

In addition, different disaster situations may occur in each of the workspaces, and a disaster (fire, explosion, etc.) generated in one workspace may affect other adjacent workspaces, resulting in human and property damage. It can add more damage. In order to prevent and prevent various disasters (fire, explosion, suffocation) on the ship under construction, a technology for correctly predicting and reporting a disaster situation that may occur in the corresponding workspace is required. Of course, it is obvious that the industrial safety accident prediction method and system of the manufacturing industry using the above-described spatial analysis may be applied to other industrial sites in the manufacturing industry.

Hereinafter, for convenience of description, a case in which the industrial safety disaster prediction method of the manufacturing industry using the spatial analysis is applied to the ship under construction will be described as an example. 1 illustrates an example in which the composite sensor devices 100 are disposed in each workspace 10 in a ship being built for the purpose of predicting the industrial safety disaster.

Workers that perform work such as welding, painting, and assembly are arranged in the work space 10, and they carry individual portable terminals 300. Safety managers with the manager terminal 400 exists inside or outside the vessel.

The composite sensor device 100 detects various sensing information at a corresponding installation point and wirelessly transmits it at a set cycle, and the transmitted sensing information is located in the general control room 20 through a separate relay means 500 ( 200). Here, the integrated control means 200 may include a function of setting and changing the period in which sensing information is transmitted by remotely controlling the complex sensor devices 100. The comprehensive control means 200 predicts and determines a disaster by using the received sensing information, and the result is a portable terminal 300, a manager terminal 400, a complex sensor device 100, and a situation notification device. (600) and the like can be sent.

Hereinafter, the composite sensor devices 100 disposed in the workspace 10 will be described in detail. In the work space 10, a plurality of complex sensor devices 100 are disposed at different positions. In addition, the complex sensor devices 100 are installed at different positions in one work space 10 to sense detection elements corresponding to respective installation positions. These complex sensor devices 100 serves to detect environmental factors causing industrial safety disasters, such as fire, explosion, suffocation.

2 shows a configuration diagram of a composite sensor device. The complex sensor device 100 is driven by its own battery unit 130. This is because the work space 10 in the vessel is mainly a sealed or poor space for safety and the majority of places where the constant power is not supplied.

The composite sensor device 100 includes sensors 110 that detect at least one of oxygen, temperature, humidity, illuminance, VOC (volatile organic compound), infrared rays, smoke, flame, human body, and gas. Is mounted. The sensors 110 are controlled by the controller 120, and the detected sensing information of each sensor 110 is wirelessly transmitted to the outside through the communication unit 140.

Here, the composite sensor device 100 operates by mounting all or part of the sensors of all the above-described types of sensors. To this end, the composite sensor device 100 is a plurality of sensors corresponding to the plurality of sensors that detect the oxygen, temperature, humidity, illuminance, VOC, infrared, smoke, flame, human body, gas in the form of a socket, a plurality of corresponding thereto Sockets (not shown). If the types of sensors that can be used for disaster prediction are eight, the total number of the sensors listed above is basically provided with a total of eight sockets corresponding thereto.

However, the main battery consumption cause of the composite sensor device 100 is due to the sensors 110, the more the sensor 110 is attached, the greater the energy consumption. In addition, the operating time (life time) of the composite sensor device 100 that operates as the battery unit 130 is very important as a performance indicator of the product.

That is, the complex sensor devices 100 installed at different locations in one workspace 10 may select only the sensors selected from the plurality of sensors to sense only the detection elements corresponding to each installation location (highly important). Make sure that it fits into the socket. In other words, the sensor that does not correspond to the installation position of the composite sensor device (100) is removed from the socket so as to reduce unnecessary power consumption of the battery unit 130. Of course, according to this, it is possible to reduce the replacement cost of the sensor, there is an advantage that can significantly reduce the cost required for the replacement of the battery unit 130, maintenance costs required for operating the system. Moreover, since the sockets for all the sensors are provided in the composite sensor device 100 in advance, after the use in the current workspace is completed, only the necessary sensors can be freely attached and reused anywhere in the other workspace. There is an advantage to this.

Here, the type of sensors to be mounted in the composite sensor device 100, the sensing angle of the sensor, the sensing distance, the intermediate obstacle, or the sensor depending on the installation position of the composite sensor device 100 in the workspace 10 It depends on the characteristics of the detection element. This is because the individual importance of the detection elements vary depending on the installation position of the composite sensor device (100).

For example, in a specific workspace 10 where welding or painting work is carried out inside a ship for docking or quarrying during the ship building process, the combined radio device installed at the bottom part has a high importance of a gas sensor and a wall or ceiling part. The combined radio device attached to the sensor increases the importance of smoke and flame sensors. According to the importance of the sensing element according to the installation position, the type of sensor mounted on the composite sensor device 100 is determined.

3 is an example of installation of the composite sensor device. This installation example is an example of installing the wireless sensor devices (devices 1, 2, 3) on a specific work space 10 inside the vessel for monitoring the fire, explosion. The cylindrical holes on the left, right, and top right corners are the entrances 11 of the workers, and the workers can descend into the work space 10 through the ladders of the upper right entrance 11.

There are three composite sensor devices 100 (devices 1, 2, and 3) in the workspace 10, and these are the bottom part (device 1) and the middle part 2 of the workspace 10. Devices) and the upper entrance (device 3). The three composite sensor devices 100 may be equipped with oxygen, temperature, humidity, illuminance, VOC, infrared, smoke, flame, human body, and gas sensors, but only some of the sensors having high importance depending on the installation location. To be mounted or operated.

That is, in the case of the floor of the workplace where the device 1 is installed, the oxygen sensor and the human body sensor are important because they are the main work space of the workers, and the VOC sensor and the gas sensor are also important because the gas may be filled on the floor. In addition, in the middle part of the work site where the device 2 is installed, the sensor has a relatively wide viewing angle compared to other installation sites, and thus the importance of illuminance, infrared, and flame sensors related to light is high. The upper entrance of the workplace where device 3 is installed is a passage through which internal air can escape to the outside, and the importance of the smoke sensor is high. Of course, devices 1 to 3 may be equipped with the above-described sensors of high importance, and devices 1 to 3 may be equipped with both temperature and humidity sensors, which are basic detection elements. Table 1 below summarizes the mounting of the sensor for each of the composite sensor device 1 to 3 according to the above example.

Oxygen Temperature Humidity Roughness VOC infrared ray Acting flame anatomy gas Multiple Sensor Unit 1 ○ ○ ○ × ○ × × × ○ ○ Multiple Sensor Unit 2 × ○ ○ ○ × ○ × ○ × × Multiple Sensor Unit 3 × ○ ○ × × × ○ × × ×

By the way, when analyzing the disaster on the workspace 10 only with the detection data (sensing information) transmitted from one composite sensor device 100, the detection angle, detection distance, intermediate obstacles, characteristics of the detection target, etc. of each sensor For this reason, some sensors mounted on the single sensor device 100 may be difficult to accurately measure or detect at all. In other words, even if all of the above 8 types of sensors are mounted and sensed in one composite sensor device 100, some of the sensors show unreliable measurement values for the reasons described above.

Therefore, in consideration of the characteristics of the installation position in the workspace 10, multiple composite sensor devices 100 are installed at different positions, and the individual detection data transmitted by these composite sensor devices 100 are mutually different. By integrating and analyzing, it is possible to further increase the accuracy and reliability of the situation determination for the workspace 10.

Further, as described above, in consideration of the main detection elements according to the detection angle, detection distance, obstacle, importance of the sensor depending on the installation position, to selectively mount the sensor to be mounted on each of the multiple sensor devices 100 Accordingly, power consumption of the battery unit 130 may be reduced, and the most optimal sensing information may be obtained.

Of course, as a method of reducing the consumption of the battery unit 130, in the state in which all the above-described sensors are mounted in the sockets of the composite sensor device 100, the total control means so that only some necessary sensors are operated. There is also a method of remotely controlling whether or not the operation of each sensor from (200).

Hereinafter, on the basis of the above-described contents, a method for predicting industrial safety disasters in the manufacturing industry using the spatial analysis will be described in detail with reference to FIGS. 4 and 5. 4 is a flowchart of a method for predicting industrial accidents in a manufacturing industry using spatial analysis according to an exemplary embodiment of the present invention. 5 is a schematic diagram of a system for the method of FIG. The system 200 includes a setting unit 210, a receiving unit 220, a combining unit 230, a DB unit 240, a predicting unit 250, a transmitting unit 260, and a display unit 270. This system 200 corresponds to the comprehensive control means described above.

First, among the list of the composite sensor devices 100 installed at different locations in one workspace 10 to sense detection elements corresponding to each installation location, the composite sensor devices to be applied to spatial analysis ( 100 is selected and set as the spatial analysis target group for the workspace 10 (S410).

Definitions and examples of detection elements corresponding to the installation positions of the respective complex sensor devices 100 have been described above. In addition, the DB unit 240 may store the list of the complex sensor devices 100 installed for each workspace 10 and information on the installation location in association with each other.

In this step S410, the form of the workspace 10, the individual locations of all the composite sensor devices 100 installed in the workspace 10, and the list of the installed composite sensor devices 100 Is displayed on the graphic through the display unit 270 and provided.

The display unit 270 corresponds to a screen output means such as a monitor. 6 illustrates an example of displaying the positions of the complex sensor devices 100 disposed in the workspace on the screen through the display unit 270. The left figure corresponds to the position of the composite sensor devices 100 shown on the front view of the actual workspace 10 and the right figure corresponds to the position of the composite sensor devices 100 shown on the top view.

In the step S510, the list of the complex sensor devices 100 to be applied to the spatial analysis among the list of the complex sensor devices 100 displayed on the display unit 270 is determined from the manager through the setting unit 210. Selected, it is set as the spatial analysis target group for the workspace (10). 6 shows a list of selected devices. For example, if there are multiple sensor devices 1,2 and 3 in one workspace 10, a list of some or all of them may be selected from the administrator and set as a spatial analysis target group for the workspace. .

This step S410 may be performed for each workspace in the vessel. That is, for each workspace including Workspace 1, Workspace 2, ... Workspace N, the above-described spatial analysis target group can be individually set for each workspace.

Thereafter, the sensing information transmitted from the complex sensor devices 100 in the workspace 10 is received through the receiver 220 of the system 200 (S420). To this end, the composite sensor devices 100 transmit each sensing information at a set period to the system 200 side.

In addition, the receiver 220 determines whether the corresponding complex sensor device 100 that has transmitted the sensing information corresponds to the spatial analysis target group. This is because the complex sensor device that does not belong to the spatial analysis target group is not included in the object of disaster prediction through the spatial analysis.

In addition, separate relay means may be disposed between the complex sensor devices 100 and the receiver 220 in the workspace 10 for smooth communication thereof. Such relay means may be located in plural inside or outside the vessel.

Next, the sensing information transmitted from the complex sensor devices 100 in a set period is stored in the DB unit 240 for each complex sensor device 100 (S430). The complex sensor devices 100 transmit their sensing information in association with a unique identification code, and the DB unit 240 stores the sensing information for each complex sensor device 100 based on the unique identification code.

Then, through the combination unit 230, the sensing information of the complex sensor devices 100 corresponding to the spatial analysis target group is extracted and combined with each other to generate data for disaster prediction in the workspace (S440). .

In this step S440, the combination unit 230, in order to generate the data for the disaster prediction, the 'recent sensing information' for each of the composite sensor devices 100 corresponding to the spatial analysis target group to the DB Extracted from section 240 and generated in combination with each other.

More specifically, the latest sensing information is transmitted within a set period from the complex sensor devices 100 and collected in the DB unit 240 within the time (ex, 10 minutes) set from the present. Includes all recent sensing information collected during the period. For example, among the sensing information of the complex sensor devices belonging to the spatial analysis target group, the data is generated by using a value 10 minutes before the present time or 10 minutes to the present value.

The reason for using 'recently sensing information' is that the disaster prediction method is for predicting a disaster predicted in the future based on the current sensing information, and the sensing information of a few hours or a few days ago is significantly used for predicting a disaster of the current situation. Because there is no. For example, 12 hours after a problem (ex, operation error, failure, battery discharge) occurs in the operation of the composite sensor device 1 of the composite sensor devices 10 in the workspace 10, and thus transmission of the sensing information is impossible. In this case, the last 12 hours of data transmitted cannot be used as data for real-time disaster prediction of the current situation. Even if it is used, the accuracy and reliability of the prediction result is inferior. Thus, the risk of disaster is incorrectly predicted as a case where there is no risk or vice versa.

On the other hand, in step S440, among the detection elements collected from the composite sensor devices 100 corresponding to the spatial analysis target group, the detection element for detecting the oxygen, temperature, humidity, illuminance, VOC, gas is the corresponding sensing information. The data is generated using the maximum value or the minimum value of. For example, as in the case of Table 1, when both the composite sensors 1, 2 and 3 have a temperature sensor, all the sensing information recently sensed from these (composite sensors 1, 2, 3) are integrated and integrated. Of the sensing information, only the maximum value or the minimum value of the sensing temperature is used to generate data for the disaster prediction. Of course, if only the composite sensor device 1 has a temperature sensor, the maximum or minimum information among the recent sensing values of the temperature sensor detected by the composite sensor device 1 is reflected in the generation of the disaster prediction data.

In addition, the detection element for detecting the infrared, smoke, flame, human body generates the data by using the sensing value of the corresponding sensing information. The sensing value may be determined based on whether the sensed sensing information deviates from a threshold range set for each detection element. For example, in the case of Table 1, there is no smoke sensor in the composite sensor devices 1 and 2, but there is a smoke sensor in the composite sensor device 3. Regardless of whether there is no smoke sensor in the composite sensor devices 1 and 2, whether the sensing value of the smoke received from the composite sensor device 3 is immediately reflected in the data generation for the disaster prediction of the workspace 10.

Of course, if there is a smoke sensor in the composite sensor device 1, 3, even if the smoke is detected only in the composite sensor device 3, it is determined that the smoke is detected through the OR operation for each device is used to generate the disaster prediction data. That is, in the case of the detection of the infrared, smoke, flame, or human body, which is a detection element whose sensing value is immediately reflected in the disaster prediction, not the sensing value itself, any of the complex sensor devices 100 in the corresponding spatial analysis target group Even if only one device is detected, it is determined that the corresponding element is detected through OR operation.

Table 2 shows a combination table of sensing information used for spatial analysis. Oxygen (O2), temperature (Temp), humidity (Humid), roughness (Bright), VOC, gas (GAS) detection values for the minimum and maximum values are used in combination, the infrared (PIR), smoke ( Fume, Flame, Human detection can be seen that the presence of the sensing value is reflected in the combination.

Thereafter, by analyzing the data for the combined disaster prediction to predict the disaster of fire, explosion, suffocation in the workspace (S450). Analysis of data for such a disaster prediction may be performed using the values of the table.

In addition, various methods may be applied to the data analysis method for the disaster prediction, for example, the heuristic algorithm method disclosed in Patent Application No. 10-2007-0140529 by the present applicant may be used. Such a heuristic algorithm can improve the reliability of disaster prediction through the normalization process and weighting for each sensing element, and has the advantage of enabling more accurate notification of prediction results and prompt response accordingly.

Of course, in addition to the heuristic algorithm described above, the situation analysis pattern can be applied. Such a pattern may be registered in advance in the DB unit 240 for the disaster analysis.

7 shows an example of a screen of pattern registration to be used for disaster analysis. FIG. 7 shows an example in which pattern 1238 is registered for a specific workspace 10 (NO1 tank; tank 1) in a ship. In addition, the list of the composite sensor devices 100 set as the spatial analysis target group in the tank No. 1 is displayed together as a unique number (01902,01903,01904,01905,01906).

FIG. 8 is information of the pattern 1238 applied to FIG. 7. Summarizing this pattern, as a result of analyzing the data generated by combining through the step S440, the oxygen concentration is 165% or less, the temperature is 250 ℃ or more, the illuminance is 1000 lx or more, infrared, smoke, flame, GAS If all are detected for more than 0 seconds, it predicts suffocation as the type of disaster and issues a warning level for this suffocation. Application of this situation analysis pattern is disclosed in more detail in patent application No. 10-2008-0040580 by the applicant.

As described above, after analyzing the disaster through the heuristic algorithm or the pattern analysis, the result of the disaster prediction for the workspace 10 is notified. The notification is transmitted to the portable terminal 300, the administrator terminal 400, the composite sensor device 100, the situation notification device 600 in the form of, for example, a text message, an e-mail, a warning alarm, or a vibration. . This enables quick response in the event of a disaster and reduces the risk of human and property damage.

Although the present invention has been described with reference to the embodiments shown in the drawings, these are merely exemplary and those skilled in the art will understand that various modifications and equivalent other embodiments are possible. Therefore, the true technical protection scope of the present invention will be defined by the technical spirit of the appended claims.

1 illustrates an example in which multiple sensor devices are disposed in each workspace of an industrial site for predicting an industrial safety accident according to an embodiment of the present invention.

2 is a configuration example of a composite sensor device according to an embodiment of the present invention.

3 is an example of installation of a composite sensor device.

4 is a flowchart of a method for predicting industrial accidents in a manufacturing industry using spatial analysis according to an exemplary embodiment of the present invention.

5 is a schematic diagram of a system for the method of FIG.

6 is an example of displaying the position of the complex sensor devices arranged in the workspace according to an embodiment of the present invention on the screen.

7 shows an example of pattern registration to be used for disaster analysis according to an embodiment of the present invention.

8 illustrates information of the pattern applied to FIG. 7.

BRIEF DESCRIPTION OF THE DRAWINGS FIG.

10: Work space 20: General control room

100: composite sensor device 200: total control means

210: setting unit 220: receiving unit

230: combination unit 240: DB unit

250: prediction unit 260: transmission unit

270: display unit

Claims (12)

Spatial analysis of the workspace by selecting a list of complex sensor devices to be applied to spatial analysis from a list of complex sensor devices installed at different locations within one workspace to sense detection elements corresponding to each installation location Setting to a target group; Receiving respective sensing information from complex sensor devices in the workspace; And And extracting sensing information of complex sensor devices corresponding to the spatial analysis target group among the complex sensor devices and combining them with each other to generate data for disaster prediction in the workspace. Setting the spatial analysis target group, Providing a graphic display of the form of the workspace, an individual location of all the composite sensor devices installed in the workspace, and a list of the installed multiple sensor devices; And And selecting a list of complex sensor devices to be applied to spatial analysis from the list of complex sensor devices and setting the group as a spatial analysis target group for the workspace. The composite sensor device, It is driven by its own battery unit, and as the detection element, so that the corresponding sensors for detecting a plurality of selected elements of oxygen, temperature, humidity, illuminance, VOC, infrared, smoke, flame, human body, gas, respectively detachable, Each of the sockets corresponding to the corresponding sensors, A method for predicting industrial safety accidents in a manufacturing industry using spatial analysis in which only some sensors of the corresponding sensors are mounted in corresponding sockets so as to sense only a detection element corresponding to each installation position. delete The method according to claim 1, The composite sensor device, Prediction of industrial safety accidents in manufacturing industry using spatial analysis in which the types of sensors to be mounted are determined according to the sensing angle, sensing distance, intermediate obstacle, or characteristics of the detection element determined according to each installation position in the workspace. Way. The method according to claim 1, The method may further include storing the sensing information transmitted from the complex sensor devices in a predetermined period in a DB for each complex sensor device. Generating data for the disaster prediction, A method for predicting occupational safety and disasters in a manufacturing industry using spatial analysis of extracting recent sensing information of each complex sensor device corresponding to the spatial analysis target group from the DB and combining them with each other. The method according to claim 4, The latest sensing information, A method for predicting industrial safety accidents in a manufacturing industry using spatial analysis including all recent sensing information collected in a period within a set time from the present among the sensing information transmitted in a set period from the complex sensor devices. The method according to claim 4, Predicting disasters related to fire, explosion and suffocation in the workspace by analyzing data for predicting the disasters; And The industrial safety accident prediction method of the manufacturing industry using the spatial analysis further comprising the step of notifying the results of the disaster prediction for the workspace. The method according to claim 5 or 6, Combining the sensing information to generate data for disaster prediction, Among the detection elements collected from the composite sensor devices corresponding to the spatial analysis target group, the detection elements related to oxygen, temperature, humidity, illuminance, VOC, and gas detection use the maximum or minimum information of the corresponding sensing information. The detection element relating to the detection of infrared rays, smoke, flames, human body is a method for predicting industrial safety accidents in the manufacturing industry using spatial analysis using the sensing value of the corresponding sensing information. delete The list of composite sensor devices installed at different locations in one workspace to sense detection elements corresponding to each installation location, the form of the workspace, and the individual locations of all composite sensor devices installed in the workspace. A display unit to display on a graphic; A setting unit configured to select a list of the composite sensor devices to be applied to the spatial analysis from the displayed list of the composite sensor devices and to set the spatial analysis target group for the workspace; Receiving unit for receiving each sensing information from the composite sensor devices in the workspace; And A combination unit for extracting sensing information of complex sensor devices corresponding to the spatial analysis target group among the complex sensor devices and combining them to generate data for disaster prediction in the workspace; The composite sensor device, It is driven by its own battery unit, and as the detection element, so that the corresponding sensors for detecting a plurality of selected elements of oxygen, temperature, humidity, illuminance, VOC, infrared, smoke, flame, human body, gas, respectively detachable, Each of the sockets corresponding to the corresponding sensors, Industrial safety accident prediction system using a spatial analysis in which only some of the sensors are mounted in the socket so as to sense only the detection element corresponding to each installation position. delete The method according to claim 9, The composite sensor device, Prediction of industrial safety accidents in manufacturing industry using spatial analysis in which the types of sensors to be mounted are determined according to the sensing angle, sensing distance, intermediate obstacle, or characteristics of the detection element determined according to each installation position in the workspace. system. The method of claim 11, The combination part, Among the detection elements collected from the composite sensor devices corresponding to the spatial analysis target group, the detection elements related to oxygen, temperature, humidity, illuminance, VOC, and gas detection may be performed using the maximum or minimum information of the corresponding sensing information. Generate data for disaster prediction, The detection element relating to the detection of infrared rays, smoke, flames, human body is an industrial safety accident prediction system of a manufacturing industry using spatial analysis for generating data for predicting the disaster by using the sensing value of the sensing information.

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