A marine LNG / diesel dual-fuel engine safety monitoring system

Abstract

The application provides a marine LNG / diesel dual-fuel engine safety monitoring system, belonging to the technical field of data processing, comprising: a monitoring module that monitors LNG supply data and diesel supply data in real time; a determination module that acquires safety preset data, determines real-time abnormal data and real-time early warning data; an early warning module that acquires a plurality of historical operation data, determines a real-time early warning automatic parameter set, a real-time automatic control instruction and a real-time early warning non-automatic parameter set, executes the real-time automatic control instruction and issues a real-time early warning alarm; an abnormality module that determines a historical abnormal parameter set of the ship, determines prediction abnormal data and issues a real-time abnormality alarm. The application can improve the intelligent level of abnormality processing and early warning processing and the accuracy of emergency response, reduces the demand for manual intervention, improves the safety and reliability of ship operation, improves system response speed and decision efficiency, and enhances the safety monitoring capability of the marine LNG / diesel dual-fuel engine.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a safety monitoring system for marine LNG / diesel dual-fuel engines. Background Technology

[0002] With the continuous development of the shipbuilding industry, especially the widespread application of LNG / diesel dual-fuel engines, higher demands are being placed on the safety monitoring systems of ship engines. Traditional monitoring systems mainly rely on human experience and simple threshold alarm mechanisms. These systems typically only issue alarms after an anomaly occurs, lacking the ability to predict potential problems and failing to achieve automatic control. For example, when a traditional monitoring system detects that the LNG tank level is too low or the diesel fuel supply pressure is too high, it can only issue a simple alarm without automatically adjusting relevant parameters or taking preventative measures. Furthermore, traditional systems are inadequate in processing complex data, predicting potential problems, and achieving automatic control, failing to meet the high safety and reliability requirements of modern ships.

[0003] In recent years, with the rapid development of sensor technology, data processing technology, and automation control technology, intelligent monitoring systems have gradually become a research hotspot. These systems, through real-time monitoring, data analysis, and automatic control, can improve the efficiency of identifying and handling potential security issues. However, existing technologies still need further improvement in areas such as multimodal data fusion, historical data utilization, and separate identification of anomalies and early warnings.

[0004] Therefore, the present invention provides a safety monitoring system for marine LNG / diesel dual-fuel engines. Summary of the Invention

[0005] This invention provides a safety monitoring system for marine LNG / diesel dual-fuel engines. By monitoring LGN supply data, diesel supply data, and acquired safety preset data in real time, it identifies real-time abnormal data and real-time early warning data. Based on acquired historical operating data and real-time early warning data, it determines and executes real-time automatic control commands, identifies a set of real-time early warning non-automatic parameters and issues real-time early warning alarms, and determines a set of historical abnormal parameters of the ship based on historical operating data, identifies predicted abnormal data, and issues real-time abnormal alarms. This system can accurately identify abnormal and early warning parameters of the ship, improve the intelligence level of abnormal and early warning processing and the accuracy of emergency response, reduce the need for manual intervention, improve the safety and reliability of ship operation, enhance system response speed and decision-making efficiency, and strengthen the safety monitoring capabilities of marine LNG / diesel dual-fuel engines.

[0006] This invention provides a safety monitoring system for marine LNG / diesel dual-fuel engines, comprising: Monitoring module: Real-time monitoring of LGN supply data based on LGN supply, and real-time monitoring of diesel supply data based on diesel supply; Determine module: acquire safety preset data, determine the set of real-time abnormal parameters and the set of real-time early warning parameters based on LGN supply data, diesel supply data and safety preset data, and determine real-time abnormal data and real-time early warning data; Early warning module: acquires multiple historical operation data, and based on all historical operation data and real-time early warning data, determines the set of real-time early warning automatic parameters, real-time automatic control commands, and real-time early warning non-automatic parameters, executes real-time automatic control commands, and issues real-time early warning alarms; Anomaly Module: Based on historical operational data, determine the set of historical anomaly parameters of the ship. Based on the set of historical anomaly parameters and real-time anomaly data, determine the predicted anomaly data and issue real-time anomaly alarms.

[0007] According to the present invention, a safety monitoring system for a marine LNG / diesel dual-fuel engine includes a monitoring module comprising: LGN Supply Data Unit: Based on the first sensor group, it monitors the LGN supply data at the current time point. The LGN supply data includes LGN storage tank data, gas supply line data, and vaporizer data. The LGN storage tank data includes real-time monitoring values ​​of multiple parameters labeled as parameters of the LGN storage tank. The gas supply line data includes real-time monitoring values ​​of multiple parameters labeled as parameters of the gas supply line. The vaporizer data includes real-time monitoring values ​​of multiple parameters labeled as parameters of the vaporizer. Diesel Supply Data Unit: Based on the second sensor group, the diesel supply data is monitored in real time at the current time point. The diesel supply data includes diesel storage tank data and fuel supply pipeline data. The diesel storage tank data includes real-time monitoring values ​​of multiple parameters labeled as diesel storage tank parameters, and the fuel supply pipeline data includes real-time monitoring values ​​of multiple parameters labeled as fuel supply pipeline parameters.

[0008] According to the present invention, a safety monitoring system for a marine LNG / diesel dual-fuel engine includes a determination module comprising: Safety Preset Data Unit: Acquires the ship's safety preset data, which includes LGN storage safety data, gas supply safety data, vaporization safety data, diesel storage safety data, and fuel supply safety data. The LGN storage safety data includes the safety range of each parameter labeled as an LGN storage tank. The gas supply safety data includes the safety range of each parameter labeled as an gas supply line. The vaporization safety data includes the safety range of each parameter labeled as a carburetor. The diesel storage safety data includes the safety range of each parameter labeled as a diesel storage tank. The fuel supply safety data includes the safety range of each parameter labeled as a fuel supply line. Real-time temperature value unit: Acquires the real-time temperature value of the ship's location; Safety tag unit: Based on LGN supply data, diesel supply data, real-time temperature values, and safety preset data, determine the first real-time safety tag and the second real-time safety tag for all parameters; Parameter set unit: Based on the first security label and the second security label of all parameters, determine the set of real-time abnormal parameters and the set of real-time early warning parameters.

[0009] According to the safety monitoring system for a marine LNG / diesel dual-fuel engine provided by the present invention, the determining module further includes: Real-time anomaly data unit: Based on all parameters in the real-time anomaly parameter set and the real-time monitoring value corresponding to each parameter, real-time anomaly data is determined; Real-time early warning data unit: Based on all parameters in the real-time early warning parameter set and the real-time monitoring value corresponding to each parameter, real-time early warning data is determined.

[0010] A safety monitoring system for a marine LNG / diesel dual-fuel engine according to the present invention includes: Historical Operation Data Unit: Acquires historical operation data within multiple specified time periods, including historical early warning data and historical anomaly data within the specified time periods; Historical early warning data unit: Extracts historical early warning data from historical operational data within each specified time period. The historical early warning data includes a set of historical early warning parameters within the specified time period, as well as the historical monitoring values, historical monitoring time points, emergency strategies, emergency strategy labels, and emergency results for each parameter in the historical early warning parameter set that is labeled with an early warning label. The emergency strategy labels include automatic control strategies and non-automatic control strategies, and the emergency results include excellent, good, and average. Automatic control parameter subset unit: Extract all parameters in the historical early warning parameter set of historical early warning data for each specified time period that have the emergency strategy label of automatic control strategy and the emergency result of excellent, and determine the automatic control parameter subset for each specified time period; Early warning automatic control parameter set unit: Based on all automatic control parameter subsets for specified time periods, determine the ship's early warning automatic control parameter set; Emergency Strategy Table Unit: Based on all historical early warning data for all specified time periods, extract all historical monitoring values ​​and emergency strategies for each parameter in the ship's early warning automatic control parameter set that are labeled as automatic control strategies and have excellent emergency results, and construct an emergency strategy table for each parameter in the ship's early warning automatic control parameter set; Real-time early warning automatic parameter set unit: Based on all parameters in the real-time early warning parameter set in the real-time early warning data and the ship's early warning automatic control parameter set, determine the real-time early warning automatic parameter set; Real-time automatic control strategy unit: Calculate the difference between the real-time monitoring value of each parameter in the real-time warning automatic parameter set and all historical monitoring values ​​in the emergency strategy table of each parameter in the real-time warning automatic parameter set, and select the emergency strategy corresponding to the historical monitoring value with the smallest difference as the real-time automatic control strategy for each parameter in the real-time warning automatic parameter set; Real-time automatic control sub-instruction unit: Based on the real-time automatic control strategy of each parameter in the real-time early warning automatic parameter set, it generates real-time automatic control sub-instructions for each parameter in the real-time early warning automatic parameter set; Real-time early warning non-automatic parameter set unit: Based on the real-time early warning parameter set and the real-time early warning automatic parameter set in the real-time early warning data, the real-time early warning non-automatic parameter set is determined.

[0011] A safety monitoring system for a marine LNG / diesel dual-fuel engine according to the present invention further includes: Real-time automatic control instruction unit: Based on the real-time automatic control sub-instructions of all parameters in the real-time early warning automatic parameter set, determine the real-time automatic control instruction, and execute the real-time automatic control sub-instruction of each parameter in the real-time automatic control instruction; Real-time early warning alarm unit: issues real-time early warning alarms for each parameter in the real-time early warning non-automatic parameter set.

[0012] According to the present invention, a safety monitoring system for a marine LNG / diesel dual-fuel engine includes an anomaly module comprising: Historical anomaly data unit: Extracts historical anomaly data from historical operational data within each specified time period. The historical anomaly data includes a subset of historical anomaly parameters within the specified time period, and the historical monitoring values ​​of each parameter in the historical anomaly parameter subset whose historical safety label is an anomaly label at all historical monitoring time points within the specified time period. Historical Anomaly Parameter Set Unit: Based on the subset of historical anomaly parameters in all historical anomaly data within a specified time period, determine the set of historical anomaly parameters for the ship.

[0013] According to the present invention, a safety monitoring system for a marine LNG / diesel dual-fuel engine, including an anomaly module, further comprises: Construction Unit: Based on the ship's historical anomaly parameter set and historical operational data for all specified time periods, construct an anomaly prediction model for the ship; Training Unit: The anomaly prediction model is trained based on the historical monitoring values ​​of the parameters with historical safety labels as anomaly labels in the historical anomaly parameter subset of all historical anomaly data for all specified time periods at all historical monitoring time points. The historical monitoring values ​​of the parameters with historical safety labels as warning labels in the historical warning parameter subset of all historical warning data for all specified time periods, as well as the historical monitoring time, are used as the output of the anomaly prediction model. Predicted anomaly data unit: The real-time monitoring value of each parameter in the real-time anomaly parameter set in the real-time anomaly data and the current time point are input into the anomaly prediction model. Based on the output of the anomaly prediction model, the predicted anomaly data is determined. The predicted anomaly data includes the predicted warning time points of all parameters in the real-time anomaly parameter set. Real-time anomaly alarm unit: issues real-time anomaly alarms based on the predicted warning time point of all parameters in each anomaly parameter set and the current time point.

[0014] Compared with the prior art, the beneficial effects of this application are as follows: By monitoring LGN supply data, diesel supply data, and acquired safety preset data in real time, the system identifies real-time abnormal data and real-time early warning data. Based on acquired historical operating data and real-time early warning data, it determines and executes real-time automatic control commands, identifies the set of real-time early warning non-automatic parameters, and issues real-time early warning alarms. Furthermore, based on historical operating data, it determines the ship's historical abnormal parameter set, identifies predicted abnormal data, and issues real-time abnormal alarms. This system can accurately identify abnormal and early warning parameters of the ship, improving the intelligence level of abnormal and early warning processing and the accuracy of emergency response, reducing the need for manual intervention, enhancing the safety and reliability of ship operation, improving system response speed and decision-making efficiency, and strengthening the safety monitoring capabilities of marine LNG / diesel dual-fuel engines. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the structure of a safety monitoring system for a marine LNG / diesel dual-fuel engine provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the determination module of a safety monitoring system for a marine LNG / diesel dual-fuel engine provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0018] Example 1

[0019] This invention provides a safety monitoring system for marine LNG / diesel dual-fuel engines, such as... Figure 1 As shown, it includes: Monitoring module: Real-time monitoring of LGN supply data based on LGN supply, and real-time monitoring of diesel supply data based on diesel supply; Determine module: acquire safety preset data, determine the set of real-time abnormal parameters and the set of real-time early warning parameters based on LGN supply data, diesel supply data and safety preset data, and determine real-time abnormal data and real-time early warning data; Early warning module: acquires multiple historical operation data, and based on all historical operation data and real-time early warning data, determines the set of real-time early warning automatic parameters, real-time automatic control commands, and real-time early warning non-automatic parameters, executes real-time automatic control commands, and issues real-time early warning alarms; Anomaly Module: Based on historical operational data, determine the set of historical anomaly parameters of the ship. Based on the set of historical anomaly parameters and real-time anomaly data, determine the predicted anomaly data and issue real-time anomaly alarms.

[0020] In this embodiment, the determination module retrieves a pre-set safety range from the system database. It compares the real-time monitoring data with the preset safety data to determine a first safety label and a second safety label for each parameter. It then determines which parameters belong to the real-time abnormal parameter set and which belong to the real-time early warning parameter set.

[0021] In this embodiment, the early warning module acquires multiple historical operating data, and based on the historical early warning data and real-time early warning data in the historical operating data, determines the set of real-time early warning automatic parameters, the real-time automatic control command, and the set of real-time early warning non-automatic parameters, executes the real-time automatic control command, and issues a real-time early warning alarm.

[0022] In this embodiment, the parameters in the real-time warning automatic parameter set can be resolved through automatic control, while the parameters in the real-time warning non-automatic parameter set require manual intervention.

[0023] In this embodiment, for all parameters that can be resolved by automatic control, real-time automatic control commands are generated and executed; for all parameters that require manual intervention, real-time early warning alarms are issued.

[0024] The beneficial effects of the above technical solution are as follows: By monitoring LGN supply data, diesel supply data, and acquired safety preset data in real time, real-time abnormal data and real-time early warning data are identified. Based on the acquired historical operating data and real-time early warning data, real-time automatic control commands are determined and executed. The set of real-time early warning non-automatic parameters is determined and real-time early warning alarms are issued. Based on historical operating data, the set of historical abnormal parameters of the ship is determined, and predicted abnormal data is determined and real-time abnormal alarms are issued. This allows for accurate identification of abnormal and early warning parameters of the ship, improving the intelligence level of abnormal and early warning processing and the accuracy of emergency response, reducing the need for manual intervention, improving the safety and reliability of ship operation, enhancing system response speed and decision-making efficiency, and strengthening the safety monitoring capabilities of marine LNG / diesel dual-fuel engines.

[0025] Example 2

[0026] This invention provides a safety monitoring system for marine LNG / diesel dual-fuel engines, including a monitoring module comprising: LGN Supply Data Unit: Based on the first sensor group, it monitors the LGN supply data at the current time point. The LGN supply data includes LGN storage tank data, gas supply line data, and vaporizer data. The LGN storage tank data includes real-time monitoring values ​​of multiple parameters labeled as parameters of the LGN storage tank. The gas supply line data includes real-time monitoring values ​​of multiple parameters labeled as parameters of the gas supply line. The vaporizer data includes real-time monitoring values ​​of multiple parameters labeled as parameters of the vaporizer. Diesel Supply Data Unit: Based on the second sensor group, the diesel supply data is monitored in real time at the current time point. The diesel supply data includes diesel storage tank data and fuel supply pipeline data. The diesel storage tank data includes real-time monitoring values ​​of multiple parameters labeled as diesel storage tank parameters, and the fuel supply pipeline data includes real-time monitoring values ​​of multiple parameters labeled as fuel supply pipeline parameters.

[0027] In this embodiment, the LNG storage tank data includes monitoring multiple parameters of the LNG storage tank, such as liquid level, pressure, and temperature. These parameters are collected in real time by sensors to ensure comprehensive monitoring of the storage tank's status.

[0028] In this embodiment, for example: Liquid level monitoring: The liquid level in the LNG storage tank is monitored in real time using a liquid level sensor to prevent the liquid level from becoming too low or too high. Pressure monitoring: The pressure in the LNG storage tank is monitored in real time using a pressure sensor to prevent excessive pressure from causing the tank to rupture. Temperature monitoring: The temperature of the LNG storage tank is monitored in real time using a temperature sensor to ensure that the temperature remains within a safe range.

[0029] In this embodiment, the gas supply pipeline data includes monitoring multiple parameters such as pressure, flow rate, and temperature. This data helps assess the operational status of the gas supply system and promptly detect problems such as leaks or blockages.

[0030] In this embodiment, for example: Pressure monitoring: The pressure of the gas supply line is monitored in real time using a pressure sensor to ensure pressure stability. Flow monitoring: The flow rate of the gas supply line is monitored in real time using a flow sensor to ensure the flow rate is within the design range. Temperature monitoring: The temperature of the gas supply line is monitored in real time using a temperature sensor to prevent excessively high or low temperatures.

[0031] In this embodiment, vaporizer data includes monitoring multiple parameters of the vaporizer, such as medium temperature, inlet and outlet pressure, and vaporization efficiency. This data ensures the vaporizer functions properly and avoids safety issues caused by incomplete vaporization.

[0032] In this embodiment, for example: Medium temperature monitoring: The temperature of the medium in the vaporizer is monitored in real time using a temperature sensor to ensure the vaporizer operates normally. Inlet and outlet pressure monitoring: The pressure at the inlet and outlet of the vaporizer is monitored in real time using pressure sensors to ensure the pressure is within a safe range. Vaporization efficiency monitoring: The vaporization efficiency of the vaporizer is comprehensively evaluated using flow sensors and temperature sensors to ensure complete LNG vaporization.

[0033] In this embodiment, the diesel storage tank data includes monitoring multiple parameters such as liquid level, temperature, and pressure. These parameters are collected in real time by sensors to ensure comprehensive monitoring of the storage tank's status.

[0034] In this embodiment, for example: Liquid level monitoring: The liquid level in the diesel storage tank is monitored in real time using a liquid level sensor to prevent the liquid level from being too low or too high. Temperature monitoring: The temperature of the diesel storage tank is monitored in real time using a temperature sensor to ensure the temperature is within a safe range. Pressure monitoring: The pressure of the diesel storage tank is monitored in real time using a pressure sensor to prevent the pressure from becoming too high.

[0035] In this embodiment, the oil supply pipeline data includes monitoring multiple parameters such as pressure, flow rate, and temperature. This data helps assess the operational status of the oil supply system and promptly detect problems such as leaks or blockages.

[0036] In this embodiment, for example: Pressure monitoring: The pressure of the oil supply line is monitored in real time using a pressure sensor to ensure pressure stability. Flow monitoring: The flow rate of the oil supply line is monitored in real time using a flow sensor to ensure the flow rate is within the design range. Temperature monitoring: The temperature of the oil supply line is monitored in real time using a temperature sensor to prevent excessively high or low temperatures.

[0037] The beneficial effects of the above technical solution are: real-time monitoring of LGN supply data based on LGN supply and real-time monitoring of diesel supply data based on diesel supply can provide data support for determining the set of real-time abnormal parameters and the set of real-time early warning parameters.

[0038] Example 3

[0039] This invention provides a safety monitoring system for marine LNG / diesel dual-fuel engines, including a determination module comprising: Safety Preset Data Unit: Acquires the ship's safety preset data, which includes LGN storage safety data, gas supply safety data, vaporization safety data, diesel storage safety data, and fuel supply safety data. The LGN storage safety data includes the safety range of each parameter labeled as an LGN storage tank. The gas supply safety data includes the safety range of each parameter labeled as an gas supply line. The vaporization safety data includes the safety range of each parameter labeled as a carburetor. The diesel storage safety data includes the safety range of each parameter labeled as a diesel storage tank. The fuel supply safety data includes the safety range of each parameter labeled as a fuel supply line. Real-time temperature value unit: Acquires the real-time temperature value of the ship's location; Safety tag unit: Based on LGN supply data, diesel supply data, real-time temperature values, and safety preset data, determine the first real-time safety tag and the second real-time safety tag for all parameters; Parameter set unit: Based on the first security label and the second security label of all parameters, determine the set of real-time abnormal parameters and the set of real-time early warning parameters.

[0040] In this embodiment, LNG storage safety data includes the safety ranges for all parameters labeled as LNG storage tank parameters, such as liquid level, pressure, and temperature. Gas supply safety data includes the safety ranges for all parameters labeled as gas supply pipeline parameters, such as pressure, flow rate, and temperature. Vaporization safety data includes the safety ranges for all parameters labeled as vaporizer parameters, such as medium temperature and inlet / outlet pressure. Diesel storage safety data includes the safety ranges for all parameters labeled as diesel storage tank parameters, such as liquid level, temperature, and pressure. Oil supply safety data includes the safety ranges for all parameters labeled as oil supply pipeline parameters, such as pressure, flow rate, and temperature.

[0041] In this embodiment, the ambient temperature at the ship's location is collected in real time by an ambient temperature sensor installed on the ship.

[0042] In this embodiment, the safety tag unit determines the first real-time safety tag for all parameters based on LGN supply data, diesel supply data, and safety preset data. The determination formula can be expressed as: ; in, This represents the first security label of the b-th parameter in data a. a=1 represents LGN storage tank data, a=2 represents gas supply line data, a=3 represents carburetor data, a=4 represents diesel storage tank data, and a=5 represents fuel supply line data. This represents the real-time monitoring value of the b-th parameter in data a. This represents the lower limit of the safe range for the b-th parameter in data a. This represents the upper limit of the safety range for the b-th parameter in data a. This indicates the proximity of the upper limit of the b-th parameter in data a. TP represents the lower limit proximity of the b-th parameter in data a, where TP represents the preset proximity threshold.

[0043] In this embodiment, when a=1, This indicates that the b-th parameter in the LGN storage tank data is a parameter labeled as an LGN storage tank, and when a=2, This indicates that the b-th parameter label in the gas supply pipeline data is a parameter of the gas supply pipeline. When a=3, This indicates that the b-th parameter label in the vaporizer data is a vaporizer parameter; when a=4, This indicates that the b-th parameter label in the diesel storage tank data is a parameter of the diesel storage tank, and when a=5, This indicates that the b-th parameter in the oil supply pipeline data is a parameter of the oil supply pipeline.

[0044] In this embodiment, This represents the real-time monitoring value of the b-th parameter in data 'a'. For example, when a=1, This represents the real-time monitoring value of the b-th LGN storage tank parameter in the LGN storage tank data. When a=2, This represents the real-time monitoring value of the b-th gas supply pipeline parameter in the gas supply pipeline data. When a=3, This represents the real-time monitoring value of the b-th carburetor parameter in the carburetor data. When a=4, This represents the real-time monitoring value of the parameter of the b-th diesel storage tank in the diesel storage tank data. When a=5, This represents the real-time monitoring value of the b-th oil supply pipeline parameter in the oil supply pipeline data.

[0045] In this embodiment, the safety tag unit determines the second real-time safety tag for all parameters based on LGN supply data, diesel supply data, and safety preset data. The determination formula can be expressed as: ; in, This represents the second security label of the b-th parameter in data a. This represents the safety buffer value of the b-th parameter in data a. Indicates temperature-sensitive factor, This represents the real-time temperature value. This indicates the critical value for high temperature. Indicates the critical value for low temperature. This represents the historical standard deviation of the b-th parameter in data a. Indicates the buffer adjustment factor. This indicates the coordinated risk value of LGN supply data and diesel supply data. Indicates the early warning risk threshold. Indicates the anomaly risk threshold. The exception judgment sub-conditions represent the exception labels, and aN1 represents the number of parameters in the data a.

[0046] In this embodiment, the real-time anomaly parameter set and the real-time early warning parameter set are labeled: Parameter set unit: Based on the security labels of all parameters, the formula for determining the real-time anomaly parameter set and the real-time early warning parameter set can be expressed as: ; ; ; Where EW represents the set of real-time early warning parameters, and AB represents the set of real-time anomaly parameters. This represents a set of data parameters representing all parameters in the LGN supply data and diesel supply data. This represents the b-th parameter in data a.

[0047] The beneficial effects of the above technical solution are as follows: by acquiring safety preset data, and based on LGN supply data, diesel supply data and safety preset data, the set of real-time abnormal parameters and the set of real-time early warning parameters can be determined. This can accurately identify abnormal parameters and early warning parameters of the ship, improve the safety and reliability of ship operation, and reduce the risk of accidents.

[0048] Example 4

[0049] This invention provides a safety monitoring system for marine LNG / diesel dual-fuel engines, including a determination module, and further comprising: Real-time anomaly data unit: Based on all parameters in the real-time anomaly parameter set and the real-time monitoring value corresponding to each parameter, real-time anomaly data is determined; Real-time early warning data unit: Based on all parameters in the real-time early warning parameter set and the real-time monitoring value corresponding to each parameter, real-time early warning data is determined.

[0050] In this embodiment, all parameters in the real-time anomaly parameter set and their corresponding real-time monitoring values ​​are integrated to determine the real-time anomaly data.

[0051] In this embodiment, all parameters in the real-time warning parameter set and their corresponding real-time monitoring values ​​are integrated to determine the real-time warning data.

[0052] The beneficial effects of the above technical solution are: determining real-time abnormal data and real-time early warning data can improve the accuracy and reliability of real-time early warning alarms and real-time abnormal alarms, reduce false alarm rates, and enhance the intelligence level and emergency response capabilities of the security monitoring system.

[0053] Example 5

[0054] This invention provides a safety monitoring system for marine LNG / diesel dual-fuel engines, including an early warning module comprising: Historical Operation Data Unit: Acquires historical operation data within multiple specified time periods, including historical early warning data and historical anomaly data within the specified time periods; Historical early warning data unit: Extracts historical early warning data from historical operational data within each specified time period. The historical early warning data includes a set of historical early warning parameters within the specified time period, as well as the historical monitoring values, historical monitoring time points, emergency strategies, emergency strategy labels, and emergency results for each parameter in the historical early warning parameter set that is labeled with an early warning label. The emergency strategy labels include automatic control strategies and non-automatic control strategies, and the emergency results include excellent, good, and average. Automatic control parameter subset unit: Extract all parameters in the historical early warning parameter set of historical early warning data for each specified time period that have the emergency strategy label of automatic control strategy and the emergency result of excellent, and determine the automatic control parameter subset for each specified time period; Early warning automatic control parameter set unit: Based on all automatic control parameter subsets for specified time periods, determine the ship's early warning automatic control parameter set; Emergency Strategy Table Unit: Based on all historical early warning data for all specified time periods, extract all historical monitoring values ​​and emergency strategies for each parameter in the ship's early warning automatic control parameter set that are labeled as automatic control strategies and have excellent emergency results, and construct an emergency strategy table for each parameter in the ship's early warning automatic control parameter set; Real-time early warning automatic parameter set unit: Based on all parameters in the real-time early warning parameter set in the real-time early warning data and the ship's early warning automatic control parameter set, determine the real-time early warning automatic parameter set; Real-time automatic control strategy unit: Calculate the difference between the real-time monitoring value of each parameter in the real-time warning automatic parameter set and all historical monitoring values ​​in the emergency strategy table of each parameter in the real-time warning automatic parameter set, and select the emergency strategy corresponding to the historical monitoring value with the smallest difference as the real-time automatic control strategy for each parameter in the real-time warning automatic parameter set; Real-time automatic control sub-instruction unit: Based on the real-time automatic control strategy of each parameter in the real-time early warning automatic parameter set, it generates real-time automatic control sub-instructions for each parameter in the real-time early warning automatic parameter set; Real-time early warning non-automatic parameter set unit: Based on the real-time early warning parameter set and the real-time early warning automatic parameter set in the real-time early warning data, the real-time early warning non-automatic parameter set is determined.

[0055] In this embodiment, the historical operation data unit extracts historical operation data from the system database for multiple time periods (such as the past week, month, three months, etc.). The historical operation data includes historical early warning data and historical anomaly data, which record various situations and their processing results in the past operation of the system.

[0056] In this embodiment, parameters with the emergency strategy label "automatic control strategy" and excellent emergency results within each specified time period are extracted to determine a subset of automatic control parameters. The automatic control parameter subset unit iterates through the historical early warning data for each time period and filters out parameters that meet the criteria. These parameters are those that have demonstrated good automatic control performance in historical operations.

[0057] In this embodiment, the early warning automatic control parameter set unit aggregates the subsets of automatic control parameters for all time periods to form a comprehensive early warning automatic control parameter set. This set includes all parameters with historically good automatic control performance, providing a foundation for subsequent real-time early warning processing.

[0058] In this embodiment, the emergency strategy table unit extracts all historical monitoring values ​​and corresponding emergency strategies for each parameter in the automatic control parameter set for early warning. The emergency strategy table records the emergency strategies for each parameter under different historical monitoring values, providing a basis for decision-making in real-time early warning processing.

[0059] In this embodiment, the real-time early warning automatic parameter set unit compares the parameters in the real-time early warning data with the early warning automatic control parameter set, and filters out the parameters that can be automatically controlled. These parameters are considered to be the parameters for which an automatic control strategy can be applied at present.

[0060] In this embodiment, the real-time automatic control strategy unit calculates the difference between the real-time monitored value and all historical monitored values ​​in the emergency strategy table for each parameter in the real-time early warning automatic parameter set. The emergency strategy corresponding to the historical monitored value with the smallest difference is selected as the real-time automatic control strategy. This method ensures that the selection of the real-time control strategy is based on historical best experience, improving the accuracy of control.

[0061] In this embodiment, the real-time automatic control sub-instruction unit generates specific control instructions according to the selected real-time automatic control strategy.

[0062] In this embodiment, the set of real-time early warning parameters and the set of real-time early warning automatic parameters in the real-time early warning data are subtracted to determine the set of real-time early warning non-automatic parameters. All parameters in the set of real-time early warning non-automatic parameters require manual intervention.

[0063] The beneficial effects of the above technical solution are as follows: by acquiring multiple historical operational data and determining the set of automatic parameters, automatic control commands, and non-automatic parameters for real-time early warning based on all historical and real-time early warning data, the intelligence level of early warning processing and the accuracy of emergency response can be improved, the need for manual intervention can be reduced, and the security and reliability of the system can be enhanced.

[0064] Example 6

[0065] This invention provides a safety monitoring system for marine LNG / diesel dual-fuel engines, including an early warning module, and further comprising: Real-time automatic control instruction unit: Based on the real-time automatic control sub-instructions of all parameters in the real-time early warning automatic parameter set, determine the real-time automatic control instruction, and execute the real-time automatic control sub-instruction of each parameter in the real-time automatic control instruction; Real-time early warning alarm unit: issues real-time early warning alarms for each parameter in the real-time early warning non-automatic parameter set.

[0066] In this embodiment, the real-time automatic control instruction unit integrates all real-time automatic control sub-instructions in the real-time early warning automatic parameter set to form a comprehensive real-time automatic control instruction. Executing this comprehensive real-time automatic control instruction ensures that the real-time automatic control sub-instructions for each parameter are executed accurately.

[0067] In this embodiment, the execution effect of each sub-instruction is monitored in real time during execution to ensure the effectiveness of the control measures. If a sub-instruction fails to execute or is ineffective, the system will record the failure and issue an alert to remind the operator to intervene manually.

[0068] In this embodiment, the real-time early warning alarm unit generates a corresponding real-time early warning alarm for each parameter in the real-time early warning non-automatic parameter set. The alarm content includes detailed information such as the parameter name, real-time monitoring value, and reason for the alarm. The alarm is issued to the operator through audible and visual alarms, display screen prompts, or remote notifications, ensuring that the operator receives the alarm promptly and takes appropriate measures.

[0069] In this embodiment, all alarms are recorded in the system database, including alarm time, parameter name, real-time monitoring value, and alarm reason. By issuing real-time warnings, operators are reminded to pay attention to parameters requiring manual intervention, ensuring that operators can take timely measures to avoid potential safety issues.

[0070] The beneficial effects of the above technical solution are as follows: executing real-time automatic control commands and issuing real-time early warning alarms can improve the automation level and response speed of the system, ensure the timeliness and effectiveness of manual intervention, enhance the safety monitoring capability of marine LNG / diesel dual-fuel engines, reduce accident risks, and improve overall operating efficiency.

[0071] Example 7

[0072] This invention provides a safety monitoring system for marine LNG / diesel dual-fuel engines, including an anomaly module: Historical anomaly data unit: Extracts historical anomaly data from historical operational data within each specified time period. The historical anomaly data includes a subset of historical anomaly parameters within the specified time period, and the historical monitoring values ​​of each parameter in the historical anomaly parameter subset whose historical safety label is an anomaly label at all historical monitoring time points within the specified time period. Historical Anomaly Parameter Set Unit: Based on the subset of historical anomaly parameters in all historical anomaly data within a specified time period, determine the set of historical anomaly parameters for the ship.

[0073] In this embodiment, the historical anomaly data unit extracts historical operational data for each specified time period from the database. From the extracted historical operational data, the system filters out historical anomaly data. This data identifies which parameters have exhibited abnormalities during historical operation. For example, the system can filter out historical anomaly data such as LNG tank level being too low, gas supply pipeline pressure being too high, and vaporizer medium temperature being abnormal.

[0074] In this embodiment, the subset of abnormal parameters refers to the set of all parameters whose historical safety tags are "abnormal" within a specified time period. For example, if the LNG storage tank level and the gas supply pipeline pressure have been abnormal within a certain time period, these two parameters will be included in the subset of historical abnormal parameters.

[0075] In this embodiment, the historical anomaly parameter set unit summarizes all historical anomaly parameter subsets within a specified time period to form a comprehensive historical anomaly parameter set.

[0076] The beneficial effects of the above technical solution are as follows: by determining the set of historical abnormal parameters of the ship based on historical operating data, potential anomalies can be accurately identified and predicted, measures can be taken in advance, the system's anomaly identification capability and anomaly handling efficiency can be improved, the risk of accidents can be reduced, and the safety and reliability of ship operation can be enhanced.

[0077] Example 8

[0078] This invention provides a safety monitoring system for marine LNG / diesel dual-fuel engines, including an anomaly module, and further comprising: Construction Unit: Based on the ship's historical anomaly parameter set and historical operational data for all specified time periods, construct an anomaly prediction model for the ship; Training Unit: The anomaly prediction model is trained based on the historical monitoring values ​​of the parameters with historical safety labels as anomaly labels in the historical anomaly parameter subset of all historical anomaly data for all specified time periods at all historical monitoring time points. The historical monitoring values ​​of the parameters with historical safety labels as warning labels in the historical warning parameter subset of all historical warning data for all specified time periods, as well as the historical monitoring time, are used as the output of the anomaly prediction model. Predicted anomaly data unit: The real-time monitoring value of each parameter in the real-time anomaly parameter set in the real-time anomaly data and the current time point are input into the anomaly prediction model. Based on the output of the anomaly prediction model, the predicted anomaly data is determined. The predicted anomaly data includes the predicted warning time points of all parameters in the real-time anomaly parameter set. Real-time anomaly alarm unit: issues real-time anomaly alarms based on the predicted warning time point of all parameters in each anomaly parameter set and the current time point.

[0079] In this embodiment, the construction unit integrates the historical set of anomaly parameters and historical operational data for all specified time periods to form a comprehensive dataset. A suitable prediction model is selected, such as a time series analysis model (e.g., ARIMA), a machine learning model (e.g., random forest, support vector machine), or a deep learning model (e.g., LSTM). Based on the integrated dataset, an anomaly prediction model is constructed to predict the time when each anomaly parameter exceeds its corresponding safety range.

[0080] In this embodiment, the training unit uses the historical monitoring values ​​of each parameter with a historical safety label as an anomaly label from the historical anomaly parameter set at all historical monitoring time points as the model input. It uses the historical monitoring values ​​of each parameter with a historical safety label as a warning label from the historical warning data of all specified time periods, along with the historical monitoring time, as the model output. The anomaly prediction model is trained using the input and output data to optimize model parameters and improve the accuracy and reliability of predictions.

[0081] In this embodiment, the real-time monitoring value of each parameter in the real-time anomaly parameter set and the current time point are input into the trained anomaly prediction model. The model predicts the potential warning time point for each parameter based on the input data.

[0082] In this embodiment, the real-time anomaly alarm unit compares the predicted warning time point with the current time point. If the predicted warning time point is close to the current time point, a real-time anomaly alarm is triggered. The alarm content includes detailed information such as parameter name, real-time monitoring value, and predicted warning time point. The alarm can be issued to the operator through audible and visual alarms, display screen prompts, or remote notifications, reminding them to take appropriate measures.

[0083] The beneficial effects of the above technical solution are as follows: Based on historical anomaly parameter sets and real-time anomaly data, predictable anomaly data can be identified and real-time anomaly alarms can be issued. This can improve the accuracy and timeliness of real-time anomaly data early warning, enhance the system's intelligence level, reduce accident risks, and improve the safety and reliability of ship operations.

[0084] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0085] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A safety monitoring system for a marine LNG / diesel dual-fuel engine, characterized in that, include: Monitoring module: Real-time monitoring of LNG supply data based on LNG supply, and real-time monitoring of diesel supply data based on diesel supply; Determine module: acquire safety preset data, determine the set of real-time abnormal parameters and the set of real-time early warning parameters based on LNG supply data, diesel supply data and safety preset data, and determine real-time abnormal data and real-time early warning data; Early warning module: acquires multiple historical operation data, and based on all historical operation data and real-time early warning data, determines the set of real-time early warning automatic parameters, real-time automatic control commands, and real-time early warning non-automatic parameters, executes real-time automatic control commands, and issues real-time early warning alarms; Anomaly Module: Based on historical operational data, determine the set of historical anomaly parameters of the ship. Based on the set of historical anomaly parameters and real-time anomaly data, use an anomaly prediction model built on the set of historical anomaly parameters of the ship and historical operational data of all specified time periods to determine the predicted anomaly data and issue real-time anomaly alarms. The determining module includes: Safety tag unit: Based on LNG supply data, diesel supply data, real-time temperature values, and safety preset data, determine the first real-time safety tag and the second real-time safety tag for all parameters; Parameter set unit: Based on the first real-time security label and the second real-time security label of all parameters, determine the real-time abnormal parameter set and the real-time early warning parameter set; The early warning module includes: Historical Operation Data Unit: Acquires historical operation data within multiple specified time periods, including historical early warning data and historical anomaly data within the specified time periods; Historical early warning data unit: Extracts historical early warning data from historical operational data within each specified time period. The historical early warning data includes a set of historical early warning parameters within the specified time period, as well as the historical monitoring values, historical monitoring time points, emergency strategies, emergency strategy labels, and emergency results for each parameter in the historical early warning parameter set that is labeled with an early warning label. The emergency strategy labels include automatic control strategies and non-automatic control strategies, and the emergency results include excellent, good, and average. Automatic control parameter subset unit: Extract all parameters in the historical early warning parameter set of historical early warning data for each specified time period that have the emergency strategy label of automatic control strategy and the emergency result of excellent, and determine the automatic control parameter subset for each specified time period; Early warning automatic control parameter set unit: Based on all automatic control parameter subsets for specified time periods, determine the ship's early warning automatic control parameter set; Emergency Strategy Table Unit: Based on all historical early warning data for all specified time periods, extract all historical monitoring values ​​and emergency strategies for each parameter in the ship's early warning automatic control parameter set that are labeled as automatic control strategies and have excellent emergency results, and construct an emergency strategy table for each parameter in the ship's early warning automatic control parameter set; Real-time early warning automatic parameter set unit: Based on all parameters in the real-time early warning parameter set in the real-time early warning data and the ship's early warning automatic control parameter set, determine the real-time early warning automatic parameter set; Real-time automatic control strategy unit: Calculate the difference between the real-time monitoring value of each parameter in the real-time warning automatic parameter set and all historical monitoring values ​​in the emergency strategy table of each parameter in the real-time warning automatic parameter set, and select the emergency strategy corresponding to the historical monitoring value with the smallest difference as the real-time automatic control strategy for each parameter in the real-time warning automatic parameter set; Real-time automatic control sub-instruction unit: Based on the real-time automatic control strategy of each parameter in the real-time early warning automatic parameter set, it generates real-time automatic control sub-instructions for each parameter in the real-time early warning automatic parameter set; Real-time automatic control instruction unit: Based on the real-time automatic control sub-instructions of all parameters in the real-time early warning automatic parameter set, determine the real-time automatic control instruction, and execute the real-time automatic control sub-instruction of each parameter in the real-time automatic control instruction; Real-time early warning non-automatic parameter set unit: Based on the real-time early warning parameter set and the real-time early warning automatic parameter set in the real-time early warning data, the real-time early warning non-automatic parameter set is determined.

2. The safety monitoring system for a marine LNG / diesel dual-fuel engine according to claim 1, characterized in that, The monitoring module includes: LNG Supply Data Unit: Based on the first sensor group, the LNG supply data is monitored in real time at the current time point. The LNG supply data includes LNG storage tank data, gas supply pipeline data, and vaporizer data. The LNG storage tank data includes real-time monitoring values ​​of multiple parameters labeled as LNG storage tank parameters. The gas supply pipeline data includes real-time monitoring values ​​of multiple parameters labeled as gas supply pipeline parameters. The vaporizer data includes real-time monitoring values ​​of multiple parameters labeled as vaporizer parameters. Diesel Supply Data Unit: Based on the second sensor group, the diesel supply data is monitored in real time at the current time point. The diesel supply data includes diesel storage tank data and fuel supply pipeline data. The diesel storage tank data includes real-time monitoring values ​​of multiple parameters labeled as diesel storage tank parameters, and the fuel supply pipeline data includes real-time monitoring values ​​of multiple parameters labeled as fuel supply pipeline parameters.

3. The safety monitoring system for a marine LNG / diesel dual-fuel engine according to claim 1, characterized in that, The module also includes: Safety Preset Data Unit: Acquires the ship's safety preset data, which includes LNG storage safety data, gas supply safety data, vaporization safety data, diesel storage safety data, and fuel supply safety data. LNG storage safety data includes the safety range of each parameter labeled as an LNG storage tank parameter. Gas supply safety data includes the safety range of each parameter labeled as an gas supply pipeline parameter. Vaporization safety data includes the safety range of each parameter labeled as a vaporizer parameter. Diesel storage safety data includes the safety range of each parameter labeled as a diesel storage tank parameter. Fuel supply safety data includes the safety range of each parameter labeled as a fuel supply pipeline parameter. Real-time temperature value unit: Obtains the real-time temperature value of the ship's location.

4. The safety monitoring system for a marine LNG / diesel dual-fuel engine according to claim 1, characterized in that, The module also includes: Real-time anomaly data unit: Based on all parameters in the real-time anomaly parameter set and the real-time monitoring value corresponding to each parameter, real-time anomaly data is determined; Real-time early warning data unit: Based on all parameters in the real-time early warning parameter set and the real-time monitoring value corresponding to each parameter, real-time early warning data is determined.

5. A safety monitoring system for a marine LNG / diesel dual-fuel engine according to claim 1, characterized in that, The early warning module also includes: Real-time early warning alarm unit: issues real-time early warning alarms for each parameter in the real-time early warning non-automatic parameter set.

6. A safety monitoring system for a marine LNG / diesel dual-fuel engine according to claim 1, characterized in that, The exception module includes: Historical anomaly data unit: Extracts historical anomaly data from historical operational data within each specified time period. The historical anomaly data includes a subset of historical anomaly parameters within the specified time period, and the historical monitoring values ​​of each parameter in the historical anomaly parameter subset whose historical safety label is an anomaly label at all historical monitoring time points within the specified time period. Historical Anomaly Parameter Set Unit: Based on the subset of historical anomaly parameters in all historical anomaly data within a specified time period, determine the set of historical anomaly parameters for the ship.

7. A safety monitoring system for a marine LNG / diesel dual-fuel engine according to claim 6, characterized in that, The exception module also includes: Construction Unit: Based on the ship's historical anomaly parameter set and historical operational data for all specified time periods, construct an anomaly prediction model for the ship; Training Unit: Based on the historical anomaly parameter set of the ship, the historical monitoring values ​​of each parameter with a historical safety label as an anomaly label in the historical anomaly parameter subset of all historical anomaly data of all specified time periods at all historical monitoring time points are used as input to the anomaly prediction model. The historical monitoring values ​​of each parameter with a historical safety label as a warning label in the historical warning parameter set of all historical warning data of all specified time periods, as well as the historical monitoring time, are used as output to train the anomaly prediction model of the ship. Predicted anomaly data unit: The real-time monitoring value of each parameter in the real-time anomaly parameter set in the real-time anomaly data and the current time point are input into the anomaly prediction model. Based on the output of the anomaly prediction model, the predicted anomaly data is determined. The predicted anomaly data includes the predicted warning time points of all parameters in the real-time anomaly parameter set. Real-time anomaly alarm unit: issues real-time anomaly alarms based on the predicted warning time point of all parameters in each anomaly parameter set and the current time point.

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