Ship hazardous gas monitoring device, method, equipment and medium

Abstract

The invention discloses a ship hazardous gas monitoring device, method and equipment and a medium, and belongs to the technical field of ship equipment. The device comprises a sampling parameter determination module for determining sampling parameters of cabin gas according to data of a current environment where a ship is located and data of articles loaded in each cabin; the gas sampling module is used for controlling a sampling unit to sample gas of each cabin according to the sampling parameters to obtain gas sample data of each cabin; the abnormal gas detection module is used for carrying out abnormal gas detection on the gas sample data to obtain an abnormal gas detection result; and the alarm information generation module is used for determining that the current abnormal gas exceeds the standard and generating alarm information if the abnormal gas detection result is higher than the abnormal gas preset safety threshold value. According to the scheme, automatic gas detection and alarm can be realized, and the gas detection flexibility and accuracy are improved. And potential dangers can be found in advance, so that proper measures can be taken for prevention and control, and the safety of the ship is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of marine equipment, and specifically relates to a monitoring device, method, equipment and medium for hazardous gases on ships. Background Art

[0002] Ships face various potential dangers at sea, including fire, explosion, and gas leaks. Therefore, to ensure the safety of crew and vessel, ships are now equipped with hazardous gas detection equipment to detect the presence of hazardous gases on board in real time, enabling early detection and appropriate measures to mitigate the risk of accidents.

[0003] In the existing technology, manual operation of hazardous gas detection equipment is usually required to obtain gas samples. Workers can use a sampler to inhale gas from the target area into a sampling bag or gas cylinder, and then send the sample to the laboratory for analysis.

[0004] However, manual sampling requires personnel to physically enter the detection area to collect gas samples, which may expose them to toxic, flammable, or other hazardous gases, posing operational risks. Furthermore, manual sampling requires personnel to enter different locations to collect gas samples, which is time-consuming and inefficient. Furthermore, improper operation can lead to low sampling accuracy. Therefore, automated gas sampling, thereby improving the flexibility, accuracy, and efficiency of hazardous gas monitoring on ships, is an urgent challenge in this field. Summary of the Invention

[0005] The present invention provides a device, method, equipment, and medium for monitoring hazardous gases on ships. These devices aim to address the safety, accuracy, flexibility, and efficiency limitations of manual sampling in existing technologies. This device enables automated gas detection and alarms, improving the flexibility and accuracy of gas detection. Furthermore, it enables early detection of potential hazards, enabling appropriate preventive and control measures to be taken, thereby enhancing ship safety.

[0006] In a first aspect, an embodiment of the present application provides a device for monitoring hazardous gases on a ship, the device comprising:

[0007] The sampling parameter determination module is used to obtain the current environmental data and the data of the loaded items from the real-time status data of the ship or the digital twin model of the ship, and determine the sampling parameters of the cabin gas based on the current environmental data of the ship and the data of the items loaded in each cabin;

[0008] A gas sampling module, configured to control a sampling unit to sample gas in each cabin according to the sampling parameters, and obtain gas sample data in each cabin;

[0009] An abnormal gas detection module is used to perform abnormal gas detection on the gas sample data to obtain abnormal gas detection results;

[0010] The alarm information generation module is used to determine that the current abnormal gas exceeds the standard and generate an alarm message if the abnormal gas detection result is higher than the preset safety threshold of the abnormal gas.

[0011] Furthermore, the device further includes a location information acquisition module, which is configured to:

[0012] Obtain the location information of each cabin in the ship;

[0013] Accordingly, the sampling parameter determination module is used to:

[0014] The sampling parameters of the cabin gas are determined based on the current environmental data of the ship, the data of the items loaded in each cabin, and the position information of each cabin in the ship.

[0015] Furthermore, the device further includes a flow coefficient determination module, which is configured to:

[0016] Determine the flow coefficient of each cabin using the pre-built cabin model and external airflow data;

[0017] Accordingly, the sampling parameter determination module is used to:

[0018] The sampling parameters of the cabin gas are determined based on the current environmental data of the ship, the data of the items loaded in each cabin, and the flow coefficient of each cabin.

[0019] Furthermore, the sampling parameters of the cabin gas include at least one of a sampling period, a sampling position, and a sampling volume in each cabin.

[0020] Furthermore, the device further includes a sampling parameter updating module, wherein the sampling parameter updating module is configured to:

[0021] According to the current abnormal gas and a predetermined sampling parameter association scheme, the sampling parameters of each cabin where the abnormal gas is detected are updated to obtain a sampling parameter update result;

[0022] The gas detection is performed again on each cabin according to the updated result of the sampling parameters.

[0023] Furthermore, the sampling parameter updating module is used to:

[0024] According to the sampling parameter update result, the updated sampling period is used to perform gas detection in each cabin;

[0025] and / or,

[0026] According to the sampling parameter update result, the updated sampling position is used to perform gas detection in each cabin;

[0027] and / or,

[0028] According to the sampling parameter update result, the updated sampling volume is used to perform gas detection on each cabin.

[0029] In a second aspect, an embodiment of the present application provides a method for monitoring hazardous gases on a ship, the method comprising:

[0030] Obtain current environmental data and loaded item data from the ship's real-time status data or the ship's digital twin model, and determine the cabin gas sampling parameters based on the ship's current environmental data and the data on items loaded in each cabin;

[0031] Controlling the sampling unit to sample the gas in each cabin according to the sampling parameters to obtain gas sample data of each cabin;

[0032] Performing abnormal gas detection on the gas sample data to obtain abnormal gas detection results;

[0033] If the abnormal gas detection result is higher than the preset safety threshold of abnormal gas, it is determined that the current abnormal gas exceeds the standard and an alarm message is generated.

[0034] Furthermore, before determining the sampling parameters of the cabin gas based on the current environmental data of the ship and the data of the items loaded in each cabin, the method further includes:

[0035] Obtain the location information of each cabin in the ship;

[0036] Accordingly, based on the current environmental data of the ship and the data of the items loaded in each cabin, the cabin gas sampling parameters are determined, including:

[0037] The sampling parameters of the cabin gas are determined based on the current environmental data of the ship, the data of the items loaded in each cabin, and the position information of each cabin in the ship.

[0038] Furthermore, before determining the sampling parameters of the cabin gas based on the current environmental data of the ship and the data of the items loaded in each cabin, the method further includes:

[0039] Determine the flow coefficient of each cabin using the pre-built cabin model and external airflow data;

[0040] Accordingly, based on the current environmental data of the ship and the data of the items loaded in each cabin, the cabin gas sampling parameters are determined, including:

[0041] The sampling parameters of the cabin gas are determined based on the current environmental data of the ship, the data of the items loaded in each cabin, and the flow coefficient of each cabin.

[0042] Furthermore, the sampling parameters of the cabin gas include at least one of a sampling period, a sampling position, and a sampling volume in each cabin.

[0043] Furthermore, after determining that the current abnormal gas exceeds the standard and generating an alarm message, the method further includes:

[0044] According to the current abnormal gas and a predetermined sampling parameter association scheme, the sampling parameters of each cabin where the abnormal gas is detected are updated to obtain a sampling parameter update result;

[0045] The gas detection is performed again on each cabin according to the updated result of the sampling parameters.

[0046] Furthermore, re-detecting gas in each cabin according to the updated result of the sampling parameters includes:

[0047] According to the sampling parameter update result, the updated sampling period is used to perform gas detection in each cabin;

[0048] and / or,

[0049] According to the sampling parameter update result, the updated sampling position is used to perform gas detection in each cabin;

[0050] and / or,

[0051] According to the sampling parameter update result, the updated sampling volume is used to perform gas detection on each cabin.

[0052] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the second aspect.

[0053] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the second aspect are implemented.

[0054] In a fifth aspect, an embodiment of the present application provides a chip, which includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method described in the second aspect.

[0055] In an embodiment of the present application, a sampling parameter determination module is used to obtain current environmental data and loaded item data from the ship's real-time status data or the ship's digital twin model, and determine the sampling parameters of the cabin gas based on the ship's current environmental data and the item data loaded in each cabin; a gas sampling module is used to control the sampling unit to perform gas sampling in each cabin according to the sampling parameters to obtain gas sample data for each cabin; an abnormal gas detection module is used to perform abnormal gas detection on the gas sample data to obtain abnormal gas detection results; an alarm information generation module is used to determine that the current abnormal gas exceeds the standard and generate an alarm message if the abnormal gas detection result is higher than the preset safety threshold of the abnormal gas. The above-mentioned ship hazardous gas monitoring device can realize automated gas detection and alarm, and improve the flexibility and accuracy of gas detection. At the same time, potential dangers can be discovered in advance so that appropriate measures can be taken for prevention and control, thereby improving the safety of the ship. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a schematic structural diagram of a monitoring device for hazardous gases on board a ship provided in Example 1 of the present application;

[0057] Figure 2 This is a schematic diagram of the structure of a monitoring device for hazardous gases on board a ship provided in Example 2 of the present application;

[0058] Figure 3 This is a flow chart of a method for monitoring hazardous gases on ships provided in Example 3 of the present application;

[0059] Figure 4 This is a schematic diagram of the structure of the electronic device provided in Example 4 of the present application. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. It should also be noted that, for ease of description, only parts related to the present application, not all of the contents, are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0061] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0062] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0063] The following, in conjunction with the accompanying drawings, describes in detail the device, method, equipment and medium for monitoring hazardous gases on ships provided by the embodiments of the present application through specific embodiments and their application scenarios.

[0064] Example 1

[0065] Figure 1 This is a schematic diagram of the structure of the monitoring device for dangerous gases in ships provided in Example 1 of this application. Figure 1 As shown, specifically including the following:

[0066] The sampling parameter determination module 101 is used to obtain current environmental data and loaded item data from the real-time status data of the ship or the digital twin model of the ship, and determine the sampling parameters of the cabin gas based on the current environmental data of the ship and the data of the items loaded in each cabin;

[0067] The gas sampling module 102 is used to control the sampling unit to perform gas sampling in each cabin according to the sampling parameters to obtain gas sample data of each cabin;

[0068] The abnormal gas detection module 103 is used to perform abnormal gas detection on the gas sample data to obtain abnormal gas detection results;

[0069] The alarm information generating module 104 is configured to determine that the current abnormal gas exceeds the standard and generate an alarm message if the abnormal gas detection result is higher than a preset safety threshold of the abnormal gas.

[0070] First of all, the usage scenario of this solution can be to use sensors to detect the environment and object data, and then transmit the data to the smart terminal. After the smart terminal determines the sampling parameters, it controls the sampling equipment to take samples, controls the gas detection equipment to perform gas detection, and transmits the detection results back to the smart terminal. The smart terminal determines whether an abnormal gas exceeds the standard based on the detection results, and generates an alarm message if it exceeds the standard.

[0071] Based on the above usage scenarios, it can be understood that the executor of this application can be a ship hazardous gas monitoring system that integrates ship data detection function, control function, gas sampling function, abnormal gas detection function and alarm function, and no excessive restrictions are made here.

[0072] In this context, a vessel refers to a large watercraft capable of sailing on water, typically comprising a hull structure, propulsion system, cabins, equipment, and navigation and communication systems. The main component of a vessel is the hull, which is composed of the planking, bottom, cabins, and deck. The propulsion system provides power and enables the vessel to move through the water. The propulsion system includes propellers, thrusters, and turbines, which propel the vessel forward, backward, and turn by rotating or generating water currents. The interior of a vessel includes multiple compartments for storing cargo, equipment, and fuel. Different types of vessels may have different compartment configurations, such as cargo holds, engine rooms, passenger cabins, and control rooms. Vessels are also equipped with various equipment and systems, such as engines, generators, pumps, ship control systems, and safety equipment, to support the vessel's operation and functions. Navigation and communication systems ensure the vessel's safe navigation on the water. These systems may include radar, GPS, sonar, compasses, and communication equipment, providing position determination, navigation information, nautical chart navigation, and communication capabilities with land and other vessels.

[0073] Real-time ship status data can be collected in real time by various sensors installed on the ship, covering various aspects of information such as the ship's position, speed, tilt angle, temperature, humidity, etc. These sensors can utilize Internet of Things (IoT) technology to achieve real-time data transmission and sharing.

[0074] A ship's digital twin model is a virtual representation of a vessel. Leveraging big data, machine learning, and simulation technologies, it comprehensively and accurately simulates and predicts a vessel's physical structure, operating status, and environmental impact. This model can reflect the vessel's actual conditions in real time, providing a more accurate basis for decision-making.

[0075] At the data fusion and analysis level, deep fusion of multi-source heterogeneous data can be employed. Specifically, in addition to real-time ship status data and digital twin models, external data sources such as satellite remote sensing data and marine environmental big data can be incorporated. For example, satellite remote sensing data provides a wide range of marine meteorological and geographic information. Fusion of this with the ship's own data provides a more comprehensive understanding of the ship's macro-environment. Leveraging the powerful parallel computing capabilities of quantum computing technology, this multi-source heterogeneous data can be rapidly processed and fused, uncovering potential connections between the data.

[0076] Using knowledge graph technology, various data types are integrated into a semantic network, clearly presenting the relationships between the data. For example, the ship's equipment status, cargo characteristics, and environmental factors are constructed into a knowledge graph, enabling the module to make more intelligent inferences and decisions based on the graph, and determine more accurate sampling parameters.

[0077] This solution can be built on a dynamic model using machine learning. Specifically, transfer learning techniques can be used to train the model using existing data from other ships or similar scenarios, and then transfer the model to the current ship for fine-tuning. This reduces reliance on large amounts of new data, allows for rapid adaptation to the characteristics of different ships, and improves the model's generalization capabilities.

[0078] The introduction of a reinforcement learning algorithm allows the module to learn and optimize its sampling parameter determination strategy through continuous practice. For example, the module can adjust subsequent sampling parameters based on the test results and actual conditions after each sampling, gradually finding the optimal sampling plan.

[0079] For real-time perception and prediction, edge intelligence and real-time perception can be employed. Specifically, edge computing devices can be deployed at key locations on the ship to perform preliminary processing and analysis of sensor data locally, extract key features, and then transmit them to the sampling parameter determination module 101. This reduces data transmission latency and improves the system's real-time performance. For example, edge computing nodes can be installed in the ship's cabin to monitor and warn of minor changes in gas concentration in real time, allowing for timely adjustment of sampling parameters. Leveraging the self-organizing and adaptive properties of wireless sensor networks, the sensor sampling frequency and data transmission method can be dynamically adjusted. As the environment changes, the sensor network can automatically optimize its configuration to ensure the most accurate real-time data.

[0080] This solution builds deep learning-based time series prediction models, such as long short-term memory (LSTM) or gated recurrent units (GRU), to predict changing trends in vessel environmental and cargo data. By predicting the environmental and cargo conditions over a period of time, sampling parameters can be adjusted in advance, improving detection foresight. Modeling and predicting complex dynamic behaviors in the data can more accurately capture the inherent patterns in the data, providing a more scientific basis for determining sampling parameters.

[0081] At the human-machine collaboration and interaction level, augmented reality (AR) can be used to assist decision-making. An AR-based interactive interface can be developed, allowing operators to intuitively view the ship's real-time status, environmental data, sampling parameters, and other information through AR devices. Operators can also interact with the module through gestures and voice, adjusting sampling parameters in real time. For example, when inspecting a ship's hold, an operator can use AR glasses to view the gas sampling recommendations for the current hold and modify them based on actual conditions.

[0082] In addition, a virtual professional system can be introduced to integrate domain expertise and experience into the system. When encountering complex situations, operators can call up the virtual professional system through the AR interface to obtain professional advice and guidance to assist in determining sampling parameters.

[0083] A crowdsourcing platform for ship monitoring data can be established to share and exchange monitoring data and experiences from different ships. Success stories and best practices from other ships can be leveraged on this platform to optimize the system based on specific circumstances. For example, when encountering a new cargo item, the module can quickly adjust sampling parameters based on the sampling parameters of other ships handling similar items. Leveraging social networks and online communities, ship operators, experts, and researchers can engage in real-time communication and discussion. Community discussions and feedback can be used to continuously refine the sampling parameter determination method.

[0084] A vessel's ambient temperature and humidity may refer to the temperature and humidity inside the vessel. Specifically, the ambient temperature inside a vessel may refer to the air temperature in various areas within the vessel. For example, inside a cabin, the ambient temperature may be controlled by the vessel's air conditioning system to maintain a comfortable temperature. On a vessel's deck, the ambient temperature may be affected by solar radiation, causing the deck surface temperature to rise. The ambient humidity inside a vessel refers to the moisture content in the air within the vessel. For example, humidity may need to be controlled inside a vessel's engine room to prevent equipment corrosion or electrical failure. Outside the vessel, humidity may be affected by evaporation from the surrounding waters and marine climate conditions. Weather conditions in the waters where the vessel is located may include temperature, humidity, wind, waves, and precipitation.

[0085] The data on items loaded in each hold may include the types of items loaded in the hold, which may include general cargo, dangerous goods, food, corrosive items, liquids and gases, electronic equipment and instruments, first aid supplies, scientific research supplies and experimental equipment, etc.

[0086] Sampling parameters include gas sampling volume, sampling location, sampling period, and sampling method. The gas sampling volume refers to the number or volume of gas samples collected from the vessel's hold. The sampling volume depends on a variety of factors, including the concentration of the gas to be detected, the sensitivity of the sampling equipment, the sampling time, and the required accuracy of the analysis. For example, if the presence of noble gases in the atmosphere is to be determined, a larger gas sample volume may be required, as these gases are very rare in the environment.

[0087] Sampling locations can be specific points within the cabin, including the center, corners, vents, and ventilation openings. For example, sampling at vents allows for obtaining samples from areas with high air flow, allowing assessment of air circulation and ventilation effectiveness within the cabin.

[0088] The sampling period is the time interval at which gas samples are taken, and can include continuous sampling, periodic sampling, response sampling, and random sampling. The sampling period can be determined based on the testing requirements, environmental conditions, and the type of goods. For example, if a ship is carrying dangerous goods, continuous sampling can be used. However, response sampling can be used in specific circumstances, such as during ship maintenance, cargo loading, and when potential hazards are identified.

[0089] Sampling methods can include active sampling, bag sampling, sorbent tube sampling, sensor monitoring, and static sampling, and can be determined based on specific needs and the sampling environment. For example, if there is a need to monitor for toxic gas leaks inside a ship, active sampling can be used. A gas sampling pump equipped with a toxic gas detector can be used to extract air samples and send the samples to an analytical instrument for testing.

[0090] Temperature, humidity, and meteorological sensors can be used to collect data about the ship's current environment. Vision sensors can be used to capture images of objects. This data is then transmitted to a smart terminal via wireless communication. The smart terminal first uses image processing and computer vision algorithms to identify object features based on the images captured by the vision sensors, thereby obtaining object data. The smart terminal then automatically calculates sampling parameters based on a pre-set algorithm, combining the environmental and object data. For example, a machine learning algorithm can be used to predict possible abnormal gas conditions based on the ship's environmental and object data, combined with known gas characteristics and potential interactions, and adjust sampling parameters accordingly. For example, if the current humidity is 75%, and the items on board are corrosive, higher humidity increases the generation of corrosive gases. In this case, the sampling period and sampling locations can be increased, particularly around the objects, to detect corrosive gas formation earlier and take timely countermeasures.

[0091] Based on the above technical solutions, optionally, the device further includes a flow coefficient determination module, and the flow coefficient determination module is used to:

[0092] Determine the flow coefficient of each cabin using the pre-built cabin model and external airflow data;

[0093] Accordingly, the sampling parameter determination module is used to:

[0094] The sampling parameters of the cabin gas are determined based on the current environmental data of the ship, the data of the items loaded in each cabin, and the flow coefficient of each cabin.

[0095] In this solution, the cabin model may refer to the modeling of the geometric structure and layout of the internal cabin of the ship, which may include information such as the size, shape, channel connection and isolation of the cabin.

[0096] External airflow data may refer to the airflow conditions in the vessel's surrounding environment, and may include information from weather data, ocean environment data, or other meteorological observation data, and may include wind speed, wind direction, and air pressure, etc. For example, the wind speed is 5 m / s, the wind direction is south, and the air pressure is 1000 Pa.

[0097] The flow coefficient is a parameter used to describe the gas flow characteristics within a cabin. It reflects the ability or degree of flow between different locations within the cabin. It can be used to assess gas diffusion within the cabin, guiding the selection of gas sampling points and the development of gas monitoring strategies. Specifically, the flow coefficient can be set between 0 and 1, with higher values indicating greater flow. For example, if the flow coefficient between compartments A and B is 0.7, gas flow between the two compartments is relatively smooth. If the flow coefficient between compartments A and B is 0.2, gas flow between the two compartments is significantly obstructed.

[0098] The intelligent terminal can pre-build a cabin model using ship design drawings and ship construction information. It then connects to meteorological sensors to obtain external airflow data. Based on the cabin model and external airflow data, it uses airflow simulation and calculation methods, such as computational fluid dynamics simulation or wind tunnel test data analysis, to deduce the airflow between cabins. Finally, based on the airflow simulation and calculation results, the flow coefficient between cabins is calculated. Specifically, this can be expressed as the following code:

[0099] ship_model=build_ship_model(ship_design_blueprint, ship_construction_info)

[0100] external_airflow_data=get_external_airflow_data(weather_sensor)

[0101] airflow_simulation=initialize_airflow_simulation(ship_model, external_airflow_data)

[0102] foreachtimestepintime_step_list:

[0103] simulate_airflow_flow(airflow_simulation, time_step)

[0104] foreachcabininship_model.cabin_list:

[0105] airflow_path=analyze_airflow_path(airflow_simulation,cabin)

[0106] analyze_and_record_airflow_path(airflow_path)

[0107] foreachadjacentcabinpairinship_model.adjacent_cabin_pairs_list:circulation_coefficient=calculate_circulation_coefficient(adjacent_cabin_pair)

[0108] record_circulation_coefficient(circulation_coefficient)

[0109] This code pre-builds a cabin model using ship design drawings and meteorological sensor data. By simulating external airflow data and using methods such as fluid dynamics simulation, it derives the airflow within the cabin and ultimately calculates the flow coefficients between cabins.

[0110] By analyzing the collected data, the intelligent terminal can identify the gas characteristics of different cabins, potential gas contamination risks, and gas diffusion patterns. Specifically, machine learning algorithms can be used for data analysis and processing. Based on the data analysis results, the intelligent terminal can determine cabin gas sampling parameters by considering factors such as the gas distribution within the cabin, the volatility or hazardousness of the items, the circulation coefficient, and monitoring requirements.

[0111] This solution determines cabin gas sampling parameters based on the vessel's current environmental data, the contents of each hold, and the flow coefficient of each hold. This effectively monitors cabin gas conditions, allowing for timely detection of abnormalities and appropriate action, thereby improving vessel safety.

[0112] On the basis of the above technical solutions, optionally, the sampling parameters of the cabin gas include at least one of a sampling period, a sampling position and a sampling volume in each cabin.

[0113] The sampling unit is the device responsible for actual gas sampling, typically connected to a sampling port. It allows sampling of gases from various compartments. It may include an extraction device, a control system, a filtration device, and a storage device. The extraction device may be a sampling pump or an air extraction pump. These devices use negative pressure or other mechanisms to extract gas from the compartment through the sampling port and introduce it into the sampling unit for processing. The control system controls the operation of the extraction device and the sampling process. It controls the extraction device's operating state and extraction rate based on preset sampling parameters, such as sample volume, sampling location, sampling cycle, and sampling method. To purify and reduce impurities, the sampling unit is typically equipped with a filtration device. This filtration device removes particulate matter, sediment, or other solid matter to ensure accurate and clean gas samples. The storage device temporarily stores collected gas samples for further analysis or processing. Specifically, it may be a gas sample bottle, air bag, or other container.

[0114] Gas sample data can be the actual measurement results of the gas in the cabin obtained by controlling the sampling unit. It is the data obtained after analyzing and testing the gas collected by the sampling port, and can include the concentration or percentage of various gas components.

[0115] The sampling unit can be controlled by an intelligent terminal. The intelligent terminal can control the sampling unit through a control interface or communication protocol connected to the sampling unit according to the sampling parameters to perform gas sampling in each cabin and obtain gas sample data from each cabin. Specifically, the intelligent terminal can communicate with the sampling unit through the control interface or communication protocol and send corresponding instructions. These instructions may include parameters such as sampling volume, sampling location, sampling cycle, and sampling method. The sampling unit performs corresponding operations based on the received instructions, including controlling the operating status of the extraction device, adjusting the opening and closing status of valves or valves, and setting the sampling cycle or trigger conditions. Finally, the sampling unit performs gas sampling according to the instructions sent by the intelligent terminal and feeds back the collected gas sample data to the intelligent terminal. This data can be transmitted through a communication channel or stored in a storage device within the sampling unit.

[0116] The abnormal gas detection result can be a conclusion drawn after analyzing and judging the collected gas sample data, which is used to determine whether there is an abnormal gas situation. It can include the identification of abnormal gas, quantitative description of abnormal gas, and visual display. Specifically, the identification of abnormal gas can identify the specific abnormal gas. For example, it can be identified as toxic gas, flammable gas or abnormal oxygen concentration. The quantitative description of abnormal gas can include the concentration of abnormal gas, the degree of exceeding the safety threshold, or other indicators related to the degree of abnormality. For example, it can be reported that the concentration of a certain gas exceeds the preset safety limit. Abnormal gas detection results can also be presented in the form of graphics, charts or visualizations to more intuitively understand and analyze the distribution, trends or changes of abnormal gas.

[0117] The smart terminal can transmit gas sample data to the gas analyzer via wireless communication technology. The gas analyzer parses the received data and then uses a preset abnormal gas detection algorithm to analyze the received gas sample data and determine abnormalities, generating corresponding abnormal gas detection results. The abnormal gas detection results are then transmitted back to the smart terminal via wireless communication technology. Specifically, the abnormal gas detection algorithm can be a statistical analysis algorithm that uses statistical methods to analyze gas sample data. For example, it can calculate statistical indicators such as the mean, standard deviation, and coefficient of variation of gas concentration.

[0118] Abnormal gas preset safety thresholds can refer to pre-set thresholds used to determine whether gas concentrations exceed safety standards during gas detection. For example, the safety threshold for carbon monoxide can be set to 35ppm, the safety threshold for ammonia can be set to 25ppm, the safety threshold for hydrogen sulfide can be set to 10ppm, and the safety threshold for combustible gas can be set to 10%LEL (Lower Explosive Limit).

[0119] Alarm information can include the abnormal gas type, abnormal gas concentration, alarm level, alarm location, alarm time, and related measures. For example, if the methane concentration in compartment A exceeds 10% LEL, the following alarm message can be generated: Sampling point 101 in compartment A detected combustible methane at 10:00 AM on May 28, 2023. The concentration has exceeded 10% LEL, and the alarm level has reached Level 2. Please immediately shut down related equipment and ventilate the compartment. Alarm levels can be divided into 1 to 5 levels, with Level 1 being the highest and Level 5 being the lowest. Alarm levels can be classified according to the degree of danger, safety impact, and urgency.

[0120] When the smart terminal receives the abnormal gas detection results, it compares them with the preset abnormal gas safety threshold. If the abnormal gas detection results are higher than the preset abnormal gas safety threshold, that is, the gas concentration exceeds the safety limit, it will be determined that the current abnormal gas exceeds the standard. At this time, the smart terminal will automatically generate a corresponding alarm message to remind the operator to take necessary measures.

[0121] In an embodiment of the present application, a sampling parameter determination module is used to obtain current environmental data and loaded item data from the ship's real-time status data or the ship's digital twin model, and determine the sampling parameters of the cabin gas based on the ship's current environmental data and the item data loaded in each cabin; a gas sampling module is used to control the sampling unit to perform gas sampling in each cabin according to the sampling parameters to obtain gas sample data for each cabin; an abnormal gas detection module is used to perform abnormal gas detection on the gas sample data to obtain abnormal gas detection results; an alarm information generation module is used to determine that the current abnormal gas exceeds the standard and generate an alarm message if the abnormal gas detection result is higher than the preset safety threshold of the abnormal gas. The above-mentioned ship hazardous gas monitoring device can realize automated gas detection and alarm, and improve the flexibility and accuracy of gas detection. At the same time, potential dangers can be discovered in advance so that appropriate measures can be taken for prevention and control, thereby improving the safety of the ship.

[0122] Based on the above technical solutions, optionally, the device further includes a sampling parameter updating module, and the sampling parameter updating module is used to:

[0123] According to the current abnormal gas and a predetermined sampling parameter association scheme, the sampling parameters of each cabin where the abnormal gas is detected are updated to obtain a sampling parameter update result;

[0124] The gas detection is performed again on each cabin according to the updated result of the sampling parameters.

[0125] In this solution, the predefined sampling parameter association scheme can be a set of sampling parameter update rules based on factors such as the type and concentration of the abnormal gas, as well as the characteristics and location of the cabin. Specifically, a set of rules can be established based on factors such as the type and concentration of the abnormal gas, the characteristics and location of the cabin, etc. to update the sampling parameters based on the abnormal gas situation. For example, if the combustible gas exceeds the standard, the sampling volume can be adjusted based on the concentration level, the density of sampling locations can be increased, or locations close to the leak source can be prioritized.

[0126] Sampling parameter update results refer to the results of adjusting or updating the sampling parameters for each compartment where abnormal gas was detected, according to the sampling parameter association scheme. These sampling parameter updates may involve increasing or decreasing the sampling volume, adjusting the sampling location, changing the sampling cycle, and adjusting the sampling method. For example, for high-concentration gases, the sampling volume may be increased from 3 liters per hour to 6 liters per hour; for toxic gases, the sampling cycle may be increased from once per hour to once every 20 minutes.

[0127] The sampling parameter association scheme can be stored in the memory in advance. The intelligent terminal calls the sampling parameter association scheme in the storage area, updates the sampling parameters of each cabin where abnormal gas is detected according to the characteristics of the abnormal gas and the association scheme, and obtains the sampling parameter update result.

[0128] In this solution, updating the sampling parameters and resetting the gas detection according to the current abnormal gas can improve the accuracy and efficiency of ship environmental monitoring, and make corresponding adjustments according to the specific conditions of the ship to improve the safety of the ship.

[0129] Based on the above technical solutions, optionally, the sampling parameter updating module is used to:

[0130] According to the sampling parameter update result, the updated sampling period is used to perform gas detection in each cabin;

[0131] and / or,

[0132] According to the sampling parameter update result, the updated sampling position is used to perform gas detection in each cabin;

[0133] and / or,

[0134] According to the sampling parameter update result, the updated sampling volume is used to perform gas detection on each cabin.

[0135] In this solution, the smart terminal first retrieves the updated sampling parameter results stored in the intelligent terminal, including information such as the sampling cycle, sampling location, and sampling volume for each cabin. The system then traverses the cabin. For each cabin, the location where the gas sample should be collected is determined based on the sampling location specified in the updated sampling parameter results. This could be the cabin center, a specific area, or a predefined key location. Based on the set sampling cycle, the intelligent terminal then transmits instructions to the sensor to perform gas testing in the cabin at the corresponding time. The sensor collects a sample of the cabin air and determines the gas conditions within the cabin by analyzing the gas composition in the sample. The sensor then transmits the test results to the intelligent terminal via wireless communication technology. Finally, for each gas test, the test results are recorded, including the test time, test location, and the detected gas composition and concentration.

[0136] First, the sampling parameter update results stored in the smart terminal can be obtained, including information such as the sampling cycle, sampling location, and sampling volume for each cabin. For each cabin, the location where the gas sample should be collected is determined based on the sampling location specified in the sampling parameter update results. This can be a specific area inside the cabin, a key location, or a pre-defined sampling point. The sensor is then placed or connected to the determined sampling location to collect gas samples from the cabin air. The sensor detects and records the gas composition and concentration in the sample, and then transmits the detection results to the smart terminal via wireless communication technology. Finally, for each gas detection, the detection results are recorded, which can include the detection time, detection location, and the detected gas composition and concentration.

[0137] First, the sampling parameter update results stored in the smart terminal can be obtained, including information such as the sampling cycle, sampling location, and sampling volume for each cabin. Then, the cabins are traversed. For each cabin, the location where the gas sample should be collected is determined based on the cabin location information. Then, based on the sampling volume specified in the sampling parameter update results, the amount of gas sample that needs to be collected in the cabin is determined. Specifically, it can be a specified volume or a set sampling time. The sensor is then placed or connected to the determined sampling location and gas sampling is performed according to the sampling volume requirements. The sensor will detect and record the gas composition and concentration in the sample and transmit the detection results to the smart terminal via wireless communication technology. Finally, for each gas detection, the detection results are recorded, which can include the detection time, detection location, and the detected gas composition and concentration.

[0138] In this solution, based on the sampling parameter update results, gas detection is performed using the updated sampling period, sampling position, and sampling volume, which can improve detection efficiency and accuracy and provide valuable data support for data analysis and decision-making.

[0139] The ship hazardous gas monitoring device in the embodiments of the present application can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, the mobile electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. The non-mobile electronic device can be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc., and the embodiments of the present application do not specifically limit this.

[0140] The ship hazardous gas monitoring device in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0141] Example 2

[0142] Figure 2 This is a schematic diagram of the structure of the monitoring device for dangerous gases in ships provided in Example 2 of this application. Figure 2 As shown, specifically including the following:

[0143] The sampling parameter determination module 201 is used to obtain current environmental data and loaded item data from the real-time status data of the ship or the digital twin model of the ship, and determine the sampling parameters of the cabin gas based on the current environmental data of the ship and the data of the items loaded in each cabin;

[0144] The gas sampling module 202 is used to control the sampling unit to perform gas sampling in each cabin according to the sampling parameters to obtain gas sample data of each cabin;

[0145] An abnormal gas detection module 203 is configured to perform abnormal gas detection on the gas sample data to obtain an abnormal gas detection result;

[0146] The alarm information generating module 204 is configured to determine that the current abnormal gas exceeds the standard and generate an alarm message if the abnormal gas detection result is higher than a preset safety threshold of the abnormal gas.

[0147] The device further includes a location information acquisition module 205, which is configured to:

[0148] Obtain the location information of each cabin in the ship;

[0149] Accordingly, the sampling parameter determination module is used to:

[0150] The sampling parameters of the cabin gas are determined based on the current environmental data of the ship, the data of the items loaded in each cabin, and the position information of each cabin in the ship.

[0151] Cabin location information refers to the precise location and layout of each cabin within the vessel, and may include cabin number or name, cabin type, cabin location, and cabin floor plan or layout. Specifically, each cabin is assigned a unique number or name to identify and distinguish it from other cabins. This number or name may be a numeric number, an alphabetical number, or a specific name.

[0152] The cabin type may refer to the function or purpose of different cabins in a ship, such as the cargo hold, passenger cabin, and deck hold.

[0153] Cabin location refers to the specific location and relative position of each cabin in a ship. It can describe the cabin's position in the front, middle, and rear parts of the ship, as well as the upper, middle, and lower decks of the ship.

[0154] By drawing a ship plan or layout, you can more clearly show the location and layout of each cabin in the ship. Specifically, it can include information such as the cabin's shape, size, relative position, and channel connections.

[0155] The location of each cabin within the vessel can be obtained through a smart terminal. Specifically, the smart terminal can connect to a vessel database and, through a query interface for ship-related information, retrieve data such as cabin number or name, cabin type, and location. The database also contains information on the vessel's structure and layout, as well as relevant floor plans or layout diagrams.

[0156] Based on the ship's environmental data and data on the items loaded in each hold, the intelligent terminal can analyze the possible gas types and concentration ranges. For example, certain items may release harmful gases or affect oxygen concentrations. Combined with cabin location information, the intelligent terminal determines the sampling location for each hold. This sampling location can be in a specific area of the hold or near specific equipment to more accurately reflect the gas conditions in that area. The sampling cycle is then determined based on the cabin location information. Cabins in different locations may have different risk levels or gas change rates, so the sampling cycle can be adjusted based on specific needs. The sampling method is then determined based on the cabin location information. This can involve direct gas sampling or real-time monitoring and data recording using gas sensors. Finally, the sampling volume for each cabin can be determined based on the cabin location information and cabin type. Some cabins may require more frequent sampling or a larger sampling volume to meet safety requirements.

[0157] In this embodiment, by acquiring the location information of each ship's compartments and determining cabin gas sampling parameters based on environmental data and cargo data, this helps improve ship safety, enabling accurate monitoring and preventative maintenance, and providing a reliable data foundation for data analysis and decision support. This allows for effective management and control of the ship's internal gas conditions, reducing potential risks and the likelihood of accidents.

[0158] Example 3

[0159] Figure 3 This is a flow chart of the method for monitoring dangerous gases on ships provided in Example 3 of this application. Figure 3 As shown, the specific steps include:

[0160] Obtain current environmental data and loaded item data from the ship's real-time status data or the ship's digital twin model, and determine the cabin gas sampling parameters based on the ship's current environmental data and the data on items loaded in each cabin;

[0161] Controlling the sampling unit to sample the gas in each cabin according to the sampling parameters to obtain gas sample data of each cabin;

[0162] Performing abnormal gas detection on the gas sample data to obtain abnormal gas detection results;

[0163] If the abnormal gas detection result is higher than the preset safety threshold of abnormal gas, it is determined that the current abnormal gas exceeds the standard and an alarm message is generated.

[0164] Furthermore, before determining the sampling parameters of the cabin gas based on the current environmental data of the ship and the data of the items loaded in each cabin, the method further includes:

[0165] Obtain the location information of each cabin in the ship;

[0166] Accordingly, based on the current environmental data of the ship and the data of the items loaded in each cabin, the cabin gas sampling parameters are determined, including:

[0167] The sampling parameters of the cabin gas are determined based on the current environmental data of the ship, the data of the items loaded in each cabin, and the position information of each cabin in the ship.

[0168] Furthermore, before determining the sampling parameters of the cabin gas based on the current environmental data of the ship and the data of the items loaded in each cabin, the method further includes:

[0169] Determine the flow coefficient of each cabin using the pre-built cabin model and external airflow data;

[0170] Accordingly, based on the current environmental data of the ship and the data of the items loaded in each cabin, the cabin gas sampling parameters are determined, including:

[0171] The sampling parameters of the cabin gas are determined based on the current environmental data of the ship, the data of the items loaded in each cabin, and the flow coefficient of each cabin.

[0172] Furthermore, the sampling parameters of the cabin gas include at least one of a sampling period, a sampling position, and a sampling volume in each cabin.

[0173] Furthermore, after determining that the current abnormal gas exceeds the standard and generating an alarm message, the method further includes:

[0174] According to the current abnormal gas and a predetermined sampling parameter association scheme, the sampling parameters of each cabin where the abnormal gas is detected are updated to obtain a sampling parameter update result;

[0175] The gas detection is performed again on each cabin according to the updated result of the sampling parameters.

[0176] Furthermore, re-detecting gas in each cabin according to the updated result of the sampling parameters includes:

[0177] According to the sampling parameter update result, the updated sampling period is used to perform gas detection in each cabin;

[0178] and / or,

[0179] According to the sampling parameter update result, the updated sampling position is used to perform gas detection in each cabin;

[0180] and / or,

[0181] According to the updated result of the sampling parameters, the updated sampling volume is used to perform gas detection on each cabin. In an embodiment of the present application, the current environmental data and the data of the items loaded are obtained from the real-time status data of the ship or the digital twin model of the ship, and the sampling parameters of the cabin gas are determined according to the current environmental data of the ship and the data of the items loaded in each cabin; the sampling unit is controlled according to the sampling parameters to perform gas sampling in each cabin to obtain the gas sample data of each cabin; the gas sample data is subjected to abnormal gas detection to obtain the abnormal gas detection result; if the abnormal gas detection result is higher than the preset safety threshold of the abnormal gas, it is determined that the current abnormal gas exceeds the standard and an alarm message is generated. Through the above-mentioned ship hazardous gas monitoring method, automated gas detection and alarm can be achieved, and the flexibility and accuracy of gas detection can be improved. At the same time, potential dangers can be discovered in advance so that appropriate measures can be taken for prevention and control, thereby improving the safety of the ship.

[0182] The method for monitoring hazardous gases on ships provided in this embodiment corresponds to the devices provided in the above embodiments and has corresponding execution processes and beneficial effects, which will not be repeated here.

[0183] Example 4

[0184] like Figure 4 As shown, an embodiment of the present application also provides an electronic device 400, including a processor 401, a memory 402, and a program or instruction stored in the memory 402 and executable on the processor 401. When the program or instruction is executed by the processor 401, each process of the above-mentioned embodiment of the ship hazardous gas monitoring device is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0185] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0186] Example 5

[0187] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned ship hazardous gas monitoring device embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0188] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk.

[0189] Example 6

[0190] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned ship hazardous gas monitoring device embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0191] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0192] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0193] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0194] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

[0195] The above are only preferred embodiments of the present application and the technical principles employed. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that are possible for those skilled in the art will not depart from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments and may include more other equivalent embodiments without departing from the concept of the present application. The scope of the present application is determined by the scope of the claims.

Claims

1. A monitoring device for dangerous gases on ships, characterized in that: The device comprises: The sampling parameter determination module is used to obtain the current environmental data and the data of the loaded items from the real-time status data of the ship or the digital twin model of the ship, and determine the sampling parameters of the cabin gas based on the current environmental data of the ship and the data of the items loaded in each cabin; A gas sampling module, configured to control a sampling unit to sample gas in each cabin according to the sampling parameters, and obtain gas sample data in each cabin; An abnormal gas detection module is used to perform abnormal gas detection on the gas sample data to obtain abnormal gas detection results; The alarm information generation module is used to determine that the current abnormal gas exceeds the standard and generate an alarm message if the abnormal gas detection result is higher than the preset safety threshold of the abnormal gas.

2. The ship hazardous gas monitoring device according to claim 1, characterized in that: The device further includes a location information acquisition module, which is configured to: Obtain the location information of each cabin in the ship; Accordingly, the sampling parameter determination module is used to: The sampling parameters of the cabin gas are determined based on the current environmental data of the ship, the data of the items loaded in each cabin, and the position information of each cabin in the ship.

3. The ship hazardous gas monitoring device according to claim 1, characterized in that: The device further comprises a flow coefficient determination module, wherein the flow coefficient determination module is configured to: Determine the flow coefficient of each cabin using the pre-built cabin model and external airflow data; Accordingly, the sampling parameter determination module is used to: The sampling parameters of the cabin gas are determined based on the current environmental data of the ship, the data of the items loaded in each cabin, and the flow coefficient of each cabin.

4. The ship hazardous gas monitoring device according to any one of claims 1 to 3, characterized in that: The sampling parameters of the cabin gas include at least one of a sampling period, a sampling position, and a sampling amount in each cabin.

5. The ship hazardous gas monitoring device according to claim 1, characterized in that: The apparatus further includes a sampling parameter updating module, wherein the sampling parameter updating module is configured to: According to the current abnormal gas and a predetermined sampling parameter association scheme, the sampling parameters of each cabin where the abnormal gas is detected are updated to obtain a sampling parameter update result; The gas detection is performed again on each cabin according to the updated result of the sampling parameters.

6. The ship dangerous gas monitoring device according to claim 5, characterized in that: The sampling parameter updating module is used for: According to the sampling parameter update result, the updated sampling period is used to perform gas detection in each cabin; and / or, According to the sampling parameter update result, the updated sampling position is used to perform gas detection in each cabin; and / or, According to the sampling parameter update result, the updated sampling volume is used to perform gas detection on each cabin.

7. A method for monitoring dangerous gases on ships, characterized in that: The method comprises: Obtain current environmental data and loaded item data from the ship's real-time status data or the ship's digital twin model, and determine the cabin gas sampling parameters based on the ship's current environmental data and the data on items loaded in each cabin; Controlling the sampling unit to sample the gas in each cabin according to the sampling parameters to obtain gas sample data of each cabin; Performing abnormal gas detection on the gas sample data to obtain an abnormal gas detection result; If the abnormal gas detection result is higher than the preset safety threshold of abnormal gas, it is determined that the current abnormal gas exceeds the standard and an alarm message is generated.

8. The method for monitoring dangerous gases in ships according to claim 7, characterized in that: Before determining the sampling parameters of the cabin gas based on the current environmental data of the ship and the data of the items loaded in each cabin, the method further includes: Obtain the location information of each cabin in the ship; Accordingly, based on the current environmental data of the ship and the data of the items loaded in each cabin, the cabin gas sampling parameters are determined, including: The sampling parameters of the cabin gas are determined based on the current environmental data of the ship, the data of the items loaded in each cabin, and the position information of each cabin in the ship.

9. An electronic device, characterized in that: It comprises a processor, a memory and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method for monitoring hazardous gases in ships as described in any one of claims 7 to 8.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the method for monitoring hazardous gases in ships as described in any one of claims 7 to 8 are implemented.

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