Intelligent factory management system based on Internet of Things

Abstract

The invention discloses an intelligent factory management system based on the Internet of Things. An operation method of the system comprises the following steps: step 1, acquiring equipment and environment data in a dangerous area by installing a sensor; step 2, analyzing real-time data of the sensor to realize risk assessment of the dangerous area; 3, establishing a risk level for the dangerous area of the chemical plant according to a risk matrix analysis result; 4, measures are adjusted or improved by evaluating the current risk condition, and the sensor network establishing and data collecting module is used for monitoring the dangerous area of the chemical plant in real time and establishing a reliable environment monitoring network by installing a sensor and collecting data; the risk assessment and matrix analysis module is used for analyzing and associating real-time data of a sensor and deeply assessing the risk level of a dangerous area by establishing a risk matrix; the method has the characteristic of comprehensive and accurate risk assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of the Internet of Things, and specifically to an intelligent factory management system based on the Internet of Things. Background Art

[0002] With the rapid development of science and technology, intelligentization and customization have become the key development directions of Chinese manufacturing enterprises. Especially in fields such as the chemical industry, the intelligent and safe management of factories has become crucial. Advanced automated and intelligent production equipment has become the infrastructure of factories, but relying solely on these devices still cannot meet the current requirements for the safety and efficiency of the production process. Against this background, the rapid development of sensor technology and the Internet of Things technology has brought new possibilities to factory management.

[0003] By widely installing Internet of Things sensors in hazardous areas, factories can monitor various key parameters in real time, such as temperature, pressure, chemical substance concentration, etc. This not only provides more comprehensive real-time data on the production process but also lays the foundation for establishing a comprehensive risk control system. However, despite the fact that sensors provide more data, there are still certain limitations in the existing technology for risk control. The current safety management systems often have difficulty fully considering the complex relationships between multiple parameters, resulting in an incomplete risk assessment. Traditional risk assessment methods are difficult to adapt to the processing of multi-dimensional data and lack comprehensiveness and flexibility. In addition, the real-time monitoring and precise control of hazardous areas are still a challenging task, which may lead to the delayed discovery and handling of potential risks. Therefore, it is necessary to design an intelligent factory management system based on the Internet of Things with a comprehensive and accurate risk assessment. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent factory management system based on the Internet of Things to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solution: An intelligent factory management system based on the Internet of Things, and the operation method of this system includes the following steps:

[0006] Step 1: Obtain device and environmental data in the hazardous area through installing sensors;

[0007] Step 2: Achieve the risk assessment of the hazardous area by analyzing the real-time data of the sensors;

[0008] Step 3: Establish a risk level for the hazardous area of the chemical plant according to the result of the risk matrix analysis;

[0009] Step 4: Adjust or improve the measures by evaluating the current risk situation.

[0010] According to the above technical solution, the step of obtaining device and environmental data in the hazardous area by installing sensors includes:

[0011] The sensors are installed in the potentially hazardous areas of the chemical plant. Temperature sensors are installed around chemical reaction equipment, at pipeline connection points, and in storage areas to monitor temperature changes. At the same time, pressure sensors are installed on high-pressure systems or other equipment that requires pressure monitoring, and corresponding air detection sensors are installed to detect the concentration of harmful substances in the air. These sensors achieve real-time data collection through Internet of Things technology and are connected to the central data collection unit through the network. The sensor network adopts an adaptive data collection frequency, which is dynamically adjusted according to the monitored environmental changes to minimize energy consumption while maintaining continuous monitoring of dangerous parameters. In the system, the central data collection unit is responsible for coordinating the work of each sensor and transmitting the collected data to the system. To improve the stability and scalability of the system, the central data collection unit adopts a redundant design to ensure that the system can still operate normally even if some sensors fail.

[0012] According to the above technical solution, the step of realizing risk assessment of the hazardous area by analyzing the real-time data of the sensors includes:

[0013] The system obtains multi-dimensional data from each sensor, including temperature, pressure, and chemical substance concentration parameters. These data form a multi-dimensional data matrix, denoted as D ij , where i represents the sample index and j represents the parameter index. For parameters with different dimensions, the system uses standardization or normalization methods through the formula: to ensure that each parameter has the same weight in the analysis. Among them, Z ij is the standardized data, μ j and δ j are the mean and standard deviation of the jth parameter respectively. The system uses principal component analysis or factor analysis to identify the main patterns and correlations in the data. These algorithms can automatically extract the main interactions between parameters and represent them as new, uncorrelated dimensions. The system establishes a correlation matrix C based on the results of multi-dimensional data analysis. The element C kl represents the degree of correlation between the Kth parameter and the Lth parameter. Through the formula The numerator part of the formula represents the covariance between the two, and the denominator represents the variance between the two. Subsequently, the correlation matrix C is transformed into a risk matrix R, and the system can clearly show the mutual relationship between each parameter. This process can be carried out using the following formula: where r klRepresents an element in the risk matrix, with a value range between [0, 100], indicating the risk relationship between parameters K and L. By establishing the risk matrix, the system can more comprehensively understand the interactions between various parameters.

[0014] According to the above technical solution, the steps of establishing the risk level for the hazardous area of the chemical plant based on the result of the risk matrix analysis include:

[0015] Based on the result of the risk matrix analysis, establish a risk level system;

[0016] Set numerical thresholds or specific parameter combinations, and preset emergency response plans for different levels.

[0017] According to the above technical solution, the steps of establishing a risk level system based on the result of the risk matrix analysis include:

[0018] Conduct risk level classification, and set numerical thresholds or specific parameter combinations to divide risks into different levels. Then, the system uses the result of the previously conducted risk matrix analysis to obtain the corresponding risk matrix R, which contains the correlations between different parameters. For each cell r kl , the system compares it with the pre-set threshold. When r kl exceeds or reaches the threshold, the corresponding area is classified into the corresponding risk level. The specific implementation process is as follows: The data collected by sensors from the chemical hazardous area, which includes temperature, pressure, and chemical substance concentration data. The system conducts risk level classification based on the interactions between these three parameters. First, it is divided into three risk levels: low risk, medium risk, and high risk. Then, the risk threshold scores are divided: 0 - 30 is low risk, 31 - 60 is medium risk, and 61 - 100 is high risk.

[0019] According to the above technical solution, the steps of setting numerical thresholds or specific parameter combinations and presetting emergency response plans for different levels include:

[0020] Each cell r in the risk matrix kl has a value range from 0 to 100, where 100 represents the highest risk and 0 represents the lowest risk. For each r kl , it is compared with the set threshold. When r kl is less than or equal to 30, the area is classified as low risk. The area is in a relatively safe state and requires routine monitoring and maintenance. When r kl is greater than 30 and less than or equal to 60, it is classified as medium risk, indicating that there are some potential risks and requires enhanced monitoring and taking some preventive measures. When r klIf it is greater than 60, it is classified as high risk, indicating a serious risk and the need to immediately take emergency measures, such as halting work or taking other strong measures. In addition, the system has pre-set emergency response plans for each risk level, including an automated alarm system, an emergency notification procedure, and the formation of an emergency response team, to ensure that appropriate measures can be taken quickly and effectively when corresponding risks occur.

[0021] According to the above technical solution, the step of adjusting or improving measures by evaluating the current risk situation includes:

[0022] By collecting feedback and data, deeply understand the contribution of these measures to risk reduction, and determine whether existing methods need to be adjusted or improved. As technology, equipment, and the business environment continue to change, new potential risks will emerge, so attention is also paid to the identification of new risk factors. In addition, according to the results of regular reviews, the risk management plan will be adjusted and improved, including updating the risk matrix, modifying the risk level classification criteria, and optimizing the monitoring system, to ensure that the risk management strategy is consistent with the actual situation. At the same time, by recording all review and improvement activities and generating regular reports, it helps to comprehensively report the risk management situation, improvement measures, and future plans to management and stakeholders, ensuring the continuous and healthy operation of the entire risk management system.

[0023] According to the above technical solution, the system includes:

[0024] A sensor network establishment and data collection module, used to monitor dangerous areas of the chemical plant in real time, and establish a reliable environmental monitoring network by installing sensors and collecting data;

[0025] A risk assessment and matrix analysis module, used to analyze and correlate real-time sensor data, and deeply evaluate the risk level of dangerous areas by establishing a risk matrix;

[0026] A risk management and continuous improvement module, used to formulate risk level standards, implement safety measures, and conduct regular reviews and improvements to ensure that the risk management strategy remains effective and adapts to the changing plant environment.

[0027] According to the above technical solution, the sensor network establishment and data collection module includes:

[0028] A sensor installation module, used to install Internet of Things sensors in dangerous areas of the chemical plant to achieve comprehensive monitoring of key parameters;

[0029] A real-time data collection module, used to collect sensor data in real time through Internet of Things technology and dynamically adjust the collection frequency to minimize energy consumption;

[0030] Data transmission and redundancy design module, which is used for the central data acquisition unit to coordinate the work of sensors, and adopts redundancy design to ensure the stability and scalability of the system.

[0031] According to the above technical solution, the risk assessment and matrix analysis module includes:

[0032] Real-time data analysis module, which is used to receive sensor data, extract the main interactions through algorithms, and form a multi-dimensional data matrix;

[0033] Correlation matrix establishment module, which is used to establish a correlation matrix, convert it into a risk matrix, and clearly show the mutual relationship between parameters;

[0034] Risk level establishment module, which is used to divide the risk levels of dangerous areas and provide a basis for subsequent safety measures and response plans.

[0035] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: Firstly, by installing Internet of Things sensors in the dangerous areas of chemical plants, the real-time monitoring of key parameters is realized. Then, risk matrix analysis is carried out on the sensor data by using algorithms such as principal component analysis to establish the correlation between parameters. Subsequently, according to the analysis results, the risk levels of dangerous areas are established, and corresponding management strategies are provided for different risks. Finally, in step four, through regular review and improvement, the system continuously adjusts measures to ensure the effectiveness and adaptability of the risk management strategy. The whole process constructs a comprehensive and real-time sensor network, providing reliable data support for the risk analysis and management of chemical plants. Description of the Drawings

[0036] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation to the present invention. In the drawings:

[0037] Figure 1 It is a flowchart of an Internet of Things-based intelligent factory management method provided by Embodiment 1 of the present invention;

[0038] Figure 2 It is a schematic diagram of the module composition of an Internet of Things-based intelligent factory management system provided by Embodiment 2 of the present invention. Detailed Embodiments

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0040] Example 1: Figure 1 The figure shows a flowchart of intelligent factory management and distribution based on the Internet of Things provided by the first embodiment of the present invention. This embodiment can be applied to the scenario of chemical plant risk analysis. This method can be executed by an intelligent factory management system based on the Internet of Things provided by this embodiment, as Figure 1 shown. The method specifically includes the following steps:

[0041] Step 1: Obtain device and environmental data in the hazardous area by installing sensors;

[0042] In the embodiment of the present invention, Internet of Things sensors are installed in the hazardous areas of chemical plants. These sensors cover various important parameters, such as temperature, pressure, and chemical substance concentration;

[0043] Exemplarily, sensors are installed in potential hazardous areas of chemical plants. Temperature sensors are installed at key positions such as around chemical reaction equipment, pipe connection points, and storage areas to monitor temperature changes. At the same time, pressure sensors are installed on high-pressure systems or other equipment that needs to monitor pressure, and corresponding air detection sensors are installed to detect the concentration of harmful substances in the air. These sensors achieve real-time data collection through Internet of Things technology and are connected to the central data collection unit through the network. The sensor network adopts an adaptive data collection frequency, which is dynamically adjusted according to the monitored environmental changes to minimize energy consumption while maintaining continuous monitoring of dangerous parameters. In the system, the central data collection unit is responsible for coordinating the work of each sensor and transmitting the collected data to the system. To improve the stability and scalability of the system, the central data collection unit adopts a redundant design to ensure that the system can still operate normally even if some sensors fail. Through this step, a chemical plant can establish a comprehensive and real-time monitoring sensor network, providing reliable data support for subsequent risk analysis and management.

[0044] Step 2: Achieve risk assessment of the hazardous area by analyzing the real-time data of the sensors;

[0045] In the embodiment of the present invention, the system receives real-time data from the sensors and conducts a risk matrix analysis to achieve a comprehensive assessment of the risks in the hazardous area;

[0046] Exemplarily, the system obtains multi-dimensional data from each sensor, including key parameters such as temperature, pressure, and chemical substance concentration. These data form a multi-dimensional data matrix, denoted as D ij , where i represents the sample index and j represents the parameter index. For parameters with different dimensions, the system uses standardization or normalization methods through the formula: to ensure that each parameter has the same weight in the analysis. Among them, Z ij is the standardized data, μj and δ j are the mean and standard deviation of the j-th parameter respectively. The system uses principal component analysis or factor analysis to identify the main patterns and correlations in the data. These algorithms can automatically extract the main interactions between parameters and represent them as new, uncorrelated dimensions. The system establishes a correlation matrix C based on the results of the multi-dimensional data analysis, where the element C kl represents the degree of correlation between the K-th parameter and the L-th parameter. Through the formula The numerator part of the formula represents the covariance between the two, and the denominator represents the variance between the two. Subsequently, the correlation matrix C is transformed into a risk matrix R, and the system can clearly show the mutual relationship between each parameter. This process can be carried out using the following formula: where r kl represents the element in the risk matrix, and its value range is between [0, 100], indicating the risk relationship between parameters K and L. By establishing the risk matrix, the system can more comprehensively understand the mutual interactions between each parameter, providing a more accurate and reliable data basis for subsequent risk level establishment and risk control.

[0047] Step 3: According to the results of the risk matrix analysis, establish a risk level for the hazardous areas of the chemical plant;

[0048] In the embodiment of the present invention, based on the results of the risk matrix analysis, a risk level for the hazardous areas of the chemical plant is established, which is divided into low risk, medium risk, and high risk levels. Different risk levels correspond to different safety measures and emergency response plans;

[0049] Exemplarily, first, the risk levels are divided, and numerical thresholds or specific parameter combinations are set to divide the risks into different levels. Then, the system uses the results of the previously performed risk matrix analysis to obtain the corresponding risk matrix R, which contains the correlations between different parameters. For each cell r kl , the system compares it with the pre-set threshold. If r kl exceeds or reaches the threshold, the corresponding area is classified into the corresponding risk level. The specific implementation process is as follows: The data collected by the sensors from the chemical hazardous areas, which includes temperature, pressure, and chemical substance concentration data. The system classifies the risk levels according to the mutual interactions between these three parameters. First, it is divided into three risk levels: low risk, medium risk, and high risk. Then, the risk threshold scores are divided: 0 - 30 is low risk, 31 - 60 is medium risk, and 61 - 100 is high risk;

[0050] Exemplarily, the value range of each cell r kl in the risk matrix is from 0 to 100, where 100 represents the highest risk and 0 represents the lowest risk. For each rkl , compare it with a set threshold. When r kl is less than or equal to 30, the area is classified as low risk, and the area is in a relatively safe state, requiring routine monitoring and maintenance. When r kl is greater than 30 and less than or equal to 60, it is classified as medium risk, indicating that there are some potential risks, and enhanced monitoring and some preventive measures are required. When r kl is greater than 60, it is classified as high risk, indicating that there are serious risks, and immediate emergency measures need to be taken, and it may be necessary to stop work or take other strong measures. In addition, the system also pre-sets emergency response plans for each risk level, including an automated alarm system, an emergency notification procedure, and the formation of an emergency response team, to ensure that appropriate measures can be taken quickly and effectively when corresponding risks occur. Through this step, the system can not only clarify the distribution of different risk levels in the dangerous area, but also provide a set of targeted safety management strategies and emergency measures for the factory, ensuring that the factory can respond quickly and effectively in different risk situations.

[0051] Step Four: Based on the assessment of the current risk situation, make adjustments or improvements to the measures.

[0052] In the embodiment of the present invention, regular risk reviews are carried out, including safety inspections, risk assessments, and data analysis, aiming to evaluate the current risk situation to verify whether the previously taken measures can still effectively reduce the risk level;

[0053] Exemplarily, by collecting feedback and data, deeply understand the contribution of these measures to reducing risks, and determine whether it is necessary to adjust or improve the existing methods. With the continuous changes in technology, equipment, and business environment, new potential risks may emerge, so attention is also paid to the identification of new risk factors. In addition, according to the results of regular reviews, the risk management plan will be adjusted and improved, including updating the risk matrix, modifying the risk level classification criteria, optimizing the monitoring system, etc., to ensure that the risk management strategy is consistent with the actual situation. At the same time, by recording all review and improvement activities and generating regular reports, it helps to comprehensively report the risk management status, improvement measures, and future plans to management and stakeholders, ensuring the continuous and healthy operation of the entire risk management system.

[0054] Embodiment Two: Embodiment Two of the present invention provides an Internet of Things-based intelligent factory management system. Figure 2 is a schematic diagram of the module composition of an Internet of Things-based intelligent factory management system provided by Embodiment Two of the present invention, as Figure 2 shown. The system includes:

[0055] Sensor network establishment and data acquisition module, used for real-time monitoring of hazardous areas in chemical plants, to establish a reliable environmental monitoring network by installing sensors and collecting data;

[0056] Risk assessment and matrix analysis module, used for analyzing and correlating real-time sensor data, to deeply evaluate the risk level of hazardous areas by establishing a risk matrix;

[0057] Risk management and continuous improvement module, used for formulating risk level criteria, implementing safety measures, and regularly reviewing and improving to ensure that the risk management strategy remains effective and adapts to the changing plant environment;

[0058] In some embodiments of the present invention, the sensor network establishment and data acquisition module includes:

[0059] Sensor installation module, used for installing Internet of Things sensors in hazardous areas of chemical plants to comprehensively monitor key parameters;

[0060] Real-time data acquisition module, used for real-time collecting sensor data through Internet of Things technology and dynamically adjusting the acquisition frequency to minimize energy consumption to the greatest extent;

[0061] Data transmission and redundancy design module, used for the central data acquisition unit to coordinate the work of sensors, and adopting redundancy design to ensure system stability and scalability;

[0062] In some embodiments of the present invention, the risk assessment and matrix analysis module includes:

[0063] Real-time data analysis module, used for receiving sensor data, extracting main interactions through algorithms to form a multi-dimensional data matrix;

[0064] Correlation matrix establishment module, used for establishing a correlation matrix and converting it into a risk matrix to clearly show the mutual relationship between parameters;

[0065] Risk level establishment module, used for dividing the risk level of hazardous areas, providing a basis for subsequent safety measures and response plans;

[0066] In some embodiments of the present invention, the risk management and continuous improvement module includes:

[0067] Risk level division standard module, used for setting risk level division standards, dividing levels by threshold comparison, and providing a basis for safety management;

[0068] Measure adjustment and improvement module, used for regularly reviewing risks and adjusting the implemented measures to ensure that the strategy is consistent with the actual situation;

[0069] A regular review and improvement reporting module is used to record review activities, generate reports for reporting to management and stakeholders, and ensure the continuous and healthy operation of the risk management system.

[0070] It should be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0071] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent factory management method based on the Internet of Things, characterized in that: The method includes the following steps: Step 1: Obtain equipment and environmental data in the hazardous area by installing sensors; Step 2: Achieve risk assessment of the hazardous area by analyzing the real-time data of the sensors; Step 3: Establish a risk level for the hazardous area of the chemical plant according to the results of the risk matrix analysis; Step 4: Adjust or improve measures by evaluating the current risk situation; The step of obtaining equipment and environmental data in the hazardous area by installing sensors includes: The sensors are installed in the potential hazardous areas of the chemical plant. Temperature sensors are installed around chemical reaction equipment, pipe connection points, and storage areas to monitor temperature changes. At the same time, pressure sensors are installed on high-pressure systems or other equipment that requires pressure monitoring, and corresponding air detection sensors are installed to detect the concentration of harmful substances in the air. These sensors achieve real-time data collection through Internet of Things technology and are connected to the central data collection unit through a network. The sensor network adopts an adaptive data collection frequency, which is dynamically adjusted according to the monitored environmental changes to minimize energy consumption while maintaining continuous monitoring of dangerous parameters. In the system, the central data collection unit is responsible for coordinating the work of each sensor and transmitting the collected data to the system. The central data collection unit adopts a redundant design to ensure that the system can still operate normally even if some sensors fail; The step of achieving risk assessment of the hazardous area by analyzing the real-time data of the sensors includes: The system obtains multi-dimensional data from various sensors, including temperature, pressure, and chemical substance concentration parameters. These data form a multi-dimensional data matrix, denoted as D ij , where i represents the sample index and j represents the parameter index. For parameters with different dimensions, the system uses standardization or normalization methods through the formula: to ensure that each parameter has the same weight in the analysis. Here, Z ij is the standardized data, μ j and δ j are the mean and standard deviation of the j-th parameter respectively. The system uses principal component analysis or factor analysis to identify the main patterns and correlations in the data. These algorithms can automatically extract the main interactions between parameters and represent them as new, uncorrelated dimensions. The system establishes a correlation matrix C based on the results of multi-dimensional data analysis. The element C kl represents the degree of correlation between the K-th parameter and the L-th parameter through the formula The numerator part of the formula represents the covariance between the two, and the denominator represents the variance between the two. Subsequently, the correlation matrix C is transformed into a risk matrix R. The system can clearly show the mutual relationship between each parameter. This process is carried out using the following formula: where r kl represents the element in the risk matrix, with a value range between [0, 100], indicating the risk relationship between parameters K and L. By establishing the risk matrix, the system can more comprehensively understand the mutual interactions between each parameter; The step of establishing a risk level for the hazardous area of the chemical plant according to the results of the risk matrix analysis includes: Establish a risk level system based on the results of the risk matrix analysis; Set numerical thresholds or specific parameter combinations and preset emergency response plans for different levels; The step of establishing a risk level system based on the results of the risk matrix analysis includes: Perform risk level classification and set numerical thresholds or specific parameter combinations for dividing risks into different levels. Then, the system utilizes the results of the previously conducted risk matrix analysis to obtain the corresponding risk matrix R, which contains the correlations between different parameters. For each cell r kl , the system compares it with the pre-set threshold. When r kl exceeds or reaches the threshold, the corresponding area is classified into the corresponding risk level. The specific implementation process is as follows: The system collects data from chemical hazardous areas by sensors, which includes temperature, pressure, and chemical substance concentration data. The system classifies the risk level based on the interactions between these three parameters. First, it is divided into three risk levels: low risk, medium risk, and high risk. Then, the risk threshold scores are divided as follows: 0 - 30 is low risk, 31 - 60 is medium risk, and 61 - 100 is high risk; The step of setting numerical thresholds or specific parameter combinations and presetting emergency response plans for different levels includes: Each cell r in the risk matrix kl has a value range from 0 to 100, where 100 represents the highest risk and 0 represents the lowest risk. For each r kl , it is compared with a set threshold. When r kl is less than or equal to 30, the area is classified as low risk, and the area is in a relatively safe state, requiring routine monitoring and maintenance. When r kl is greater than 30 and less than or equal to 60, it is classified as medium risk, indicating that there are some potential risks, and enhanced monitoring and some preventive measures are required. When r kl is greater than 60, it is classified as high risk, indicating that there are serious risks, and immediate emergency measures, such as halting work or taking other strong measures, are required. In addition, the system also pre-sets emergency response plans for each risk level, including an automated alarm system, an emergency notification procedure, and the formation of an emergency response team, to ensure that appropriate measures can be taken quickly and effectively when corresponding risks occur.

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