Liquefied natural gas (LNG) ship material loading and unloading balance dynamic monitoring cloud platform and method

Abstract

The invention discloses an LNG ship material loading and unloading balance dynamic monitoring cloud platform and method, and relates to the technical field of material monitoring. A monitoring end dynamically corrects a target flow curve according to actual operation conditions and influence factors, a sensing end collects real-time sensor data of an operation site, and based on an obstacle recognition and state change mechanism, operation abnormity is automatically sensed; and a control end continuously monitors the state change of a target flow curve and the collected parameters, automatically adjusts a material balance judgment window, calculates material balance according to an adjusted parameter system, and automatically starts a linkage mechanism if the material balance deviation exceeds a deviation threshold value. The monitoring platform realizes layered acquisition and multi-source fusion processing of information by performing function decoupling and logic linkage, forms dynamic closed-loop control on the whole operation process, realizes whole-process series analysis of multi-point data, and improves the control force on the whole material motion trail.

Description

Technical Field

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

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

[0003] Traditional LNG ship loading and unloading material monitoring systems mostly adopt a fixed point + fixed time window monitoring method. The monitoring content is limited to single-point data (such as flow meters and liquid level meters), which makes it difficult to cover the entire process and full-link material flow process of the operation. There are the following defects: 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 on-site changes; 2. The monitoring results rely on fixed reference values, and the response to sudden interference (such as equipment abnormalities and weather fluctuations) is delayed; 3. The overall system is unable to achieve trend correction and target update, resulting in the plan scheduling and actual material delivery being out of sync.

[0004] Based on this, the present invention proposes a cloud platform and method for dynamic monitoring of material balance in loading and unloading of LNG ships. By performing functional decoupling and logical linkage, hierarchical collection and multi-source fusion processing of information are realized, a dynamic closed-loop control of the entire operation process is formed, and full-process serial analysis of multi-point data is realized, thereby improving the control over the overall material movement trajectory. Summary of the invention

[0005] The purpose of the present invention is to provide a cloud platform and method for dynamic monitoring of material balance of LNG ship loading and unloading, so as to solve the shortcomings of the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a dynamic monitoring method for LNG ship loading and unloading material balance, the monitoring method comprising the following steps: The monitoring end collects global data and dynamically modifies the target flow curve according to the actual operation conditions and influencing factors; The sensing end collects real-time sensor data at the work site, automatically senses work abnormalities based on obstacle recognition and state change mechanisms, and adaptively adjusts data collection parameters; The control end continuously monitors the state changes of the target flow curve and the collected 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.

[0007] Preferably, the control end continuously monitors the state changes of the target flow curve and the acquisition parameters and automatically adjusts the material balance judgment window, including the following steps: 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 material balance calculation. The expression is: , where is the material balance judgment window after adjustment. is the material balance judgment window before adjustment. is the trend influencing factor.

[0008] Preferably, the control end calculates the material balance according to the adjusted parameter system, and if the material balance deviation exceeds the deviation threshold, the linkage mechanism is automatically started, including the following steps: The accumulated material balance deviation is calculated 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 per unit time, is the output flow per unit time; The obtained material balance deviation is compared with a preset first deviation threshold and a second deviation threshold, wherein 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; If the material balance deviation is greater than or equal to the first deviation threshold, and the material balance deviation is 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, the cumulative input flow is analyzed to be 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, the cumulative input flow is analyzed to be excessively higher than the cumulative output flow, and the linkage mechanism needs to be activated.

[0009] Preferably, the sensing end collects real-time sensor data at the work site, automatically senses work abnormalities based on obstacle recognition and state change mechanisms, and adaptively adjusts 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 status and loading and unloading arm action status; Through real-time data analysis, abnormal situations that occur during the operation are identified, including: When the data exceeds the normal range, it is marked as abnormal, and frequency analysis and signal processing technology are used to identify jitter or instability of the device. Liquid level mutation detection: By analyzing the rate of liquid level change, detect whether the liquid level has abnormal fluctuations, monitor the sudden changes in pipeline pressure, analyze the frequency of pressure fluctuations, and determine whether there is equipment failure or abnormal pipeline leakage; Adjust the granularity, frequency, and point priority of data collection according to the real-time status and dynamic changes of the work site.

[0010] Preferably, according to the real-time status and dynamic changes of the work site, the granularity, frequency and point priority of data collection are adjusted, including the following steps: During the operation, when abnormal fluctuations or status changes are identified, the data collection time interval is automatically shortened to improve the data collection granularity; When equipment anomalies or changes in operating status are detected, the frequency of data collection is increased; Dynamically adjust the collection priority of different points according to changes in on-site operation status.

[0011] Preferably, identifying abnormal situations occurring during the operation process through real-time data analysis includes the following steps: When the monitoring value exceeds the normal range, an abnormal alarm is automatically triggered, and frequency analysis and signal processing technology are used to identify the jitter or instability of the equipment; Process the acceleration sensor or vibration sensor data of the device, use Fourier transform to perform frequency domain analysis on the vibration signal, extract the frequency components in the signal, and identify the high-frequency components. Fourier transform is used to convert the time domain signal into the frequency domain signal. The expression is: , where is the spectrum of the signal, is the original sampled data, is the frequency, is the number of data points, j is the imaginary unit; 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.

[0012] Preferably, the calculation logic of the liquid level change rate is: obtain the liquid level difference by subtracting the liquid level at the previous moment from the current moment, and obtain the liquid level change rate by dividing the liquid level difference by the time interval of each liquid level data; The calculation logic of the pressure change rate is: obtain the pressure difference by subtracting the pressure at the previous moment from the current moment pressure, and obtain the pressure change rate by dividing the pressure difference by the time interval of each pressure data.

[0013] Preferably, 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 overall LNG flow, changes in ship tank capacity, trunk pipeline flow, and loading and unloading plan progress indicators, and set the target flow curve based on the loading and unloading plan and the initial system configuration; Obtain external influencing factors, conduct comparative analysis based on real-time data and set benchmarks, identify the source of deviation, dynamically correct the target flow curve, and set a new global benchmark.

[0014] Preferably, external influencing factors are obtained, and a comparative analysis is performed based on real-time data and a set benchmark. After the deviation source is identified, the target flow curve is dynamically corrected, including the following steps: Define the target flow curve: , get the current actual traffic data: , calculate the flow deviation between the target flow curve and the actual flow: , where is the flow deviation at time t; The target flow curve is adjusted in real time according to the external factor correction factor, and 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 correction factor for external factors.

[0015] This application also proposes a cloud platform for dynamic monitoring of material balance of LNG ship loading and unloading, including a data monitoring layer, a field perception layer, and a regulation control layer; Data monitoring layer: used to collect global data and dynamically modify the target flow curve according to actual operating conditions and influencing factors; On-site perception layer: used to collect real-time sensor data at the work site, automatically perceive work abnormalities based on obstacle recognition and state change mechanisms, and adaptively adjust data collection parameters; Adjustment control layer: continuously monitor the state changes of the target flow curve and the collected parameters, automatically adjust the material balance judgment window, and calculate the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, the linkage mechanism is automatically started.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention dynamically corrects the target flow curve according to the actual operation conditions and influencing factors through the monitoring end, collects real-time sensor data at the operation site through the sensing end, automatically senses operation abnormalities based on the obstacle recognition and state change mechanism, and adaptively adjusts the data acquisition parameters. The control end continuously monitors the state changes of the target flow curve and the acquisition parameters, automatically adjusts the material balance judgment window, and calculates the material balance according to the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, the linkage mechanism is automatically started. The monitoring platform realizes hierarchical information collection and multi-source fusion processing through functional decoupling and logical linkage, forms a dynamic closed-loop control of the entire operation process, realizes the full-process serial analysis of multi-point data, and improves the control over the overall material movement trajectory. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 The present invention is a flow chart of the method.

[0019] Figure 2 This is a mind map of the present invention.

[0020] Figure 3 It is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1: Please refer to Figure 1 and Figure 2 As shown, the LNG ship loading and unloading material balance dynamic monitoring method described in this embodiment includes the following steps: The monitoring end collects global data, such as overall LNG flow, changes in ship tank capacity, trunk pipeline flow, loading and unloading plan progress, etc., and dynamically corrects the target flow curve according to the actual operation situation and influencing factors (weather, equipment, etc.), realizing real-time update of plan trends and automatic calibration of global benchmarks. The perception end collects real-time sensor data at the operation site, including liquid level, temperature, pressure, valve status, loading and unloading arm action status, etc., reflecting the immediate status and dynamic changes during the operation process. Based on the obstacle recognition and state change mechanism, it automatically perceives operation anomalies (such as equipment jitter, liquid level mutation, etc.), and adaptively adjusts data acquisition parameters (granularity, frequency, point priority) to achieve rapid response to on-site dynamics. The control end continuously monitors the state changes of the target flow curve and 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, the linkage mechanism is automatically activated (including feedback to the monitoring end that the loading and unloading plan may not be reached, suggesting to adjust the progress, sending data verification and equipment inspection instructions to the perception end, and activating the alarm mechanism to notify the operator or suspend loading and unloading).

[0023] This application uses the monitoring end to dynamically correct 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. Based on the obstacle recognition and state change mechanism, it automatically senses operation abnormalities and adaptively adjusts the data acquisition parameters. The control end continuously monitors the state 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 started. The monitoring platform realizes hierarchical information collection and multi-source fusion processing through functional decoupling and logical linkage, forms a dynamic closed-loop control of the entire operation process, realizes the full-process serial analysis of multi-point data, and improves the control over the overall material movement trajectory.

[0024] Embodiment 2: The monitoring end collects global data, such as overall LNG flow, changes in ship tank capacity, trunk pipeline flow, loading and unloading plan progress, etc., and dynamically corrects the target flow curve according to the actual operation situation and influencing factors (weather, equipment, etc.), so as to realize real-time update of the plan trend and automatic calibration of the global benchmark, including the following steps: Obtain real-time data from the LNG loading and unloading system, including key indicators such as overall LNG flow, changes in ship tank capacity, trunk pipeline flow, loading and unloading plan progress, etc. Set the target flow curve based on the loading and unloading plan and the initial configuration of the system (such as ideal loading and unloading speed, expected changes in ship tank volume). The target flow curve should take into account flow fluctuations when ships enter and leave the port, as well as changes in the operation plan. Obtain external influencing factors, such as weather (wind speed, temperature, humidity, etc.), equipment status (equipment operation status, failure rate, maintenance plan, etc.), compare and analyze real-time data with the set benchmark, identify the source of deviation (such as unstable flow, equipment abnormality, etc.), dynamically correct the target flow curve, and set a new global benchmark.

[0025] In the dynamic monitoring process of LNG ship loading and unloading material balance, 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. Obtaining external influencing factors, comparing and analyzing the real-time data with the set benchmark, identifying the deviation source, dynamically correcting the target flow curve, and setting a new global benchmark, including the following steps: Collect weather factors that affect loading and unloading operations, such as wind speed, temperature, humidity, etc. Collect operating status data of key equipment, including operating conditions, failure rates, maintenance plans, etc. Define the target flow curve based on the loading and unloading plan and initial settings: ,The target flow curve is usually an ideal flow change curve, which represents the ,change of flow over time without considering interference factors.

[0026] Get the current actual traffic data: , reflects the real-time changes in flow during on-site loading and unloading, compares real-time flow data with the target flow curve, calculates deviations and analyzes possible sources of deviations, and calculates the flow deviation between the target flow curve and the actual flow: , where The flow deviation at time t is used to compare the deviation between the target flow and the actual flow in real time, reflecting whether the flow is stable, and then analyzing whether the flow target needs to be adjusted. The deviation mainly comes from external factors such as weather changes and equipment abnormalities. The target flow curve is adjusted in real time based on the external factor correction factor. , the dynamically corrected target flow curve will become the new global benchmark for further adjusting the operation plan.

[0027] According to the influence of external factors (such as wind speed, temperature, equipment status, etc.) on the flow, the target flow curve is corrected. The calculation formula of the external correction factor is as follows: , where is the external factor correction factor, is the weather correction factor, The external factor correction factor is used to comprehensively evaluate the impact of weather and equipment status on flow targets and adjust the target flow curve. The flow targets under different weather conditions or equipment status should be appropriately adjusted to avoid operational deviations caused by external factors.

[0028] The dynamically corrected target flow curve can be calculated using the following formula: , where is the target flow curve after dynamic correction, is the target flow curve before dynamic correction, is the flow deviation, The target flow curve after dynamic correction is used as the new global benchmark flow curve to reflect the new standard flow provided for the system after dynamic correction as the correction factor for external factors.

[0029] When the wind speed increases, the flow resistance increases, which may lead to a decrease in LNG transportation efficiency and a decrease in the flow target. If the wind speed is high, the flow value of the target flow curve should be reduced. Especially in high wind speed conditions, the maximum flow limit can be preset to avoid unstable flow or equipment overload. When the temperature rises, the viscosity of LNG decreases and the fluidity increases. At this time, the target flow should be appropriately increased. When the failure rate increases, it indicates that the equipment fails frequently, the flow will be greatly affected, and the flow target needs to be reduced.

[0030] Normalize the wind speed, temperature and equipment failure rate so that the value range of wind speed, temperature and equipment failure rate is mapped to [0,1]. Subtract the normalized temperature from the wind speed from the equipment failure rate to obtain the external factor correction factor. When the external factor correction factor is large, it means 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 is stable and efficient. 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 stability and efficiency of the flow rate. Therefore, the target flow rate should be reduced.

[0031] The sensing end collects real-time sensor data at the operation site, including liquid level, temperature, pressure, valve status, loading and unloading arm action status, etc., to reflect the immediate status and dynamic changes during the operation. Based on the obstacle recognition and state change mechanism, it automatically senses operation anomalies (such as equipment jitter, liquid level mutation, etc.), and adaptively adjusts data collection parameters (granularity, frequency, point priority) to achieve rapid response to on-site dynamics, including the following steps: Through the sensor network, various data at the work site are collected in real time, including: liquid level (reflecting changes in the LNG liquid level in the storage tank), temperature (temperature changes in the liquid have a direct impact on the LNG loading and unloading process), pressure (pressure changes in ships and pipelines affect flow and equipment operation), valve status (valve switch status determines flow regulation) and loading and unloading arm action status (reflecting the operation of the loading and unloading arm to ensure a smooth loading and unloading process).

[0032] Through real-time data analysis, abnormal situations that may occur during the operation, such as equipment failure or sudden changes, can be identified, including: Data monitoring and threshold judgment: Set a reasonable threshold range (such as the normal range of liquid level and pressure). When the data exceeds the normal range, the system will immediately mark it as abnormal.

[0033] Device jitter identification: Through small fluctuations or irregular changes in sensor data, frequency analysis and signal processing techniques (such as Fourier transform and filtering) are used to identify device jitter or instability.

[0034] Liquid level mutation detection: By analyzing the rate of liquid level change, detect whether there is abnormal fluctuation in the liquid level. Rapid liquid level changes may be an abnormal situation in loading and unloading operations.

[0035] Pressure fluctuation judgment: monitor sudden changes in pipeline pressure, analyze the frequency of pressure fluctuations, and determine whether there are abnormal conditions such as equipment failure or pipeline leakage.

[0036] According to the needs of real-time data collection, possible abnormal conditions in the operation process are identified, including equipment failure, liquid level changes and pressure fluctuations. Through real-time data analysis, possible abnormal conditions in the operation process, such as equipment failure or sudden changes, are identified, including the following steps: Get real-time sensor data, monitor key variables such as liquid level and pressure, and set a reasonable normal range for each monitoring indicator. Taking liquid level as an example, set the normal range of liquid level as: , where It is the current liquid level. When the monitoring value exceeds the normal range, the system automatically triggers an abnormal alarm.

[0037] Through the small fluctuations or irregular changes in the 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 acceleration sensor or vibration sensor data of the device is processed, and the vibration signal is analyzed in the frequency domain using Fourier transform to extract the frequency components in the signal and identify the high-frequency components (indicating the jitter or instability of the device). Fourier transform is used to convert the time domain signal into the frequency domain signal. 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 an imaginary unit. Through spectrum analysis, we can identify whether there are periodic high-frequency components in the signal, indicating that the device is jittering or unstable. Through Fourier transform analysis, we can identify small fluctuations or periodic jitters in the operation of the device, which helps to identify potential device failures. The intensity distribution of different frequency components can be observed through the spectrum graph. If there are periodic components in the signal, there will be obvious peaks in the spectrum. Analyze the spectrum and set the frequency threshold 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 frequency of the device.

[0038] By analyzing the rate of change of the liquid level, detect whether there is abnormal fluctuation in the liquid level. Rapid changes in the liquid level may be an abnormal situation in loading and unloading operations. The calculation logic of the liquid level change rate is: obtain the liquid level difference by subtracting the liquid level at the previous moment from the current moment, and obtain the liquid level change rate by dividing the liquid level difference by the time interval of each liquid level data. If the liquid level change rate is greater than the change threshold, it is marked as a sudden change in the liquid level. By judging the liquid level change rate, abnormal fluctuations in the liquid level can be discovered in time to avoid accidents such as overflow or lack of liquid.

[0039] Monitor sudden changes in pipeline pressure, analyze the frequency of pressure fluctuations, and determine whether there are abnormal conditions such as equipment failure or pipeline leakage. The calculation logic of the pressure change rate is: obtain the pressure difference by subtracting the pressure at the previous moment from the current moment pressure, and obtain the pressure change rate by dividing the pressure difference by the time interval of each pressure data. If the pressure change rate is greater than the change threshold, it is marked as pressure fluctuation. The pressure change rate is used to determine whether there is an abnormal pressure fluctuation, so that problems such as pipeline leakage and equipment failure can be discovered in time.

[0040] According to the real-time status and dynamic changes of the work site, adjust the granularity, frequency and point priority of data collection to optimize the collection process and improve monitoring accuracy and response speed.

[0041] Adaptive granularity adjustment: During the operation process, when the system identifies abnormal fluctuations or status changes, it automatically shortens the data collection time interval and increases the data collection granularity (for example, changing the original data collection once a minute to once a second). In the absence of abnormalities, the system maintains the normal data collection granularity.

[0042] Adaptive sampling frequency adjustment: When an abnormality in the device or a change in the operating status is detected, the frequency of data collection is increased to ensure a quick response. For example, when a device jitter occurs, the collection frequency is increased to high-frequency mode to continuously monitor the device status. When the device is operating normally, the sampling frequency can be appropriately reduced to reduce the computing burden.

[0043] Dynamic point priority adjustment: Dynamically adjust the collection priority of different points according to the changes in the on-site operation status. For example, when an abnormality occurs in a tank area or pipeline section, the sampling priority of this 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 locations), a higher priority is always maintained to ensure that their data collection is not missed.

[0044] According to the real-time status and dynamic changes of the work site, adjust the granularity, frequency and point priority of data collection, including the following steps: Detect abnormalities in equipment status or operation process. Abnormal fluctuations include equipment jitter, sudden change in liquid level, and pressure fluctuation. If abnormalities exist, automatically shorten the time interval for data collection (for example, change the original collection cycle from once per minute to once per second), thereby improving the granularity of data collection.

[0045] The sampling frequency is dynamically adjusted based on the results of real-time status analysis. If an abnormality is detected, the sampling frequency is immediately increased; when the equipment returns to normal, the frequency is gradually reduced. According to the changes in the operating status, such as liquid level changes, pressure changes, etc., it is determined which sensor points are abnormal or critical, and the priority of these points is increased. For sensor points with abnormalities (such as key valves, liquid level, pressure sensors, etc.), their sampling priority is increased to ensure that these data will not be missed.

[0046] When an abnormal situation is detected, respond promptly and take appropriate measures. When the system identifies an abnormality (such as equipment jitter, sudden change in liquid level, etc.), it automatically triggers the alarm mechanism and notifies the operator. Provide the type, severity and possible impact of the abnormality to assist in decision-making. Start the data correction mechanism to verify the abnormal data and eliminate erroneous data or abnormal fluctuations. Re-verify the sensor data involved in the abnormal event to ensure data accuracy. Issue inspection instructions to the equipment management system, requiring relevant equipment to be inspected and maintained. When the system detects changes in liquid level or pressure, it automatically prompts the operator to check the relevant equipment.

[0047] 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, the linkage mechanism is automatically started (including feedback to the monitoring end that the loading and unloading plan may not be achieved, suggesting to adjust the progress, sending data verification and equipment inspection instructions to the sensing end, and starting the alarm mechanism to notify the operator or suspend loading and unloading), including the following steps: 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 material balance calculation. The expression is: , where is the material balance judgment window after adjustment. is the material balance judgment window before adjustment. is the trend influencing factor, and the accumulated 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; The calculation logic of the trend impact factor is: obtain the flow deviation and sensor data deviation, sum up all sensor data deviations to obtain the equipment impact index, the expression is: , where is the device impact index, is the number of sensor data, is the ith sensor data deviation value. The flow deviation and the device impact index are normalized so that the value range of the flow deviation and the device impact index is mapped to [0,1]. The flow deviation and the device impact index after normalization are summed to obtain the trend impact factor.

[0048] The obtained material balance deviation is compared with a preset first deviation threshold and a second deviation threshold, wherein 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; If the material balance deviation is greater than or equal to the first deviation threshold, and the material balance deviation is 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, the cumulative input flow is analyzed to be 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, the cumulative input flow is analyzed to be excessively higher than the cumulative output flow, and the linkage mechanism needs to be activated.

[0049] According to the judgment logic of material balance deviation, the control end can automatically start different linkage mechanisms according to different material balance deviation ranges. The specific linkage mechanisms are as follows: 1. Material balance deviation is less than the first deviation threshold: Analysis: The cumulative input flow is excessively lower than the cumulative output flow: Analyze the causes and optimize the input flow: When the control end identifies that the input flow is too low, possible causes include: input equipment failure or flow drop, which may be caused by abnormalities in equipment such as pumps, valves, and pipelines. External environmental factors (such as wind speed, temperature, etc.) affect the input flow. Feedback to the monitoring end that the input flow is low, it is recommended to check the operating status of related input equipment, especially key components such as pumps, valves, and filters, to check for failures or maintenance issues. If the system identifies that there is a fault in the equipment, it automatically starts equipment inspection and maintenance instructions and repairs it in time. According to the current operating conditions, adjust the loading and unloading plan, increase the input flow, or adjust the input flow setting. Feedback adjustment suggestions to the operator, and it is recommended to optimize the input process or increase the working efficiency of the input equipment.

[0050] 2. Material balance deviation is greater than the second deviation threshold: Analysis: The cumulative input flow is excessively higher than the cumulative output flow: Analyze the causes and optimize the output flow: When 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. The output equipment (such as unloading pumps, valves, and unloading arms) fails or performs poorly. Feedback to the monitoring end that the input flow is too high, it is recommended to check the status of related output equipment (such as unloading pumps, valves, and loading and unloading arms) to ensure that the equipment is operating normally and avoid excessive input flow that exceeds the system's tolerance. If the output equipment fails, start equipment inspection and instruct maintenance. Adjust the loading and unloading plan, appropriately reduce the input flow or increase the output flow to ensure stable operation of the system and avoid excessive backlogs. If necessary, adjust the operation schedule and coordinate other related equipment for optimization.

[0051] Alarm mechanism: If the material balance deviation exceeds the set deviation threshold range, the control end will immediately trigger the alarm mechanism to notify the relevant operators to take measures. Provide real-time feedback and suggestions to operators (such as equipment failure, flow abnormality, loading and unloading plan adjustment, etc.).

[0052] Adjust flow settings and progress: Analyze deviations through the control end and start the linkage mechanism to optimize loading and unloading flow, ensure smooth operation, and avoid equipment damage or operation interruption caused by flow imbalance. Equipment inspection and repair: When abnormal flow is identified, the control end can start equipment inspection and repair instructions in real time to ensure that the equipment is repaired in time to avoid affecting operation efficiency. Operator feedback and progress adjustment: The linkage mechanism will feedback the adjustment plan to the operator to ensure that the operation progress and material flow are optimized and adjusted in time. Through these automated linkage mechanisms, the material balance during the loading and unloading process of LNG ships can be adjusted and optimized in real time, improving the safety, stability and efficiency of operations.

[0053] Example 3: Please refer to Figure 3As shown, the LNG ship loading and unloading material balance dynamic monitoring cloud platform described in this embodiment includes a data monitoring layer, a field perception layer, and a regulation control layer; Data monitoring layer: collects global data, such as overall LNG flow, changes in ship tank capacity, trunk pipeline flow, loading and unloading plan progress, etc., dynamically corrects the target flow curve according to the actual operation situation and influencing factors (weather, equipment, etc.), realizes real-time update of the plan trend and automatic calibration of the global benchmark, and sends the corrected target flow curve to the regulation control layer; On-site perception layer: collects real-time sensor data at the operation site, including liquid level, temperature, pressure, valve status, loading and unloading arm action status, etc., to reflect the immediate status and dynamic changes during the operation process. Based on the obstacle recognition and state change mechanism, it automatically perceives operation anomalies (such as equipment jitter, liquid level mutation, etc.), and adaptively adjusts data collection parameters (granularity, frequency, point priority) to achieve rapid response to on-site dynamics. The adjusted data collection parameters are sent to the regulation control layer; Adjustment control layer: continuously monitor the state 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, the linkage mechanism is automatically started (including feedback to the monitoring end that the loading and unloading plan may not be achieved, suggesting to adjust the progress, sending data verification and equipment inspection instructions to the perception end, and starting the alarm mechanism to notify the operator or suspend loading and unloading).

[0054] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0055] 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 invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A dynamic monitoring method for material balance of LNG ship loading and unloading, characterized by: The monitoring method comprises the following steps: The monitoring end collects global data and dynamically modifies the target flow curve according to the actual operation conditions and influencing factors; The sensing end collects real-time sensor data at the work site, automatically senses work abnormalities based on obstacle recognition and state change mechanisms, and adaptively adjusts data collection parameters; The control end continuously monitors the state changes of the target flow curve and the collected 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.

2. The method for dynamic monitoring of material balance of LNG ship loading and unloading according to claim 1, characterized in that: The control end continuously monitors the state changes of the target flow curve and the acquisition parameters, and automatically adjusts the material balance judgment window, including the following steps: 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 material balance calculation. The expression is: , where is the material balance judgment window after adjustment. is the material balance judgment window before adjustment. is the trend influencing factor.

3. The method for dynamic monitoring of material balance of LNG ship loading and unloading according to claim 2 is 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, the linkage mechanism is automatically activated, including the following steps: The accumulated material balance deviation is calculated 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 per unit time, is the output flow per unit time; The obtained material balance deviation is compared with a preset first deviation threshold and a second deviation threshold, wherein 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; If the material balance deviation is greater than or equal to the first deviation threshold, and the material balance deviation is 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, the cumulative input flow is analyzed to be 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, the cumulative input flow is analyzed to be excessively higher than the cumulative output flow, and the linkage mechanism needs to be activated.

4. The method for dynamic monitoring of material balance of LNG ship loading and unloading according to claim 3 is characterized in that: The sensing end collects real-time sensor data at the work site, automatically senses work abnormalities based on obstacle recognition and state change mechanisms, and adaptively adjusts 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 status and loading and unloading arm movement status; Through real-time data analysis, abnormal situations that occur during the operation are identified, including: When the data exceeds the normal range, it is marked as abnormal, and frequency analysis and signal processing technology are used to identify jitter or instability of the device. Liquid level mutation detection: By analyzing the rate of liquid level change, detect whether the liquid level has abnormal fluctuations, monitor the sudden changes in pipeline pressure, analyze the frequency of pressure fluctuations, and determine whether there is equipment failure or abnormal pipeline leakage; Adjust the granularity, frequency, and point priority of data collection according to the real-time status and dynamic changes of the work site.

5. The method for dynamic monitoring of material balance of LNG ship loading and unloading according to claim 4 is characterized in that: According to the real-time status and dynamic changes of the work site, adjust the granularity, frequency and point priority of data collection, including the following steps: During the operation, when abnormal fluctuations or status changes are identified, the data collection time interval is automatically shortened to improve the data collection granularity; When equipment anomalies or changes in operating status are detected, the frequency of data collection is increased; Dynamically adjust the collection priority of different points according to changes in on-site operation status.

6. The method for dynamic monitoring of material balance of LNG ship loading and unloading according to claim 4, characterized in that: Through real-time data analysis, abnormal situations that occur during the operation are identified, including the following steps: When the monitoring value exceeds the normal range, an abnormal alarm is automatically triggered, and frequency analysis and signal processing technology are used to identify the jitter or instability of the equipment; Process the acceleration sensor or vibration sensor data of the device, use Fourier transform to perform frequency domain analysis on the vibration signal, extract the frequency components in the signal, and identify the high-frequency components. Fourier transform is used to convert the time domain signal into the frequency domain signal. The expression is: , where is the spectrum of the signal, is the original sampled data, is the frequency, is the number of data points, j is the imaginary unit; 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.

7. The method for dynamic monitoring of material balance of LNG ship loading and unloading according to claim 6, characterized in that: The calculation logic of the liquid level change rate is: obtain the liquid level difference by subtracting the liquid level at the previous moment from the current moment, and obtain the liquid level change rate by dividing the liquid level difference by the time interval of each liquid level data; The calculation logic of the pressure change rate is: obtain the pressure difference by subtracting the pressure at the previous moment from the current moment pressure, and obtain the pressure change rate by dividing the pressure difference by the time interval of each pressure data.

8. The method for dynamic monitoring of material balance of LNG ship loading and unloading according to claim 7, characterized in that: The monitoring end collects global data and dynamically modifies 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 overall LNG flow, changes in ship tank capacity, trunk pipeline flow, and loading and unloading plan progress indicators, and set the target flow curve based on the loading and unloading plan and the initial system configuration; Obtain external influencing factors, conduct comparative analysis based on real-time data and set benchmarks, identify the source of deviation, dynamically correct the target flow curve, and set a new global benchmark.

9. The method for dynamic monitoring of material balance of LNG ship loading and unloading according to claim 8, characterized in that: Obtain external influencing factors, compare and analyze the real-time data with the set benchmark, identify the deviation source, and dynamically correct the target flow curve, including the following steps: Define the target flow curve: , get the current actual traffic data: , calculate the flow deviation between the target flow curve and the actual flow: , where is the flow deviation at time t; The target flow curve is adjusted in real time according to the external factor correction factor, and 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 correction factor for external factors.

10. A cloud platform for dynamic monitoring of material balance of LNG ship loading and unloading, used to implement the monitoring method according to any one of claims 1 to 9, characterized in that: Including data monitoring layer, on-site perception layer, and regulation and control layer; Data monitoring layer: used to collect global data and dynamically modify the target flow curve according to actual operating conditions and influencing factors; On-site perception layer: used to collect real-time sensor data at the work site, automatically perceive work abnormalities based on obstacle recognition and state change mechanisms, and adaptively adjust data collection parameters; Adjustment control layer: continuously monitor the state changes of the target flow curve and the collected parameters, automatically adjust the material balance judgment window, and calculate the material balance based on the adjusted parameter system. If the material balance deviation exceeds the deviation threshold, the linkage mechanism is automatically started.

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