Automatic control method for BOG cryogenic recovery system for LNG marine storage tanks

Abstract

The present invention relates to the field of automated control technology, specifically to an automated control method for a BOG cryogenic recovery system for an LNG marine storage tank, comprising the following steps: obtaining BOG pressure fluctuation data and temperature difference trends, analyzing pressure rise rates and cooling efficiency changes, identifying pressure peaks and cooling lag sections, screening inefficient sections and marking optimized units, identifying energy matching, analyzing energy loss distribution, identifying over-limit nodes and fault signals, and obtaining an energy and fault synchronization control table. The present invention, through real-time data monitoring and intelligent analysis, can accurately control the BOG recovery process, optimize energy flow and cooling processing, improve the efficiency of the cryogenic recovery system, monitor BOG pressure and temperature difference changes, analyze pressure fluctuations and cooling lags, ensure continuous improvement in condensation efficiency, accurately adjust control parameters, avoid energy loss and improve equipment energy efficiency, optimize energy consumption and equipment fault management, and ensure efficient and safe operation of the cryogenic recovery system.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to an automatic control method for a BOG cryogenic recovery system for an LNG marine storage tank. Background Art

[0002] The field of automation and control technology involves the design and implementation of various automation systems, focusing on automation equipment, process control systems, regulatory control strategies, and real-time monitoring and optimization management. The core mission of this technology is to automate the operation of various equipment and optimize resource utilization through precise control methods. Automation and control technology is widely used in various industries, including manufacturing, energy, transportation, and environmental protection, and plays a key role in the control and regulation of complex systems. Among these, conventional automation and control methods for LNG marine tank BOG cryogenic recovery systems employ automated control technology to cryogenically recover BOG (Boil-Off Gas) from LNG marine tanks. This method primarily addresses the recovery and cooling of BOG from LNG marine tanks. Traditional technologies manage the BOG recovery process through mechanical or manual adjustments, relying on manual or semi-automatic control to adjust various system parameters. By introducing automated control technology and leveraging real-time data acquisition and intelligent analysis, the BOG recovery process can be precisely controlled, ensuring the efficient operation of the cryogenic recovery system. During implementation, this automated control method combines sensor monitoring data, process regulation algorithms, and feedback control mechanisms to automatically adjust various operating parameters to achieve optimized BOG recovery and cooling.

[0003] Existing technologies primarily rely on manual adjustment and semi-automatic control, which lacks precise control over the BOG recovery process and is prone to unstable cooling efficiency and excessive energy loss. Since manual or semi-automatic adjustment cannot monitor all influencing factors in real time, optimizing equipment energy efficiency and fault management is difficult, often leading to system inefficiency and energy waste. Existing technologies also suffer from lags in energy flow and fault detection, resulting in delayed fault diagnosis and the inability to detect and address equipment failures at an early stage, thus impacting the smooth progress of the entire recovery process. This can lead to overcooling or excessive energy consumption, reducing the overall efficiency and stability of the cryogenic recovery system. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an automated control method for a BOG cryogenic recovery system for an LNG marine storage tank.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an automated control method for a BOG cryogenic recovery system for an LNG marine storage tank, comprising the following steps:

[0006] S1: Obtain pressure fluctuation data and temperature difference trends from the BOG pressure change monitoring unit and temperature sensing unit in LNG marine storage tanks, analyze the pressure rise rate and cooling efficiency changes per unit time, identify pressure peak sections and cooling lag sections, and generate BOG treatment priority division results;

[0007] S2: Based on the BOG treatment priority classification results, the energy conversion frequency of the condensing module and the subcooling cycle component in the low-temperature recovery system is extracted, the condensing efficiency change trend is detected, the inefficient sections are screened and the units to be optimized are marked, and the condensing efficiency adjustment nodes are generated;

[0008] S3: Based on the condensing efficiency adjustment node, extract the operating cycle data and energy consumption value of the low-temperature recovery system, identify the matching degree of energy input and output per unit time, sort the equipment energy efficiency, and generate an energy efficiency allocation optimization list;

[0009] S4: Based on the energy efficiency allocation optimization list, extract the control unit data of the condensing module and the subcooling cycle component, identify the key node status in the energy flow path, analyze the energy loss distribution, and generate an energy loss distribution map.

[0010] As a further solution of the present invention, the BOG treatment priority division results include pressure fluctuation amplitude, cooling efficiency change trend, energy consumption intensive section, and condensation priority identification; the condensation efficiency adjustment node includes condensation frequency change point, cooling efficiency fluctuation range, condensation abnormality unit, and optimization trigger condition; the energy efficiency allocation optimization list includes equipment energy input intensity, equipment energy output efficiency, energy efficiency priority, and energy consumption priority; the energy loss distribution map includes energy flow node status, total energy loss, distribution uniformity index, and abnormal energy node mark.

[0011] As a further solution of the present invention, the steps for obtaining the BOG processing priority division result are specifically as follows:

[0012] S111: Based on the pressure fluctuation data and temperature difference trends of the BOG pressure change monitoring unit and temperature sensing unit in the LNG marine storage tank, combined with the output efficiency value of the condensing module at the corresponding time point, the time series is divided into segments according to the efficiency change, and the pressure and temperature change trends in each segment are analyzed to obtain the pressure and temperature interval mapping value;

[0013] S112: extracting adjacent extreme value points of the pressure fluctuation data and the temperature change data according to the pressure and temperature interval mapping values, identifying a pressure and temperature difference ratio sequence, comparing the fluctuation amplitude with a reference value, screening the difference period numbers, and obtaining a fluctuation offset period number sequence;

[0014] S113: Based on the fluctuation offset cycle number sequence, extract the segment condensing efficiency change data, calculate the time difference between the condensing efficiency growth and decay intervals, divide the fluctuation segments based on the efficiency change speed and cycle frequency, and generate the BOG treatment priority division result.

[0015] As a further solution of the present invention, the step of obtaining the condensing efficiency adjustment node is specifically as follows:

[0016] S211: Based on the BOG treatment priority classification result, the energy demand segments of the condensing module and the subcooling cycle component in the low-temperature recovery system are extracted, the condensing frequency, cooling cycle, energy state value, and module voltage feedback of the segment are called, the unit cycle condensing efficiency and time difference are identified, and a segment condensing frequency sequence is generated;

[0017] S212: Based on the segment condensation frequency sequence, the voltage stability rate, cooling module energy balance value and component position offset coefficient in each condensation node are collected. By comparing the proportional relationship between the parameters and the condensation efficiency change threshold, the change amplitude value of the inefficient node is calculated, and compared with the action cycle benchmark fluctuation amplitude point by point to obtain the condensation efficiency adjustment node.

[0018] As a further solution of the present invention, the steps of obtaining the energy efficiency allocation optimization list are specifically as follows:

[0019] S311: Based on the condensing efficiency adjustment node, extracting the operating cycle data of the low-temperature recovery system, the operating cycle data of the condensing module, and the operating cycle data of the subcooling cycle component, identifying the energy consumption value, and analyzing the matching interval between the energy input and output per unit time by comparing the continuous interval in the equipment operating cycle data with the energy consumption value to obtain the unit energy efficiency matching interval;

[0020] S312: calling the unit energy efficiency matching interval, calculating the energy efficiency deviation index value by differentiating the matching interval values ​​of the equipment in the self-operation cycle, and comparing and arranging the index values ​​to generate an energy efficiency allocation optimization list.

[0021] As a further solution of the present invention, the steps for obtaining the energy loss distribution map are specifically as follows:

[0022] S411: Based on the energy efficiency allocation optimization list, call the real-time energy output data of the condensing module and the subcooling cycle component, record the status of the starting node, transfer node, and terminal node in the energy transmission path, identify the total energy of each path and the difference between the states of adjacent nodes, and generate a path node set;

[0023] S412: For the path node set, identify the path energy sum and the node state difference, calculate the path loss index, compare it with a preset node loss threshold, screen out paths with energy loss exceeding the threshold or with a sudden change in state difference, and generate abnormal path identifiers;

[0024] S413: Extract the node status and energy loss of the abnormal path according to the abnormal path identifier, write the filtered path data into the distributed storage module in timestamp order, analyze the energy flow association between nodes, and obtain an energy loss distribution map.

[0025] As a further embodiment of the present invention, the method further comprises step S5:

[0026] S5: Based on the energy loss distribution map, extract the node output values ​​of the condensing module and the subcooling cycle component, analyze the number of zero output segments in consecutive cycles, compare them with the standard fault interval, identify the over-limit nodes and fault markers and partition switching signals, and obtain the low-temperature recovery system energy and fault synchronization control table;

[0027] The low-temperature recovery system energy and fault synchronization control table includes the number of zero output cycles, over-limit fault nodes, fault mark status, and partition switching identifier.

[0028] As a further solution of the present invention, the steps for obtaining the low-temperature recovery system energy and fault synchronization control table are specifically as follows:

[0029] S511: Based on the energy loss distribution map, extract the node output values ​​of the condensing module, the subcooling cycle component, and the low-temperature recovery system, divide the time periods into continuous cycles, determine whether the node output value in each period is zero, and count the number of time periods in which the node has zero value in the period to obtain the number of zero output periods of the node period;

[0030] S512: Call the number of zero output segments of the node cycle, determine whether the node exceeds the normal range based on the difference between the number of zero output segments of the node and the set standard fault interval, bind the exceeding node index with the corresponding cycle number, filter the abnormal nodes, record the cycle performance of the exceeding node, and obtain the exceeding node location value;

[0031] S513: Based on the over-limit node positioning value, call the partition switching threshold, compare the number of consecutive abnormal cycles with the threshold, mark the partition status of the node that meets the switching conditions, summarize the current output value of the node and the switching status parameters, and obtain the low-temperature recovery system energy and fault synchronization control table.

[0032] Compared with the prior art, the advantages and positive effects of the present invention are:

[0033] The present invention precisely controls the BOG recovery process through real-time data monitoring and intelligent analysis, effectively identifying and optimizing energy flow and cooling processes, significantly improving the operating efficiency of the low-temperature recovery system. By monitoring BOG pressure and temperature differential changes and analyzing the relationship between pressure fluctuations and cooling lag, priority allocation results are generated and inefficient sections are optimized, ensuring continuous improvement in condensation efficiency. Based on energy efficiency allocation optimization and energy loss distribution maps, various control parameters can be precisely adjusted to optimize equipment energy efficiency and effectively avoid energy loss. By promptly identifying and marking faulty nodes, unexpected downtime and failures during low-temperature recovery system operation are reduced, improving the stability and reliability of the low-temperature recovery system. This not only improves recovery efficiency but also optimizes energy consumption and equipment fault management, ensuring the efficient and safe operation of the low-temperature recovery system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0035] Figure 2 This is a flow chart for obtaining the BOG processing priority division result in the present invention;

[0036] Figure 3 This is a flow chart for obtaining the condensing efficiency adjustment node in the present invention;

[0037] Figure 4 A flowchart for obtaining the energy efficiency allocation optimization list in the present invention;

[0038] Figure 5 This is a flow chart for obtaining the energy loss distribution map in the present invention;

[0039] Figure 6 This is a flow chart for obtaining the energy and fault synchronization control table of the low-temperature recovery system in the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0041] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined. Example 1

[0042] See also Figure 1 The present invention provides a technical solution: an automated control method for a BOG cryogenic recovery system for an LNG marine storage tank, comprising the following steps:

[0043] S1: Obtain pressure fluctuation data and temperature difference trends from the BOG pressure change monitoring unit and temperature sensing unit in LNG marine storage tanks, analyze the pressure rise rate and cooling efficiency changes per unit time, identify pressure peak sections and cooling lag sections, and generate BOG treatment priority division results;

[0044] S2: Based on the BOG treatment priority classification results, the energy conversion frequency of the condensing module and the subcooling cycle components in the low-temperature recovery system is extracted, the condensing efficiency change trend is detected, the inefficient sections are screened and the units that need to be optimized are marked, and the condensing efficiency adjustment nodes are generated;

[0045] S3: Adjust the nodes based on condensing efficiency, extract the operating cycle data and energy consumption value of the low-temperature recovery system, identify the matching degree of energy input and output per unit time, sort the equipment energy efficiency, and generate an energy efficiency allocation optimization list;

[0046] S4: Based on the energy efficiency allocation optimization list, extract the control unit data of the condensing module and the subcooling cycle component, identify the key node states in the energy flow path, analyze the energy loss distribution, and generate an energy loss distribution map;

[0047] S5: Based on the energy loss distribution map, extract the node output values ​​of the condensing module and the subcooling cycle component, analyze the number of zero output segments in consecutive cycles, compare them with the standard fault interval, identify the over-limit nodes and fault marks and partition switching signals, and obtain the energy and fault synchronization control table of the low-temperature recovery system.

[0048] The BOG treatment priority division results include pressure fluctuation amplitude, cooling efficiency change trend, energy consumption intensive section, and condensation priority mark. The condensation efficiency adjustment nodes include condensation frequency change point, cooling efficiency fluctuation range, condensation abnormality unit, and optimization trigger conditions. The energy efficiency allocation optimization list includes equipment energy input intensity, equipment energy output efficiency, energy efficiency priority, and energy consumption priority. The energy loss distribution map includes energy flow node status, total energy loss, distribution uniformity index, and abnormal energy node mark. The low-temperature recovery system energy and fault synchronization control table includes the number of zero output cycles, over-limit fault nodes, fault mark status, and partition switching mark.

[0049] See also Figure 2 ,The specific steps for obtaining the BOG processing priority division results are:

[0050] S111: Based on the pressure fluctuation data and temperature difference trends of the BOG pressure change monitoring unit and temperature sensing unit in the LNG marine storage tank, combined with the output efficiency value of the condensing module at the corresponding time point, the time series is divided into segments according to the efficiency change, and the pressure and temperature change trends in each segment are analyzed to obtain the pressure and temperature interval mapping value;

[0051] Based on the pressure fluctuation data and temperature difference trends obtained by the BOG pressure change monitoring unit and temperature sensing unit in the LNG marine storage tank, combined with the output efficiency value of the condensing module corresponding to the time point, the pressure fluctuation data is first analyzed. During the 24-hour monitoring period, the pressure data is collected in real time by an independent pressure sensor and recorded every 10 seconds. By performing Fourier transform on the continuous pressure data sequence, its fluctuation components on different time scales can be identified. For example, within a specific time period, if the tank pressure rises from the initial 150kPa to 165kPa and then drops to 148kPa, the pressure fluctuation shows obvious periodic characteristics. At the same time, the temperature sensing unit monitors the temperature of multiple locations inside the storage tank in real time, and calculates the difference between each point and the preset reference temperature (for example, -162°C) to form temperature difference trend data. For example, when the top temperature of the storage tank is monitored to be -158°C and the bottom temperature is -160°C, the temperature differences from the reference temperature are 4°C and 2°C respectively, indicating that there is a temperature difference. Then, combined with the output efficiency value of the condensing module corresponding to the time point, the efficiency value indicates the ability of the condensing module to process BOG per unit time. For example, from 2 a.m. to 4 a.m., the output efficiency of the condensing module is 85%, while from 10 a.m. to 12 a.m., due to the increase in ambient temperature, its efficiency drops to 70%. %, by analyzing the efficiency values, identifying the key time points of efficiency changes, for example, when the efficiency drops from 85% to 70%, it is marked as a change point, and the entire time series is divided into different segments based on the efficiency change points. For example, a 24-hour day is divided into several segments, such as 0:00-6:00, 6:00-12:00, 12:00-18:00, and 18:00-24:00. The condensing efficiency remains relatively stable or shows a specific trend in each segment. In each divided segment, the pressure and temperature change trends in the segment are further analyzed, and the maximum, minimum, and average pressure values ​​in the segment are calculated. and standard deviation, and calculate the maximum, minimum, average and standard deviation of the temperature. For example, in the section from 0:00 to 6:00, the tank pressure fluctuates between 145kPa and 155kPa, and the temperature varies between -161°C and -159°C, while the output efficiency of the condensing module is 88%. Through statistical analysis, the pressure and temperature interval mapping value is obtained, which represents the range or characteristics of the pressure and temperature fluctuations in a specific condensing efficiency section. For example, in a section with a condensing efficiency of 85% to 90%, the pressure interval mapping value is [145kPa, 155kPa], and the temperature interval mapping value is [-161°C, -159°C].

[0052] S112: Extracting adjacent extreme value points of the pressure fluctuation data and the temperature change data based on the pressure and temperature interval mapping values, identifying a pressure and temperature difference ratio sequence, comparing the fluctuation amplitude with a reference value, screening the difference period numbers, and obtaining a fluctuation offset period number sequence;

[0053] According to the pressure and temperature range mapping values ​​obtained above, adjacent extreme points are extracted from the monitored pressure fluctuation data. If the pressure data sequence is [150kPa, 152kPa, 149kPa, 153kPa, 148kPa, 155kPa], the adjacent extreme points are (150kPa, 152kPa), (152kPa, 149kPa), (149kPa, 153kPa), etc., and the adjacent extreme points are extracted from the temperature change data. Extract adjacent extreme points. If the temperature data sequence is [-160℃, -159℃, -161℃, -158℃, -162℃, -157℃], the adjacent extreme points are (-160℃, -159℃), (-159℃, -161℃), (-161℃, -158℃), etc. Then, based on the adjacent extreme points, calculate the pressure and temperature difference ratio sequence. For example, for pressure, calculate the pressure difference between adjacent extreme points, such as For temperature, calculate the temperature difference between adjacent extreme points, calculate the ratio between the pressure difference and the temperature difference, and form a difference ratio sequence. For example, if the pressure difference at a certain time point is 2kPa and the temperature difference is 1℃, the difference ratio is 2; then, compare the difference ratio sequence with the preset benchmark value to screen out the difference cycle number. The benchmark value refers to the standard used to measure whether the BOG pressure and temperature fluctuations are abnormal. Its setting needs to comprehensively consider the design specifications, operating experience and historical data of the LNG storage tank. For example, by statistically analyzing the historical operating data of the LNG storage tank, the average and standard deviation of the BOG pressure and temperature difference ratio in the past year are calculated, and the benchmark value is set to the average plus two times the standard deviation to cover 95% of the normal fluctuation range. Assuming that the historical data analysis results show that the average value of the pressure and temperature difference ratio is 1.5 and the standard deviation is 0.2, then the benchmark value can be set to Specifically, if the calculated difference ratio exceeds 1.9, it is determined that the fluctuation amplitude is large, and the corresponding cycle number is recorded. For example, within a one-hour monitoring cycle, if the difference ratios at the 5th minute and the 12th minute are 2.1 and 2.5, respectively, both exceeding 1.9, cycle numbers 5 and 12 are screened out. In this way, cycles in which the pressure and temperature fluctuation amplitudes significantly deviate from the normal range are identified. The screened cycle numbers are combined to obtain a fluctuation deviation cycle number sequence, which identifies the time periods when the BOG pressure and temperature fluctuations are abnormal.

[0054] S113: Based on the fluctuation offset cycle number sequence, extract the section condensing efficiency change data, calculate the time difference between the condensing efficiency growth and decay intervals, divide the fluctuation segments based on the efficiency change speed and cycle frequency, and generate the BOG treatment priority division result;

[0055] Based on the fluctuation offset cycle number sequence obtained above, the condensing efficiency change data of the segment corresponding to the cycle is extracted. If the fluctuation offset cycle number sequence includes the 5th, 12th, and 20th cycles, the condensing efficiency data of the segment to which the cycle belongs is extracted. The data includes the change process of the condensing efficiency from one stable value to another stable value, for example, from 88% to 75%, or from 70% to 82%. The time difference between the condensing efficiency growth interval and the decay interval is calculated. For example, if the condensing efficiency takes 10 minutes to increase from 75% to 82%, the time difference between the growth interval is 10 minutes; if the condensing efficiency takes 8 minutes to decrease from 88% to 75%, the time difference between the decay interval is 8 minutes. The fluctuation segments are divided based on the efficiency change speed and cycle frequency. The efficiency change speed refers to the change amplitude of the condensing efficiency per unit time. For example, if the efficiency increases from 75% to 82% within 10 minutes, the change speed is 82. The cycle frequency refers to the number of fluctuation offsets that occur per unit time. For example, 3 fluctuation offsets are detected within 1 hour, the cycle frequency is 82. The BOG treatment fluctuation range is 3 times / hour. Through a comprehensive analysis of the efficiency change rate, cycle frequency, and the time difference between the growth and decay intervals of condensing efficiency, BOG treatment fluctuation ranges are identified. For example, when the efficiency change rate is fast (greater than 1% / minute) and the cycle frequency is high (greater than 2 times / hour), this range is designated as a high-priority treatment range. This is because rapid and frequent fluctuations mean that the BOG system requires more active intervention. The combined use of these parameters enables more precise and accurate BOG treatment priority division, generating a BOG treatment priority division result that associates different fluctuation ranges with corresponding treatment priorities. For example, within the fluctuation offset cycle number sequence, a range with a condensing efficiency decay rate greater than 1% / minute and a frequency greater than 2 times / hour is marked as "high priority," a range with a condensing efficiency decay rate between 0.5% and 1% / minute and a frequency between 1 and 2 times / hour is marked as "medium priority," and a range with a condensing efficiency decay rate less than 0.5% / minute and a frequency less than 1 time / hour is marked as "low priority."

[0056] See also Figure 3 , the specific steps for obtaining the condensing efficiency adjustment node are:

[0057] S211: Based on the BOG treatment priority classification results, the energy demand segments of the condensing module and the subcooling cycle components in the low-temperature recovery system are extracted. The condensing frequency, cooling cycle, energy status value, and module voltage feedback of the segments are called up to identify the condensing efficiency and time difference per unit cycle, and generate a segment condensing frequency sequence.

[0058] Based on the BOG treatment priority classification results obtained above, the energy demand segments of the condensing module and the subcooling cycle components are extracted from the low-temperature recovery system. If the BOG treatment priority classification results show that the BOG treatment priority is "high priority" within a specific time period, such as 3:00 a.m. to 5:00 a.m., the energy demand data of the condensing module (for example, the refrigerant R134a compressor) and the subcooling cycle components (for example, the plate heat exchanger) during this time period are extracted. The data reflects the energy input required to maintain a stable tank pressure. The condensing frequency, cooling cycle, energy status value, and module voltage feedback data of this segment are called. The condensing frequency refers to the number of times the condensing module starts and stops per unit time, for example, 5 starts per hour and 5 cooling cycles per hour. It refers to the time required from the start of cooling to reaching the target temperature, for example, 20 minutes; the energy status value refers to the current energy consumption level of the condensing module, for example, 15kW; the module voltage feedback refers to the actual operating voltage of the condensing module, for example, 380V. By calling real-time data, for example, in a specific "high priority" section, the condensing frequency is 8 times / hour, the cooling cycle is 15 minutes, the energy status value is 18kW, and the module voltage feedback is 375V. The data is used to identify the unit cycle condensing efficiency and time difference. The unit cycle condensing efficiency is obtained by real-time monitoring of the ratio of the cooling capacity of the condensing module to the input power. For example, in a certain 10-minute cycle, the cooling capacity is 1000MJ and the input power is 20kW, then the condensing efficiency is ,The time difference refers to the difference between the actual cooling time and the ,ideal cooling time. For example, if the ideal cooling time is 12 minutes and ,the actual cooling time is 15 minutes, then the time difference is 3 minutes. ,Based on the identification results, a segment condensation frequency ,sequence is generated. For example, in a high priority segment, the condensation frequency ,sequence [8 times / hour, 7 times / hour, 9 times / hour] indicates the operating frequency ,of the condensation module at different time points.

[0059] S212: Based on the segment condensation frequency sequence, the voltage stability rate, cooling module energy balance value, and component position offset coefficient of each condensation node are collected. By comparing the proportional relationship between the parameters and the condensation efficiency change threshold, the formula is used:

[0060] ;

[0061] Calculate the change amplitude of the inefficient node and compare it point by point with the action cycle benchmark fluctuation amplitude to obtain the condensing efficiency adjustment node;

[0062] in, Indicates the change amplitude value of the inefficient node, is the total number of condensation nodes, is the voltage stability rate of the i-th condensation node, is the standard value of voltage stability, is the energy balance value of the cooling module at the i-th condensation node, is the reference temperature of the cooling module, is the condensation efficiency of the i-th node, is the change threshold of condensation efficiency;

[0063] The "inefficient node variation value" refers to the change in the operating efficiency of a node in the condensing system. Specifically, when the changes in the node's voltage stability, the energy balance value of the cooling module, and the condensing efficiency exceed the preset threshold, the system will evaluate the node's inefficiency by calculating these changes. The core purpose of this indicator is to identify inefficient nodes that may exist in the system and to promptly adjust their operating parameters or take optimization measures to avoid a serious decline in system energy efficiency. The rationality of the inefficient node variation value lies in its ability to provide early warning to the system, ensuring that the system can correct the problem before it expands, thereby effectively improving overall energy efficiency and extending the service life of the equipment.

[0064] According to the segment condensation frequency sequence obtained above, the voltage stability rate, cooling module energy balance value and component position offset coefficient in each condensation node are collected, and the voltage stability rate It refers to the The ratio of the voltage fluctuation range of a condensation node in unit time to the average voltage. If the voltage of a condensation node fluctuates between 370V and 390V and the average voltage is 380V, then its voltage stability rate is , cooling module energy balance value It refers to the The difference between the actual cooling energy and the theoretical cooling energy of the cooling module of the condensing node. For example, the actual cooling energy is 1000MJ and the theoretical cooling energy is 1100MJ. The energy balance value is , component position offset coefficient It refers to the The deviation between the physical position of a condensation node and the ideal design position can be quantitatively determined by setting an offset distance threshold. For example, if the offset distance is less than 1mm, the coefficient is 0.1; if the offset distance is between 1mm and 5mm, the coefficient is 0.5; if the offset distance is greater than 5mm, the coefficient is 1.0. By comparing the proportional relationship between the parameter and the condensation efficiency change threshold, the condensation efficiency change threshold is The setting of needs to be combined with the performance curve and historical operation data of the condensing module. For example, according to the factory parameters of the condensing module, its normal working efficiency fluctuation range is within ±5%, so the condensing efficiency change threshold can be set to 0.05, that is, 5%. If the deviation between the actual efficiency and the theoretical efficiency exceeds 0.05, it is considered that there is an efficiency problem. Then, the formula is used. Calculate the change amplitude of inefficient nodes ;

[0065] The benefit of this formula lies in that it comprehensively evaluates the magnitude of changes in inefficient nodes by combining voltage stability, cooling module energy balance values, and condensing efficiency change thresholds. This allows for more comprehensive and accurate identification of potential problems in the condensing system. For example, when voltage stability deviates from standard values, cooling module energy balance values ​​deviate from the reference temperature, and condensing efficiency significantly decreases, the formula can amplify the impact of these adverse factors, thereby more effectively identifying inefficient nodes.

[0066] Voltage stability standard value The setting refers to the national grid standard or the recommended value of the equipment manufacturer. For example, for industrial electrical equipment, the voltage stability standard value is set to 0.98, which means that the voltage fluctuation should be within ±2% of the rated voltage. The reference temperature of the cooling module is The setting refers to the design operating temperature of the cooling module. For example, for the LNG ship cooling module, its design operating temperature is -160℃, so the reference temperature can be set to -160℃;

[0067] Assuming there are three condensing nodes, the voltage stability rate, cooling module energy balance value, condensing efficiency, and condensing efficiency change threshold are shown in Table 1.

[0068] Table 1: Condensation node data table

[0069]

[0070] As shown in Table 1, the voltage stability standard value Set to 0.98, the reference temperature of the cooling module Set to -160℃, condensation efficiency change threshold Set to 0.05;

[0071] Substitute the parameters into the formula to calculate the change amplitude of the inefficient node: ;

[0072] Node 1: ;

[0073] Node 2: ;

[0074] Node 3: ;

[0075] ;

[0076] Calculated inefficient node change value Then, the value is compared point by point with the preset action cycle reference fluctuation amplitude. The action cycle reference fluctuation amplitude refers to the fluctuation range allowed by the condensing system under normal operating conditions. Its setting refers to the design tolerance and operation stability requirements of the condensing system. For example, under normal operating conditions, the fluctuation amplitude of the condensing system does not exceed 0.2, so the action cycle reference fluctuation amplitude can be set to 0.2. If it is greater than 0.2, the node is considered to be an inefficient node and recorded. In this way, the condensing efficiency adjustment node is obtained. The node identifies the condensing module or component that needs to be adjusted for efficiency. For example, the above calculation results If the fluctuation amplitude is greater than the benchmark value of 0.2, it indicates that there are inefficient nodes and efficiency adjustments are needed.

[0077] See also Figure 4 ,The specific steps for obtaining the energy efficiency allocation optimization list are:

[0078] S311: Based on the condensing efficiency adjustment node, extract the operating cycle data of the low-temperature recovery system, the operating cycle data of the condensing module, and the operating cycle data of the subcooling cycle component, identify the energy consumption value, and analyze the matching interval between energy input and output per unit time by comparing the continuous interval in the equipment operating cycle data with the energy consumption value to obtain the unit energy efficiency matching interval;

[0079] According to the condensing efficiency adjustment node obtained above, the operating cycle data of the low-temperature recovery system, the operating cycle data of the condensing module and the operating cycle data of the subcooling cycle component are extracted. For example, if a condensing node is determined to need adjustment, its operating data in the past 24 hours are extracted, including detailed operating records of the low-temperature recovery system (such as the overall energy consumption curve), the condensing module (such as the compressor start and stop time, operating power) and the subcooling cycle component (such as the heat exchanger inlet and outlet temperature, flow). The data reflects the operating status of each device in different time periods and identifies the energy consumption value in the operating data. For example, by integrating the power sensor data of the condensing module, its total power consumption in a specific operating cycle can be calculated in kWh. For example, a condensing module is running for 2 hours. 10kWh of electricity is consumed in one hour. By comparing the continuous intervals in the equipment operation cycle data with the energy consumption value, the matching range between energy input and output per unit time is analyzed. For example, the total energy input (such as power input) and total energy output (such as heat converted from cooling effect) of the low-temperature recovery system in a certain hour are compared. If the energy input is 100MJ and the energy output is 80MJ, the matching degree is 80%. The matching degree interval refers to the fluctuation range of the matching degree between energy input and output within a certain operation cycle. For example, if the matching degree fluctuates between 75% and 85%, the matching degree interval is [75%, 85%]. The unit energy efficiency matching degree interval is obtained, which reflects the efficiency range of energy utilization of the low-temperature recovery system under specific operating conditions.

[0080] S312: Call the unit energy efficiency matching interval, and use the matching interval value of the differentiated device in the self-operation cycle to use the formula:

[0081] ;

[0082] Calculate the energy efficiency deviation index value, compare and arrange the index values, and generate an energy efficiency allocation optimization list;

[0083] in, Represents the energy efficiency deviation index value, Represents the energy efficiency matching degree of the jth device, Represents the average value of device matching. represents the power consumption of the jth device, Represents the average value of the device's power consumption, represents the operating cycle of the jth device, Represents the total number of devices;

[0084] The "Energy Efficiency Deviation Index" measures the difference between a device's energy efficiency performance and its ideal state during its operating cycle. It calculates the deviation index by comparing the device's actual energy efficiency match with the expected value, taking into account factors such as the device's power consumption and operating cycle. The key purpose of this metric is to compare the energy efficiency performance of different devices, thereby identifying those with significant energy efficiency deviations and prioritizing optimization and adjustment. Its rationale lies in its ability to help focus resources on improving devices with significant energy efficiency deviations, avoiding energy waste caused by energy efficiency mismatches and improving overall system efficiency. Regular calculation and adjustment can optimize device energy use and reduce overall energy consumption.

[0085] Call the unit energy efficiency matching interval obtained above. If the unit energy efficiency matching interval is [0.75, 0.85], it means that the energy utilization efficiency under normal operating conditions is between 75% and 85%. By differentiating the matching interval values ​​of the equipment in the self-operating cycle, for example, in a certain operating cycle, the matching degree of the condensing module is 0.78, the matching degree of the subcooling cycle component is 0.82, and the matching degree of the low-temperature recovery system as a whole is 0.80. The values ​​reflect the actual energy efficiency performance of different equipment in their respective operating cycles. The formula is used. Calculate the energy efficiency deviation index value ;

[0086] The benefit of this formula lies in that it can more comprehensively evaluate a device's energy efficiency performance by comprehensively considering the device's energy efficiency matching and power consumption. In particular, the inclusion of the operating cycle as the square root term in the denominator allows for more sensitive identification of devices with short operating cycles but large power consumption fluctuations, preventing abnormalities in short-term operating devices from being masked by the average value.

[0087] Average device matching The method of obtaining is to monitor the energy efficiency matching of all devices in the system in real time and calculate the average value. For example, there are 3 devices with energy efficiency matching degrees of 0.78, 0.82, and 0.80 respectively, and the average value is ; Average value of device power consumption The method for obtaining is to monitor the real-time power consumption of all devices in the system and calculate the average value. For example, if the power consumption of three devices is 15kW, 12kW, and 18kW respectively, the average value is ;

[0088] Assume that there are three devices, and their energy efficiency matching, power consumption, and operation cycle are shown in Table 2.

[0089] Table 2: Equipment energy efficiency data table

[0090]

[0091] As shown in Table 2, the total number of devices , the average value of device matching , the average value of the device power consumption ;

[0092] Substitute the parameters into the formula to calculate the energy efficiency deviation index value:

[0093] Device 1: ;

[0094] Device 2: ;

[0095] Device 3: ;

[0096] ;

[0097] Calculated energy efficiency deviation index value , compare and arrange the index values ​​to generate an energy efficiency allocation optimization list. For example, sort the energy efficiency deviation index values ​​of all devices from large to small to obtain a priority list. The larger the deviation value of the device, the more prominent its energy efficiency problem is, and energy efficiency optimization needs to be prioritized. For example, according to the calculation results, the energy efficiency deviation index values ​​of device 2 and device 3 (0.366 and 0.346) are relatively large, and the energy efficiency deviation index value of device 1 (0.02) is small. The energy efficiency allocation optimization list shows that device 2 and device 3 need to be optimized for energy efficiency first. This list provides a basis for energy efficiency allocation optimization.

[0098] See also Figure 5 , the specific steps for obtaining the energy loss distribution map are:

[0099] S411: Based on the energy efficiency allocation optimization list, the real-time energy output data of the condensing module and the subcooling cycle component are called, the status of the starting node, transfer node, and terminal node in the energy transmission path is recorded, the total energy of each path and the difference between the status of adjacent nodes are identified, and a path node set is generated;

[0100] Based on the energy efficiency allocation optimization list obtained above, the real-time energy output data of the condensing module and the subcooling cycle component are called. If the optimization list indicates that a condensing module needs attention, its energy output data is read in real time, including cooling capacity, heat rejection, etc. The data reflects the actual working performance of the module at a certain moment, and records the status of the starting node, transfer node and terminal node in the energy transmission path. The starting node refers to the source of energy generation, for example, the compressor outlet of the condensing module. The transfer node is the key point that the energy passes through during the transmission process, for example, the inlet and outlet of the heat exchanger. The terminal node is the location where the energy finally arrives, for example, the inside of the LNG storage tank. The node status includes temperature, pressure, flow, etc. Parameters such as quantity, for example, the compressor outlet temperature is recorded as 50℃ and the pressure is 1.5MPa; the total energy amount and the state difference of adjacent nodes of each path are identified. The total energy amount refers to the total energy transmitted from the starting node to the terminal node on a specific path. For example, a path transmits 100MJ of energy. The state difference of adjacent nodes refers to the difference in state parameters between two adjacent nodes on the energy transmission path. For example, the inlet temperature of the heat exchanger is -150℃ and the outlet temperature is -160℃, then the temperature difference is 10℃. A path node set is generated, which contains detailed information of all energy transmission paths, including the nodes on the path, the total energy amount of each node and the state difference between adjacent nodes.

[0101] S412: For the set of path nodes, identify the difference between the total path energy and the node status, calculate the path loss index, compare it with a preset node loss threshold, screen out paths with energy loss exceeding the threshold or with a sudden change in status difference, and generate abnormal path identifiers;

[0102] For the path node set obtained above, identify the path energy sum and the node state difference. For example, extract the total energy value of each energy transmission path (for example, 100MJ) and the temperature difference of each adjacent node on the path (for example, 10°C) from the path node set, and calculate the path loss index. The path loss index is an indicator that measures the degree of energy loss during transmission. Its calculation method can refer to the law of conservation of energy. For example, compare the actual energy output with the theoretical energy output. If the theoretical output is 100MJ and the actual output is 90MJ, the loss index is The closer the index value is to 1, the greater the loss. The path loss index is compared with the preset node loss threshold to screen out paths where the energy loss amplitude exceeds the threshold or the state difference suddenly changes. The node loss threshold refers to the limit of the maximum energy loss allowed in the energy transmission path. Its setting refers to the design energy consumption standard of the equipment and the system operation experience. For example, through the analysis of the historical operation data of the LNG recovery system, it is found that under normal circumstances, the average energy loss index is 0.05 and the maximum does not exceed 0.1. Therefore, the node loss threshold can be set to 0.1. If the calculated path loss index is 0.15, it exceeds 0.1. Threshold, indicating that there is abnormal energy loss on the path; state difference mutation refers to abnormal and large changes in parameters such as temperature and pressure between adjacent nodes in a short period of time. For example, during normal operation, the temperature difference between adjacent nodes is between 2-5°C. If the temperature difference suddenly reaches 15°C, it is determined to be a state difference mutation. For example, if a loss index of 0.15 is detected on a certain path, and the temperature difference between adjacent nodes suddenly changes from the normal 3°C to 15°C, the path is marked as abnormal and an abnormal path identifier is generated. This identifier indicates the specific energy transmission path in the low-temperature recovery system with abnormal energy loss or state mutation, so that subsequent key analysis can be carried out.

[0103] S413: Extracting the node status and energy loss of the abnormal path based on the abnormal path identifier, writing the filtered path data into the distributed storage module in timestamp order, analyzing the energy flow correlation between the nodes, and obtaining an energy loss distribution map;

[0104] According to the abnormal path identification obtained above, the node status and energy loss of the abnormal path are extracted. If the identification indicates that there is an abnormality in a certain energy path from the condensing module to the storage tank, the real-time temperature, pressure, flow and other state parameters of each node on the path are extracted, as well as the actual energy loss on the path (for example, calculated by subtracting the outlet energy from the inlet energy). For example, on a certain abnormal path, the outlet temperature of the condensing module is -155°C, the temperature of a node in the middle of the pipeline is -150°C, the inlet temperature of the storage tank is -140°C, and the energy loss is 5MJ. The filtered path data is written into the distributed storage module in timestamp order. The distributed storage module can ensure the security and traceability of the data. For example, the above-mentioned The node status data and energy loss amount obtained are written to a NoSQL database along with the timestamp of when they occurred (for example, 11:30:00 on June 13) to ensure that the data is stored in chronological order to facilitate subsequent query and analysis. Finally, the energy flow associations between nodes are analyzed to obtain an energy loss distribution map. For example, by performing big data analysis on the data in the distributed storage module, an energy flow diagram is constructed to identify concentrated areas and key nodes of energy loss, thereby clearly showing the specific distribution of energy loss within the low-temperature recovery system. For example, it is found that energy loss is mainly concentrated in a section of pipe with poor insulation performance or an inefficient valve, and the loss will show a specific pattern over time.

[0105] See also Figure 6 ,The steps for obtaining the energy and fault synchronization control table for the low temperature recovery system are as follows:

[0106] S511: Based on the energy loss distribution map, the node output values ​​of the condensing module, the subcooling cycle component, and the low-temperature recovery system are extracted. The time periods are divided into continuous periods. It is determined whether the node output value in each period is zero. The number of time periods in which the node has a zero value in the period is counted to obtain the number of zero-output periods of the node period.

[0107] Based on the energy loss distribution map obtained above, the node output values ​​of the condensing module, subcooling cycle component, and low-temperature recovery system are extracted. If the map shows that there is energy loss in the condensing module, its cooling capacity, compressor power and other output values ​​are extracted. If there is loss in the subcooling cycle component, its heat exchange efficiency, outlet temperature and other output values ​​are extracted. If there is loss in the low-temperature recovery system as a whole, its total energy recovery efficiency and other output values ​​are extracted. The output values ​​reflect the actual performance of each device in operation. The time period is divided into continuous cycles. For example, the 24-hour operation data of a day is divided into several continuous 1-hour time periods, so that the node output in each time period can be analyzed independently to determine the performance of each time period. Whether the output value of the internal node is zero. For example, within a one-hour period, check whether the cooling capacity of the condensing module is continuously zero, or whether the outlet temperature of the subcooling cycle component does not reach the set value for a long time (that is, the output value has not changed effectively, which can be regarded as zero output). Zero output indicates that the device is in a shutdown, faulty or inefficient operating state. Count the number of time periods in which the node has a zero value within the cycle. For example, within a one-hour period, if the cooling capacity of the condensing module is zero for 30 minutes, then record the 30-minute zero output period of the node within that period. The number of zero output segments of the node cycle is obtained. This number reflects the length of time that the device failed to effectively output energy or operate normally within a specific operating cycle.

[0108] S512: Call the number of zero output segments in the node cycle, determine whether the node exceeds the normal range based on the difference between the number of zero output segments of the node and the set standard fault interval, bind the index of the exceeding node to the corresponding cycle number, filter out abnormal nodes, record the cycle performance of the exceeding node, and obtain the exceeding node location value;

[0109] Call the number of zero output segments of the node cycle obtained above. If the number of zero output segments of a condensing module in a certain cycle is 30 minutes, judge whether the node is out of the normal range based on the difference between the number of zero output segments of the node and the set standard fault interval. The standard fault interval difference refers to the threshold for measuring whether the zero output time of the device is normal. Its setting refers to the design life, maintenance cycle and historical fault data of the device. For example, by analyzing the historical fault records of the same type of condensing modules, it is found that when the zero output time exceeds 15 minutes, the probability of device failure increases significantly. Therefore, the standard fault interval difference can be set to 15 minutes. Specifically, if the number of zero output segments of the node (for example, 30 minutes) exceeds 15 minutes, it is determined that the node is out of the normal range. If the number of zero output segments of the condensing module is 30 minutes, and the standard fault interval difference is 15 minutes, then 30 If the time interval is greater than 15 minutes, the condensation module is determined to be out of the normal range. The out-of-limit node index is bound to the corresponding cycle number, and the abnormal node is screened. The out-of-limit node index uniquely identifies the location of the node. For example, the index of condensation module 1 is CM001, and the cycle number identifies the time period in which the abnormality occurred. For example, the cycle number 06:13-11:00 represents the period from 11:00 to 12:00 on June 13. For example, if the number of zero-output segments of condensation module 1 exceeds the limit, "CM001" is bound to "06:13-1100" and screened as an abnormal node. The cycle performance of the out-of-limit node is recorded. For example, the specific zero-output time and operating parameters of condensation module 1 in the period 06:13-1100 are recorded to obtain the out-of-limit node location value, which accurately indicates which nodes in the system have abnormal zero output and in which time period.

[0110] S513: Based on the exceeding node location value, the partition switching threshold is called, the number of consecutive abnormal cycles is compared with the threshold, the partition status of the node that meets the switching condition is marked, the current output value of the node and the switching state parameter are summarized, and the energy and fault synchronization control table of the low-temperature recovery system is obtained;

[0111] Based on the over-limit node positioning value, for example, the positioning value indicates that the condensing module 1 has an abnormal zero output in the 0613-1100 cycle, and the partition switching threshold is called. The partition switching threshold refers to the critical value used to determine whether the system needs to switch the operating mode. Its setting refers to the redundancy design, safe operation requirements and fault recovery strategy. If there are multiple condensing modules and the design allows some modules to be shut down, when the number of consecutive abnormal cycles reaches a certain value, in order to ensure the overall stable operation, partition switching is required. For example, the partition switching threshold is set to 3 consecutive abnormal cycles. This means that if a node has abnormal zero output for 3 consecutive cycles, it is necessary to consider switching to the backup partition or taking fault handling measures. The number of consecutive abnormal cycles is related to the partition switching. For example, if condensing module 1 has experienced zero output abnormality for four consecutive cycles, and the partition switching threshold is three cycles, then 4 is greater than 3, and the switching condition is met. The partition status of the node that meets the switching condition is marked. For example, the condensing partition to which condensing module 1 belongs is marked as "needs switching", indicating that the partition needs fault isolation or load transfer. The current output value of the node and the switching state parameters are summarized. For example, the current actual cooling capacity (if any), fault status (e.g., zero output) of condensing module 1, and the switching status of the partition to which it belongs (e.g., "needs switching") are summarized, thereby obtaining the energy and fault synchronization control table of the low-temperature recovery system. This table provides a basis for real-time decision-making, so that energy distribution adjustment or fault isolation can be carried out in a timely manner when an abnormality is detected.

[0112] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. The automated control method for the BOG cryogenic recovery system of an LNG marine storage tank is characterized in that: The following steps are involved: S1: Obtain pressure fluctuation data and temperature difference trends from the BOG pressure change monitoring unit and temperature sensing unit in LNG marine storage tanks, analyze the pressure rise rate and cooling efficiency changes per unit time, identify pressure peak sections and cooling lag sections, and generate BOG treatment priority division results; S2: Based on the BOG treatment priority classification results, the energy conversion frequency of the condensing module and the subcooling cycle component in the low-temperature recovery system is extracted, the condensing efficiency change trend is detected, the inefficient sections are screened and the units to be optimized are marked, and the condensing efficiency adjustment nodes are generated; S3: Based on the condensing efficiency adjustment node, extract the operating cycle data and energy consumption value of the low-temperature recovery system, identify the matching degree of energy input and output per unit time, sort the equipment energy efficiency, and generate an energy efficiency allocation optimization list; S4: Based on the energy efficiency allocation optimization list, extract the control unit data of the condensing module and the subcooling cycle component, identify the key node status in the energy flow path, analyze the energy loss distribution, and generate an energy loss distribution map The steps for obtaining the BOG processing priority division result are as follows: S111: Based on the pressure fluctuation data and temperature difference trends of the BOG pressure change monitoring unit and temperature sensing unit in the LNG marine storage tank, combined with the output efficiency value of the condensing module at the corresponding time point, the time series is divided into segments according to the efficiency change, and the pressure and temperature change trends in each segment are analyzed to obtain the pressure and temperature interval mapping value; S112: extracting adjacent extreme value points of the pressure fluctuation data and the temperature change data according to the pressure and temperature interval mapping values, identifying a pressure and temperature difference ratio sequence, comparing the fluctuation amplitude with a reference value, screening the difference period numbers, and obtaining a fluctuation offset period number sequence; S113: Based on the fluctuation offset cycle number sequence, extract the section condensing efficiency change data, calculate the time difference between the condensing efficiency growth and decay intervals, divide the fluctuation segments based on the efficiency change speed and cycle frequency, and generate the BOG treatment priority division result; The steps for obtaining the condensing efficiency adjustment node are specifically as follows: S211: Based on the BOG treatment priority classification result, the energy demand segments of the condensing module and the subcooling cycle component in the low-temperature recovery system are extracted, the condensing frequency, cooling cycle, energy state value, and module voltage feedback of the segment are called, the unit cycle condensing efficiency and time difference are identified, and a segment condensing frequency sequence is generated; S212: Based on the segment condensation frequency sequence, the voltage stability rate, cooling module energy balance value and component position offset coefficient in each condensation node are collected. By comparing the proportional relationship between the parameters and the condensation efficiency change threshold, the change amplitude value of the inefficient node is calculated, and compared with the action cycle benchmark fluctuation amplitude point by point to obtain the condensation efficiency adjustment node.

2. The automatic control method for the BOG cryogenic recovery system for LNG marine storage tanks according to claim 1, characterized in that: The BOG treatment priority division results include pressure fluctuation amplitude, cooling efficiency change trend, energy consumption intensive section, and condensation priority identifier. The condensation efficiency adjustment node includes condensation frequency change point, cooling efficiency fluctuation range, condensation abnormality unit, and optimization trigger condition. The energy efficiency allocation optimization list includes equipment energy input intensity, equipment energy output efficiency, energy efficiency priority, and energy consumption priority. The energy loss distribution map includes energy flow node status, total energy loss, distribution uniformity index, and abnormal energy node mark.

3. The automatic control method for the BOG cryogenic recovery system for LNG marine storage tanks according to claim 1, characterized in that: The steps for obtaining the energy efficiency allocation optimization list are specifically as follows: S311: Based on the condensing efficiency adjustment node, extracting the operating cycle data of the low-temperature recovery system, the operating cycle data of the condensing module, and the operating cycle data of the subcooling cycle component, identifying the energy consumption value, and analyzing the matching interval between the energy input and output per unit time by comparing the continuous interval in the equipment operating cycle data with the energy consumption value to obtain the unit energy efficiency matching interval; S312: calling the unit energy efficiency matching interval, calculating the energy efficiency deviation index value by differentiating the matching interval values ​​of the equipment in the self-operation cycle, and comparing and arranging the index values ​​to generate an energy efficiency allocation optimization list.

4. The automatic control method for the BOG cryogenic recovery system for LNG marine storage tanks according to claim 3 is characterized in that: The steps for obtaining the energy loss distribution map are specifically as follows: S411: Based on the energy efficiency allocation optimization list, call the real-time energy output data of the condensing module and the subcooling cycle component, record the status of the starting node, transfer node, and terminal node in the energy transmission path, identify the total energy of each path and the difference between the states of adjacent nodes, and generate a path node set; S412: For the path node set, identify the path energy sum and the node state difference, calculate the path loss index, compare it with a preset node loss threshold, screen out paths with energy loss exceeding the threshold or with a sudden change in state difference, and generate abnormal path identifiers; S413: Extract the node status and energy loss of the abnormal path according to the abnormal path identifier, write the filtered path data into the distributed storage module in timestamp order, analyze the energy flow association between nodes, and obtain an energy loss distribution map.

5. The automatic control method for the BOG cryogenic recovery system for LNG marine storage tanks according to claim 1, characterized in that: The method further comprises step S5: S5: Based on the energy loss distribution map, extract the node output values ​​of the condensing module and the subcooling cycle component, analyze the number of zero output segments in consecutive cycles, compare them with the standard fault interval, identify the over-limit nodes and fault markers and partition switching signals, and obtain the low-temperature recovery system energy and fault synchronization control table; The low-temperature recovery system energy and fault synchronization control table includes the number of zero output cycles, over-limit fault nodes, fault mark status, and partition switching identifier.

6. The automatic control method for the BOG cryogenic recovery system for LNG marine storage tanks according to claim 5, characterized in that: The steps for obtaining the low-temperature recovery system energy and fault synchronization control table are specifically as follows: S511: Based on the energy loss distribution map, extract the node output values ​​of the condensing module, the subcooling cycle component, and the low-temperature recovery system, divide the time periods into continuous cycles, determine whether the node output value in each period is zero, and count the number of time periods in which the node has zero value in the period to obtain the number of zero output periods of the node period; S512: Call the number of zero output segments of the node cycle, determine whether the node exceeds the normal range based on the difference between the number of zero output segments of the node and the set standard fault interval, bind the exceeding node index with the corresponding cycle number, filter the abnormal nodes, record the cycle performance of the exceeding node, and obtain the exceeding node location value; S513: Based on the over-limit node positioning value, call the partition switching threshold, compare the number of consecutive abnormal cycles with the threshold, mark the partition status of the node that meets the switching conditions, summarize the current output value of the node and the switching status parameters, and obtain the low-temperature recovery system energy and fault synchronization control table.

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