Ship fuel filling whole-process explosion-proof safety monitoring system

Abstract

The present application relates to the technical field of explosion-proof early warning, and discloses a ship fuel filling whole-process explosion-proof safety monitoring system, comprising a data acquisition unit, a risk analysis unit, a filling analysis unit, a risk calculation unit and an explosion-proof early warning unit; the monitoring nodes are arranged in the ship through the data acquisition unit, and sea wind data, flammable gas concentration, ship body posture data and filling data are synchronously acquired; the environmental risk index generated by the risk analysis unit can accurately reflect the influence of the environment on gas diffusion; the operation vulnerability coefficient generated by the filling analysis unit is used for capturing the cooperative risk of fuel filling operation and ship body movement; the safety risk situation field constructed by the risk calculation unit realizes risk space positioning; finally, the explosion-proof early warning unit generates an explosion-proof early warning value and triggers a graded safety early warning instruction, so that explosion-proof early warning can be carried out in time, and the safety during fuel filling is ensured.

Description

Technical Field

[0001] This invention relates to the field of explosion-proof early warning technology, specifically to an explosion-proof safety monitoring system for the entire process of ship fuel refueling. Background Technology

[0002] In existing ship fuel filling explosion prevention safety monitoring, explosion prevention safety monitoring usually adopts a fixed detector + threshold alarm mode. For example, combustible gas sensors are installed at key points such as filling port and fuel tank hatch to monitor the gas concentration at fixed locations. When the concentration reaches the preset alarm threshold, an early warning is triggered.

[0003] However, the above-mentioned explosion-proof early warning methods still have the following defects: In the open ocean, when a ship is refueling, the entire ship is in continuous rolling, pitching and heave motion due to the sea voyage, and is affected by unpredictable sea winds. The fuel leaked during refueling is volatile, and the resulting flammable gas spreads rapidly with the hull movement and airflow, and the diffusion trajectory is an irregular curve. Existing monitoring methods may fail to provide timely early warning because the flammable gas is diluted by the airflow and the concentration does not meet the standard, resulting in poor safety monitoring. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an explosion-proof safety monitoring system for the entire process of ship fuel refueling, thus solving the aforementioned problems.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution: The ship fuel refueling process explosion-proof safety monitoring system includes: The data acquisition unit is used to set up multiple monitoring nodes on the target vessel to acquire the sea breeze data and combustible gas concentration of each monitoring node in real time, and at the same time acquire the hull attitude data and fuel filling data of the target vessel in real time. The risk analysis unit is used to calculate the pre-processed ship attitude data and sea wind data to assess the comprehensive impact of the current marine environment on the diffusion and accumulation of combustible gases, and obtain the environmental risk index. The refueling analysis unit is used to calculate the pre-processed hull attitude data and refueling data, analyze the risk level of the refueling operation in a dynamic ship environment, and obtain the operational vulnerability coefficient. The risk calculation unit is used to analyze the distribution of combustible gas during the refueling process based on the combustible gas concentration and environmental risk index, and correlate it with the operational vulnerability coefficient to obtain a safety risk situation field. The explosion-proof early warning unit is used to analyze the safety risk situation field, obtain the explosion-proof early warning value, and trigger a safety early warning command based on the explosion-proof early warning value.

[0006] Furthermore, calculations are performed on the preprocessed ship attitude data and sea wind data to assess the comprehensive impact of the current marine environment on the diffusion and accumulation of combustible gases, resulting in an environmental risk index, including: Calculate the wind speed and direction in the sea breeze data to generate wind field clearing potential energy that represents the current wind field's ability to actively disperse combustible gases; Based on the roll and pitch angles in the ship's attitude data, the aerodynamic damping effect generated by the ship's motion is analyzed, and the spatial vortex drag coefficient is generated.

[0007] Furthermore, calculations are performed on the preprocessed ship attitude data and sea wind data to assess the comprehensive impact of the current marine environment on the diffusion and accumulation of combustible gases, resulting in an environmental risk index, which also includes: By combining the wind field clearing potential energy with the spatial vortex drag coefficient, the coupling effect between wind driving force and ship motion resistance is analyzed to generate the environmental suppression field strength. By analyzing the impact of heave in ship attitude data on atmospheric turbulence, a turbulence intensity correction factor is obtained. Based on the turbulence intensity correction factor, the environmental suppression field strength is corrected to obtain an environmental risk index representing the risk of gas accumulation.

[0008] Furthermore, calculations are performed on the preprocessed hull attitude data and refueling data to analyze the risk level of the refueling operation in a dynamic ship environment, yielding an operational vulnerability coefficient, including: Based on the pressure and flow rate in the refueling data, the stability of the refueling operation is analyzed, and the operation disturbance energy value is generated. Based on the roll and pitch angles in the ship's attitude data, the periodic motion of the ship is analyzed to generate an attitude stress risk domain representing the risk of leakage. A collaborative analysis of the operational disturbance energy value and attitude stress risk domain is conducted to assess the impact of operational disturbance and hull motion on the refueling process and generate an environmental operational risk level.

[0009] Furthermore, calculations are performed on the preprocessed hull attitude data and refueling data to analyze the risk level of the refueling operation in a dynamic ship environment, obtaining the operational vulnerability coefficient. This also includes: Based on the refueling duration in the refueling data, the periodic characteristics of the target vessel's heave motion are analyzed to generate a time-series risk amplification factor; By fusing environmental operational risk and time-series risk amplification factor, an operational vulnerability coefficient representing the overall risk of the current refueling operation is generated.

[0010] Furthermore, based on the combustible gas concentration and environmental hazard index, the distribution of combustible gas during the refueling process is analyzed and correlated with the operational vulnerability coefficient to obtain the safety risk situation field, including: By integrating the combustible gas concentration and environmental hazard index of each monitoring node, the abnormal accumulation of combustible gas concentration is analyzed, and its spatial vector is calculated to generate an abnormal sign vector representing the hazard signs and their development direction. By combining the abnormal symptom vector with the operational vulnerability coefficient, the severity of potential risks under the refueling operation state is assessed, and a risk intensity index is generated.

[0011] Furthermore, based on the combustible gas concentration and environmental hazard index, the distribution of combustible gas during the refueling process is analyzed and correlated with the operational vulnerability coefficient to obtain a safety risk situation field, which also includes: Based on the risk intensity index and monitoring nodes, the areas that currently require key monitoring are delineated, and the spatial hot zone range is generated; By integrating abnormal symptom vectors, risk intensity indices, and spatial hot zone ranges, a safety risk situation field is constructed.

[0012] Furthermore, the safety risk situation field is analyzed to obtain explosion-proof early warning values, including: Based on the safety risk situation field, the changes in combustible gas concentration within the space thermal zone are analyzed to generate field strength focusing intensity.

[0013] Furthermore, the safety risk situation field is analyzed to obtain explosion-proof early warning values, which also include: Based on the security risk situation field, we analyze the abnormal symptom vector and risk intensity index to generate a situation evolution entropy that represents the short-term development trend of risk. By integrating field strength focusing intensity with situational evolution entropy, an explosion-proof early warning value is generated.

[0014] Furthermore, based on the explosion-proof warning value, a safety warning instruction is triggered, including: Analyze the explosion-proof warning values ​​and generate graded safety warning instructions.

[0015] In summary, the present invention has the following main beneficial effects: By deploying monitoring nodes in the data acquisition unit, sea breeze data, combustible gas concentration, ship attitude data, and refueling data are acquired simultaneously, breaking the spatial limitations of traditional single-point monitoring. The risk analysis unit calculates the wind field clearance potential energy and spatial vortex drag coefficient, and combines the environmental suppression field strength and time flow intensity correction factor to generate an environmental risk index, accurately reflecting the comprehensive impact of sea breeze and ship roll, pitch, and heave on the diffusion and accumulation of combustible gases. Meanwhile, the refueling analysis unit generates operational disturbance energy values ​​based on refueling pressure and flow velocity, and combines the ship's motion characteristics to obtain the attitude stress risk domain, environmental operational risk level, and time-series risk amplification factor, ultimately merging them into an operational vulnerability coefficient. This accurately captures the risks associated with the dynamic coordination between fuel refueling operations and ship dynamics. At the same time, the safety risk situation field constructed by the risk calculation unit and the explosion-proof early warning unit generate explosion-proof early warning values, triggering graded safety early warning commands. This solution effectively adapts to the dynamic motion of ships and the influence of unpredictable sea breezes, avoids the problem of missed reporting caused by combustible gas dilution, provides timely explosion-proof early warnings, achieves early risk prediction, and ensures safe monitoring during fuel refueling. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the explosion-proof safety monitoring system for the entire process of ship fuel refueling according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] refer to Figure 1 The ship fuel refueling process explosion-proof safety monitoring system includes: The data acquisition unit is used to set up multiple monitoring nodes on the target vessel to acquire the sea breeze data and combustible gas concentration of each monitoring node in real time, and at the same time acquire the hull attitude data and fuel filling data of the target vessel in real time. Multiple monitoring points are located in the following areas: deck area, fuel tank area, fuel filling area, etc. Ship attitude data includes: roll angle, pitch angle, heave, etc. Sea breeze data includes: wind speed, wind direction, etc. The refueling data includes: refueling pressure, flow rate, refueling duration, etc. The risk analysis unit is used to calculate the pre-processed ship attitude data and sea wind data to assess the comprehensive impact of the current marine environment on the diffusion and accumulation of combustible gases, and obtain the environmental risk index. The refueling analysis unit is used to calculate the pre-processed hull attitude data and refueling data, analyze the risk level of the refueling operation in a dynamic ship environment, and obtain the operational vulnerability coefficient. The risk calculation unit is used to analyze the distribution of combustible gas during the refueling process based on the combustible gas concentration and environmental risk index, and correlate it with the operational vulnerability coefficient to obtain a safety risk situation field. The explosion-proof early warning unit is used to analyze the safety risk situation field, obtain the explosion-proof early warning value, and trigger a safety early warning command based on the explosion-proof early warning value.

[0019] In one embodiment, the preprocessed ship attitude data and sea wind data are used to calculate and assess the comprehensive impact of the current marine environment on the diffusion and accumulation of combustible gases, resulting in an environmental hazard index, including: The wind speed and direction in the sea breeze data are calculated to generate wind field clearing potential energy, which represents the current wind field's ability to actively disperse combustible gases. Specifically, this includes: acquiring 8 consecutive sets of sea breeze data from each monitoring node in real time, with an 8-second interval between each set; removing outliers where the wind speed value of a single set exceeds the average of the 8 sets of sea breeze data for that monitoring node; and using the average of the remaining sea breeze data as the base wind speed for each monitoring node. A 180-degree sector-shaped azimuth domain is defined centered on each monitoring node. The central direction of this azimuth domain is the direction from the fuel filling area towards the monitoring node. When the wind direction (i.e., the direction from which the wind is blowing) falls within this azimuth domain, it indicates that the wind is coming from the fuel filling area, which is beneficial for dispersing combustible gases from the filling area towards the monitoring node. The number of times the wind direction falls within this azimuth domain in 8 sets of data is recorded as the number of times the wind is effectively cleared. The effective wind clearing count is divided by 8 and then multiplied by 100% to obtain the percentage of effective wind directions. When the effective wind direction percentage is less than 50%, the effective wind direction percentage is divided by 0.5 and then multiplied by the base wind speed to obtain the effective wind speed of the monitoring node; when the effective wind direction percentage is greater than or equal to 50%, the base wind speed is directly used as the effective wind speed of the monitoring node. Meanwhile, if the roll angle of the target vessel is ≤5 degrees, the effective wind speed is directly used as the corrected wind speed; if the roll angle of the target vessel is ≥10 degrees, the effective wind speed is multiplied by 0.9 to obtain the corrected wind speed; otherwise, the effective wind speed is multiplied by 0.75 to obtain the corrected wind speed. The corrected wind speed is the wind field clearance potential energy of each monitoring node, which reflects the wind field's ability to actively disperse combustible gases.

[0020] Based on the roll and pitch angles in the ship's attitude data, the aerodynamic damping effect generated by the ship's motion is analyzed to generate a spatial vortex drag coefficient. Specifically, this involves: real-time acquisition of roll and pitch angle data at a sampling frequency of once per second, continuously collecting 10 samples; for each sample, calculating the vector sum of the roll and pitch angles as the instantaneous attitude amplitude; calculating the standard deviation of the instantaneous attitude amplitude for the 10 samples; and classifying the motion into three levels based on the standard deviation: a standard deviation < 2 degrees indicates stability, with a basic damping coefficient of 1; a standard deviation between 2 and 5 degrees indicates moderate motion, with a basic damping coefficient of 1.5; and a standard deviation > 5 degrees indicates severe motion, with a basic damping coefficient of 2. Simultaneously calculate the average absolute values ​​of the roll and pitch angles to obtain the average attitude angle; multiply the average attitude angle by a scaling factor of 0.05 to obtain the attitude damping increment; then add the basic damping coefficient to the attitude damping increment to obtain the spatial vortex drag coefficient, which is used to reflect the obstruction of gas diffusion by the airflow vortex drag caused by the hull rolling.

[0021] In one embodiment, the preprocessed ship attitude data and sea wind data are used to calculate and assess the comprehensive impact of the current marine environment on the diffusion and accumulation of combustible gases, resulting in an environmental hazard index. This also includes: By aggregating the wind field clearing potential energy and the spatial vortex drag coefficient, the coupling effect between wind driving force and ship motion resistance is analyzed to generate the environmental suppression field strength. Specifically, this includes: calculating the difference and average value of the maximum and minimum values ​​of the wind field clearing potential energy for all wind fields, and obtaining the wind field potential energy range and the overall wind driving force benchmark; then dividing the wind field potential energy range by the overall wind driving force benchmark to obtain the interference factor, which is used to reflect the local wind field non-uniformity caused by the ship structure; and subtracting 1 from the spatial vortex drag coefficient to obtain the pure motion damping increment. Then, the pure motion damping increment is multiplied by the interference factor to obtain the comprehensive damping effect value. The comprehensive damping effect value is then subtracted from the overall wind driving force benchmark. The calculation result is then mapped to the range of 0-100 to obtain the environmental suppression field strength. The environmental suppression field strength is used to reflect the overall suppression effect of the environment on the diffusion or accumulation of combustible gases under the combined action of wind driving force and ship motion resistance.

[0022] The influence of heave and sag in ship attitude data on atmospheric turbulence is analyzed to obtain a turbulence intensity correction factor. Based on this correction factor, the environmental suppression field strength is adjusted to obtain an environmental risk index representing the risk of gas accumulation. Specifically, this involves: continuously collecting 15 instantaneous heave and sag values ​​per minute; calculating the absolute value of the difference between two adjacent instantaneous values ​​to obtain 14 heave and sag amplitude sequences; calculating the average value of these sequences to obtain the average heave and sag rate of change, representing the intensity of ship motion within the period; and simultaneously calculating the standard deviation of the heave and sag amplitude sequences to obtain the heave and sag fluctuation intensity, representing the irregularity of motion. The atmospheric turbulence influence value is obtained by adding the average rate of change of heave and the intensity of heave fluctuation. If the atmospheric turbulence influence value is ≤1, the turbulence intensity correction factor is 1; otherwise, the turbulence intensity correction factor is 1 minus (atmospheric turbulence influence value minus 1) multiplied by 0.15. The environmental suppression field strength is multiplied by the turbulence intensity correction factor, and the calculation result is normalized to the 0-1 interval to obtain the environmental risk index representing the risk of gas accumulation.

[0023] By comprehensively analyzing pre-processed ship attitude data and sea wind data, the wind field clearing potential energy and spatial vortex drag coefficient are accurately calculated. Combined with the environmental suppression field strength and turbulence intensity correction factor, an environmental hazard index is generated. It is no longer limited to fixed-point gas concentration monitoring, but fully considers the dynamic changes of ship roll angle, pitch angle, heave and sag, as well as the influence of wind speed and wind direction. It accurately captures the irregular diffusion trajectory of combustible gases under the coupling effect of ship motion and sea wind, avoiding the problem of missed reports due to airflow dilution and insufficient concentration. It can comprehensively assess the combined impact of the environment on the diffusion and accumulation of combustible gases, facilitate timely explosion prevention warnings, improve the safety monitoring efficiency during the refueling process of ocean-going vessels, and reduce safety risks.

[0024] In one embodiment, the preprocessed hull attitude data and refueling data are calculated to analyze the risk level of the refueling operation in a dynamic ship environment, resulting in an operational vulnerability coefficient, including: Based on the pressure and flow rate data from the refueling data, the stability of the refueling operation is analyzed, and the operational disturbance energy value is generated. Specifically, this includes: simultaneously acquiring pressure and flow rate data at a sampling frequency of 5 Hz within a 30-second calculation window; for pressure data, calculating the average difference between all its maxima and minima to obtain the pressure pulsation amplitude; for flow rate data, calculating the absolute value of the flow rate change between each sampling point and the previous sampling point, and calculating the 85th quantile of these absolute values ​​of change to obtain the flow rate abrupt change characteristic value. Multiply the mean of all pressure data in the calculation window by 0.15 to obtain the pressure fluctuation threshold; if the pressure pulsation amplitude is less than the pressure fluctuation threshold, the pressure stability component is recorded as 1, otherwise the pressure stability component is 1.5. The pressure stability component is used to represent the pressure stability during the filling operation. Multiply the mean of all velocity data within the calculation window by 0.15 to obtain the velocity fluctuation threshold; if the velocity mutation characteristic value is less than the velocity fluctuation threshold, the velocity stability component is 1, otherwise the velocity stability component is 1.3; the velocity stability component is used to represent the stability of the velocity during the refueling operation; multiply the pressure stability component by the velocity stability component to obtain the operation disturbance energy value, which is used to reflect the instability risk of the refueling operation itself.

[0025] Based on the roll and pitch angles in the ship's attitude data, the periodic motion of the ship is analyzed to generate an attitude stress risk domain representing leakage risk. Specifically, this includes: continuously acquiring time series data of roll and pitch angles within a dynamic observation window with a period of five seconds; calculating the cross-correlation coefficient between the roll angle time series and its own roll angle time series lagged by one period to obtain the roll motion attenuation coefficient; obtaining the pitch motion attenuation coefficient of the pitch angle series using the same calculation method; and adding the two attenuation coefficients to obtain the motion spectral density factor, which is used to represent the energy concentration of the ship's periodic rolling motion. Simultaneously, the real-time phase difference between the roll and pitch time series within the same observation window is calculated. The absolute value of the real-time phase difference is divided by 180 to obtain the motion phase lag angle. The motion spectral density factor is multiplied by the motion phase lag angle to obtain the attitude stress risk domain representing the leakage risk.

[0026] A synergistic analysis of operational disturbance energy and attitude stress risk domain is conducted to assess the impact of operational disturbance and hull motion on the refueling process and generate an environmental operational risk level. Specifically, this includes: synchronously collecting time series data of operational disturbance energy and attitude stress risk domain at one-second intervals within a two-minute dynamic observation window; analyzing the synergistic trend of these two series: for each sampling moment, if the operational disturbance energy is higher than its own average value within the dynamic observation window, and the attitude stress risk domain is also higher than its own average value within the window, then that moment is marked as a double high-pressure moment. The total number of dual high-pressure moments within the dynamic observation window is counted, and this total is divided by the total number of sampling points in the dynamic observation window to obtain the high-risk co-occurrence rate. The high-risk co-occurrence rate reflects the probability that operational instability and hull stress risk occur simultaneously in time, thereby reflecting the impact of operational disturbances and hull motion on the refueling process. In each dual high-pressure moment, the operational disturbance energy value is multiplied by the attitude stress risk domain to obtain the initial co-occurrence risk. Then, the mean of all initial co-occurrence risks is calculated to obtain the risk intensity under the co-occurrence state. Finally, the high-risk co-occurrence rate is multiplied by the risk intensity and then multiplied by 100 to obtain the environmental operational risk level. The environmental operational risk level reflects the refueling operation risk under the combined effect of operational disturbances and hull dynamic motion.

[0027] In one embodiment, the preprocessed hull attitude data and refueling data are calculated to analyze the risk level of the refueling operation in a dynamic ship environment, and an operational vulnerability coefficient is obtained. The method also includes: Based on the refueling duration in the refueling data, analyze the periodic characteristics of the target vessel's heave motion and generate a time-series risk amplification factor. Specifically, this includes: starting from the start of the fuel refueling operation, synchronously and continuously recording the vessel's heave time series, using the data from the first ten minutes as the initial learning window; if the fuel refueling operation duration is less than ten minutes, then using the current total refueling duration as the initial learning window. Calculate the average of all local maxima of heave within the initial learning window as the initial feature amplitude; identify the complete heave motion cycle within the initial learning window and calculate its average period as the initial feature period. Then, taking one minute as an assessment period, the average absolute deviation of heave within the current assessment period is calculated. This average absolute deviation is divided by the initial characteristic amplitude to obtain the relative rate of change of amplitude. The Hurst exponent for the entire heave time series from the start of the fuel refueling operation to the current moment is calculated. The Hurst exponent is used to reflect the long-term memory and trend persistence of the motion process. The total refueling time is divided by the initial characteristic period to obtain the cumulative number of periods. Finally, the relative rate of change of amplitude, the Hurst exponent, and the cumulative number of periods are multiplied together, and the calculation result is normalized to the 0-1 interval. This is the time series risk amplification factor, which is used to reflect the amplification effect of the periodic characteristics of ship heave motion on operational risk.

[0028] The environmental operational risk level and the time-series risk amplification factor are fused to generate an operational vulnerability coefficient representing the overall risk of the current refueling operation. Specifically, this includes: establishing a dynamic reference time window with a length ten times that of the initial characteristic period; continuously recording the environmental operational risk level within the reference time window to form an environmental operational risk level sequence; and calculating the upper quartile of the environmental operational risk level sequence, which is used as the benchmark value for high-risk operations in the current stage. If the current environmental operational risk level is less than the high-risk operational benchmark, then the environmental operational risk level is used as the unamplified vulnerability median value; otherwise, the current environmental operational risk level is used as the base, and the time-series risk amplification factor is used as the exponent for power operation to obtain the unamplified vulnerability median value. Dividing the unamplified vulnerability median value by the maximum value in the environmental operational risk sequence within the reference time window yields the operational vulnerability coefficient, which represents the overall risk of the current injection operation.

[0029] By assessing the impact of sea wind and ship attitude on gas diffusion through wind field removal potential energy and spatial vortex drag coefficient, and by accurately analyzing the synergistic risks of refueling pressure, flow velocity stability, and ship roll, pitch, and heave based on operational disturbance energy, attitude stress risk domain, environmental operational risk level, and time-series risk amplification factor, the system can dynamically capture the synchronous risks of operational disturbances and ship motion, avoid the problem of unreported gas dilution, improve the safety monitoring efficiency of ocean fuel refueling, and ensure the timeliness of early warning.

[0030] In one embodiment, based on the combustible gas concentration and environmental hazard index, the distribution of combustible gas during the refueling process is analyzed and correlated with the operational vulnerability coefficient to obtain a safety risk situation field, including: By integrating the combustible gas concentration and environmental hazard index of each monitoring node, the abnormal accumulation of combustible gas concentration is analyzed, and its spatial vector is calculated to generate anomaly vectors representing hazard signs and their development direction. Specifically, this includes: identifying the node with the highest combustible gas concentration among all monitoring nodes as a high-concentration focus; calculating the average and standard deviation of combustible gas concentration at all monitoring nodes, and marking monitoring nodes that are one standard deviation above the average as potential anomalies. For each potential anomaly point, the minimum value of the combustible gas concentration of all monitoring nodes is subtracted from its combustible gas concentration to obtain the absolute concentration advantage value; at the same time, the reciprocal of the environmental risk index is multiplied by 10 to obtain the local environmental diffusion resistance coefficient. Divide the absolute concentration dominance value by the local environmental diffusion resistance coefficient to obtain the weighted anomaly intensity of potential anomalies. Using the high-concentration focus as the reference origin, each potential anomaly forms a logical vector with it. The direction of the logical vector points from the high-concentration focus to the potential anomaly. This direction needs to be converted into a specific angle. During the conversion, the ship's current true course is considered, where the true course is the geographic course angle, with true north as 0 degrees and measured clockwise. Determine the relative bearing of the potential anomaly relative to the high-concentration focus in the ship's coordinate system, such as forward, aft, left, or right. Then, convert this relative bearing to the geographic bearing angle: add the bearing angle to the ship's geographic course angle and take the modulus of 360 degrees, ensuring the value is between 0 and 360 degrees. The conversion is then complete. For example, if the ship's heading is 45 degrees, northeast, and the potential anomaly is located directly to the right of the high-concentration focus, which is 90 degrees in the ship's coordinate system, then the geographical bearing is 45 degrees + 90 degrees = 135 degrees; and the magnitude of the logical vector is the weighted anomaly intensity of the potential anomaly. By synthesizing all the logical vectors, we can obtain the abnormal sign vector that represents the danger signs and their development direction.

[0031] By combining the anomaly vector with the operational vulnerability coefficient, the severity of potential risks under the refueling operation is assessed, and a risk intensity index is generated. Specifically, the modulus of the anomaly vector is used as the intensity of abnormal gas accumulation, and the moving average of the modulus over the past three minutes is calculated to obtain the accumulation intensity benchmark. For the wind direction and wind speed data of all monitoring nodes in the fuel refueling area within the current three minutes, the wind speed value of each node is used as the weight, and its wind direction azimuth is converted into a unit vector and then weighted and summed. The direction of the resulting composite vector is the effective dominant wind direction. The wind direction azimuth is a geographic coordinate system, which is rotated clockwise with due north as 0 degrees. The effective dominant wind direction is also a geographic coordinate system. Match the direction of the anomalous sign vector with the effective prevailing wind direction: calculate the absolute value of the angle between the two. If the absolute value of the angle is ≤30 degrees, it is considered that the directions are consistent, and the operational vulnerability coefficient is multiplied by 1.2; otherwise, it is multiplied by 0.8 to obtain the wind field-corrected operational risk value. Finally, add the modulus of the anomalous sign vector to the wind field-corrected operational risk value and then subtract the concentration intensity benchmark. Map the calculation result to the range of 0-100 to obtain the risk intensity index. The risk intensity index is used to reflect the severity of potential risks under the refueling operation state.

[0032] In one embodiment, the distribution of combustible gas during the refueling process is analyzed based on the combustible gas concentration and environmental hazard index, and correlated with the operational vulnerability coefficient to obtain a safety risk situation field. This also includes: Based on the risk intensity index and monitoring nodes, the area requiring key monitoring is delineated, generating a spatial hot zone range. Specifically, this includes: normalizing the risk intensity index to the 0-1 interval to obtain the basic range coefficient; using all potential anomaly points identified during the generation of abnormal symptom vectors as core nodes to form a core node set; calculating the standard deviation and mean of combustible gas concentrations for all core nodes in the core node set, dividing the standard deviation by the mean to obtain the concentration distribution dispersion; and adding the basic range coefficient to the concentration distribution dispersion to obtain the comprehensive expansion factor. For each core node in the core node set, the distance between the monitoring node and all other monitoring nodes is calculated, and the monitoring node with the closest distance is identified as the neighboring node. The distance between the monitoring node and the neighboring node is calculated, and the distance is multiplied by a comprehensive expansion factor to obtain the initial hot zone radius centered on the core node. Finally, a geometric union operation is performed on the circular areas covered by all core nodes and their corresponding initial hot zone radii. The resulting continuous area is the area that needs to be monitored in the current key area, thus obtaining the spatial hot zone range.

[0033] By integrating the abnormal symptom vector, risk intensity index, and spatial hot zone range, a safety risk situation field is constructed. Specifically, this includes: using the geometric centroid of the spatial hot zone range as the hazard location coordinate; and normalizing the risk intensity index to the 0-1 interval as a global risk intensity scalar covering the entire spatial hot zone range to represent the overall severity. After normalizing the magnitude of the abnormal sign vector to the 0-1 range, it is used as a scalar of risk development momentum; the direction of the direction in the abnormal sign vector is used as the direction of the dominant risk trend. Then, by encapsulating the hazard location coordinates, global risk intensity scalar, risk development trend scalar, risk dominant trend direction, and spatial hot zone range, the safety risk situation field can be obtained.

[0034] By integrating combustible gas concentration, environmental hazard index, and operational vulnerability coefficient, and by generating anomaly symptom vectors, risk intensity indices, and spatial hot zone ranges, a safety risk situation field is constructed. This allows for the precise location of high-concentration focal points and potential anomalies. Combined with the ship's geographical heading angle to change azimuth, the direction and intensity of risk development are captured. Simultaneously, by linking the operational vulnerability coefficient, the system adapts to the effects of sea winds, ship roll angle, pitch angle, and heave, avoiding the problem of gas dilution underreporting and improving the safety protection capabilities of ocean bunkering.

[0035] In one embodiment, the safety risk situation field is analyzed to obtain explosion-proof warning values, including: Based on the safety risk situation field, the changes in combustible gas concentration within the space hot zone are analyzed to generate field strength focusing. Specifically, this includes: for the combustible gas concentration of all monitoring nodes within the space hot zone, calculating the difference between the maximum and minimum values ​​of these combustible gas concentrations to obtain the overall concentration range; for each monitoring node within the space hot zone, calculating the absolute value of the difference between the combustible gas concentration of that monitoring node and the combustible gas concentration of its nearest neighboring monitoring node to obtain local concentration abrupt changes. Divide the local concentration mutation by the overall concentration range and add 1 to obtain the dynamic modulation factor. Finally, multiply the overall concentration range by the dynamic modulation factor to obtain the field intensity focusing degree. The field intensity focusing degree is used to reflect the degree of spatial focusing of combustible gas concentration within the space hot zone.

[0036] In one embodiment, analyzing the safety risk situation field to obtain an explosion-proof warning value further includes: Based on the security risk situation field, the abnormal symptom vector and risk intensity index are analyzed to generate a situation evolution entropy that represents the short-term development trend of risk. Specifically, a five-minute rolling time window is set, and for the security risk situation field, a continuous sequence of risk-dominant trend directions is obtained within the rolling time window at ten-second intervals. Calculate whether two adjacent risk-dominant trend directions in the risk-dominant trend direction sequence have changed. If they have changed, the change state is 1; otherwise, the change state is 0, thus obtaining the change state sequence. Calculate the proportion of states with 1 in the change state sequence as the directional instability rate. The ratio of the maximum to the minimum magnitude of the abnormal symptom vector within the rolling time window is used as the intensity fluctuation ratio; and the directional instability rate is multiplied by the intensity fluctuation ratio to obtain the situation evolution entropy, which represents the short-term development trend of the risk.

[0037] The explosion-proof warning value is generated by fusing the field strength focusing degree with the situation evolution entropy. Specifically, the situation evolution entropy is divided by 10 and then added by 1, which is then converted into a composite modulation factor of not less than 1. The field strength focusing degree is used as the base, and the composite modulation factor is used as the exponent for power operation. The result of the power operation is taken as the common logarithm with base 10, and then the calculation result is mapped to the range of 0-100 to obtain the explosion-proof warning value. The core function of exponentiation is that when the degree of dynamic disorder of risk represented by the situation evolution entropy is higher, the composite modulation factor is larger, thereby producing a nonlinear amplification effect on the field strength focusing degree representing the spatial risk concentration; conversely, the amplification effect is gentler.

[0038] In one embodiment, a safety warning command is triggered based on an explosion-proof warning value, including: Analyze the explosion-proof warning values ​​and generate graded safety warning instructions. Specifically, this includes: calculating the 75th percentile and 25th percentile of the explosion-proof warning values ​​in the last 15 minutes, using the 25th percentile as the low-risk dynamic threshold and the 75th percentile as the high-risk dynamic threshold. If the explosion-proof warning value is less than the low-risk dynamic threshold, a Level 1 safety warning instruction will be triggered directly. If the explosion-proof warning value is greater than the high-risk dynamic threshold, a level 3 safety warning instruction will be triggered directly. Otherwise, calculate the rate of change of the current explosion-proof warning value relative to the explosion-proof warning value of the previous minute. If the rate of change is positive, it indicates that the risk is rising and triggers a level two safety warning instruction; if the rate of change is not positive, it indicates that the risk is falling and triggers a level one safety warning instruction. The severity of the warnings is ranked as follows: Level 3 security warning instruction > Level 2 security warning instruction > Level 1 security warning instruction.

[0039] By capturing the concentration of combustible gases within a hot zone of space using field strength focusing intensity, and combining this with situational evolution entropy to predict short-term risk development trends, explosion-proof warning values ​​are generated. Based on these values, level one, level two, and level three safety warning instructions are assigned to accurately avoid the problem of unreported gas dilution, thereby improving the timeliness and relevance of early warnings for ocean fuel refueling.

[0040] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A ship fuel refueling full-process explosion-proof safety monitoring system, characterized in that, include: The data acquisition unit is used to set up multiple monitoring nodes on the target vessel to acquire the sea breeze data and combustible gas concentration of each monitoring node in real time, and at the same time acquire the hull attitude data and fuel filling data of the target vessel in real time. The risk analysis unit is used to calculate the pre-processed ship attitude data and sea wind data to assess the comprehensive impact of the current marine environment on the diffusion and accumulation of combustible gases, and obtain the environmental risk index. The refueling analysis unit is used to calculate the pre-processed hull attitude data and refueling data, analyze the risk level of the refueling operation in a dynamic ship environment, and obtain the operational vulnerability coefficient. The risk calculation unit is used to analyze the distribution of combustible gas during the refueling process based on the combustible gas concentration and environmental risk index, and correlate it with the operational vulnerability coefficient to obtain a safety risk situation field. The explosion-proof early warning unit is used to analyze the safety risk situation field, obtain the explosion-proof early warning value, and trigger a safety early warning command based on the explosion-proof early warning value.

2. The explosion-proof safety monitoring system for the entire process of ship fuel refueling according to claim 1, characterized in that, The preprocessed ship attitude data and sea wind data are used to calculate and assess the comprehensive impact of the current marine environment on the diffusion and accumulation of combustible gases, resulting in an environmental risk index, including: Calculate the wind speed and direction in the sea breeze data to generate wind field clearing potential energy that represents the current wind field's ability to actively disperse combustible gases; Based on the roll and pitch angles in the ship's attitude data, the aerodynamic damping effect generated by the ship's motion is analyzed, and the spatial vortex drag coefficient is generated.

3. The explosion-proof safety monitoring system for the entire process of ship fuel refueling according to claim 2, characterized in that, The preprocessed ship attitude data and sea wind data are used to calculate and assess the comprehensive impact of the current marine environment on the diffusion and accumulation of combustible gases, resulting in an environmental risk index, which also includes: By combining the wind field clearing potential energy with the spatial vortex drag coefficient, the coupling effect between wind driving force and ship motion resistance is analyzed to generate the environmental suppression field strength. By analyzing the impact of heave in ship attitude data on atmospheric turbulence, a turbulence intensity correction factor is obtained. Based on the turbulence intensity correction factor, the environmental suppression field strength is corrected to obtain an environmental risk index representing the risk of gas accumulation.

4. The explosion-proof safety monitoring system for the entire process of ship fuel refueling according to claim 1, characterized in that, The preprocessed hull attitude data and refueling data are used to calculate and analyze the risk level of the refueling operation in a dynamic ship environment, resulting in an operational vulnerability coefficient, including: Based on the pressure and flow rate in the refueling data, the stability of the refueling operation is analyzed, and the operation disturbance energy value is generated. Based on the roll and pitch angles in the ship's attitude data, the periodic motion of the ship is analyzed to generate an attitude stress risk domain representing the risk of leakage. A collaborative analysis of the operational disturbance energy value and attitude stress risk domain is conducted to assess the impact of operational disturbance and hull motion on the refueling process and generate an environmental operational risk level.

5. The explosion-proof safety monitoring system for the entire process of ship fuel refueling according to claim 4, characterized in that, The preprocessed hull attitude data and refueling data are used to calculate and analyze the risk level of the refueling operation in a dynamic ship environment, resulting in an operational vulnerability coefficient. This also includes: Based on the refueling duration in the refueling data, the periodic characteristics of the target vessel's heave motion are analyzed to generate a time-series risk amplification factor; By fusing environmental operational risk and time-series risk amplification factor, an operational vulnerability coefficient representing the overall risk of the current refueling operation is generated.

6. The explosion-proof safety monitoring system for the entire process of ship fuel refueling according to claim 5, characterized in that, Based on the combustible gas concentration and environmental hazard index, the distribution of combustible gas during the refueling process is analyzed and correlated with the operational vulnerability coefficient to obtain the safety risk situation field, including: By integrating the combustible gas concentration and environmental hazard index of each monitoring node, the abnormal accumulation of combustible gas concentration is analyzed, and its spatial vector is calculated to generate an abnormal sign vector representing the hazard signs and their development direction. By combining the abnormal symptom vector with the operational vulnerability coefficient, the severity of potential risks under the refueling operation state is assessed, and a risk intensity index is generated.

7. The explosion-proof safety monitoring system for the entire process of ship fuel refueling according to claim 6, characterized in that, Based on the combustible gas concentration and environmental hazard index, the distribution of combustible gas during the refueling process is analyzed and correlated with the operational vulnerability coefficient to obtain a safety risk situation field, which also includes: Based on the risk intensity index and monitoring nodes, the areas that currently require key monitoring are delineated, and the spatial hot zone range is generated; By integrating abnormal symptom vectors, risk intensity indices, and spatial hot zone ranges, a safety risk situation field is constructed.

8. The explosion-proof safety monitoring system for the entire process of ship fuel refueling according to claim 7, characterized in that, An analysis of the safety risk situation field yields explosion-proof early warning values, including: Based on the safety risk situation field, the changes in combustible gas concentration within the space thermal zone are analyzed to generate field strength focusing intensity.

9. The explosion-proof safety monitoring system for the entire process of ship fuel refueling according to claim 8, characterized in that, The analysis of the safety risk situation field yields explosion-proof early warning values, which also include: Based on the security risk situation field, we analyze the abnormal symptom vector and risk intensity index to generate a situation evolution entropy that represents the short-term development trend of risk. By integrating field strength focusing intensity with situational evolution entropy, an explosion-proof early warning value is generated.

10. The explosion-proof safety monitoring system for the entire process of ship fuel refueling according to claim 9, characterized in that, Based on the explosion-proof warning value, a safety warning instruction is triggered, including: Analyze the explosion-proof warning values ​​and generate graded safety warning instructions.

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