LNG Ship Loading and Unloading Material Balance Dynamic Monitoring Cloud Platform and Method

Abstract

The present invention discloses a dynamic monitoring cloud platform and method for the material balance of LNG ship loading and unloading, which relates to the technical field of material monitoring. The monitoring end dynamically corrects the target flow curve according to the actual operation conditions and influencing factors. The sensing end collects real-time sensor data at the operation site, and based on the obstacle recognition and state change mechanism, automatically senses operation anomalies and adaptively adjusts the data acquisition parameters. The control end continuously monitors the state changes of the target flow curve and acquisition parameters, automatically adjusts the material balance judgment window, and calculates the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, the linkage mechanism is automatically activated. The monitoring platform realizes hierarchical acquisition of information and multi-source fusion processing through functional decoupling and logical linkage, forms a dynamic closed-loop control over the entire operation process, realizes the full-process series analysis of multi-point data, and improves the control over the overall material movement trajectory.

Description

Technical Field

[0001] The present invention relates to the technical field of material monitoring, and particularly to a dynamic monitoring cloud platform and method for the material balance of LNG ship loading and unloading. Background Art

[0002] With the continuous optimization of the global energy structure, liquefied natural gas (LNG) has become one of the important energy transportation forms due to its clean, efficient, and safe characteristics. In the LNG industrial chain, ship loading and unloading operations, as a key link, are directly related to the accuracy of energy measurement, the level of loading and unloading efficiency, and the safety of the operation process.

[0003] Most traditional LNG ship loading and unloading material monitoring systems adopt a monitoring method of fixed points + fixed time windows, and the monitoring content is limited to single-point data (such as flow meters, level gauges), making it difficult to cover the material flow process of the entire operation process and the entire link, and there are the following defects:

[0004] 1. Lack of the ability to perceive the overall trend of loading and unloading, and unable to dynamically perceive the deviation between the planned progress and the on-site changes;

[0005] 2. The monitoring results rely on fixed reference values, and the response to sudden interferences (such as equipment abnormalities, weather fluctuations) is lagging;

[0006] 3. The overall system cannot achieve trend correction and target update, resulting in the out-of-sync between the planned scheduling and the actual material transportation.

[0007] Based on this, the present invention proposes a dynamic monitoring cloud platform and method for the material balance of LNG ship loading and unloading. By performing functional decoupling and logical linkage, it realizes hierarchical collection and multi-source fusion processing of information, forms a dynamic closed-loop control over the entire operation process, realizes the full-process series analysis of multi-point data, and improves the control ability of the overall material movement trajectory. Summary of the Invention

[0008] The purpose of the present invention is to provide a dynamic monitoring cloud platform and method for the material balance of LNG ship loading and unloading to solve the deficiencies in the background art.

[0009] To achieve the above purpose, the present invention provides the following technical solution: A dynamic monitoring method for the material balance of LNG ship loading and unloading, the monitoring method includes the following steps:

[0010] The monitoring end collects global data and dynamically corrects the target flow curve according to the actual operation situation and influencing factors;

[0011] The perception end collects real-time sensor data of the operation site, and based on the obstacle recognition and state change mechanism, automatically perceives operation abnormalities and adaptively adjusts the data collection parameters;

[0012] The control end continuously monitors the status changes of the target flow curve and the acquisition parameters, automatically adjusts the material balance judgment window, and calculates the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, the linkage mechanism is automatically activated.

[0013] Preferably, the control end continuously monitors the status changes of the target flow curve and the acquisition parameters, and automatically adjusts the material balance judgment window, including the following steps:

[0014] According to the changes in the target flow curve and the acquisition parameter status, the control end automatically adjusts the time period for material balance calculation. The expression is: , where is the adjusted material balance judgment window, is the material balance judgment window before adjustment, is the trend influence factor.

[0015] Preferably, the control end calculates the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, the linkage mechanism is automatically activated, including the following steps:

[0016] Calculate the cumulative material balance deviation based on the adjusted material balance judgment window. The expression is: , where are the start and end times of the adjusted time window, is the material balance deviation, is the input flow per unit time, is the output flow per unit time;

[0017] Compare the obtained material balance deviation with the preset first deviation threshold and the second deviation threshold. The first deviation threshold is less than the second deviation threshold, and the first deviation threshold is used to analyze whether the cumulative input flow is excessively lower than the cumulative output flow, and the second deviation threshold is used to analyze whether the cumulative input flow is excessively higher than the cumulative output flow;

[0018] If the material balance deviation is greater than or equal to the first deviation threshold and less than or equal to the second deviation threshold, it indicates that the logistics is in a balanced state;

[0019] If the material balance deviation is less than the first deviation threshold, analyze that the cumulative input flow is excessively lower than the cumulative output flow, and the linkage mechanism needs to be activated. If the material balance deviation is greater than the second deviation threshold, analyze that the cumulative input flow is excessively higher than the cumulative output flow, and the linkage mechanism needs to be activated.

[0020] Preferably, the sensing end collects the real-time sensor data at the operation site, automatically senses the operation anomalies based on the obstacle recognition and status change mechanism, and adaptively adjusts the data acquisition parameters, including the following steps:

[0021] Through the sensor network, various data at the operation site are collected in real time, including liquid level, temperature, pressure, valve status, and the movement status of loading and unloading arms;

[0022] Through real-time data analysis, abnormal situations occurring during the operation process are identified, including:

[0023] When the data exceeds the normal range, it is marked as abnormal, and frequency analysis and signal processing techniques are used to identify the jitter or instability of the equipment.

[0024] Liquid level mutation detection: By analyzing the rate of change of the liquid level, it is detected whether there is abnormal fluctuation in the liquid level, the mutation of the pipeline pressure is monitored, the pressure fluctuation frequency is analyzed, and it is judged whether there are abnormal situations such as equipment failures or pipeline leaks;

[0025] According to the real-time status and dynamic changes at the operation site, the granularity, frequency, and point priority of data collection are adjusted.

[0026] Preferably, according to the real-time status and dynamic changes at the operation site, adjusting the granularity, frequency, and point priority of data collection includes the following steps:

[0027] During the operation process, when abnormal fluctuations or status changes are identified, the time interval of data collection is automatically shortened to improve the data collection granularity;

[0028] When equipment abnormalities or operation status changes are detected, the data collection frequency is increased;

[0029] According to the changes in the on-site operation status, the collection priorities of different points are dynamically adjusted.

[0030] Preferably, through real-time data analysis, identifying abnormal situations occurring during the operation process includes the following steps:

[0031] When the monitored value exceeds the normal range, an abnormal alarm is automatically triggered, and frequency analysis and signal processing techniques are used to identify the jitter or instability of the equipment;

[0032] Process the data of the acceleration sensor or vibration sensor of the equipment, perform frequency-domain analysis on the vibration signal using Fourier transform, extract the frequency components in the signal, identify the high-frequency components, and Fourier transform is used to convert the time-domain signal into a frequency-domain signal, and the expression is: , where is the spectrum of the signal, is the original sampled data, is the frequency, is the number of data points, and j is the imaginary unit;

[0033] If the liquid level change rate is greater than the change threshold, it is marked as a liquid level mutation. If the pressure change rate is greater than the change threshold, it is marked as a pressure fluctuation.

[0034] Preferably, the calculation logic of the liquid level change rate is as follows: obtain the liquid level difference by subtracting the liquid level at the previous moment from the liquid level at the current moment, and obtain the liquid level change rate by dividing the liquid level difference by the time interval between each liquid level data.

[0035] The calculation logic of the pressure change rate is as follows: obtain the pressure difference by subtracting the pressure at the previous moment from the pressure at the current moment, and obtain the pressure change rate by dividing the pressure difference by the time interval between each pressure data.

[0036] Preferably, the monitoring end collects global data and dynamically corrects the target flow curve according to the actual operation situation and influencing factors, including the following steps:

[0037] Obtain real-time data from the LNG loading and unloading system, including the total LNG flow rate, the change in the ship's tank capacity, the main pipeline flow rate, and the progress index of the loading and unloading plan, and set the target flow curve based on the loading and unloading plan and the initial system configuration.

[0038] Obtain external influencing factors, conduct a comparative analysis based on the real-time data and the set benchmark, dynamically correct the target flow curve after identifying the deviation source, and set a new global benchmark.

[0039] Preferably, obtain external influencing factors, conduct a comparative analysis based on the real-time data and the set benchmark, and dynamically correct the target flow curve after identifying the deviation source, including the following steps:

[0040] Define the target flow curve: , obtain the current actual flow data: , calculate the flow deviation between the target flow curve and the actual flow: , where is the flow deviation at time t.

[0041] Adjust the target flow curve in real time according to the external factor correction factor, and the expression is: , where is the dynamically corrected target flow curve, is the target flow curve before dynamic correction, is the flow deviation, is the external factor correction factor.

[0042] This application also proposes a dynamic monitoring cloud platform for the material balance of LNG ship loading and unloading, including a data monitoring layer, a field perception layer, and an adjustment control layer;

[0043] Data Monitoring Layer: Used to collect global data and dynamically correct the target flow curve according to the actual operation conditions and influencing factors;

[0044] On-site Sensing Layer: Used to collect real-time sensor data of the operation site. Based on the obstacle recognition and status change mechanism, it automatically senses operation anomalies and adaptively adjusts the data collection parameters;

[0045] Adjustment and Control Layer: Continuously monitors the status changes of the target flow curve and collection parameters, automatically adjusts the material balance judgment window, and calculates the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, it automatically activates the linkage mechanism.

[0046] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0047] In the present invention, the monitoring end dynamically corrects the target flow curve according to the actual operation conditions and influencing factors, the sensing end collects real-time sensor data of the operation site, automatically senses operation anomalies based on the obstacle recognition and status change mechanism, and adaptively adjusts the data collection parameters. The control end continuously monitors the status changes of the target flow curve and collection parameters, automatically adjusts the material balance judgment window, and calculates the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, it automatically activates the linkage mechanism. The monitoring platform realizes hierarchical collection and multi-source fusion processing of information through functional decoupling and logical linkage, forms a dynamic closed-loop control over the entire operation process, realizes full-process series analysis of multi-point data, and improves the control ability of the overall material movement trajectory. Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is the method flow chart of the present invention.

[0050] Figure 2 It is the mind map of the present invention.

[0051] Figure 3 It is the system architecture diagram of the present invention. Detailed Embodiments

[0052] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0053] Embodiment 1: Please refer to Figure 1 and Figure 2 As shown, the dynamic monitoring method for the material balance of LNG ship loading and unloading in this embodiment includes the following steps:

[0054] The monitoring end collects global data, such as the total LNG flow rate, the change in the ship's tank capacity, the main pipeline flow rate, the progress of the loading and unloading plan, etc., and dynamically corrects the target flow curve according to the actual operation conditions and influencing factors (weather, equipment, etc.) to achieve real-time update of the planned trend and automatic calibration of the global benchmark. The sensing end collects real-time sensor data at the operation site, including liquid level, temperature, pressure, valve status, loading and unloading arm movement status, etc., to reflect the immediate status and dynamic changes during the operation. Based on the obstacle recognition and status change mechanism, it automatically senses operation anomalies (such as equipment jitter, sudden change in liquid level, etc.), and adaptively adjusts the data collection parameters (granularity, frequency, point priority) to achieve a rapid response to the on-site dynamics. The control end continuously monitors the status changes of the target flow curve and the collection parameters, automatically adjusts the material balance judgment window (for example, time period, number of sampling points), and calculates the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, it automatically activates the linkage mechanism (including feedback to the monitoring end that the loading and unloading plan may be unreachable, suggesting adjustment of the progress, issuing data verification and equipment inspection instructions to the sensing end, and activating the alarm mechanism to notify the operator or pause the loading and unloading).

[0055] In this application, the monitoring end dynamically corrects the target flow curve according to the actual operation conditions and influencing factors, the sensing end collects real-time sensor data at the operation site, automatically senses operation anomalies based on the obstacle recognition and status change mechanism, and adaptively adjusts the data collection parameters. The control end continuously monitors the status changes of the target flow curve and the collection parameters, automatically adjusts the material balance judgment window, and calculates the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, it automatically activates the linkage mechanism. The monitoring platform realizes hierarchical collection of information and multi-source fusion processing through functional decoupling and logical linkage, forms a dynamic closed-loop control over the entire operation process, realizes full-process series analysis of multi-point data, and improves the control ability of the overall material movement track.

[0056] Embodiment 2: The monitoring end collects global data, such as the overall LNG flow rate, the change in the ship's tank capacity, the main pipeline flow rate, the progress of the loading and unloading plan, etc., and dynamically corrects the target flow curve according to the actual operation conditions and influencing factors (weather, equipment, etc.) to achieve real-time update of the planned trend and automatic calibration of the global benchmark, including the following steps:

[0057] Obtain real-time data from the LNG loading and unloading system, including key indicators such as the overall LNG flow rate, the change in the ship's tank capacity, the main pipeline flow rate, the progress of the loading and unloading plan, etc., and set the target flow curve based on the loading and unloading plan and the initial system configuration (such as the ideal loading and unloading speed, the expected change in the ship's tank volume). The target flow curve should consider the flow fluctuations when the ship enters and leaves the port and the changes in the operation plan.

[0058] Obtain external influencing factors, such as weather (wind speed, temperature, humidity, etc.), equipment status (equipment operation conditions, failure rate, maintenance plan, etc.), conduct comparative analysis based on the real-time data and the set benchmark, identify the deviation sources (such as unstable flow, equipment anomalies, etc.), dynamically correct the target flow curve, and set a new global benchmark.

[0059] During the dynamic monitoring of the material balance in the LNG ship loading and unloading process, it is crucial to collect external influencing factors (such as weather, equipment status, etc.) in real time and correct the target flow curve based on these data. After obtaining the external influencing factors, conducting comparative analysis based on the real-time data and the set benchmark, identifying the deviation sources, dynamically correcting the target flow curve, and setting a new global benchmark, including the following steps:

[0060] Collect weather factors affecting the loading and unloading operation, such as wind speed, temperature, humidity, etc., collect the operation status data of key equipment, including operation conditions, failure rate, maintenance plan, etc., and define the target flow curve according to the loading and unloading plan and the initial setting: , The target flow curve is usually an ideal flow change curve, which represents the change of flow with time when interference factors are not considered.

[0061] Obtain the current actual flow data: , which reflects the real-time change of the flow rate during the on-site loading and unloading process. Compare the real-time flow data with the target flow curve, calculate the deviation and analyze the possible deviation sources, and calculate the flow deviation between the target flow curve and the actual flow: , where is the flow deviation at time t. The flow deviation is used to compare the deviation between the target flow and the actual flow in real time, reflects whether the flow is stable, and further analyzes whether it is necessary to adjust the flow target. The deviation mainly comes from external factors such as weather changes and equipment anomalies, and the target flow curve is adjusted in real time according to the external factor correction factor. , the dynamically corrected target flow curve will become the new global benchmark to further adjust the operation plan.

[0062] According to the influence of external factors (such as wind speed, temperature, equipment status, etc.) on the flow rate, correct the target flow curve. The calculation formula for the external correction factor is as follows: , where is the external factor correction factor, is the weather factor correction factor, is the equipment status correction factor. The external factor correction factor is used to comprehensively evaluate the influence of weather and equipment status on the flow rate target and adjust the target flow curve. The flow rate target under different weather conditions or equipment status should be appropriately adjusted to avoid operation deviations caused by external factors.

[0063] The dynamically corrected target flow curve can be calculated by the following formula: , where is the dynamically corrected target flow curve, is the target flow curve before dynamic correction, is the flow deviation, is the external factor correction factor. Take the dynamically corrected target flow curve as the new global benchmark flow curve, which reflects the new standard flow rate provided to the system after dynamic correction.

[0064] When the wind speed increases, the resistance of the flow rate increases, which may lead to a decrease in the LNG transportation efficiency and a reduction in the flow rate target. If the wind speed is high, the flow rate value of the target flow curve should be reduced. Especially in the case of high wind speed, a maximum flow rate limit can be preset to avoid flow rate instability or equipment overload. When the temperature rises, the viscosity of LNG decreases and its fluidity increases. At this time, the target flow rate should be appropriately increased. When the failure rate increases, it indicates that the number of equipment failures is frequent, and the flow rate will be greatly affected, and the flow rate target needs to be reduced.

[0065] Normalize the wind speed, temperature, and equipment failure rate so that the value ranges of the wind speed, temperature, and equipment failure rate are mapped to [0,1]. Subtract the wind speed and equipment failure rate from the normalized temperature to obtain the external factor correction factor. When the external factor correction factor is large, it indicates that the temperature is high, the wind speed is low, and the equipment failure rate is low. These conditions are conducive to LNG loading and unloading operations, and the flow rate stability and efficiency are high. Therefore, the target flow rate can be appropriately increased. When the external factor correction factor is small, the temperature is low, the wind speed is high, and the equipment failure rate is high. These conditions are not conducive to LNG loading and unloading operations and may reduce the flow rate stability and efficiency. Therefore, the target flow rate should be reduced.

[0066] The sensing end collects real-time sensor data of the operation site, including liquid level, temperature, pressure, valve status, loading and unloading arm movement status, etc., which reflects the immediate status and dynamic changes during the operation process. Based on the obstacle recognition and status change mechanism, it automatically senses operation anomalies (such as equipment jitter, sudden liquid level change, etc.), and adaptively adjusts the data collection parameters (granularity, frequency, point priority) to achieve a rapid response to the on-site dynamics, including the following steps:

[0067] Through the sensor network, various data of the operation site are collected in real time, including: liquid level (reflecting the change of LNG liquid level in the storage tank), temperature (the change of liquid temperature has a direct impact on the LNG loading and unloading process), pressure (the pressure change in the ship and pipeline affects the flow rate and equipment operation), valve status (the valve opening and closing status determines the flow rate adjustment), and loading and unloading arm movement status (reflecting the operation of the loading and unloading arm to ensure the smoothness of the loading and unloading process).

[0068] Through real-time data analysis, identify possible abnormal situations during the operation process, such as equipment failures or sudden changes, including:

[0069] Data monitoring and threshold judgment: Set reasonable threshold ranges (such as the normal ranges of liquid level and pressure). When the data exceeds the normal range, the system immediately marks it as abnormal.

[0070] Equipment jitter identification: Through the small fluctuations or irregular changes in the sensor data, use frequency analysis and signal processing techniques (such as Fourier transform, filtering) to identify the jitter or instability of the equipment.

[0071] Sudden liquid level change detection: By analyzing the rate of change of the liquid level, detect whether there is an abnormal fluctuation in the liquid level. An overly rapid change in the liquid level may be an abnormal situation during the loading and unloading operation.

[0072] Pressure fluctuation judgment: Monitor sudden changes in pipeline pressure, analyze the pressure fluctuation frequency, and judge whether there are abnormal situations such as equipment failures or pipeline leaks.

[0073] According to the requirements of real-time data collection, identify possible abnormal situations during the operation process, including equipment failures, liquid level changes, and pressure fluctuations, etc. Through real-time data analysis, identify possible abnormal situations during the operation process, such as equipment failures or sudden changes, including the following steps:

[0074] Obtain real-time sensor data, monitor key variables such as liquid level and pressure, and set a reasonable normal range for each monitoring index. Taking the liquid level as an example, the normal range of the liquid level is set as: , where is the current liquid level. When the monitored value exceeds the normal range, the system automatically triggers an abnormal alarm.

[0075] By detecting minute fluctuations or irregular changes in sensor data, frequency analysis and signal processing techniques (such as Fourier transform and filtering) are used to identify the jitter or instability of the device. The data from the device's acceleration sensor or vibration sensor is processed. The Fourier transform is used to perform frequency-domain analysis on the vibration signal, extract the frequency components in the signal, and identify the high-frequency components (indicating the jitter or instability of the device). The Fourier transform is used to convert a time-domain signal into a frequency-domain signal, and the expression is: , where is the spectrum of the signal, is the original sampled data, is the frequency, is the number of data points, and j is the imaginary unit. Through spectrum analysis, it is identified whether there are periodic high-frequency components in the signal, indicating that the device is jittering or unstable. Through Fourier transform analysis, minute fluctuations or periodic jitters during the operation of the device are identified, which helps to identify potential device failures. The intensity distribution of different frequency components can be observed through the spectrogram. If there are periodic components in the signal, there will be obvious peaks in the spectrum. The spectrum is analyzed and a frequency threshold is set to distinguish high-frequency components. If the spectrum of the signal exceeds the frequency threshold, the frequencies corresponding to these components can be considered as the jitter frequencies of the device.

[0076] By analyzing the rate of change of the liquid level, it is detected whether there are abnormal fluctuations in the liquid level. Too rapid a change in the liquid level may be an abnormal situation during loading and unloading operations. The calculation logic for the rate of change of the liquid level is: the liquid level difference is obtained by subtracting the previous liquid level from the current liquid level, and the rate of change of the liquid level is obtained by dividing the liquid level difference by the time interval between each liquid level data. If the rate of change of the liquid level is greater than the change threshold, it is marked as a liquid level mutation. By judging the rate of change of the liquid level, abnormal fluctuations in the liquid level can be detected in a timely manner, avoiding accidents such as overflow or lack of liquid.

[0077] Monitor sudden changes in pipeline pressure, analyze the pressure fluctuation frequency, and judge whether there are abnormal situations such as device failures or pipeline leaks.

[0078] The calculation logic for the rate of change of pressure is: the pressure difference is obtained by subtracting the previous pressure from the current pressure, and the rate of change of pressure is obtained by dividing the pressure difference by the time interval between each pressure data. If the rate of change of pressure is greater than the change threshold, it is marked as a pressure fluctuation. By judging the rate of change of pressure to determine whether there are abnormal pressure fluctuations, problems such as pipeline leaks and device failures can be detected in a timely manner.

[0079] According to the real-time status and dynamic changes at the operation site, adjust the granularity, frequency, and point priority of data collection to optimize the collection process and improve the monitoring accuracy and response speed.

[0080] Adaptive Granularity Adjustment: During the operation process, when the system detects abnormal fluctuations or state changes, it automatically shortens the data collection time interval to increase the data collection granularity (for example, changing the original data collection once per minute to once per second). In the absence of abnormalities, the system maintains the normal data collection granularity.

[0081] Adaptive Sampling Frequency Adjustment: When detecting equipment abnormalities or changes in the operation state, the data collection frequency is increased to ensure a quick response. For example, when equipment jitter occurs, the collection frequency is increased to the high-frequency mode to continuously monitor the equipment state. When the equipment is operating normally, the sampling frequency can be appropriately reduced to reduce the computational burden.

[0082] Dynamic Point Priority Adjustment: According to the changes in the on-site operation state, the collection priorities of different points are dynamically adjusted. For example, when an abnormality occurs in a certain tank area or pipeline section, the sampling priority of that location is increased, and the sampling frequency of other locations can be appropriately reduced. For key points (such as key valves and important liquid level positions), a high priority is always maintained to ensure that their data collection is not missed.

[0083] Adjust the data collection granularity, frequency, and point priority according to the real-time state and dynamic changes of the operation site, including the following steps:

[0084] Detect abnormalities in the equipment state or during the operation process. Abnormal fluctuations include equipment jitter, sudden liquid level changes, and pressure fluctuations. If abnormalities exist, automatically shorten the data collection time interval (for example, changing the original collection cycle from once per minute to once per second) to increase the data collection granularity.

[0085] Dynamically adjust the sampling frequency based on the analysis results of the real-time state. If an abnormality is detected, immediately increase the sampling frequency; when the equipment returns to normal, the frequency gradually decreases. According to changes in the operation state, such as liquid level changes and pressure changes, determine which sensor points are abnormal or critical, and increase the priority of these points. For the sensor points where abnormalities occur (such as key valves, liquid levels, and pressure sensors), increase their sampling priority to ensure that this data is not missed.

[0086] When detecting abnormal situations, respond in a timely manner and take corresponding measures. When the system identifies an abnormality (such as equipment jitter, sudden liquid level change, etc.), it automatically triggers an alarm mechanism to notify the operator. Provide the type of abnormality, severity, and possible impacts to assist in decision-making. Start the data correction mechanism to verify the abnormal data, exclude incorrect data or abnormal fluctuations. Re-verify the sensor data involved in the abnormal event to ensure data accuracy. Send a patrol instruction to the equipment management system, requiring relevant equipment to be inspected and maintained. When the system detects changes in the liquid level or pressure, automatically prompt the operator to check the relevant equipment.

[0087] The control end continuously monitors the state changes of the target flow curve and the acquisition parameters, automatically adjusts the material balance judgment window (such as time period, number of sampling points), and calculates the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, it automatically activates the linkage mechanism (including feedback to the monitoring end that the loading and unloading plan may be unreachable, suggesting adjusting the progress, sending data verification and equipment inspection instructions to the sensing end, and activating the alarm mechanism to notify the operator or pause the loading and unloading), including the following steps:

[0088] According to the changes in the target flow curve and the state of the acquisition parameters, the control end automatically adjusts the time period for calculating the material balance. The expression is: , where is the adjusted material balance judgment window, is the material balance judgment window before adjustment, is the trend influence factor, and the cumulative material balance deviation is calculated based on the adjusted material balance judgment window. The expression is: , where are the start and end times of the adjusted time window, is the material balance deviation, is the input flow per unit time, is the output flow per unit time;

[0089] The calculation logic of the trend influence factor is: obtain the flow deviation and the sensor data deviation, sum all the sensor data deviations to obtain the equipment influence index. The expression is: , where is the equipment influence index, is the number of sensor data, is the deviation value of the i-th sensor data. Normalize the flow deviation and the equipment influence index so that the value ranges of the flow deviation and the equipment influence index are mapped to between [0, 1]. Sum the normalized flow deviation and the equipment influence index to obtain the trend influence factor.

[0090] Compare the obtained material balance deviation with the preset first deviation threshold and the second deviation threshold. The first deviation threshold is less than the second deviation threshold, and the first deviation threshold is used to analyze whether the cumulative input flow is excessively lower than the cumulative output flow, and the second deviation threshold is used to analyze whether the cumulative input flow is excessively higher than the cumulative output flow;

[0091] If the material balance deviation is greater than or equal to the first deviation threshold and less than or equal to the second deviation threshold, it indicates that the logistics is in a balanced state;

[0092] If the material balance deviation is less than the first deviation threshold and it is analyzed that the cumulative input flow is excessively lower than the cumulative output flow, the linkage mechanism needs to be activated. If the material balance deviation is greater than the second deviation threshold and it is analyzed that the cumulative input flow is excessively higher than the cumulative output flow, the linkage mechanism needs to be activated.

[0093] According to the judgment logic of the material balance deviation, the control end can automatically activate different linkage mechanisms according to different material balance deviation ranges. The specific content of the linkage mechanism is as follows:

[0094] 1. The material balance deviation is less than the first deviation threshold: Analyze that the cumulative input flow is excessively lower than the cumulative output flow:

[0095] Analyze the reasons and optimize the input flow: The control end identifies that the input flow is too low. Possible reasons include: malfunctions or flow decreases in the input equipment, such as abnormalities in pumps, valves, pipelines, etc. External environmental factors (such as wind speed, temperature, etc.) affect the input flow. Feedback to the monitoring end that the input flow is low, and it is recommended to check the operating status of relevant input equipment, especially key components such as pumps, valves, and filters, to check for malfunctions or maintenance issues. If the system identifies a malfunction in the equipment, automatically start the equipment inspection and repair instructions and repair it in a timely manner. According to the current operation situation, adjust the loading and unloading plan, increase the input flow or adjust the setting of the input end flow. Feedback the adjustment suggestions to the operator and recommend optimizing the input process or increasing the working efficiency of the input equipment.

[0096] 2. The material balance deviation is greater than the second deviation threshold: Analyze that the cumulative input flow is excessively higher than the cumulative output flow:

[0097] Analyze the reasons and optimize the output flow: The control end identifies that the input flow is too high. Possible reasons include: the input flow is too large, exceeding the processing capacity, resulting in insufficient output flow. Malfunctions or poor performance of the output equipment (such as unloading pumps, valves, unloading arms). Feedback to the monitoring end that the input flow is too high, and it is recommended to check the status of relevant output equipment (such as unloading pumps, valves, loading and unloading arms) to ensure the normal operation of the equipment and avoid excessive input flow exceeding the system's tolerance. If there is a malfunction in the output equipment, start the equipment inspection and instruct for repair. Adjust the loading and unloading plan, appropriately reduce the input flow or increase the output flow to ensure the stable operation of the system and avoid excessive backlog. When necessary, adjust the operation progress and coordinate other relevant equipment for optimization.

[0098] Alarm mechanism: If the material balance deviation exceeds the set deviation threshold range, the control end immediately triggers the alarm mechanism to notify relevant operators to take measures. Provide real-time feedback and suggestions to the operator (such as equipment malfunctions, flow abnormalities, loading and unloading plan adjustments, etc.).

[0099] Adjusting Flow Settings and Progress: Analyze deviations through the control terminal and initiate the linkage mechanism to optimize the loading and unloading flow, ensure the smooth progress of operations, and avoid equipment damage or operation interruption caused by unbalanced flow. Equipment Inspection and Repair: When abnormal flow is identified, the control terminal can immediately initiate equipment inspection and repair instructions to ensure that the equipment is promptly maintained and avoid affecting operation efficiency. Operator Feedback and Progress Adjustment: The linkage mechanism will provide feedback on the adjustment plan to the operator to ensure that the operation progress and material flow are promptly optimized and adjusted. Through these automated linkage mechanisms, the material balance during the LNG ship loading and unloading process can be adjusted and optimized in real time, improving the safety, stability, and efficiency of operations.

[0100] Example 3: Please refer to Figure 3 As shown, the dynamic monitoring cloud platform for the material balance of LNG ship loading and unloading in this example includes a data monitoring layer, a field perception layer, and an adjustment control layer;

[0101] Data Monitoring Layer: Collect global data, such as the overall LNG flow, changes in the ship's tank capacity, main pipeline flow, loading and unloading plan progress, etc. Dynamically correct the target flow curve according to the actual operation situation and influencing factors (weather, equipment, etc.) to achieve real-time update of the planned trend and automatic calibration of the global benchmark. The corrected target flow curve is sent to the adjustment control layer;

[0102] Field Perception Layer: Collect real-time sensor data at the operation site, including liquid level, temperature, pressure, valve status, loading and unloading arm movement status, etc., to reflect the immediate status and dynamic changes during the operation. Based on the obstacle recognition and status change mechanism, automatically sense operation anomalies (such as equipment jitter, sudden liquid level changes, etc.), and adaptively adjust the data collection parameters (granularity, frequency, point priority) to achieve a rapid response to the on-site dynamics. The adjusted data collection parameters are sent to the adjustment control layer;

[0103] Adjustment Control Layer: Continuously monitor the status changes of the target flow curve and the collection parameters, automatically adjust the material balance judgment window (such as time period, number of sampling points), and calculate the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, automatically initiate the linkage mechanism (including feedback to the monitoring terminal that the loading and unloading plan may be unreachable, suggesting progress adjustment, sending data verification and equipment inspection instructions to the perception terminal, and activating the alarm mechanism to notify the operator or pause the loading and unloading).

[0104] In the description of this specification, the descriptions referring to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples.

[0105] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific embodiments. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A dynamic monitoring method for the material balance of LNG ship loading and unloading, characterized in that: The monitoring method includes the following steps: The monitoring end collects global data and dynamically corrects the target flow curve according to the actual operation situation and influencing factors. The expression is: , where is the target flow curve after dynamic correction, is the target flow curve before dynamic correction, is the flow deviation, is the external factor correction factor; The sensing end collects real-time sensor data of the operation site, automatically senses operation anomalies based on the obstacle recognition and state change mechanism, and adaptively adjusts the data collection parameters; The control terminal continuously monitors the state changes of the target flow curve and the acquisition parameters, and automatically adjusts the material balance judgment window. The expression is: , where is the adjusted material balance judgment window, is the material balance judgment window before adjustment, is the trend influence factor, and the material balance is calculated based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, the linkage mechanism is automatically activated; The calculation logic of the trend impact factor is as follows: sum up all the sensor data deviations to obtain the device impact index, and the expression is: , where is the device impact index, is the number of sensor data, is the deviation value of the i-th sensor data. Sum up the flow deviation and the device impact index after normalization to obtain the trend impact factor.

2. The dynamic monitoring method for the material balance during the loading and unloading of LNG ships according to claim 1, characterized in that: The control end calculates the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, it automatically activates the linkage mechanism, including the following steps: Calculate the cumulative material balance deviation based on the adjusted material balance judgment window, and the expression is: , where are the start and end times of the adjusted time window, is the material balance deviation, is the input flow rate per unit time, is the output flow rate per unit time; Compare the obtained material balance deviation with a preset first deviation threshold and a second deviation threshold. The first deviation threshold is less than the second deviation threshold. The first deviation threshold is used to analyze whether the cumulative input flow is excessively lower than the cumulative output flow, and the second deviation threshold is used to analyze whether the cumulative input flow is excessively higher than the cumulative output flow; If the material balance deviation is greater than or equal to the first deviation threshold and less than or equal to the second deviation threshold, it indicates that the logistics is in a balanced state; If the material balance deviation is less than the first deviation threshold, analyze that the cumulative input flow is excessively lower than the cumulative output flow, and the linkage mechanism needs to be activated. If the material balance deviation is greater than the second deviation threshold, analyze that the cumulative input flow is excessively higher than the cumulative output flow, and the linkage mechanism needs to be activated.

3. The dynamic monitoring method for the material balance during the loading and unloading of LNG ships according to claim 2, wherein: The sensing end collects real-time sensor data of the operation site, automatically senses operation anomalies based on the obstacle recognition and state change mechanism, and adaptively adjusts the data collection parameters, including the following steps: Through the sensor network, various data of the operation site are collected in real time, including liquid level, temperature, pressure, valve state, and loading and unloading arm movement state; Through real-time data analysis, identify the abnormal situations that occur during the operation process, including: Mark as abnormal when the data exceeds the normal range, and use frequency analysis and signal processing techniques to identify the jitter or instability of the equipment; Liquid level mutation detection: By analyzing the rate of change of the liquid level, detect whether there is abnormal fluctuation of the liquid level, monitor the mutation of the pipeline pressure, analyze the pressure fluctuation frequency, and judge whether there is equipment failure or pipeline leakage abnormal situation; According to the real-time state and dynamic changes of the operation site, adjust the granularity, frequency, and point priority of data collection.

4. The dynamic monitoring method for the material balance during the loading and unloading of LNG ships according to claim 3, characterized in that: According to the real-time state and dynamic changes of the operation site, adjust the granularity, frequency, and point priority of data collection, including the following steps: During the operation process, when abnormal fluctuations or state changes are identified, automatically shorten the data collection time interval and increase the data collection granularity; When equipment anomalies or operation state changes are detected, increase the data collection frequency; Dynamically adjust the collection priority of different points according to the changes in the on-site operation state.

5. The dynamic monitoring method for the material balance during the loading and unloading of LNG ships according to claim 4, characterized in that: Through real-time data analysis, identify the abnormal situations that occur during the operation process, including the following steps: When the monitored value exceeds the normal range, automatically trigger an abnormal alarm, and use frequency analysis and signal processing techniques to identify the jitter or instability of the equipment; Process the data of the acceleration sensor or vibration sensor of the equipment, perform frequency domain analysis on the vibration signal using Fourier transform, extract the frequency components in the signal, identify the high-frequency components. Fourier transform is used to convert the time-domain signal into the frequency-domain signal, and the expression is: , where is the spectrum of the signal, is the original sampled data, is the frequency, is the number of data points, and j is the imaginary unit; If the liquid level change rate is greater than the change threshold, it is marked as a sudden liquid level change. If the pressure change rate is greater than the change threshold, it is marked as a pressure fluctuation.

6. The dynamic monitoring method for the material balance during the loading and unloading of LNG ships according to claim 5, wherein: The calculation logic of the liquid level change rate is as follows: the liquid level difference is obtained by subtracting the liquid level at the previous moment from the liquid level at the current moment, and the liquid level change rate is obtained by dividing the liquid level difference by the time interval between each liquid level data. The calculation logic of the pressure change rate is as follows: the pressure difference is obtained by subtracting the pressure at the previous moment from the pressure at the current moment, and the pressure change rate is obtained by dividing the pressure difference by the time interval between each pressure data.

7. The dynamic monitoring method for the material balance during the loading and unloading of LNG ships according to claim 6, characterized in that: The monitoring end collects global data and dynamically corrects the target flow curve according to the actual operation conditions and influencing factors, including the following steps: Obtain real-time data from the LNG loading and unloading system, including the total LNG flow rate, the change in the ship's tank capacity, the main pipeline flow rate, and the progress index of the loading and unloading plan, and set the target flow curve based on the loading and unloading plan and the initial system configuration. Obtain external influencing factors, conduct a comparative analysis based on the real-time data and the set benchmark, dynamically correct the target flow curve after identifying the deviation source, and set a new global benchmark.

8. The dynamic monitoring method for the material balance during the loading and unloading of LNG ships according to claim 7, characterized in that: Obtain external influencing factors, conduct a comparative analysis based on the real-time data and the set benchmark, dynamically correct the target flow curve after identifying the deviation source, including the following steps: Define the target flow curve: , obtain the current actual flow data: , calculate the flow deviation between the target flow curve and the actual flow: , where is the flow deviation at time t.

9. The LNG ship loading and unloading material balance dynamic monitoring cloud platform is used to implement the monitoring method described in any one of claims 1-8, and is characterized in that: It includes a data monitoring layer, a field perception layer, and an adjustment control layer; Data monitoring layer: used to collect global data and dynamically correct the target flow curve according to the actual operation conditions and influencing factors. The expression is: , where is the target flow curve after dynamic correction, is the target flow curve before dynamic correction, is the flow deviation, is the external factor correction factor; Field perception layer: used to collect real-time sensor data at the operation site, automatically perceive operation anomalies based on the obstacle recognition and state change mechanism, and adaptively adjust the data collection parameters; Adjustment control layer: Continuously monitor the status changes of the target flow curve and acquisition parameters, and automatically adjust the material balance judgment window. The expression is: , where is the adjusted material balance judgment window, is the material balance judgment window before adjustment, is the trend influence factor, and calculate the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, automatically start the linkage mechanism; The calculation logic of the trend impact factor is as follows: Sum up all the sensor data deviations to obtain the device impact index, and the expression is: , where is the device impact index, is the number of sensor data, is the deviation value of the i-th sensor data. Sum up the flow deviation and the device impact index after normalization processing to obtain the trend impact factor.

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