An oil inventory warning method based on real-time perception

Abstract

The present application provides an oil inventory warning method based on real-time perception, including: obtaining in real time the suction pressure data of the vapor recovery device, monitoring the real-time change trend of the suction pressure, and if the pressure change exceeds the normal fluctuation range, determining that there is an abnormal fluctuation in the suction pressure; if the abnormal fluctuation degree of the suction pressure exceeds the preset fluctuation threshold, obtaining the real-time pressure values of each storage tank from the pressure sensors of multiple storage tanks, calculating the difference between the current pressure and the target pressure, and determining the distribution characteristics of the pressure fluctuation; obtaining the oil flow data of each storage tank through the oil metering device, combining the changes before and after the adjustment of the valve opening, analyzing the increase or decrease of the loss amount and the change of the loss rate after the adjustment of the valve opening, and establishing a mapping relationship between the valve opening and the loss.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an oil inventory warning method based on real-time perception. Background Art

[0002] During the operation of an oil depot, when multiple storage tanks are operating simultaneously, the pressure fluctuation characteristics of the oil-gas balance system become a key issue. When the suction pressure of the oil-gas recovery device fluctuates, this fluctuation directly affects the gas-phase pressure balance inside each storage tank. Due to differences in oil types, liquid levels, and operating states among different storage tanks, the response speed and sensitivity to pressure are different. When propagating pressure fluctuations within the system, this difference can cause pressure imbalance between storage tanks, thereby affecting the oil-gas recovery efficiency. The edge computing unit undertakes the responsibilities of real-time monitoring and coordinated control during this process. It needs to dynamically adjust the opening degree of the pressure release valve according to the pressure data of each storage tank to ensure the stability of the overall system pressure. However, this adjustment is not a simple linear relationship. When the opening degree of the valve of a certain storage tank increases to release excess pressure, the pressure changes of other storage tanks may be further disturbed, resulting in increased pressure fluctuations within the system. This chain reaction may trap the control strategy of the edge computing unit in a dilemma of repeated adjustment. In addition, the change in the opening degree of the pressure release valve directly affects the evaporation loss of the oil product. When the valve opening is too large, the oil-gas emission increases, resulting in increased oil product loss and thus affecting inventory management; when the valve opening is too small, the pressure inside the storage tank may exceed the safety threshold, increasing the risk. Therefore, when the edge computing unit coordinates the valve opening, it needs to find an optimal solution between pressure balance and oil product loss. This complex dynamic balance relationship places higher requirements on the real-time performance and accuracy of the system. Summary of the Invention

[0003] To solve the above technical problems, the present invention provides an oil inventory warning method based on real-time perception, mainly including:

[0004] Real-time obtain the suction pressure data of the oil-gas recovery device, monitor the real-time change trend of the suction pressure. If the pressure change exceeds the normal fluctuation range, it is determined that there is an abnormal fluctuation in the suction pressure;

[0005] If the abnormal fluctuation degree of the suction pressure exceeds the preset fluctuation threshold, obtain the real-time pressure values of each storage tank from the pressure sensors of multiple storage tanks, calculate the difference between the current pressure and the target pressure, and determine the distribution characteristics of the pressure fluctuation;

[0006] According to the distribution characteristics of the pressure fluctuation, adopt a cooperative control algorithm based on pressure balance to determine the opening degree adjustment value of the pressure release valve of each storage tank, and send the valve opening degree adjustment value to the pressure release valve control system of each storage tank to adjust the valve opening degree in real time;

[0007] Monitor the pressure values of each storage tank after adjustment, and determine whether the preset pressure balance state standard is reached. If not, obtain the corresponding temperature gradient distribution according to the magnitude and change trend of the pressure difference, calculate the difference in gas expansion coefficients in each storage tank, and combine the pressure linkage relationship between the storage tanks to re-determine the opening adjustment value of the pressure relief valve for each storage tank;

[0008] Obtain the oil product flow data of each storage tank through the oil product metering device, and analyze the increase or decrease of the loss amount and the change of the loss rate after the valve opening adjustment in combination with the changes before and after the valve opening adjustment, and establish a mapping relationship between the valve opening and the loss;

[0009] Based on the mapping relationship between the valve opening and the loss, adjust the valve opening, including adjusting the step size, adjustment frequency and adjustment timing of the valve opening change, monitor the oil product loss data after adjustment in real time, judge the effectiveness of the optimization strategy according to the loss amount and loss rate indicators, and dynamically adjust the opening adjustment value according to the monitoring results;

[0010] Combine the inventory warning system, integrate the pressure fluctuation, valve opening and oil product loss data into the inventory management platform, cooperate to control the operation status of multiple storage tanks and monitor the whole process. If the loss exceeds the preset loss threshold, an early warning signal will be sent through the inventory management platform.

[0011] Furthermore, obtain the suction pressure data of the oil and gas recovery device in real time, monitor the real-time change trend of the suction pressure. If the pressure change exceeds the normal fluctuation range, it is determined that there is an abnormal fluctuation in the suction pressure, including: according to the pressure value records collected at the pressure sensor detection points, obtain the suction pressure data with a sampling accuracy within the range of kilopascals through the pressure sampling frequency, correct and compensate the pressure data with the pressure compensation coefficient obtained based on historical data statistics to obtain the real-time pressure value, store the data according to the pressure monitoring time period, calculate the pressure change rate by using the ratio of the pressure change amount per unit time to the pressure value at the previous moment, draw the pressure fluctuation trend curve through time series data processing, calculate the pressure difference sequence within two adjacent monitoring time periods, obtain the pressure fluctuation amplitude through the sliding window method, and judge the pressure fluctuation state from the preset pressure fluctuation range threshold; if the pressure fluctuation amplitude exceeds the upper limit of the preset threshold, determine the abnormal pressure level according to the deviation value between the real-time pressure value and the preset reference pressure, record the abnormal pressure data by using the data association method based on the time stamp, construct a pressure fluctuation model by using the polynomial regression method, extract the fluctuation characteristic parameters through the model coefficients and fitting errors, and establish a pressure fluctuation characteristic vector; classify and identify the abnormal pressure fluctuation by using the support vector machine algorithm according to the pressure fluctuation characteristic vector, and obtain the characteristic of different types of abnormal pressure fluctuation patterns through cluster analysis.

[0012] Further, if the abnormal fluctuation degree of the suction pressure exceeds the preset fluctuation threshold, obtain the real-time pressure values of each storage tank from the pressure sensors of multiple storage tanks, calculate the difference between the current pressure and the target pressure, and determine the distribution characteristics of the pressure fluctuation, including: obtaining the pressure time-series data from the pressure sensors of multiple storage tanks according to the storage tank pressure sampling interval, calculating the abnormal degree of fluctuation through comparison with the preset pressure fluctuation threshold range, recording the fluctuation trigger time for the data with abnormal degree exceeding the threshold, obtaining the real-time pressure values of each storage tank, calculating the pressure deviation sequence using the preset target pressure reference value, and statistically obtaining the mean value, maximum value, and minimum value of the fluctuation amplitude from the pressure deviation sequence; performing spectral analysis on the pressure deviation sequence through fast Fourier transform, extracting the main frequency component from the spectral data to obtain the pressure fluctuation frequency characteristics, and dividing the high-frequency fluctuation interval and the low-frequency fluctuation interval according to the fluctuation frequency characteristics; counting the start and end times of the pressure fluctuation for different fluctuation intervals, calculating the fluctuation duration through the time stamp difference, and performing data smoothing processing on the fluctuation duration sequence using the exponential smoothing method; constructing a feature vector according to the mean value of the fluctuation amplitude, the fluctuation frequency characteristics, and the smoothed duration sequence, establishing a pressure fluctuation feature model through the support vector regression algorithm; classifying the pressure fluctuation feature model using the clustering algorithm, and extracting the fluctuation distribution characteristics from the clustering results, including the statistical characteristics of the fluctuation amplitude distribution, frequency distribution, and duration distribution.

[0013] Further, according to the distribution characteristics of the pressure fluctuation, adopt a collaborative control algorithm based on pressure balance to determine the opening adjustment value of the pressure release valve of each storage tank, and send the valve opening adjustment value to the pressure release valve control system of each storage tank to adjust the valve opening in real time, including: normalizing the pressure data of the storage tank group according to the distribution characteristics of the pressure fluctuation, dividing the fluctuation interval through the preset pressure fluctuation threshold, and calculating the mean value of the pressure deviation for the fluctuation interval to obtain the pressure target value of the storage tank group; for the deviation amount between the real-time pressure of each storage tank and the pressure target value of the storage tank group, constructing a pressure adjustment rule using the fuzzy neural network algorithm to obtain the pressure release rate and adjustment priority of each storage tank; calculating the initial adjustment amount of the valve opening of each storage tank according to the pressure release rate, determining the maximum opening limit value of the valve through the pressure difference ratio between the pressure in the storage tank and the network back pressure to obtain the actual adjustment amount of the valve, arranging the sequence of the valve opening change of each storage tank according to the adjustment priority, and discretizing the valve opening adjustment amount through the preset unit adjustment cycle; generating a valve control sequence according to the discretized opening adjustment amount, calculating the valve opening adjustment compensation value using the pressure gradient of adjacent storage tanks to obtain the valve opening adjustment instruction, sending a control signal to the pressure release valve of each storage tank according to the adjustment cycle, and judging the adjustment execution state through the feedback collected valve opening execution value.

[0014] Furthermore, a collaborative control algorithm based on pressure balance is adopted. According to the difference between the real-time pressure value and the target pressure value of each storage tank, combined with the amplitude, frequency, and duration of pressure fluctuations, the opening adjustment percentage of the pressure release valve of each storage tank is calculated to generate a valve opening adjustment value, including: normalizing the deviation between the real-time pressure and the target pressure of each storage tank according to the storage tank pressure difference, quantifying the fluctuation intensity with the pressure fluctuation amplitude, calculating the number of fluctuations per unit time through the fluctuation frequency, and recording the fluctuation interval from the fluctuation duration; constructing a pressure adjustment matrix for the fluctuation intensity, calculating the adjustment urgency through the number of fluctuations per unit time, determining the adjustment action time using the fluctuation interval, obtaining the pressure adjustment priority order, dividing the adjustment levels, calculating the initial adjustment amount by the product of the pressure release rate and the adjustment level, and obtaining the valve opening change curve from the adjustment action time; performing piecewise linearization on the valve opening change curve, sampling the opening change through the valve response time, and calculating the opening value of the sampling point using the linear interpolation method; generating a valve adjustment sequence according to the sampling point opening value, discretizing the adjustment sequence using the quantization interval to obtain a valve opening adjustment step sequence; associating the opening adjustment step sequence with the time stamp, calculating the execution time of each adjustment step through the adjustment period, and generating a valve opening adjustment value from the execution time sequence.

[0015] Furthermore, monitor the pressure values of each storage tank after adjustment, and judge whether the preset pressure balance state standard is reached. If not, according to the magnitude and change trend of the pressure difference, obtain the corresponding temperature gradient distribution, calculate the difference in gas expansion coefficients in each storage tank, and combine the pressure linkage relationship between the storage tanks to re-determine the opening adjustment value of the pressure release valve of each storage tank, including: obtaining the pressure time series data from the storage tank pressure sensor according to the monitoring time period, calculating the pressure deviation value through comparison with the preset pressure balance threshold, calculating the pressure change rate using the sliding window method, and judging the balance state from the pressure deviation value and the change rate. For the storage tank group that has not reached the balance state, use the temperature sensor array to obtain the temperature field distribution data, calculate the temperature gradient field through spatial interpolation, and obtain the temperature change rate of each storage tank from the temperature change trend. Calculate the gas expansion coefficient of each storage tank according to the ideal gas state equation, establish a gas thermodynamic characteristic curve using the storage tank pressure value and the temperature change rate, and obtain the pressure-temperature compensation coefficient from the characteristic curve. Construct a pressure transfer function for the pressure deviation value between adjacent storage tanks, correct the pressure transfer gain through the pressure-temperature compensation coefficient, and obtain the storage tank pressure linkage coefficient from the corrected transfer function. Calculate the pressure adjustment weight according to the storage tank pressure linkage coefficient, classify the weight using the fuzzy control rule base, and determine the pressure adjustment priority of each storage tank from the classification result. For the corresponding relationship between the pressure adjustment priority and the gas expansion coefficient, use a proportional-integral controller to calculate the valve opening compensation amount, and generate a valve opening adjustment value from the compensation amount sequence.

[0016] Further, obtain the oil flow data of each storage tank through the oil metering device. Combine the changes before and after the valve opening adjustment, analyze the increase or decrease of the loss amount and the change of the loss rate after the valve opening adjustment, and establish the mapping relationship between the valve opening and the loss, including: obtain the flow data sequence from the oil metering device according to the flow sampling frequency, record the time points before and after the opening adjustment using the valve response characteristic curve, and obtain the real-time flow change data during the valve opening adjustment through time series data processing. Calculate the loss amount per unit time for the real-time flow change data, perform data calibration using a preset reference loss value, and statistically analyze the cumulative change of the loss amount through a time window of a fixed size. Construct a time series feature vector based on the cumulative change of the loss amount, calculate the loss change rate using the time-weighted average method, and obtain the loss trend characteristics of each time interval through piecewise linear fitting. Extract the valve opening change interval for the loss trend characteristics, calculate the discrete sampling points during the opening adjustment process using linear interpolation, and fit the opening adjustment curve by the least squares method. Establish the corresponding relationship according to the opening adjustment curve and the loss trend characteristics, construct the loss prediction function using polynomial regression, and determine the polynomial order through cross-validation. Calculate the loss increment corresponding to the opening adjustment amount for the loss prediction function, optimize the parameters of the prediction function using the gradient descent method, and obtain the mapping relationship between the valve opening and the loss from the optimized function.

[0017] Further, based on the mapping relationship between the valve opening and the loss, adjust the valve opening, including adjusting the step size, adjustment frequency, and adjustment timing of the valve opening change, and real-time monitor the oil loss data after the adjustment. Judge the effectiveness of the optimization strategy according to the loss amount and loss rate indicators, and dynamically adjust the opening adjustment value according to the monitoring results, including: discretize the opening adjustment value of the valve according to the preset opening change step size, divide the time interval using a fixed adjustment period, screen the opening adjustment moment through the minimum adjustment interval, and determine the effective adjustment interval from the preset upper and lower limits of the opening adjustment. Extract the opening adjustment curve for the effective adjustment interval, record the loss amount per unit time using the loss data acquisition device, calculate the integral value of the loss amount through a continuous monitoring window, and judge the current loss state from the preset loss threshold. Construct the oil loss feature matrix according to the loss state data, calculate the loss change trend using the time-weighted method, determine the opening adjustment priority through the trend prediction function, evaluate the adjustment effect from the combined criterion of the loss amount and the loss rate, divide the loss control area, construct the opening adjustment compensation function using the piecewise linearization method, update the compensation coefficient through the adaptive optimization algorithm, and extract the adjustment correction amount from the compensated opening value sequence. Generate a new opening control instruction according to the adjustment correction amount, sort the instruction execution moments using the response time-weighted method, and obtain the real-time opening adjustment value through dynamic feedback correction. Calculate the valve response characteristic curve for the real-time opening adjustment value, predict the loss change after the opening adjustment using the polynomial fitting method, and dynamically update the opening adjustment sequence from the prediction result.

[0018] Furthermore, combined with the inventory warning system, the pressure fluctuation, valve opening and oil loss data are integrated into the inventory management platform, and the operation status of multiple tanks is collaboratively controlled and monitored throughout the process. If the loss exceeds the preset loss threshold, an early warning signal is issued through the inventory management platform, including: obtaining pressure data from the tank pressure sensor according to the data acquisition cycle, using the valve position feedback device to record the opening value, collecting oil loss data through the loss meter, and establishing a multi-parameter association sequence from the data time tag. For the multi-parameter association sequence, data segmentation is performed according to a fixed time window, abnormal data is eliminated using the time series verification rule, and the operation process feature vector is generated through data standardization processing. The tank operation state matrix is constructed, and the inventory change relationship of adjacent tanks is calculated using the state correlation function. The operation stability of the tank group is judged by the inventory fluctuation rate. The loss influencing factor is calculated for the operation status of the tank group, and the linkage control command is generated using the multi-tank collaborative operation rule, and the operation parameters of each tank are adjusted through the control command actuator. According to the comparison between the real-time loss data and the preset loss threshold, the graded warning rule is used to generate the warning level, and the warning information is output through the warning signal generator. Key status parameters are extracted from the warning information, and emergency response instructions are generated using the inventory warning processing function, which are then sent to each tank control unit through the instruction distribution mechanism.

[0019] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0020] The present invention discloses an oil inventory early warning method based on real-time perception. The method monitors the suction pressure of multiple storage tanks in real time. When abnormal fluctuations are detected, the pressure distribution characteristics are quickly analyzed, and the opening of the pressure release valve of each storage tank is dynamically adjusted using a collaborative control algorithm based on pressure balance. Combined with the temperature gradient and the difference in gas expansion coefficient, the present invention can accurately control the pressure balance between multiple storage tanks. At the same time, by establishing a correlation model between valve opening and oil loss, the present invention can minimize oil loss while ensuring pressure balance. In addition, the present invention also combines pressure control with inventory management to achieve full-process monitoring and early warning of the operating status of multiple storage tanks. This intelligent pressure control method not only improves the operating efficiency of the oil and gas recovery device, but also effectively reduces oil loss, providing an innovative tank management solution for the petrochemical industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a flowchart of an oil inventory early warning method based on real-time perception.

[0022] Figure 2 This is another flow chart of an oil inventory early warning method based on real-time perception according to the present invention. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] As Figure 1-2 , a method for warning of oil product inventory based on real-time perception in this embodiment may specifically include:

[0025] S101 If the oil and gas recovery device is operating, the edge computing unit collects the suction pressure data in real time and monitors its change trend. When the pressure change exceeds the preset normal range, it is determined that there is an abnormal fluctuation, and the fluctuation characteristics are extracted to determine the abnormal level.

[0026] S1011 The edge computing unit collects the suction pressure data of the oil and gas recovery pipeline through a pressure sensor, sets the sampling frequency to 10 times per second, controls the accuracy within 0.1 kPa, corrects the pressure value using the pressure compensation coefficient based on historical data to obtain the real-time pressure data. For the real-time pressure data, it is stored in a 15-minute cycle, the ratio of the pressure change amount per unit time to the value at the previous moment is calculated to generate the pressure change rate, and the pressure fluctuation trend curve is drawn through time series processing. The difference between the maximum and minimum pressures within the window is calculated using a 60-second sliding window to obtain the fluctuation amplitude. If the amplitude exceeds the preset range of plus or minus 2 kPa, the abnormal data is recorded.

[0027] S1012 According to the deviation between the real-time pressure value and the preset reference pressure of minus 3 kPa, the abnormal level is determined. If the deviation is greater than 1 kPa, it is a mild abnormality; if it is greater than 2 kPa, it is a moderate abnormality; if it is greater than 3 kPa, it is a severe abnormality. For the abnormal data, a 3rd-order polynomial regression is used to construct a fluctuation model, and the model coefficients and root mean square error are extracted as characteristic parameters. When the error is less than 0.5 kPa, the model is confirmed to be valid. Based on the characteristic parameters, a fluctuation feature vector is constructed, and the support vector machine algorithm is used to classify the abnormal types, and a feature library of three modes of pipeline blockage, leakage, and equipment failure is established in combination with cluster analysis.

[0028] In the embodiment of the present application, S101 realizes the real-time perception of the suction pressure through high-frequency sampling and time series analysis to ensure the timely identification of abnormal fluctuations. In an alternative embodiment, the sampling frequency can be adjusted according to the actual scenario, and the feature extraction algorithm can also be replaced with a neural network model to improve the classification accuracy.

[0029] S102 If it is detected that the abnormal fluctuation of the suction pressure exceeds the preset threshold, the real-time pressure data of each storage tank is collected from the pressure sensors of multiple storage tanks, the deviation value from the target pressure is calculated and a pressure deviation sequence is generated. Based on this sequence, the fluctuation distribution characteristics are analyzed, including the fluctuation amplitude, frequency, and duration, and a pressure fluctuation model is constructed through the feature vector and the distribution characteristics are extracted.

[0030] S1021 Use multi-tank pressure sensors to obtain pressure time-series data at a sampling frequency of 10 times per second. Combine with a preset fluctuation threshold of plus or minus 2 kPa to compare the real-time pressure values, determine the degree of abnormal fluctuation and record the trigger moment. For example, when the pressure of tank No. 3 rises from -3.5 kPa to -1.2 kPa, the trigger time is 10:15:30 am. At the same time, collect the real-time pressures of other tanks, such as -2.8 kPa for tank No. 1, -2.5 kPa for tank No. 2, and -2.9 kPa for tank No. 4. Calculate the pressure deviation sequences of each tank using the target pressure reference value of -3 kPa. Among them, the deviation of tank No. 3 reaches 1.8 kPa, and the deviations of other tanks are between 0.2 and 0.5 kPa. Statistically analyze the sequences to obtain a mean fluctuation amplitude of 1.2 kPa, a maximum value of 1.8 kPa, and a minimum value of 0.3 kPa.

[0031] S1022 Apply the fast Fourier transform to the pressure deviation sequences for spectral analysis. Extract the main frequency components from the spectral data, such as 0.5 Hz and 2 Hz. Divide the low-frequency fluctuation interval to reflect slow changes and the high-frequency fluctuation interval to represent rapid perturbations respectively. Statistically analyze the start and end times of the fluctuations. For example, from 10:15 to 10:25, a total of 600 seconds. Use the exponential smoothing method with a smoothing coefficient of 0.3 to process the duration sequence. After eliminating random noise, construct a 12-dimensional feature vector in combination with the mean fluctuation amplitude and frequency characteristics, including amplitude standard deviation, peak factor, frequency bandwidth, and duration variance, etc. Generate a pressure fluctuation model through the support vector regression algorithm and classify it into the "rapid pressure rise accompanied by high-frequency perturbation" mode through cluster analysis.

[0032] In the embodiment of this application, the characteristics of pressure fluctuations are accurately captured through high-frequency sampling and spectral analysis. In an alternative embodiment, the sampling interval can be adjusted according to the scenario, and the clustering algorithm can be replaced by other methods to improve the efficiency of feature extraction.

[0033] S103 If the distribution characteristics of pressure fluctuations are obtained, use a collaborative control algorithm based on pressure equilibrium. Calculate the adjustment value of the pressure release valve opening according to the deviation between the real-time pressure of each tank and the target pressure and the fluctuation characteristics, and send the adjustment value to the valve control system of each tank to achieve real-time opening adjustment.

[0034] S1031 Normalize the pressure data of the storage tank group. Map the real-time pressure values such as -2.8 kPa, -3.2 kPa, -2.5 kPa, -3.5 kPa, -2.9 kPa, -3.1 kPa to the interval from 0 to 1. Divide the high-pressure and low-pressure fluctuation intervals through the preset fluctuation threshold of ±0.5 kPa. Calculate the average pressure deviation to obtain the target pressure of -3.0 kPa. Establish adjustment rules using the fuzzy neural network algorithm based on the deviation of each storage tank from the target value, and output the pressure release rate from 0.2 kPa per minute to 1.0 kPa per minute and the five-level adjustment priority. Combine the release rate and the pressure difference ratio of 0.625 to calculate the initial adjustment amount such as 40% and limit the maximum opening to 60%, obtain the actual adjustment amount of 35%, sort by priority and discretize it into a 7-sequence of 5% step sizes with a 1-minute period.

[0035] S1032 Construct a pressure adjustment matrix according to the fluctuation amplitude of 0.8 kPa, the frequency of 4 times per minute, and the duration of 600 seconds. Calculate the adjustment urgency coefficient of 0.9 and determine the priority order. Obtain the initial adjustment amount of 50% by multiplying the release rate and the adjustment level. Generate the opening change curve and sample it in segments. Rapidly adjust 40% in the first 300 seconds and finely adjust 10% in the last 300 seconds. Calculate the sampled point values through linear interpolation, discretize it into 12 step sizes with a 5% quantization interval, and associate the time stamps to generate an adjustment instruction set to ensure that the No. 3 storage tank is preferentially adjusted and gradually approaches the target pressure.

[0036] S1033 Send the valve opening adjustment instruction to the pressure release valve control system of each storage tank. Taking the No. 1 storage tank as an example, the opening gradually increases from 15% in 7% steps to the target value. The feedback execution value of 21.8% has a deviation from the instruction of less than 1%, confirming normal adjustment. Compensate 2% in combination with the pressure gradient of 0.3 kPa between adjacent storage tanks to achieve the dynamic balance of the pressure of the storage tank group.

[0037] In the embodiment of the present application, the valve opening adjustment is optimized through the cooperative control algorithm. In the optional embodiment, the sampling frequency or fuzzy rules can be adjusted to adapt to different scenarios, ensuring pressure balance while improving the execution efficiency.

[0038] S104 Monitor the adjusted pressure values of each storage tank and compare them with the preset balance standard. If the balance state is not reached, obtain the temperature gradient distribution and the difference in gas expansion coefficients according to the pressure difference and the change trend, and recalculate the opening adjustment value of the pressure release valve in combination with the pressure linkage characteristics between the storage tanks.

[0039] S1041 Collect the time-series data of the storage tank pressure at a frequency of once per second through a pressure sensor. For example, the real-time pressures of 5 storage tanks are -2.8 kPa, -3.2 kPa, -2.5 kPa, -3.4 kPa, and -2.7 kPa. Calculate the deviation from the target pressure of -3.0 kPa using the preset balance threshold of plus or minus 0.3 kPa. Analyze the pressure change rate using a 60-second sliding window. After determining that the storage tank group is not in a balanced state, call the temperature sensor array to collect temperature field data, such as 28 degrees Celsius at the top and 22 degrees Celsius at the bottom of the No. 2 storage tank. Generate a temperature gradient field through Kriging spatial interpolation and calculate the temperature change rate of 1.5 degrees Celsius per hour.

[0040] S1042 Calculate the gas expansion coefficient based on the ideal gas state equation, which is 1.0 at -3.0 kPa and 25 degrees Celsius, and increases to 1.15 after the No. 2 storage tank is heated. Combine the pressure data and the temperature change rate to construct a thermodynamic characteristic curve, and obtain that the pressure compensation coefficient increases by 0.02 for every 1-degree Celsius increase in temperature. Construct a pressure transfer function for adjacent storage tanks, such as No. 1 and No. 2. The initial gain of 0.8 is corrected to 0.92 by the compensation coefficient. Calculate the coupling coefficient to reflect the coupling. Use fuzzy control rules to divide the weights into 5 levels according to the deviation and temperature influence. The weight of the No. 2 storage tank is the highest at 0.9, and those of No. 1 and No. 3 are 0.6, and the rest are from 0.3 to 0.4.

[0041] S1043 Determine the adjustment priority according to the weight classification. Use a proportional-integral controller to calculate the valve opening compensation amount with a proportional coefficient of 0.8 and an integral time constant of 30 seconds. The initial compensation of the No. 2 storage tank is 25% and is corrected to 28.75%. Generate a sequence containing 6 incremental adjustment values from 4% to 6% and send it to the valve control system to achieve stable pressure adjustment.

[0042] S1044 Optimize the opening adjustment through the collaborative analysis of pressure and temperature. In alternative embodiments, the window length or interpolation method can be adjusted to improve the accuracy and ensure the dynamic balance of the pressure of the storage tank group.

[0043] S105 Collect the oil flow data of each storage tank through an oil metering device. Combine the change characteristics before and after the valve opening adjustment, analyze the increasing and decreasing trends and rate changes of the loss amount after adjustment, and establish the relationship between the loss and the opening to optimize the valve control strategy.

[0044] S1051 Obtain the flow data sequence from the oil metering device at a sampling frequency of once per second. For example, when the valve opening is adjusted from 15% to 35% in 300 seconds, the flow rate decreases from 180 cubic meters per hour to 160 cubic meters per hour. Use the valve response characteristic curve to record the adjustment time point, process the time-series data to generate the real-time flow change trend, calculate the loss amount per unit time using a 30-second time window, and compare the cumulative change with the reference loss value of 2 cubic meters per hour. The initial loss increases to 3.5 cubic meters per hour and stabilizes at 2.8 cubic meters per hour later.

[0045] S1052 Construct a time series feature vector based on cumulative loss data, including 10 sampling periods with weights decreasing from 1.0 to 0.1. Use the time-weighted average method to calculate the loss change rate. The loss is 0.15 cubic meters per hour in the first 10 minutes and then decreases to 0.05 cubic meters per hour. Divide the rapid rise section and the slow adjustment section by piecewise linear fitting. Extract 30 discrete sampling points within the opening change range, where the opening increases from 15% to 35%. Use linear interpolation to calculate the adjustment curve, which rises rapidly to 28% in the first 120 seconds and then slowly approaches the target value.

[0046] S1053 The error between the opening adjustment curve and the actual curve fitted by the least squares method is less than 1%. Combine the loss trend characteristics and use a third-order polynomial regression to construct a prediction function. Divide 500 groups of data into training and test sets according to 8:2, and optimize the parameters by cross-validation. It shows that when the opening is between 20% and 30%, the loss increment increases by about 0.1 cubic meters per hour for every 1% increase. Use the gradient descent method to iterate 500 times with a learning rate of 0.01 to optimize the function, and obtain the mapping relationship that the inflection point of loss is at 25% opening and the subsequent growth rate slows down.

[0047] In the embodiment of the present application, loss prediction is achieved through the correlation analysis of flow rate and opening. In alternative embodiments, the time window or the order of the polynomial can be adjusted to improve the accuracy.

[0048] S106 Optimize the valve opening adjustment strategy based on the relationship between loss and opening. Adjust the step size, frequency, and timing, and monitor the oil product loss data in real time. Evaluate the effect using the loss amount and rate, and dynamically correct the opening adjustment value to improve the control accuracy.

[0049] S1061 Discretize the valve opening adjustment value with a 5% step size. Set a 300-second adjustment period and a 60-second minimum interval to divide it into 6 adjustment units. The effective range is from 15% to 45%. Through the loss data acquisition device, sample and record the loss change every 10 seconds when the opening is adjusted from 25% to 35%. Calculate the integral value of the loss amount in a 120-second monitoring window. When it exceeds the threshold of 3 cubic meters per hour, it is determined as a high-loss state.

[0050] S1062 Construct a feature matrix based on the loss state, including three-dimensional data of loss amount, rate, and duration. Analyze the trend of 4-hour data with exponentially decaying weights. The loss rises rapidly when the opening is between 35% and 40%, and is relatively flat when it is between 30% and 35%. Accordingly, divide the high, medium, and low adjustment priorities. Use piecewise linearization to generate a compensation function. The coefficient is 0.8 when the rate exceeds 0.5 cubic meters per hour, otherwise it is 1.2. The adaptive algorithm updates the correction amount to plus or minus 2% every 60 seconds.

[0051] S1063 generates control instructions based on the correction amount and sorts them by weighted response time. Fast instructions less than 5 seconds have a weight of 0.8, slow instructions greater than 5 seconds have a weight of 0.5, and feedback correction deviations are 1% and 2% respectively. A third-order polynomial is used to fit the response characteristic curve, with an error less than 0.5%. It is predicted that when the opening increases by 2%, the loss will increase by 0.2 cubic meters per hour. The large adjustment will be divided into 8 small steps, with dynamic changes at intervals of 60 to 120 seconds.

[0052] S1064 optimizes the opening adjustment through dynamic feedback and prediction. In an optional embodiment, the sampling frequency or weight coefficient can be adjusted to adapt to the scenario to ensure smooth loss control.

[0053] S107 integrates pressure fluctuation, valve opening and oil loss data into the inventory management platform through the inventory early warning system, realizing coordinated control and full-process monitoring of the operating status of multiple storage tanks. When the loss exceeds the preset threshold, it sends out an early warning signal to optimize management efficiency.

[0054] S1071 collects pressure data such as negative 3.2 kPa from pressure sensors of 8 storage tanks at a cycle of 1 second, the valve position feedback device records the opening value such as 35%, and the loss meter obtains the loss volume such as 2.5 cubic meters per hour. It uses time tags to generate multi-parameter correlation sequences, adopts 5-minute window segmentation and uses verification rules to eliminate abnormal data with pressure changes exceeding 1 kPa, opening changes exceeding 10%, and loss changes exceeding 1 cubic meter per hour. After standardization, a feature vector containing volatility, adjustment rate and change rate is formed.

[0055] S1072 constructs an operation state matrix based on the eigenvector and calculates the inventory change relationship between adjacent tanks. For example, the fluctuation rate of tank No. 3 is 2% per hour, which is 0.5% higher than that of other tanks. The operation abnormality is determined by the state association function, and the linkage control instruction is generated by combining the pressure, opening and loss influence factor 0.8. The opening of tank No. 3 is reduced by 5% and the adjacent tanks are compensated. The instruction is adjusted by the actuator to adjust the operating parameters.

[0056] S1073 compares the real-time loss with the threshold of 3 cubic meters, 2.5 cubic meters and 2 cubic meters per hour to divide the warning into level one to level three. The signal generator outputs information including level, time and parameters. According to the rules of level one closing the valve, level two reducing the opening and level three encrypting monitoring, emergency instructions are generated and sent to the control unit through the distribution mechanism to realize coordinated regulation.

[0057] S1074 optimizes storage tank management through multi-parameter coordination and graded warning. In an optional embodiment, the collection cycle or threshold can be adjusted to suit the scenario.

[0058] The above embodiments are only one of the preferred embodiments of the present invention and should not be used to limit the protection scope of the present invention. Any meaningless changes or polishing made on the main design concept and spirit of the present invention, as long as the technical problems solved are still consistent with those of the present invention, should be included in the protection scope of the present invention.

Claims

1. An oil inventory warning method based on real-time perception, characterized in that, The method includes: Obtaining the suction pressure data of the oil and gas recovery device in real time, monitoring the real-time change trend of the suction pressure. If the pressure change exceeds the normal fluctuation range, it is determined that there is abnormal fluctuation in the suction pressure; If the abnormal fluctuation degree of the suction pressure exceeds the preset fluctuation threshold, obtain the real-time pressure values of each storage tank from the pressure sensors of multiple storage tanks, calculate the difference between the current pressure and the target pressure, and determine the distribution characteristics of the pressure fluctuation; According to the distribution characteristics of the pressure fluctuation, adopt a cooperative control algorithm based on pressure balance to determine the opening adjustment value of the pressure release valve of each storage tank, send the valve opening adjustment value to the pressure release valve control system of each storage tank, and adjust the valve opening in real time; Monitor the pressure values of each storage tank after adjustment, and judge whether the preset pressure balance state standard is reached. If not, obtain the corresponding temperature gradient distribution according to the magnitude and change trend of the pressure difference, calculate the difference in gas expansion coefficients in each storage tank, and combine the pressure linkage relationship between the storage tanks to re-determine the opening adjustment value of the pressure release valve of each storage tank; Obtain the oil product flow data of each storage tank through the oil product metering device, analyze the increase or decrease of the loss amount and the change of the loss rate after the valve opening adjustment in combination with the change before and after the valve opening adjustment, and establish a mapping relationship between the valve opening and the loss; Based on the mapping relationship between the valve opening and the loss, adjust the valve opening, including adjusting the step size, adjustment frequency and adjustment timing of the valve opening change, monitor the oil product loss data after adjustment in real time, judge the effectiveness of the optimization strategy according to the loss amount and loss rate indicators, and dynamically adjust the opening adjustment value according to the monitoring results; Combined with the inventory warning system, integrate the pressure fluctuation, valve opening and oil product loss data into the inventory management platform, cooperate to control the operation status of multiple storage tanks and monitor the whole process. If the loss exceeds the preset loss threshold, send a warning signal through the inventory management platform.

2. The method according to claim 1, characterized in that, The obtaining the suction pressure data of the oil and gas recovery device in real time, monitoring the real-time change trend of the suction pressure. If the pressure change exceeds the normal fluctuation range, it is determined that there is abnormal fluctuation in the suction pressure includes: According to the pressure value records collected at the pressure sensor detection points, obtain the real-time pressure value through correction and compensation with the pressure compensation coefficient; Calculate the pressure change rate for the real-time pressure value by using the ratio of the pressure change amount within a unit time to the pressure value at the previous moment, and obtain the pressure fluctuation trend curve through time series data processing; Obtain the pressure fluctuation amplitude by using the sliding window method according to the pressure fluctuation trend curve. If the pressure fluctuation amplitude exceeds the preset threshold upper limit, determine the pressure abnormality level according to the deviation value between the real-time pressure value and the preset reference pressure; Construct a pressure fluctuation model by using the polynomial regression method for the pressure abnormality level, extract the fluctuation characteristic parameters through the model coefficients and fitting errors of the pressure fluctuation model, and obtain the abnormal pressure fluctuation classification result by using the support vector machine algorithm according to the fluctuation characteristic parameters.

3. The method according to claim 1, wherein The if the abnormal fluctuation degree of the suction pressure exceeds the preset fluctuation threshold, obtain the real-time pressure values of each storage tank from the pressure sensors of multiple storage tanks, calculate the difference between the current pressure and the target pressure, and determine the distribution characteristics of the pressure fluctuation includes: Compare and calculate the sampled data of the storage tank pressure according to a preset pressure fluctuation threshold range to obtain the abnormal degree value of the fluctuation and the pressure value of the storage tank at the moment of the fluctuation trigger; Perform deviation calculation on the storage tank pressure value by using a preset target pressure reference value to obtain a pressure deviation sequence; According to the pressure deviation sequence, statistically calculate the mean value, maximum value and minimum value of the fluctuation amplitude; Perform spectrum analysis on the pressure deviation sequence through fast Fourier transform, extract the main frequency component, and divide the high-frequency fluctuation interval and the low-frequency fluctuation interval; Construct a feature vector according to the mean value of the fluctuation amplitude, the fluctuation frequency characteristics and the fluctuation duration sequence, establish a pressure fluctuation feature model through a support vector regression algorithm, and perform clustering analysis on the pressure fluctuation feature model to obtain the fluctuation distribution characteristics.

4. The method according to claim 1, wherein According to the distribution characteristics of the pressure fluctuation, adopt a cooperative control algorithm based on pressure balance to determine the opening adjustment value of the pressure release valve of each storage tank, and send the valve opening adjustment value to the pressure release valve control system of each storage tank to adjust the valve opening in real time, including: Adopt normalization processing for the pressure data of the storage tank group to obtain the pressure fluctuation distribution characteristics, divide the fluctuation interval according to the pressure fluctuation distribution characteristics through a preset pressure fluctuation threshold, and calculate the mean value of the pressure deviation to obtain the pressure target value of the storage tank group; According to the deviation amount between the pressure target value of the storage tank group and the real-time pressure of each storage tank, adopt a fuzzy neural network algorithm to construct a pressure adjustment rule to obtain the pressure release rate and adjustment priority of each storage tank; Calculate the initial adjustment amount of the valve opening of each storage tank by using the pressure release rate, and determine the maximum opening limit value of the valve according to the pressure difference ratio between the pressure in the storage tank and the back pressure of the pipeline network to obtain the actual adjustment amount of the valve; Arrange the actual adjustment amount of the valve according to the adjustment priority in time sequence, and perform discretization processing on the valve opening adjustment amount through a preset unit adjustment period to obtain a valve control sequence.

5. The method according to claim 4, characterized in that, It also includes: Adopt a cooperative control algorithm based on pressure balance, and calculate the opening adjustment percentage of the pressure release valve of each storage tank according to the difference between the real-time pressure value and the target pressure value of each storage tank, combined with the fluctuation amplitude, frequency and duration of the pressure fluctuation, to generate a valve opening adjustment value, specifically including: Perform normalization processing on the deviation between the real-time pressure and the target pressure of the storage tank, and quantify the fluctuation intensity from the pressure fluctuation amplitude obtained by the normalization processing; construct a pressure adjustment matrix for the fluctuation intensity, calculate the number of fluctuations per unit time through the pressure adjustment matrix to obtain the adjustment urgency, and obtain the pressure adjustment priority order from the adjustment urgency; divide the adjustment level according to the pressure adjustment priority order, calculate the initial adjustment amount by using the product of the adjustment level and the pressure release rate, and obtain the valve opening change curve from the initial adjustment amount; sample the valve opening change curve, calculate the opening value of the sampling point by using the linear interpolation method, generate a valve adjustment sequence from the opening value of the sampling point, and perform discretization processing on the adjustment sequence through a quantization interval to obtain a valve opening adjustment step sequence.

6. The method according to claim 1, characterized in that Monitor the pressure values of each storage tank after adjustment, and determine whether the preset pressure balance state standard is reached. If not, obtain the corresponding temperature gradient distribution according to the magnitude and change trend of the pressure difference, calculate the gas expansion coefficient difference in each storage tank, and combine the pressure linkage relationship between the storage tanks to re-determine the opening adjustment value of the pressure relief valve for each storage tank, including: For the pressure time series data obtained by the storage tank pressure sensor, calculate the pressure deviation value according to the preset pressure balance threshold, and use the sliding window method to obtain the pressure change rate, and judge the balance state from the pressure deviation value and the pressure change rate; If the storage tank group does not reach the balance state, use the temperature sensor array to obtain the temperature field distribution data, calculate the temperature gradient field by spatial interpolation method, and obtain the storage tank temperature change rate from the temperature gradient field; Establish a gas thermodynamic characteristic curve according to the storage tank temperature change rate and the pressure time series data, and obtain the pressure temperature compensation coefficient from the gas thermodynamic characteristic curve; Construct a pressure transfer function for the pressure deviation values of adjacent storage tanks, use the pressure temperature compensation coefficient to correct the pressure transfer gain, obtain the storage tank pressure linkage coefficient from the pressure transfer function, and determine the pressure adjustment priority according to the storage tank pressure linkage coefficient.

7. The method according to claim 1, wherein The oil product flow data of each storage tank is obtained through the oil product metering device, and the increase and decrease of the loss amount and the change of the loss rate after the valve opening adjustment are analyzed in combination with the changes before and after the valve opening adjustment, and the mapping relationship between the valve opening and the loss is established, including: Obtain the opening adjustment time point recorded in the valve response characteristic curve, and collect the real-time flow change data output by the flow metering device for the time point; Calculate the loss amount per unit time according to the real-time flow change data, and use a preset time window to accumulate and statistically obtain the loss accumulation data for the loss amount per unit time; Calculate the loss change rate by the time weighted average method for the loss accumulation data to obtain the loss trend characteristics; Extract the valve opening change interval according to the loss trend characteristics, use the linear interpolation method and the least square method to construct the opening adjustment curve, and obtain the mapping relationship between the valve opening and the loss through polynomial regression.

8. The method according to claim 1, wherein Based on the mapping relationship between the valve opening and the loss, adjust the valve opening, including adjusting the step size, adjustment frequency and adjustment timing of the valve opening change, and monitor the oil product loss data after adjustment in real time. Judge the effectiveness of the optimization strategy according to the loss amount and loss rate indicators, and dynamically adjust the opening adjustment value according to the monitoring results, including: Discretize the valve opening adjustment value according to the preset opening change step size, and obtain the opening adjustment time sequence through a fixed adjustment period; Sample the opening adjustment time sequence, and calculate the loss amount integral value from the sampled data; Construct an oil product loss characteristic matrix according to the loss amount integral value, and obtain the loss change trend curve by the time weighted method; Divide the loss control area for the loss change trend curve, use the piecewise linearization method to construct the opening adjustment compensation function, and obtain the opening adjustment correction amount from the compensation function.

9. The method according to claim 1, characterized in that The combined inventory early warning system integrates the pressure fluctuation, valve opening and oil loss data into the inventory management platform, coordinates the operation status of multiple storage tanks and monitors the whole process. If the loss exceeds the preset loss threshold, an early warning signal is issued through the inventory management platform, including: Obtain the pressure data collected by the tank pressure sensor, the opening value recorded by the valve position feedback device, and the oil loss data collected by the loss meter, and generate a multi-parameter correlation sequence through the data time tag; According to the multi-parameter association sequence, data is segmented according to a fixed time window, and a time series verification rule is used to remove abnormal data to obtain a feature vector of the operation process; The operation process characteristic vector is used to construct a storage tank operation state matrix, and the inventory change relationship of adjacent storage tanks is calculated through a state correlation function to obtain the inventory fluctuation rate; The loss impact factor is calculated based on the inventory fluctuation rate, and the multi-tank collaborative operation rules are used to generate linkage control instructions, and the operating parameters of each tank are adjusted through the control instruction executor.

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