Centralized monitoring management system based on comprehensive data of pipeline conveying station yard

Abstract

The invention belongs to the technical field of pipeline transportation monitoring management, and particularly discloses a centralized monitoring management system based on pipeline transportation station comprehensive data, which comprises an out-of-limit event identification module, a flow pressure coupling analysis module, a pressure safety updating module and an early warning trigger judgment module. According to the method, the output flow out-of-limit event is identified, the parameter is effectively moved forward to the upstream flow disturbance parameter, the problem of early warning lag is solved, the out-of-limit event is taken as a starting point, a pressure prediction track is generated through flow and pressure coupling analysis, a risk path is pre-judged before actual overpressure, and the early warning effect is improved. And the early warning mode is promoted to change from post-event alarm to pre-event prediction. Meanwhile, by quantifying the comprehensive efficiency attenuation degree and dynamically reducing the pressure safety upper limit, the early warning threshold is matched with the current real anti-interference capacity in real time, it is guaranteed that early warning can be triggered earlier and more accurately when the adjusting capacity is reduced, and the pipeline conveying safety is guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of pipeline transportation monitoring and management technology, and specifically relates to a centralized monitoring and management system based on comprehensive data from pipeline transportation stations. Background Technology

[0002] As a core hub in the natural gas supply chain, the safe and stable operation of liquefied natural gas (LNG) receiving terminals is of paramount importance. Equipment within these terminals, such as large LNG storage tanks and submerged combustion vaporizers, operate under harsh conditions of low temperature and high pressure, making condition monitoring a key focus of safety management.

[0003] In actual operation, safety monitoring at LNG receiving terminals faces two contradictions. Firstly, in terms of process, the pressure status of storage tanks is directly affected by the dynamic balance between the LNG export and BOG processing subsystems. Secondly, in terms of monitoring technology, existing systems generally employ decentralized, independent monitoring and fixed static alarm thresholds. This early warning mode leads to the following drawbacks: 1. When the outflow rate increases irregularly, it immediately disrupts the original gas-liquid balance, and the resulting increase in tank pressure usually has a certain delay. Currently, each independent subsystem can only respond to its own parameter exceeding limits retrospectively, and cannot predict whether there will be future pressure anomalies in the early stages of flow rate changes, leading to untimely early warnings.

[0004] 2. Performance degradation in equipment such as BOG compressors or reflux valves reduces the system's dynamic adjustment capability to load fluctuations, making storage tanks more prone to overpressure during flow changes. Existing static alarm thresholds cannot capture this decline in system dynamic disturbance immunity caused by gradual changes in equipment performance, resulting in insufficient early warning sensitivity. Summary of the Invention

[0005] In view of this, in order to solve the above problems, a centralized monitoring and management system based on comprehensive data from pipeline transportation stations is proposed.

[0006] The objective of this invention can be achieved through the following technical solution: This invention provides a centralized monitoring and management system based on comprehensive data from pipeline transportation stations. The system includes: an over-limit event identification module, which identifies over-limit events of the outgoing flow based on the currently collected real-time outgoing flow of the LNG outgoing pipeline.

[0007] After identifying the event, the flow-pressure coupling analysis module uses the event trigger time as the time reference point and, based on the real-time collected tank pressure and outflow data, outputs the predicted pressure trajectory of the LNG tank within a preset future time period through coupling analysis.

[0008] The pressure safety update module quantifies the overall efficiency degradation of the BOG compressor based on the operating performance data of the BOG compressor and the actual opening data of the BOG return pipeline regulating valve, and dynamically lowers the pressure safety upper limit according to the degradation.

[0009] The early warning trigger judgment module determines that an early warning command is triggered and executes the early warning action when any point on the predicted trajectory exceeds the upper limit of the adjustment pressure safety limit.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By identifying the event of exceeding the limit of the outflow, the present invention effectively switches the trigger point of risk monitoring from the traditional tank pressure parameter to the more forward-looking outflow disturbance, thereby providing an accurate starting time point for the analysis of the subsequent pressure prediction trajectory, effectively overcoming the inherent problem of early warning lag caused by only comparing the tank pressure with the preset static threshold.

[0011] (2) This invention takes the identified outflow exceeding the limit event as the starting point, performs coupled analysis based on the real-time collected tank pressure and flow data, and outputs a pressure prediction trajectory. It can predict the change path and over-limit risk before the actual pressure exceeds the static threshold, thereby changing the risk warning method from post-event alarm to pre-event warning, realizing the effective forward shift of safety warning in the time dimension, and intuitively presenting the pressure change trend through the trajectory form.

[0012] (3) By quantifying the overall performance degradation of the BOG compressor and dynamically lowering the upper limit of pressure safety based on the degradation, this invention effectively solves the problem of insufficient sensitivity of static threshold warning caused by gradual changes in equipment performance, and ensures that the warning can be triggered earlier and more accurately when the adjustment capability decreases. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the system module connections of the present invention.

[0015] Figure 2 This is a schematic diagram of the overall implementation process of the present invention.

[0016] Figure 3 This is a schematic diagram of the outflow flow exceeding the limit event identification process of the present invention. Detailed Implementation

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

[0018] Please see Figure 1 and Figure 2 As shown, the present invention provides a centralized monitoring and management system based on comprehensive data from pipeline transportation stations. The system includes: an over-limit event identification module, a flow-pressure coupling analysis module, a pressure safety update module, and an early warning trigger judgment module.

[0019] In the above, the flow and pressure coupling analysis module is connected to the over-limit event identification module and the pressure safety update module, respectively. The pressure safety update module is also connected to the early warning trigger judgment module.

[0020] The over-limit event identification module identifies over-limit events in the outflow of LNG based on the real-time collected outflow of the LNG outflow pipeline.

[0021] In actual operation, the outflow of LNG receiving terminals is dynamically affected by factors such as downstream pipeline demand and equipment start-up and shutdown. Setting a fixed over-limit alarm value for the outflow alone cannot effectively distinguish between short-term fluctuations and conditions with cumulative risks. For example, when the outflow operates at a relatively flat level close to the upper limit for an extended period due to sustained high downstream demand, although the instantaneous flow rate change is small and does not trigger a rate-of-change alarm, the sustained high load will stably and significantly consume the LNG in the storage tanks, causing the BOG (boil-off gas) generation rate to remain high, potentially leading to over-limit risks in the storage tank pressure.

[0022] Based on this, the present invention identifies events that exceed the export flow limit by analyzing the export flow rate of the LNG export pipeline, thereby accurately screening out events that truly require subsequent risk analysis and achieving both accuracy and foresight in early warning.

[0023] As a preferred example, please refer to Figure 3 As shown, the specific implementation process for identifying outflow exceeding the limit event includes: First, the measurement values ​​of the corresponding mass flow meter installed on the LNG outflow pipeline are collected in real time at a fixed sampling frequency (e.g., once per second). Furthermore, to eliminate instantaneous noise interference, the raw data is smoothed using a first-order low-pass digital filter to filter out high-frequency measurement noise and obtain data reflecting the true flow change.

[0024] Subsequently, for each latest flow rate sample value, its rate of change within a preset time window (e.g., 30 seconds) is calculated. Specifically, a linear regression method can be used to fit the outflow flow rate value within this time window, and its slope can be used as the rate of change. The linear regression method is a well-known algorithm in the field and will not be described in detail here.

[0025] Next, based on the rate of change, it is determined whether the following conditions are met: Condition 1: The rate of change exceeds the preset step rate of change threshold. The step rate of change threshold can be set to 15% of the designed rated outgoing flow. Depending on the different control accuracy requirements of the receiving station, this threshold can be adjusted between 10% and 20%. For example, if the control accuracy requirement is high, it can be reduced to 10% of the designed rated outgoing flow, and if the control accuracy requirement is low, it can be relaxed to 20% of the rated outgoing flow.

[0026] Condition 2: The duration for which the outgoing flow exceeds the preset flow threshold exceeds the preset duration. The preset flow threshold can be set to 85% of the rated outgoing flow, and the preset duration can be set to 10 minutes.

[0027] Finally, if either condition 1 or condition 2 is met, an outflow exceeding the limit event is identified, and the trigger time of the outflow exceeding the limit event is marked at least.

[0028] This invention effectively shifts the trigger point for risk monitoring from traditional tank pressure parameters to more forward-looking external flow disturbances by identifying events that exceed the external flow limit. This provides an accurate starting point for the analysis of subsequent pressure prediction trajectories and effectively overcomes the inherent early warning lag problem of simply comparing tank pressure with a preset static threshold.

[0029] After identifying the event, the flow-pressure coupling analysis module takes the event trigger as the starting point and outputs the predicted pressure trajectory of the LNG storage tank within a preset time period through coupling analysis based on the real-time collected tank pressure and external flow data.

[0030] In the process of LNG receiving terminal's LNG export to BOG processing and then to storage tanks, changes in export flow rate are the primary source of disturbance causing pressure fluctuations in storage tanks. However, due to the inherent mechanical inertia in LNG vaporization, BOG generation, gas compression, and reflux regulation, the final impact of flow rate changes on storage tank pressure has a significant process delay (ranging from several minutes to tens of minutes). Existing DCS or SCADA systems can only display real-time values ​​and historical curves of storage tank pressure, and cannot predict subsequent energy change trends in the early stages of flow rate changes. This forces operators to only take action when pressure gauge pointers approach or exceed static red lines, resulting in a certain lag in early warning and intervention.

[0031] Based on this, the present invention integrates tank pressure and external flow data, and outputs the predicted pressure trajectory of LNG tanks within a preset time period through coupled analysis, thereby dynamically deducing future pressure trends to achieve early warning and timely intervention.

[0032] As a preferred example, after receiving a signal indicating that the outflow flow exceeds the limit, the flow-pressure coupling analysis module generates a pressure prediction trajectory according to the following steps: S1. Using the trigger time T0 of the outflow exceeding the limit event as the time reference point, synchronously collect and cache the LNG outflow time series data and LNG storage tank pressure time series data from T0 back to the current time for a preset period (e.g., the past 30 minutes), and construct them into flow series and pressure series respectively.

[0033] S2. Calculate the first-order difference values ​​of the flow rate sequence and the pressure sequence respectively to obtain the flow rate change rate sequence and the pressure rate change rate sequence.

[0034] S3. Calculate the time offsets of the flow rate change series and the pressure rate change series. Cross-correlation coefficients Find the Offset to obtain the maximum value ,Should This refers to the lag time in which the change in flow rate precedes the change in pressure under the current operating conditions, and its maximum value is... That is, the target cross-correlation coefficient, which characterizes the strength of the correlation between the two.

[0035] S4. Perform linear fitting on the data of the flow sequence within the most recent short time window (e.g., 2 minutes before and after the triggering of the outflow flow limit event) to obtain the slope of the current flow change trend at the time of the event triggering.

[0036] S5. Using the tank pressure value P0 collected at the event trigger time T0 as the prediction starting point, and the current time as the recursive calculation starting point, based on the lag time, the target cross-correlation coefficient, and the slope of the current flow rate change trend, the predicted pressure value sequence for the future preset time period is recursively calculated according to a preset fixed time step (e.g., 10 seconds). The recursive calculation process is as follows: S51. Determine whether the cumulative time elapsed from the starting point of the recursive calculation to the current time point is less than or equal to the lag time.

[0037] S52. If yes, then set the current time weight to 0; otherwise, calculate the positive difference between the cumulative time and the lag time, use the positive difference as input, and set the current time weight through the exponential decay function.

[0038] S53. The product of the target cross-correlation coefficient, the slope of the current flow change trend, the fixed time step, and the current time weight is used as the pressure change increment at the current time point.

[0039] S54. Add the pressure change increment to the predicted pressure value at the previous time point to obtain the predicted pressure value at the current time point.

[0040] S55. Repeat the iterative process of steps S51 to S54 until the preset future prediction period (e.g., the next 30 minutes) is covered, to obtain a series of discrete future time points and their corresponding predicted pressure values, and output them as a sequence of predicted pressure values ​​for the preset future period.

[0041] Understandably, the formula for the exponential decay function is: .

[0042] in, This represents the positive difference between the cumulative time and the lag time. This represents the response time constant, typically set to 5 to 15 minutes, and preferably set to 15 minutes in this invention. This indicates the calculation of an exponential function.

[0043] This indicates the current time weight, which is used to quantify the degree of influence of the pressure change increment contributed by the current flow change trend (flow change trend slope) during the recursive calculation process. This weight increases as the prediction time moves into the future. It keeps increasing, leading to The value of the function gradually decreases from 1 to 0. The closer the value is to 1, the greater the contribution of the flow trend to the increase in pressure change at that moment. The closer the value is to 0, the smaller the contribution of the flow trend to the increase in pressure change at that moment.

[0044] Considering that in the actual operation of LNG receiving terminals, when the outflow rate changes abruptly, its impact on the tank pressure is not instantaneous and fully manifested. The tank usually has inherent thermal and fluid inertia, which causes the pressure response to begin to appear after a certain lag time. With the heat exchange and other processing by the BOG compressor, the intensity of this impact will gradually weaken over time and eventually tend to a new equilibrium.

[0045] Based on the above description, to reflect this delayed-onset and gradually decaying physical process and avoid distortion or divergence in long-term predictions caused by simple linear extrapolation, this invention preferably uses an exponential decay function to quantify the temporal changes in the intensity of this influence. This ensures that a clear trend is reflected in the short term and that stable convergence is maintained in the long term, thus better reflecting reality.

[0046] S6. Perform curve fitting on the predicted pressure value sequence for the future preset time period to generate a pressure prediction trajectory curve, and output the pressure prediction trajectory of the LNG storage tank within the future preset time period.

[0047] Further explanation is needed during the execution of steps S1 to S6, including the following formula for calculating the cross-correlation coefficient: .

[0048] In the formula, This is the cross-correlation coefficient. Its absolute value is between 0 and 1, representing the time offset. The degree of linear correlation between changes in flow rate and changes in pressure. The closer the value is to 1, the stronger the correlation between the two at that lag time.

[0049] Let be the pressure change rate sequence, representing the th Rate of change of pressure at each moment Let be a sequence of flow rate changes, representing the th . The rate of change of outflow at each time point.

[0050] and They are and The average value within the calculation time window.

[0051] The covariance term is used to calculate the variance at the offset. Then, the degree of synchronization between the two rate of change sequences. When the flow rate increases ( If the pressure also increases after the value is positive (i.e., when it is positive), then... If the product is positive, the two are positively correlated. A larger value indicates a larger time lag offset. The more consistent the trends of the two in the same or opposite directions, the better.

[0052] This is a normalization factor used to standardize the covariance term calculation results to the [-1,1] interval, making it easier to compare the correlation strength at different time periods.

[0053] It is important to note that the determination of the lag time between flow rate changes and pressure changes also includes a verification step: Determine whether the target cross-correlation coefficient is greater than or equal to a preset correlation threshold.

[0054] If the correlation coefficient is greater than or equal to the correlation threshold, the time offset corresponding to the maximum value of the cross-correlation coefficient is output as the final lag time; otherwise, a preset default lag time is output. This default value is determined based on the average lag time of the receiving station during historical operation, and can be set to 10 minutes, for example.

[0055] The preset correlation threshold typically ranges from 0.5 to 0.8, and is preferably set to 0.7 in this invention. That is, when the target cross-correlation coefficient is greater than 0.7, the time offset corresponding to the maximum value of the output cross-correlation coefficient is the final lag time.

[0056] It should also be noted that if an outflow exceeding the limit event is identified again during the pressure prediction process, steps S1 to S6 above will be repeated with the latest event trigger time as the reference point, thereby updating the pressure prediction trajectory.

[0057] Furthermore, after identifying an event where the outflow exceeds the limit, the pressure prediction trajectory analysis is not immediately initiated. The pressure prediction trajectory analysis is only initiated when the BOG compressor, the BOG return pipeline regulating valve, and the LNG outflow pipeline all meet the normal operating conditions marked at the factory settings. Otherwise, an equipment abnormality warning command is directly output.

[0058] This invention takes the identified outflow exceeding the limit event as the starting point, performs coupled analysis based on the real-time collected tank pressure and flow data, and outputs a pressure prediction trajectory. It can predict the change path and over-limit risk before the actual pressure exceeds the static threshold, thereby changing the risk warning method from post-event alarm to pre-event warning. It realizes the effective forward shift of safety warning in the time dimension, and at the same time, it intuitively presents the pressure change trend in the form of a trajectory.

[0059] The pressure safety update module quantifies the overall efficiency degradation of the BOG compressor based on the operating performance data of the BOG compressor and the actual opening data of the BOG return pipeline regulating valve, and dynamically lowers the pressure safety upper limit according to the degradation.

[0060] In LNG receiving terminals, BOG compressors and BOG reflux control valves are the main equipment for maintaining stable tank pressure. Focusing solely on static parameters such as compressor exhaust temperature or valve opening has two major limitations: First, it cannot quantify the gradual degradation of equipment performance, such as the slow decrease in efficiency due to compressor blade fouling or slight jamming of valve positioners. Second, it cannot assess the coordinated control effectiveness of the compressor and valves.

[0061] In actual dynamic adjustment, if the lag in valve response and the slowness of compressor output change cannot be matched, it will weaken the ability to cope with load fluctuations and lead to the failure of synergy. However, the failure of synergy cannot be reflected by the static parameters of any single device.

[0062] Based on this, the present invention integrates the BOG compressor and the BOG reflux regulating valve, and evaluates their comprehensive performance under stable and unstable operating conditions, providing a precise basis for dynamically adjusting the pressure safety upper limit.

[0063] As a preferred example, the specific implementation process for quantifying the overall performance degradation of a BOG compressor includes: A1. Real-time acquisition of BOG compressor operating performance data, including at least one of inlet pressure, inlet temperature and outlet pressure. Simultaneously, real-time acquisition of the actual opening data of the BOG return pipeline regulating valve, including at least the valve opening command, the actual valve opening value and the valve opening command issuance timestamp, and identification of the current operating condition, which is either a stable operating condition or an unstable operating condition.

[0064] Among them, identifying the current operating condition includes: A11, intercepting the actual opening data and the outflow of LNG from the pipeline according to a preset time interval (e.g., 30 seconds) to obtain the actual opening time sequence data and outflow time sequence data under each time window.

[0065] A12. Select the maximum and minimum values ​​from the actual opening data, calculate the difference to obtain the actual opening difference, and perform differential calculation on the time series data of outgoing flow to obtain the outgoing flow change rate.

[0066] A13. Based on the actual opening difference and the rate of change of outflow, determine whether the following conditions are met: The actual opening difference is consistently less than the preset opening difference threshold over multiple consecutive time windows.

[0067] The absolute value of the rate of change of outbound flow is consistently less than the preset stable rate of change threshold for flow over multiple consecutive time windows.

[0068] A14. If all of the above conditions are met simultaneously, the current operating condition is identified as a stable operating condition; otherwise, it is an unstable operating condition.

[0069] Considering that the instantaneous value of valve opening or a single change in flow rate may only reflect normal adjustment actions or random fluctuations, and is insufficient to characterize whether a steady state has been entered or exited, this invention extracts time-series data at preset time intervals and calculates the actual opening difference and the rate of change of outgoing flow rate. Only when the actual opening difference is consistently less than a preset opening difference threshold is the current operating condition identified as stable. This fundamentally avoids misjudgments of operating conditions caused by instantaneous data disturbances and ensures the accuracy of subsequent comprehensive performance degradation analysis.

[0070] It should be added that the preset opening difference threshold can be set to 1% to 5% of the full stroke of the regulating valve. For example, if the full opening is 100%, the threshold can preferably be set to 2%. The preset flow rate stability change rate threshold can be set to 0.5% to 2% of the rated output flow per minute. In this invention, it is preferably set to 2% per minute. The number of consecutive time windows can be set to 3 to 5. In this invention, it is preferably set to 5. That is, when the actual opening difference is continuously less than 2% and the preset flow rate stability change rate threshold is less than 2% per minute for five consecutive time windows, the current operating condition is a stable operating condition.

[0071] A2. If the current operating condition is stable, the moment when the actual opening degree changes more than the preset change threshold will be taken as the starting point of the collaborative analysis cycle.

[0072] A3. Using the starting point as a reference, extract the outlet pressure data of the BOG compressor and the actual opening data of the regulating valve within the corresponding preset time period after the starting point to construct the outlet pressure data sequence and the actual opening data sequence.

[0073] A4. Analyze the synchronization deviation index between the actual opening data sequence and the outlet pressure data sequence on the time axis, and calculate the relative deviation value between the outlet pressure and the theoretical expected value. Use the relative deviation value as the outlet pressure accuracy index.

[0074] The analysis method for the synchronization deviation index is as follows: calculate the first difference between the actual opening data sequence and the outlet pressure data sequence to obtain the actual opening change sequence and the outlet pressure change sequence.

[0075] In the change sequence, adjacent sampling points where the identification symbol changes and the absolute value of the change exceeds a preset threshold are identified, and the time corresponding to these adjacent sampling points is recorded as an inflection point. For the actual opening change sequence, the preset threshold can be set based on the valve adjustment amplitude. For example, it can be set to 1% to 3% of the full stroke of the regulating valve. For the outlet pressure change sequence, the preset threshold can be set based on the normal operating pressure range of the storage tank. For example, it can be set to 0.5% to 1% of the normal operating pressure range of the storage tank.

[0076] The inflection points of the actual opening change sequence and the outlet pressure change sequence are matched in chronological order, and the absolute value of the time difference between each pair of matched inflection points is calculated.

[0077] The average of the absolute time difference for all successfully matched inflection point pairs is calculated, and this average value is used as the synchronization deviation index.

[0078] There is typically a time delay between the valve's adjustment action and the compressor's outlet pressure response. However, when equipment performance deteriorates, such as when the compressor response slows down or the valve positioning lags, this delay can become abnormally large or unstable. Independent monitoring of the compressor or valve alone cannot capture this time-series-level coordinated failure. Therefore, this invention assesses the synchronicity of the actions by analyzing the inflection point correspondence between the actual opening change sequence and the outlet pressure change sequence. This effectively identifies the degradation of dynamic regulation capability caused by gradual changes in equipment performance. The smaller the synchronicity deviation index, the better the coordination and the more agile the dynamic response; the larger the synchronicity deviation index value, the worse the coordination and the slower or disconnected the response.

[0079] Furthermore, by using the synchronicity deviation index as one of the input variables for the comprehensive performance decay analysis, it is integrated with the outlet pressure accuracy index to form the comprehensive performance decay, which is ultimately used to drive the adaptive dynamic adjustment of the pressure safety upper limit.

[0080] It should also be noted that, as a preferred implementation method for calculating the theoretical expected value of the outlet pressure, this invention employs an interpolation query method based on the compressor's rated performance curve, the specific process of which is as follows: First, obtain the real-time operating parameters of the compressor in the current steady-state stage, including actual speed, inlet volumetric flow rate, inlet pressure, and inlet temperature.

[0081] Then, from the preset compressor speed-pressure ratio-flow rated performance curve, locate the speed characteristic curve that corresponds to or is closest to the current actual speed. The rated performance curve is derived from the technical manual or factory acceptance test report provided by the compressor manufacturer, and is a benchmark characterizing the performance of the equipment under healthy and rated conditions.

[0082] Next, using the current inlet volumetric flow rate as input, the corresponding theoretical pressure ratio is calculated through linear interpolation on the selected speed characteristic curve. The pressure ratio is defined as the ratio of the outlet pressure to the inlet pressure. Linear interpolation is a current processing method and will not be elaborated further.

[0083] Finally, the theoretical pressure ratio is multiplied by the real-time measured inlet pressure to calculate the theoretical expected value of the outlet pressure under the current operating conditions.

[0084] A5. Normalize the synchronization deviation index and the outlet pressure accuracy index, and perform linear weighted fusion calculation on the processing results to output the comprehensive efficiency reduction of the BOG compressor. The normalization can preferably adopt the minimum-maximum normalization method, which is an existing normalization processing method, and the specific formula will not be shown.

[0085] Understandably, considering that the degradation of dynamic response (increased synchronization deviation index) usually leads to a more direct decrease in the equipment's disturbance immunity, the weight of the synchronization deviation index is set higher than that of the outlet pressure accuracy index, and the sum of the two is 1. As a preferred example, the weights of the synchronization deviation index and the outlet pressure accuracy index can be 0.6 and 0.4, respectively.

[0086] A6. If the current operating condition is unstable, the normalized result of the synchronization deviation index shall be used as the overall efficiency degradation of the BOG compressor.

[0087] Under stable operating conditions, the compressor operates steadily, and its outlet pressure directly reflects its internal mechanical efficiency. By assessing the deviation between the outlet pressure and the theoretical expected value—that is, by evaluating the outlet pressure accuracy index—the permanent capacity reduction caused by wear, scaling, etc., can be quantified. Simultaneously, a synchronization deviation index is calculated based on the delay time between valve adjustment commands and compressor pressure response under this operating condition to detect the degradation of the control loop's dynamic characteristics. Integrating these two indicators enables early, two-dimensional diagnosis of equipment health, thereby identifying performance degradation trends before observable faults or static parameter exceedances occur.

[0088] Under unstable operating conditions, the compressor outlet pressure, dominated by fluid inertia, cannot characterize steady-state output capacity, thus rendering the outlet pressure accuracy index meaningless. In this case, using the synchronization deviation index as a measure of overall performance degradation can directly reflect its real-time coordination and response capabilities under actual disturbances.

[0089] By varying the overall performance attenuation under different operating conditions, a fundamental shift from static, passive alarm to dynamic, proactive defense has been achieved.

[0090] In traditional LNG receiving terminal monitoring, the pressure safety limit for storage tanks is typically a static value obtained by looking up a table based on a fixed liquid level. This setting method implicitly assumes that the BOG compressor and regulating valves, which ensure pressure safety, are always in good working order and at their rated performance. However, in actual operation, the performance of these devices gradually declines due to wear, scaling, aging, and other factors, leading to a decrease in their actual BOG handling capacity (including maximum output and response speed). If the static safety limit is still used in this situation, it will result in delayed warnings and fail to provide effective protection in the early stages of equipment performance degradation.

[0091] Based on this, the present invention dynamically lowers the upper limit of pressure safety based on the overall efficiency degradation of the BOG compressor. The specific lowering process includes: The current liquid level of the LNG storage tank is collected in real time. Based on the liquid level-pressure safety correspondence specified in the pre-established pressure safety operation manual or tank design data, the upper limit of the benchmark pressure corresponding to this liquid level is retrieved.

[0092] The difference between 1 and the current performance degradation is taken as the adjustment coefficient. The product of the adjustment coefficient and the reference pressure safety limit is output as the adjusted pressure safety limit. This process is a continuous dynamic adjustment process. Whenever the comprehensive performance degradation is updated, the above calculation is re-executed to ensure that the safety limit is always synchronized with the current actual state of the equipment.

[0093] This invention effectively solves the problem of insufficient sensitivity of static threshold warning caused by gradual changes in equipment performance by quantifying the overall efficiency decline of the BOG compressor and dynamically lowering the upper limit of pressure safety based on the decline. This ensures that the warning can be triggered earlier and more accurately when the adjustment capability decreases.

[0094] The warning trigger judgment module determines that an advance warning command is triggered and executes the warning action when any point on the predicted trajectory exceeds the upper limit of the adjustment pressure safety limit.

[0095] In summary, this invention effectively shifts parameters to upstream flow disturbance parameters by identifying outflow exceeding the limit event, thus solving the problem of delayed early warning. Starting from this exceeding event, pressure prediction trajectory is generated through coupled analysis of flow and pressure, enabling the prediction of risk paths before actual overpressure and promoting the transformation of early warning mode from post-event alarm to pre-event prediction.

[0096] Furthermore, by quantifying the overall performance degradation and dynamically lowering the upper limit of pressure safety, the warning threshold is matched in real time with the current actual anti-interference capability.

[0097] Meanwhile, through the synergistic effect of the above modules, an adaptive early warning closed loop is formed to predict external disturbances and dynamically perceive internal capabilities, thereby improving the LNG receiving station's ability to provide early warning and proactive defense against coupled safety risks under complex operating conditions.

[0098] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A centralized monitoring and management system based on comprehensive data from pipeline transportation stations, characterized in that, The system includes: The over-limit event identification module identifies over-limit events in the LNG export flow rate based on the real-time collected data of the LNG export flow rate. After identifying the event, the flow-pressure coupling analysis module uses the event trigger time as the time reference point and, based on the real-time collected tank pressure and outflow data, outputs the predicted pressure trajectory of the LNG tank within a preset future time period through coupling analysis. The pressure safety update module quantifies the overall efficiency degradation of the BOG compressor based on the operating performance data of the BOG compressor and the actual opening data of the BOG return pipeline regulating valve, and dynamically lowers the pressure safety upper limit according to the degradation. The early warning trigger judgment module determines that an early warning command is triggered and executes the early warning action when any point on the predicted trajectory exceeds the upper limit of the adjustment pressure safety limit.

2. The centralized monitoring and management system based on integrated data from pipeline transportation stations as described in claim 1, characterized in that: Identifying outbound flow exceeding limits includes: Calculate the rate of change of the outflow volume within a preset time window; Based on the rate of change, determine whether the following conditions are met: The rate of change exceeds the preset step rate of change threshold; The outflow rate has exceeded the preset flow rate threshold for an extended period of time. If any of the above conditions are met, an outflow exceeding the limit event is identified.

3. The centralized monitoring and management system based on integrated data from pipeline transportation stations as described in claim 1, characterized in that: The analysis and output process of the pressure prediction trajectory for LNG storage tanks includes: Starting from a time reference point, trace back the time series data of LNG export flow and LNG storage tank pressure up to the current time period to construct flow series and pressure series respectively; Calculate the first difference between the flow rate series and the pressure series to obtain the flow rate change rate series and the pressure rate change rate series; Calculate the cross-correlation coefficient between the flow rate change series and the pressure rate change series, determine the lag time in which the flow rate change leads the pressure change based on the cross-correlation coefficient, and use the cross-correlation coefficient corresponding to the lag time as the target cross-correlation coefficient; By performing linear fitting on the flow values ​​in the most recent time window in the flow sequence, the slope of the current flow change trend can be obtained; Using the tank pressure collected at the moment the event is triggered as the predicted pressure value at the first time point, and based on the lag time, the target cross-correlation coefficient and the slope of the current flow rate change trend, the predicted pressure value sequence for the future preset time period is calculated recursively according to a preset fixed time step. Curve fitting is performed on the predicted pressure value sequence for a future preset time period to generate a pressure prediction trajectory curve, and the output is the pressure prediction trajectory of the LNG storage tank within the future preset time period.

4. The centralized monitoring and management system based on integrated data from pipeline transportation stations as described in claim 3, characterized in that: The method for determining the lag time in which flow rate changes precede pressure changes is as follows: identify the time offset corresponding to the maximum value of the cross-correlation coefficient, and use this time offset as the lag time.

5. A centralized monitoring and management system based on integrated data from pipeline transportation stations as described in claim 4, characterized in that: The process of determining the lag time also includes a verification step: Determine whether the target cross-correlation coefficient is greater than or equal to a preset correlation threshold; If the correlation coefficient is greater than or equal to the correlation threshold, the time offset corresponding to the maximum value of the cross-correlation coefficient is output as the final lag time; otherwise, the preset default lag time is output.

6. The centralized monitoring and management system based on integrated data from pipeline transportation stations as described in claim 3, characterized in that: The recursive calculation of the predicted pressure value sequence for a future preset time period is achieved through the following iterative steps: The current moment is taken as the starting point of the recursive calculation, and the starting pressure value is taken as the predicted pressure value at the first time point. For each point in time within the preset future time period, perform the following sub-steps: Determine whether the cumulative time elapsed from the starting point of the recursive calculation to the current time point is less than or equal to the lag time; If yes, then set the current time weight to 0; otherwise, calculate the positive difference between the cumulative time and the lag time, use the positive difference as input, and set the current time weight through the exponential decay function. Multiply the target cross-correlation coefficient, the slope of the current flow change trend, the fixed time step, and the current time weight to obtain the pressure change increment at the current time point; Add the pressure change increment to the predicted pressure value at the previous time point to obtain the predicted pressure value at the current time point; After completing the calculations for all time points, the output is a sequence of predicted pressure values ​​arranged in chronological order.

7. A centralized monitoring and management system based on integrated data from pipeline transportation stations as described in claim 1, characterized in that: The overall performance degradation of the BOG compressor includes: Real-time monitoring of the actual opening data of the regulating valve and the flow rate of the LNG export pipeline; identification of the current operating condition, which is either a stable operating condition or an unstable operating condition; If the current operating condition is stable, the moment when the actual opening degree changes more than the preset change threshold will be taken as the starting point of the collaborative analysis cycle. Using the starting point as a reference, the outlet pressure data of the BOG compressor and the actual opening data of the regulating valve are extracted within a preset time period after the starting point to construct the outlet pressure data sequence and the actual opening data sequence. Analyze the synchronization deviation index between the actual opening data sequence and the outlet pressure data sequence on the time axis, and at the same time calculate the relative deviation value between the outlet pressure and the theoretical expected value, and use the relative deviation value as the outlet pressure accuracy index. The synchronization deviation index and the outlet pressure accuracy index are normalized, and the results are weighted and fused to calculate the overall efficiency degradation of the BOG compressor. If the current operating condition is unstable, the normalized result of the synchronization deviation index will be used as the overall efficiency degradation of the BOG compressor.

8. The centralized monitoring and management system based on integrated data from pipeline transportation stations as described in claim 7, characterized in that: The identification of the current working condition includes: The actual opening data and the outflow of LNG from the pipeline are extracted according to the preset time interval to obtain the actual opening time series data and outflow time series data under each time window. The maximum and minimum values ​​are selected from the actual opening data, and the difference is calculated to obtain the actual opening difference. The difference is then calculated on the time series data of the outflow to obtain the rate of change of the outflow. Based on the actual opening difference and the rate of change of outflow, determine whether the following conditions are met: The actual opening difference is consistently less than the preset opening difference threshold over multiple consecutive time windows; The absolute value of the rate of change of outbound flow is consistently less than the preset stable rate of change threshold for flow over multiple consecutive time windows; If all of the above conditions are met simultaneously, the current operating condition is identified as a stable operating condition; otherwise, it is an unstable operating condition.

9. A centralized monitoring and management system based on integrated data from pipeline transportation stations as described in claim 7, characterized in that: The analysis method for the synchronization deviation index is as follows: Calculate the first difference between the actual opening data sequence and the outlet pressure data sequence to obtain the actual opening change sequence and the outlet pressure change sequence; In the change sequence, the adjacent sampling points where the symbol changes and the absolute value of the change exceeds a preset threshold are identified, and the time corresponding to the adjacent sampling point is recorded as an inflection point. The inflection points of the actual opening change sequence and the outlet pressure change sequence are matched in chronological order, and the absolute value of the time difference between each pair of matched inflection points is calculated. The average of the absolute time difference for all successfully matched inflection point pairs is calculated, and this average value is used as the synchronization deviation index.

10. A centralized monitoring and management system based on integrated data from pipeline transportation stations as described in claim 1, characterized in that: Dynamically lowering the upper limit of pressure safety specifically includes: Collect the current liquid level of the LNG storage tank and retrieve the corresponding benchmark pressure safety upper limit. The difference between 1 and the current performance degradation is taken as the adjustment coefficient, and the product of the adjustment coefficient and the reference pressure safety limit is output as the adjusted pressure safety limit.

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