Intelligent risk monitoring system with optimized energy efficiency for high-risk industrial site

Abstract

The present invention relates to an intelligent risk monitoring system with optimized energy efficiency for a high-risk industrial site, the system comprising: a plurality of IoT sensor devices installed at a high-risk industrial site; a gateway for collecting sensor data from the IoT sensor devices and transmitting the sensor data to a management server; and the management server for receiving the sensor data to determine whether a risk has occurred, notifying each IoT sensor device of a risk situation, and performing control to optimize a sensor data collection frequency on the basis of the risk situation, wherein the management server sets adjacent IoT sensor devices as virtual groups on the basis of environmental data including temperature, precipitation, humidity, wind speed, and wind direction of a corresponding day and installation positions of the respective IoT sensor devices, and performs differential management by grading risk levels of the IoT sensor devices for each virtual group.

Description

Intelligent risk monitoring system optimizing energy efficiency in high-risk industrial sites

[0001] The present invention relates to an intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites, and more specifically, to an intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites by utilizing multiple IoT sensor devices installed in high-risk industrial sites to detect the occurrence of risks such as fire, explosion, and gas leak in real time, and optimizing the operation of each sensor based on environmental data and risk levels to minimize energy consumption.

[0002] Currently, there are approximately 200,000 types of chemical substances in circulation worldwide, and about 3,000 new types of chemical substances are developed every year. In Korea, as of 2018, it is estimated that about 30,900 workplaces are handling about 29,000 types of chemical substances.

[0003] From 2016 to 2020, a total of 351 chemical accidents occurred, averaging about 70 per year. By region, the highest number of accidents were recorded in Gyeonggi, Gyeongbuk, Ulsan, Chungnam, and Jeonnam, in that order. In Korea, rapid urbanization, industrialization, and uncontrolled development during the formation of modern cities resulted in industrial complexes and residential areas being located in close proximity without adequate buffer green spaces. The damage caused by the hydrogen fluoride leak at Hub Global Co., Ltd. in Gumi, Gyeongbuk, clearly demonstrated the severity of this problem, which has since been recurring in incidents such as the leaks at SK Materials in Yeongju, Gyeongbuk, and Ire Chemical in Seo-gu, Incheon. In effect, this is a structural issue that affects virtually every part of the country without exception.

[0004] In contrast, Europe has been preventing the recurrence of similar accidents by continuously establishing response systems for chemical accidents through the 'Seveso Directives II and III' following the Seveso chemical accident in Italy in 1976, which include the preparation of process safety reports, land use regulations in surrounding areas, establishment of emergency response plans, activation of risk communication, and accident investigation and sharing of lessons learned.

[0005] As the use of domestic toxic substances and chemicals continues to increase due to the development of national new growth engine industries and technologies such as semiconductors, displays, and solar power, there is a need for an integrated system that can efficiently apply standard operating procedures to the field according to the types of hazardous substances and the forms of leakage, fire, and explosion, and rapidly transmit them to relevant agencies and on-site personnel.

[0006] In addition, in the event of a disaster involving the leakage of hazardous chemicals within an industrial complex, a system is also needed that can account for both the primary accident, which involves direct human and material damage in the affected area at the time of the leak, and the secondary damage to nearby areas that spreads over time.

[0007] As prior art for solving these conventional problems, there is Registered Patent No. 10-2244634, 'IOT-based hazardous chemical leak monitoring system'.

[0008] Registered Patent No. 10-2244634 relates to at least one IoT (Internet of Things) device that is arranged in a plurality of matrix arrays in an area or factory at risk of hazardous chemical leakage, measures the concentration of at least one hazardous chemical in the air through at least one hazardous chemical leak detection sensor, and transmits pre-stored unique identification information along with each measured hazardous chemical concentration data; a data processing device that receives the unique identification information along with each hazardous chemical concentration data transmitted from each IoT device, assigns metadata through a multi-channel encoded at different bit rates according to the type and size of each hazardous chemical concentration data based thereon, converts each hazardous chemical concentration data with assigned metadata into concentration status information data of each hazardous chemical corresponding to a cloud-compatible protocol, and transmits the concentration status information data of each hazardous chemical converted and transmitted from the data processing device, and allows an administrator to visually monitor the leakage status of each hazardous chemical based thereon.

[0009] Registered Patent No. 10-2244634 has a structural limitation in that it cannot quickly and accurately identify the initial location where an accident such as an explosion, fire, or gas leak occurred, as well as the path along which the fire, smoke, or gas travels.

[0010] Therefore, the applicant filed and obtained a patent for a 'risk monitoring system that rapidly detects the location and direction of progression of hazards occurring in high-risk industrial sites' (Registered Patent No. 10-2552726).

[0011] Registered Patent No. 10-2552726 has the advantage of easily identifying the occurrence of risks and the movement paths of risks at industrial sites by having multiple IoT sensor devices collect sensor data and transmit it to a gateway in areas where IoT sensor devices are intensively installed, and the gateway then transmits the collected sensor data to a management server.

[0012] However, registered patent No. 10-2552726 has a problem in which each sensor operates continuously, resulting in excessive energy consumption, so optimization is required in terms of energy efficiency.

[0013] Therefore, the present invention aims to overcome the limitations of the prior art and provide a system capable of minimizing unnecessary energy consumption by optimizing the energy efficiency of each sensor while monitoring the occurrence of risks in real time. This enhances safety in high-risk industrial sites and promotes the efficient utilization of energy resources.

[0014] This invention was developed to improve upon the aforementioned problems and aims to provide an intelligent risk monitoring system that detects various risks, such as fire, explosion, and gas leaks that may occur in high-risk industrial sites in real time, and maximizes energy efficiency by optimizing the operation of each sensor. Specifically, it has the following objectives.

[0015] Rapid detection and response to hazardous situations: Installed IoT sensors detect hazardous situations in real time and transmit the data to a management server to quickly determine the level of risk, thereby preventing the spread of accidents.

[0016] Energy Efficiency Optimization: The management server analyzes data collected from each IoT sensor device to assess risk, and accordingly optimizes the operating mode (sleep mode or emergency mode) and data collection frequency of each sensor to minimize unnecessary energy consumption.

[0017] Intelligent Power Management: The management server efficiently manages power consumption by adjusting the operating frequency of sensors by time and gradually switching to sleep mode when risks are resolved. This balances energy usage and maximizes overall energy efficiency.

[0018] Risk Prediction and Grouping Management: By predicting risks based on sensor data and grouping sensors in adjacent locations to manage risk levels at the group level, it strengthens monitoring of high-risk areas and enables energy conservation.

[0019] To achieve the above objectives, the present invention comprises a plurality of IoT sensor devices installed in a high-risk industrial site, a gateway that collects sensor data from the IoT sensor devices and transmits it to a management server, and a management server that receives the sensor data, determines whether a risk has occurred, notifies each IoT sensor device of the risk situation, and controls the frequency of sensor data collection to be optimized based on this. The management server sets IoT sensor devices in adjacent locations into virtual groups based on environmental data such as the day's temperature, precipitation, humidity, wind speed, and wind direction, and the installation location of each IoT sensor device, and classifies the risk level of the IoT sensor devices for each virtual group to manage them differentially.

[0020] The above IoT sensor device includes a low-power mode control unit that switches to sleep mode to minimize energy consumption when the risk level is medium or lower, and an emergency mode switching unit that switches from sleep mode to emergency mode to collect data when the risk level is high, and each IoT sensor device can operate in sleep mode or emergency mode upon receiving a risk level notification from a management server.

[0021] The above gateway may be configured to include a buffer module that temporarily stores sensor data in memory to prevent data loss in the event of a network failure, a data transmission unit that transmits sensor data collected from each IoT sensor device and stored in the buffer module to a management server in real time, a data transmission optimization unit that optimizes the data transmission frequency according to the command of the management server, and a risk data priority transmission unit that transmits the sensor data to the management server first when it is determined that the sensor data collected from the IoT sensor device and stored in the buffer module is at a dangerous level.

[0022] At this time, it is preferable that the buffer module be configured to include a data transmission control unit configured to transmit data stored in memory to a management server at specific intervals or immediately when the network status is restored, and a memory identification transmission management unit that identifies sensor data in memory and transmits it preferentially, which the risk data priority transmission unit determines to be at a dangerous level.

[0023] The above management server may be configured to include a sensor grouping module that predicts dangerous situations and the direction of progression of danger based on environmental data such as temperature, precipitation, humidity, wind speed, and wind direction, as well as the installation location and sensor data of IoT sensor devices, and dynamically groups each IoT sensor device accordingly, and an intelligent sensor priority processing unit that minimizes energy consumption by dynamically adjusting the risk priority of the groups of IoT sensor devices grouped by the sensor grouping module and immediately generates a warning in high-risk situations.

[0024] At this time, the management server may be configured to include a power management stage control unit that operates the sensors in the corresponding area in emergency mode when a dangerous situation is detected in a group of IoT sensor devices grouped by the sensor grouping module, and switches the sensors to sleep mode in stages after the danger is resolved, and a real-time energy control unit that monitors the energy usage of the IoT sensor devices in real time and automatically adjusts it as needed.

[0025] In addition, the management server may be configured to further include a subgroup sensing timing control unit that, when a dangerous situation is detected in a group of IoT sensor devices, divides the IoT sensor devices within the group into multiple subgroups, wherein the IoT sensor devices belonging to each subgroup are divided in a manner that allows them to be widely distributed in the region to which the group belongs, and transmits sensing control information to the IoT sensor devices belonging to each subgroup so that the divided multiple subgroups transmit sensor data sequentially.

[0026] It is desirable that the above-mentioned subgroup sensing timing control unit be composed of IoT sensor devices belonging to each subgroup that are widely distributed in the region to which the group belongs and whose types of sensor data do not overlap.

[0027] According to the present invention with the above configuration, the following effects are achieved.

[0028] Rapid and accurate response to hazardous situations: Since each IoT sensor device detects hazardous situations such as fires, explosions, and gas leaks occurring in high-risk industrial sites in real time and transmits them to a management server, the present invention enables a rapid response to hazardous situations. This allows for quick initial response in the event of an accident, thereby minimizing casualties and property damage.

[0029] Optimization of Energy Consumption: The management server dynamically controls the operating mode of each IoT sensor to reduce unnecessary energy consumption. Sensors are switched to sleep mode to consume minimal energy when the risk is low, and only switch to emergency mode to collect data frequently when the risk is high. This maximizes the overall system energy efficiency and minimizes the power consumption of the sensors.

[0030] Intelligent Group Management and Efficient Risk Monitoring: The management server groups adjacent IoT sensors based on their installation locations. Since grouped sensors are managed differentially according to the risk level of each group, sensors in high-risk areas collect data more frequently. This enables intensive monitoring of risk zones and allows for efficient resource allocation.

[0031] Prevention of data loss during network failures: The buffer module embedded in the gateway allows for the temporary storage of data even during network failures. By transmitting the data to the management server after network recovery, data loss can be prevented. This plays a crucial role in enhancing system reliability and maintaining data consistency.

[0032] Real-time Energy Adjustment and Balanced Power Distribution: The management server monitors the energy usage of sensors in real time and automatically adjusts power consumption when necessary. By adjusting sensor operation frequency based on specific time zones or hazardous situations, it ensures balanced energy usage and prevents sudden spikes in energy consumption. This maximizes overall energy efficiency and contributes to enhancing the reliability of power management.

[0033] Enhancement of Safety in High-Risk Industrial Sites: By rapidly detecting hazardous situations and enabling appropriate warnings and responses, this system significantly improves the safety of workers and facilities in high-risk industrial environments. Through such intelligent systems, unpredictable accidents are prevented in advance, and the stability of industrial sites is enhanced.

[0034] Cost Reduction: Energy costs can be reduced by optimizing energy consumption and decreasing unnecessary sensor operations. Additionally, rapid response in the event of an accident minimizes potential damage, thereby reducing post-accident recovery costs.

[0035] Overall, the present invention has the effect of significantly improving the safety of the working environment and the economic efficiency of operations by rapidly responding to various risks that may occur in high-risk industrial sites and efficiently managing energy.

[0036] Figure 1 is a diagram illustrating the configuration of an intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites according to the present invention.

[0037] Figure 2 is a photograph of an IoT sensor device installed at the site used in the present invention.

[0038] FIG. 3 is a functional block diagram of an IoT sensor device, a gateway, and a management server constituting an intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites according to the present invention.

[0039] Figure 4 is an example in which the small group sensing timing control unit of the intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites according to the present invention divides IoT sensor devices belonging to virtual group 1 into multiple small groups.

[0040] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings.

[0041] However, the present invention is not limited to the embodiments disclosed below but will be implemented in various different forms.

[0042] The embodiments described in this specification are provided to ensure that the disclosure of the invention is complete and to fully inform those skilled in the art of the scope of the invention.

[0043] And the present invention is defined only by the scope of the claims.

[0044] Accordingly, in some embodiments, well-known components, well-known operations, and well-known techniques are not specifically described to avoid the invention being interpreted ambiguously.

[0045] Additionally, throughout the specification, the same reference numerals refer to the same components, and the terms used (mentioned) in this specification are for describing embodiments and are not intended to limit the invention.

[0046] In this specification, the singular form includes the plural form unless specifically stated otherwise in the text, and components and operations referred to as 'comprising (or comprising)' do not exclude the presence or addition of one or more other components and operations.

[0047] Unless otherwise defined, all terms used in this specification (including technical and scientific terms) may be used in a meaning that is commonly understood by those skilled in the art to which the present invention belongs.

[0048] Also, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless otherwise defined.

[0049] Hereinafter, preferred embodiments of the present invention will be described with reference to the attached drawings.

[0050] Referring to FIGS. 1 to 4, an intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites is configured to include IoT sensor devices (101 to 110), gateways (201 to 203), and a management server (300).

[0051] Multiple IoT sensor devices (101-110) are installed at high-risk industrial sites. The IoT sensor devices (101-110) include various sensor devices such as hazardous gas sensors, fire sensors, and precipitation sensors. The IoT sensor devices (101-110) collect hazardous gas measurement data, fire smoke measurement data, precipitation data, etc., as sensor data and transmit them to gateways (201-203).

[0052] The gateway (201~203) groups IoT sensor devices (101~110) that transmit sensor data to it, receives sensor data from IoT sensor devices (101~110) belonging to the group, and transmits it to the management server (300).

[0053] In FIG. 1, four IoT sensor devices (101 to 104) are shown transmitting sensor data to the gateway (201), but this is not necessarily limited thereto. Three IoT sensor devices (105 to 107) transmitting sensor data to the gateway (202) and three IoT sensor devices (108 to 110) transmitting sensor data to the gateway (203) are also given as examples and are not necessarily limited thereto.

[0054] Referring to FIG. 1, each gateway (201, 202, 203) groups the IoT sensor devices (101 to 110) that transmit sensor data to it, respectively, and this is referred to as a physical group.

[0055] When the management server (300) receives sensor data, it checks whether the standard value for each type of sensor data is exceeded to determine whether a risk has occurred at the industrial site, notifies each IoT sensor device (101~110) at the industrial site where the risk has occurred of the risk situation, and controls the frequency of sensor data collection to be optimized based on this.

[0056] The management server (300) sets IoT sensor devices (101~110) in adjacent locations into virtual groups based on environmental data of the day's temperature, precipitation, humidity, wind speed, and wind direction, and the installation location of each IoT sensor device (101~110), and classifies the risk level of the IoT sensor device (100) for each virtual group and manages them differentially. It performs a control function that maximizes energy efficiency by optimizing the frequency of data collected by each sensor.

[0057] Even if the physical groups are the same, the virtual groups can be different, and even if the physical groups are different, the virtual groups can be the same.

[0058] The management server (300) performs a control function that optimizes the frequency of sensor data collected by each IoT sensor device (101~110) and maximizes energy efficiency by differentially managing the risk level of the IoT sensor device (100) by virtual group.

[0059] Environmental data such as temperature, precipitation, humidity, wind speed, and wind direction are not collected by IoT sensor devices (101–110), but by the Korea Meteorological Administration or official meteorological agencies. This environmental data is provided by the Korea Meteorological Administration and is collected via the agency's website or API.

[0060] For example, if the management server (300) determines that the environmental data indicates a high temperature, low humidity, a wind speed of 5 m / s or higher, and a southwest wind direction, and if a fire and gas leak are detected in the sensor data collected in area A, the management server (300) does not set all sensors adjacent to area A into a single virtual group, but rather sets the sensors located in the southwest direction adjacent to area A, considering the direction of the wind, into a virtual group. In this case, if the wind speed is fast, the range of sensors included in the virtual group is further expanded.

[0061] Sensors within this virtual group are configured to collect data more frequently, at 30-second intervals. On the other hand, sensors in area B, which is adjacent to area A but has a medium risk level considering wind direction and speed, are configured to collect data at 10-minute intervals to reduce energy consumption. Sensors in area C, which has a low risk level, are configured to collect data at 20 to 30-minute intervals to reduce energy consumption.

[0062] The management server (300) sets the sensors located in the southwest direction adjacent to area A, considering the direction of wind movement, as virtual group 1, and sets the risk level of the IoT sensor devices (101~110) belonging to virtual group 1 to grade 1. Then, it transmits the risk level grade 1 to the IoT sensor devices (101~110) belonging to virtual group 1. At this time, when the management server (300) transmits to the gateway (201~203), the gateway (201~203) transmits the risk level grade 1 to the corresponding IoT sensor devices (101~110). The management server (300) sets the sensors in area B, where the risk level is medium, as virtual group 2, sets the risk level of the IoT sensor devices (101~110) belonging to virtual group 2 to grade 2, and transmits the risk level grade 2 to the corresponding IoT sensor devices (101~110) in the same way. Sensors in low-risk area C are set as virtual group 3 and transmit risk level 3 in the same way.

[0063]

[0064] For example, if a fire and gas leak are detected in area A, the wind speed is 7 m / s, and the wind direction is southwest, the IoT sensor devices (101–110) located within a range of 50 to 100 m in the southwest direction are sensors located in a high-risk area. The IoT sensor devices (101–110) in this area are set to the same virtual group and set to risk level 1. Risk level 1 causes the IoT sensor devices (101–110) to collect sensor data at intervals of 20 to 30 seconds and transmit it to the management server (300).

[0065] And the IoT sensor devices (101~110) located within a range of 50 to 100m in the southeast direction of area A are in a medium risk area. That is, fire or gas may change its direction of travel and proceed to the southeast. The IoT sensor devices (101~110) in this area are set as the same virtual group and set as risk level 2. Risk level 2 causes the IoT sensor devices (101~110) to collect sensor data every 10 minutes and transmit it to the management server (300).

[0066] And the IoT sensor devices (101~110) located within a range of 50 to 100m in the northeast direction of area A are in a low-risk area. That is, it is an area where there is no concern about the spread of fire or gas. The IoT sensor devices (101~110) in this area are set as the same virtual group and set as risk level 3. Risk level 3 causes the IoT sensor devices (101~110) to collect sensor data every 20 minutes and transmit it to the management server (300).

[0067] Then, if a fire and gas leak are detected in area A and the wind speed is slow (1 to 3 m / s), the IoT sensor devices (101 to 110) located within a range of 20 to 50 m in the southwest direction of area A are set as the same virtual group and the risk level is set to Grade 1. That is, if the spread of fire and gas is expected to be slow due to the slow wind speed, the area range of Grade 1 risk is set to a smaller size.

[0068] However, if the progression of the risk is rapid, multiple virtual groups 1 with high risk levels can be set.

[0069]

[0070] The IoT sensor device (101~110) is configured to include a low-power mode control unit (120) and an emergency mode switching unit (130).

[0071] The low-power mode control unit (120) switches to sleep mode when the risk level is medium or lower to minimize energy consumption. In the above example, the sensors of virtual group 2 with a medium risk level and the sensors of virtual group 3 with a low risk level are switched to sleep mode to reduce the frequency of collecting sensor data and save power usage.

[0072] This can contribute to reducing overall energy consumption at industrial sites. Sleep mode generally maintains only the sensor's core functions, while minimizing data collection frequency or other auxiliary features.

[0073] The emergency mode switching unit (130) switches from sleep mode to emergency mode when the risk level is high and collects data. In the above example, the sensors of virtual group 1 with a high risk level switch to emergency mode to collect sensor data more frequently and transmit it to the management server (300).

[0074] As described above, each IoT sensor device (101~110) receives a risk level notification from the management server (300) and operates in sleep mode or emergency mode.

[0075] This mode switching mechanism enables IoT sensor devices (101 to 110) to respond appropriately to the situation, thereby optimizing energy consumption while ensuring safety at industrial sites. This is particularly useful in industrial sites with high energy consumption and is a key function that can maximize the efficiency of the entire sensor network.

[0076]

[0077] The gateway (201~203) transmits data collected from each IoT sensor device (101~110) to the management server (300) in real time. The gateway (201~203) is configured to include a buffer module (210), a data transmission unit (220), a data transmission optimization unit (230), and a risk data priority transmission unit (240).

[0078] The buffer module (210) temporarily stores data in memory to prevent data loss in the event of a network failure.

[0079] For example, if the network between the management server (300) and the gateways (201-203) is temporarily unstable, the buffer module (210) temporarily stores the data and then transmits the data to the management server (300) once the network is restored. This is to prevent data loss that may occur due to network failures and to reliably deliver critical risk data to the management server (300).

[0080] The data transmission unit (220) transmits sensor data collected from each IoT sensor device (101~110) to the management server (300) in real time. The gateway (201~203) registers the type and location information of the IoT sensor devices (101~110) that transmit sensor data to it and manages them as groups.

[0081] Gateway 201 registers the types and location information of IoT sensor devices (101–104) that transmit sensor data to it and manages them as groups. Gateway 202 registers the types and location information of IoT sensor devices (105–107) that transmit sensor data to it and manages them as groups. Gateway 203 registers the types and location information of IoT sensor devices (108–110) that transmit sensor data to it and manages them as groups. A group refers to a physical group that actually transmits sensor data to the gateway.

[0082]

[0083] The data transmission optimization unit (230) optimizes the sensor data transmission frequency according to the command of the management server (300). The sensor data transmission frequency is determined according to the risk level transmitted by the management server (300). The data transmission optimization unit (230) transmits the risk level notification received from the management server (300) to the IoT sensor devices (101-110).

[0084] The risk data priority transmission unit (240) prioritizes transmitting the sensor data to the management server (300) when it is determined that the data collected from the IoT sensor devices (101 to 110) and stored in the buffer module (210) is at a dangerous level.

[0085] For example, if a hazardous substance measured by an IoT sensor device (101-110) installed in a specific area of ​​an industrial site exceeds a specific threshold, the risk data priority transmission unit (240) transmits the corresponding sensor data to the management server (300) first, enabling a rapid response. This enhances safety at the site and allows for a quick response to emergency situations.

[0086] That is, when sensor data is received from multiple IoT sensor devices (101 to 110), it is generally common to transmit it to the management server (300) in the order in which it was received. However, if the hazardous substance measured by the IoT sensor devices (101 to 110) installed in a specific area exceeds a specific threshold, the sensor data is transmitted to the management server (300) first, regardless of the order in which the sensor data was received.

[0087] To this end, the gateways (201 to 203) are equipped with edge computing capabilities to go beyond simply relaying sensor data and to be able to analyze sensor data and transmit it preferentially in specific situations.

[0088] Gateways (201–203) perform some processing locally using edge computing functions before transmitting all sensor data to the management server (300). Gateways (201–203) are equipped with the ability to quickly process sensor data collected from IoT sensor devices (101–110) locally to identify high-risk sensor data and transmit it prioritized. This reduces network traffic and minimizes the response time to dangerous situations.

[0089] The gateway (201~203) stores threshold values ​​corresponding to the level of danger for each type of sensor data collected from IoT sensor devices (101~110) in its internal memory through edge computing functions, and determines the risk level of the sensor data based on this.

[0090] Through edge computing capabilities, the gateways (201-203) can classify sensor data and assign transmission priorities according to risk level, so that the risk data priority transmission unit (240) can prioritize transmitting sensor data collected from the corresponding IoT sensor devices (101-110) to the management server (300) when a risk occurs.

[0091]

[0092] The buffer module (210) is configured to include a data transmission control unit (211) and a memory identification transmission management unit (212).

[0093] The data transmission control unit (211) is configured to transmit data stored in memory to the management server (300) at specific intervals, or to transmit it immediately when the network status is restored.

[0094] The memory identification transmission management unit (212) identifies sensor data in memory that the risk data priority transmission unit (240) determines to be at a dangerous level and transmits it preferentially.

[0095]

[0096] The management server (300) is configured to include a sensor grouping module (310) and an intelligent sensor priority processing unit (320).

[0097] The sensor grouping module (310) predicts dangerous situations and the direction of progression of danger based on environmental data such as temperature, precipitation, humidity, wind speed, and wind direction, the installation locations of IoT sensor devices (101-110), and sensor data of IoT sensor devices (101-110), and dynamically groups each IoT sensor device (101-110) accordingly. The grouping of IoT sensor devices (101-110) is dynamically changed according to the environmental data and the direction of progression of danger. Here, the group refers not to a physical group, but to the virtual group described above.

[0098] Above, we examined an example of setting up virtual groups by region and setting risk levels based on environmental data such as temperature, wind direction, and wind speed. Risk levels for each virtual group are set as Grade 1, Grade 2, Grade 3, and, depending on the speed of risk spread, the same grade may be set for different virtual groups that are geographically adjacent.

[0099] The intelligent sensor priority processing unit (320) dynamically adjusts the risk priority of groups of IoT sensor devices (101~110) grouped by the sensor grouping module (310) to minimize energy consumption and immediately generates a warning in high-risk situations.

[0100] Depending on changes in environmental data, the direction of the risk progression may change, and the degree of risk may weaken depending on changes in environmental data. The dynamic groups of the IoT sensor devices (101~110) change depending on the direction of the risk progression and the degree of the risk. When the dynamic groups change, the risk levels of those groups may also change.

[0101] The intelligent sensor priority processing unit (320) can dynamically adjust the risk level of each group of virtual groups of IoT sensor devices (101~110) grouped by the sensor grouping module (310) according to the risk situation to raise or lower the risk level, and release the risk level of virtual groups that have no risk.

[0102]

[0103] The management server (300) is configured to include a power management stage control unit (330), a real-time energy adjustment unit (340), and a small group sensing timing control unit (350).

[0104] The power management stage control unit (330) intensively operates the sensors in the corresponding area when a dangerous situation is detected in the group of IoT sensor devices (101~110) grouped by the sensor grouping module (310), and switches the sensors to sleep mode in stages after the danger is resolved.

[0105] The real-time energy adjustment unit (340) monitors the energy usage of the IoT sensor devices (101 to 110) in real time and automatically adjusts it when necessary.

[0106] When a dangerous situation is detected in a group of IoT sensor devices (101 to 110), the subgroup sensing timing control unit (350) divides the IoT sensor devices (101 to 110) within the group into multiple subgroups, wherein the IoT sensor devices (101 to 110) belonging to each subgroup are divided in a direction that is widely distributed in the region to which the group belongs, and transmits sensing control information to the IoT sensor devices (101 to 110) belonging to each subgroup so that the divided multiple subgroups sequentially transmit sensor data.

[0107] Specifically, the subgroup sensing timing control unit (350) consists of IoT sensor devices (101 to 110) belonging to each subgroup that are widely distributed in the region to which the group belongs and do not have overlapping types of sensor data.

[0108] Referring to Figure 4, when a fire and gas leak are detected in area A and the wind speed is 7 m / s and the wind direction is southwest, sensors located in the southwest direction adjacent to area A, taking into account the direction of the wind, are set as virtual group 1.

[0109] IoT sensor devices (101–110) located within a 100m range in the southwest direction of area A are sensors located in a high-risk area and are set to risk level 1. Risk level 1 requires the IoT sensor devices (101–110) to collect sensor data at intervals of 20 to 30 seconds and transmit it to the management server (300). However, if there are many IoT sensor devices (101–110) corresponding to risk level 1, the power consumption of the IoT sensor devices (101–110) is high, and there is a problem that the gateway (201–203) and the management server (300) are subjected to a significant load in processing the sensor data.

[0110] The present invention solved this problem as follows.

[0111] The small group sensing timing control unit (350) divides the IoT sensor devices (101~110) belonging to virtual group 1 into multiple small groups.

[0112] Referring to FIG. 4, an example is shown in which a virtual group 1 includes three types of IoT sensor devices (101 to 110): a fire detection sensor, an ammonia detection sensor, and a hydrogen fluoride detection sensor. The group is divided into a total of six subgroups, from subgroup 1 to subgroup 6, and the IoT sensor devices belonging to each subgroup are distributed widely across the region to which the virtual group 1 belongs.

[0113] Looking at the three sensors belonging to subgroup 1, the hydrogen fluoride detection sensor is located at the top, the fire detection sensor is in the left area, and the ammonia detection sensor is located in the right area. Looking at the three sensors belonging to subgroup 2, the fire detection sensor is on the right, the ammonia detection sensor is in the left direction from the bottom, and the hydrogen fluoride detection sensor is in the bottom area. In this way, the IoT sensor devices (101~110) belonging to each subgroup are divided in a direction that allows them to be widely distributed within the area to which the group belongs.

[0114] The remaining subgroups are also divided in this manner. However, looking at Subgroups 5 and 6, it can be seen that the three sensors are not widely distributed within the region belonging to the virtual group 1, but are located somewhat in the center. When the sensors belonging to the virtual group 1 are divided into multiple subgroups, only the sensors located in the center remain at the very end; therefore, the subgroups located at the end consist of these sensors.

[0115] In addition, it is important that the IoT sensor devices (101–110) belonging to each subgroup are widely distributed in the region to which the group belongs and consist of IoT sensor devices that do not have overlapping types of sensor data. As shown in Fig. 4, it can be seen that the sensors belonging to each subgroup consist of different types of IoT sensor devices.

[0116] Sensing control information is transmitted to IoT sensor devices (101~110) belonging to each of the six divided subgroups, from subgroup 1 to subgroup 6, so that they sequentially transmit sensor data.

[0117] The IoT sensor devices (101 to 110) belonging to subgroup 1 transmit sensor data, and after 20 to 30 seconds, the IoT sensor devices (101 to 110) belonging to subgroup 2 transmit sensor data, and after 20 to 30 seconds, the IoT sensor devices (101 to 110) belonging to subgroup 3 transmit sensor data, and so on, in a sequential manner. The same applies to the remaining subgroups.

[0118] In this way, instead of a single virtual group in the danger area, multiple subgroups are set up within it, and sensor data can be controlled to be collected sequentially on a subgroup basis. As the virtual group 1 as a whole corresponds to a risk level of 1 and an emergency mode, the management server (300) receives sensor data from the subgroups belonging to virtual group 1 at intervals of 20 to 30 seconds.

[0119] Three sensor data belonging to subgroup 1 are transmitted, then three sensor data belonging to subgroup 2 are transmitted after 20 to 30 seconds, then subgroups 3 through 6 are operated in the same way, and then three sensor data belonging to subgroup 1 are transmitted again.

[0120] By configuring each subgroup to include different types of sensors, power consumption can be minimized while monitoring the risk situation of the entire virtual Group 1 area in real time.

[0121]

[0122] The present invention is not limited to the specific preferred embodiments described above, and it is obvious that anyone with ordinary knowledge in the technical field to which the invention pertains can make various modifications without departing from the essence of the invention as claimed in the claims, and that such modifications fall within the scope of the claims.

[0123]

[0124] 101~110: IoT Sensor Devices

[0125] 120: Low-power mode controller

[0126] 130: Emergency Mode Switching Section

[0127] 201~203: Gateway

[0128] 210: Buffer Module

[0129] 211: Data transmission control unit

[0130] 212: Memory Identification Transfer Management Unit

[0131] 220: Data transmission section

[0132] 230: Data Transmission Optimizer

[0133] 240: Risk Data Priority Transmission Unit

[0134] 300: Management Server

[0135] 310: Sensor grouping module

[0136] 320: Intelligent Sensor Priority Processing Unit

[0137] 330: Power management stage regulator

[0138] 340: Real-time Energy Adjustment Unit

[0139] 350: Subgroup Sensing Timing Control Unit

Claims

1. Multiple IoT sensor devices installed at high-risk industrial sites and; A gateway that collects sensor data from the above-mentioned IoT sensor device and transmits it to a management server; and A management server that receives the sensor data, determines whether a risk has occurred, notifies each IoT sensor device of the risk situation, and controls the sensor data collection frequency to optimize it based on this; An intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites, characterized by the above-mentioned management server setting IoT sensor devices in adjacent locations into virtual groups based on environmental data of the day's temperature, precipitation, humidity, wind speed, and wind direction and the installation location of each IoT sensor device, and classifying the risk level of IoT sensor devices by virtual group for differential management.

2. In Claim 1, The above IoT sensor device is A low-power mode control unit that switches to sleep mode to minimize energy consumption when the risk level is medium or lower; and It includes an emergency mode switching unit that switches from sleep mode to emergency mode to collect data when the risk level is high; An intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites, characterized in that each IoT sensor device receives a risk level notification from a management server and operates in sleep mode or emergency mode.

3. In Claim 1, The above gateway is A buffer module that temporarily stores sensor data in memory to prevent data loss in the event of a network failure; A data transmission unit that transmits sensor data collected from each IoT sensor device and stored in the buffer module to a management server in real time; A data transmission optimization unit that optimizes the data transmission frequency according to the command of the management server; and An intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites, comprising: a risk data priority transmission unit that prioritizes the transmission of sensor data to a management server when sensor data collected from an IoT sensor device and stored in the buffer module is determined to be at a dangerous level.

4. In Claim 3, The above buffer module is A data transmission control unit configured to transmit data stored in memory to a management server at specific intervals or to transmit immediately when the network status is restored; and An intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites, comprising: a memory identification transmission management unit that identifies sensor data determined to be at a dangerous level by the above-mentioned risk data priority transmission unit and transmits it preferentially from memory.

5. In Claim 1, The above management server is A sensor grouping module that predicts dangerous situations and the direction of progression of danger based on environmental data of temperature, precipitation, humidity, wind speed, and wind direction, the installation location of IoT sensor devices, and sensor data, and dynamically groups each IoT sensor device accordingly; and An intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites, comprising: an intelligent sensor priority processing unit that dynamically adjusts the risk priority of groups of IoT sensor devices grouped by the sensor grouping module to minimize energy consumption and immediately generates a warning in high-risk situations.

6. In Claim 5, The above management server is A power management stage control unit that operates the sensors in the corresponding area in emergency mode when a dangerous situation is detected in a group of IoT sensor devices grouped by a sensor grouping module, and switches the sensors to sleep mode in stages after the danger is resolved; and An intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites, comprising a real-time energy adjustment unit that monitors the energy usage of IoT sensor devices in real time and automatically adjusts it when necessary.

7. In Claim 6, The above management server is An intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites, characterized by further including a subgroup sensing timing control unit that, when a dangerous situation is detected in a group of IoT sensor devices, divides the IoT sensor devices within the group into multiple subgroups, divides the IoT sensor devices belonging to each subgroup in a direction that is widely distributed in the region to which the group belongs, and transmits sensing control information to the IoT sensor devices belonging to each subgroup so that the divided multiple subgroups sequentially transmit sensor data.

8. In Claim 7, An intelligent risk monitoring system that optimizes energy efficiency in high-risk industrial sites, characterized in that the above-mentioned subgroup sensing timing control unit comprises IoT sensor devices belonging to each subgroup that are widely distributed in the region to which the group belongs and do not have overlapping types of sensor data.

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