A method of calibrating the capacity of a fuel station ground tank

Abstract

The application discloses a kind of calibration methods of gas station ground tank capacity, through the fixed time oil height data set obtained by gas station ground tank installed tank liquid level instrument, and the oiling data set of ground tank obtained by gas station oiling control system in time recording, comparison and form data pair set;Then the data in data pair set is carried out data preliminary processing and data depth processing, and then the oil height volume table of ground tank is calculated and counted, to complete the whole process of calibration of gas station ground tank capacity.The calibration method of gas station ground tank capacity of the application can effectively calibrate the actual capacity of ground tank when it is placed in gas station, and can effectively establish standard oil height volume table, which is convenient for intuitive display of actual capacity of oil tank in later period, simple and efficient, high accuracy, easy to use, and conducive to industrial application.

Description

Technical Field

[0001] This invention relates to the field of container calibration technology, specifically a method for calibrating the capacity of underground tanks at gas stations. Background Technology

[0002] Real-time monitoring of the fuel level in underground tanks at gas stations is crucial for their operation and management. Current technologies for measuring the fuel level inside tanks typically use a scale with a fixed end attached to a probe. In operation, the probe is lowered into the tank until it reaches the bottom, then retracted. The fuel level is determined by the mark left on the scale. However, the angle at which the tank is placed in the gas station, as well as deformation during transportation or use, significantly impacts its actual capacity. Therefore, current technologies cannot accurately measure the real-time capacity of the tanks, hindering gas station operation and management. Summary of the Invention

[0003] The purpose of this invention is to provide a method for calibrating the capacity of underground tanks at gas stations, so as to solve the above-mentioned defects.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for calibrating the capacity of underground tanks at gas stations includes the following steps:

[0006] S1. Through the refueling control system of the gas station and the oil tank level gauge installed in the underground tank of the gas station, the oil level data in the underground tank is recorded at regular intervals to form the timed oil level dataset of the underground tank.

[0007] S2. Record each fuel dispensing data of multiple fuel nozzles connected to the gas station's underground tank in a timely manner through the gas station's refueling control system, including the time of nozzle lifting, the time of nozzle hanging, the amount of fuel dispensed in liters, and the nozzle number; for several fuel dispensing data, establish a fuel dispensing dataset for the underground tank according to the principle of merging fuel dispensing data with overlapping times, including the start time of each fuel dispensing, the end time of each fuel dispensing, and the corresponding amount of fuel dispensed in liters.

[0008] S3. The timed oil level dataset obtained in step S1 and the oil dispensing dataset obtained in step S2 are compared to form a data pair set. Then, the data in the data pair set is subjected to preliminary data processing and in-depth data processing to calculate and statistically determine the oil level volume table of the underground tank, thereby completing the entire process of calibrating the underground tank capacity of the gas station.

[0009] In step S3, the specific operation of comparing and forming data pairs is as follows: taking a certain oil delivery data in the oil delivery data of the tank as the center, and combining the oil level data of the tank before and after the oil delivery, a data pair corresponding to the oil delivery is formed; by combining the data pairs corresponding to each oil delivery, a data pair set can be formed.

[0010] The data pairs include: tank number, and the start time, end time, volume of oil dispensed, oil level before and after each dispensing.

[0011] In step S3, the preliminary data processing specifically involves: removing data from abnormal periods within the entire fuel dispensing period, and dividing the entire data set into multiple smaller segments, analyzing each segment separately, and adding auxiliary columns.

[0012] The abnormal time periods include: the time period when the level gauge data is missing and the time period when oil is introduced; the time period when the level gauge data is missing refers to the time period when the time difference between two adjacent level gauge data is greater than 10 minutes, and the time period when oil is introduced refers to the time period when the level gauge height increases over time; the auxiliary columns include: height difference column and scale average volume column.

[0013] Preferably, in step S3, the data depth processing specifically includes the following steps:

[0014] A. Merge data:

[0015] A1. To match the 1mm accuracy of the oil tank level gauge, merge the data in rows with height differences less than 1.2mm in chronological order.

[0016] A2. Merge data where the time interval between the end time of the previous fuel dispensing and the start time of the next fuel dispensing is less than 1 minute.

[0017] B. Discarding data: The variance is calculated based on the average volume of the scale readings in the previous and subsequent oil delivery data. If the average volume fluctuates too much and exceeds the predetermined threshold, the data will be merged and the variance will be recalculated. If the fluctuation still exceeds the predetermined threshold, the current data will be discarded and a new data segment will start from the subsequent data.

[0018] Preferably, in step S3, the specific steps for calculating and statistically analyzing the oil level volume table of the underground tank are as follows:

[0019] C. Intermediate Standard Tank: After preliminary and in-depth data processing, an intermediate standard tank capacity table is formed according to the order of the tank level height scale values. The intermediate standard tank capacity table includes the tank number, scale value, and one or more average volume values ​​under that scale value.

[0020] D. Final Standard Tank: Analyze and calculate the multiple average volume values ​​corresponding to each scale value in the intermediate standard tank capacity table in step C of the previous step, select the most likely value, and compile the final standard oil height and volume table of the ground tank.

[0021] Preferably, in step D, the logical method for analyzing, calculating, and selecting the most likely value is as follows:

[0022] D1. If there is only one average volume value corresponding to the scale value of the can capacity, then directly select that average volume value.

[0023] D2. If there are two average volume values ​​corresponding to the scale value of the can capacity, select the value of the column with the longest span; if the span of columns is equal, select the first one.

[0024] D3. If there are more than two average volume values ​​corresponding to this scale value of the can's capacity, then calculate the variance based on the cumulative values:

[0025] D3.1 If the calculated variance is less than 2.5, then take the value of the column with the most rows.

[0026] D3.2 If the calculated variance is > 2.5, continue to determine:

[0027] D3.21. If the value in the column with the most rows is within the mean ± 1 standard deviation, then select that value;

[0028] D3.2.2 If the value in the column with the most rows is not within the mean ± 1 standard deviation, then take all the values ​​within the mean ± 1 standard deviation and calculate the average, then select the value closest to the mean.

[0029] The beneficial effects of this invention are as follows:

[0030] The present invention provides a method for calibrating the capacity of underground tanks at gas stations. This method can effectively calibrate the actual capacity of the underground tanks, taking into account the angular deviation when the tanks are actually placed at the gas station and the deformation of the tanks during transportation or use. It can also effectively establish a standard oil level volume table, which facilitates the intuitive display of the actual capacity of the oil tanks in the later stages. The method is simple, efficient, accurate, and easy to use, and is conducive to industrial application. Detailed Implementation

[0031] The present invention will be further described below with reference to the embodiments. It should be noted that these are merely examples and descriptions of the inventive concept. 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 inventive concept or exceed the scope defined in the claims, they should all be considered to fall within the protection scope of the present invention.

[0032] Example 1:

[0033] A method for calibrating the capacity of underground tanks at gas stations includes the following steps:

[0034] S1. After the underground tank is installed in the gas station or after a period of use, the oil level data in the underground tank is recorded periodically by the gas station's refueling control system and the oil tank level gauge installed in the underground tank, forming a timed oil level dataset for the underground tank.

[0035] The aforementioned timed oil level dataset includes: oil tank code, recording time, oil level, and other data, the specific format of which is shown in Table 1 below.

[0036] Table 1 - Timed Oil Level Dataset

[0037]

[0038] S2. The refueling control system of the gas station records each fuel dispensing data of multiple fuel nozzles connected to several underground tanks within a certain range in a timely manner, including the time of nozzle lifting, the time of nozzle hanging, the amount of fuel dispensed in liters, the nozzle number, etc. The specific format is shown in Table 2 below.

[0039] Table 2 - Dispensing data for each fuel dispenser from multiple fuel nozzles

[0040]

[0041]

[0042] For several oil delivery data points, an oil delivery dataset for the tank is established according to the principle of merging oil delivery data points with overlapping times. This dataset includes the start time, end time, and corresponding oil delivery volume for each delivery, as shown in Table 3 below.

[0043] Table 3 - Data set of oil transfer from underground tanks

[0044]

[0045] S3. The timed oil level dataset obtained in step S1 and the oil dispensing dataset obtained in step S2 are compared to form a data pair set. Then, the data in the data pair set is subjected to preliminary data processing and in-depth data processing to calculate and statistically determine the oil level volume table of the underground tank, thereby completing the entire process of calibrating the underground tank capacity of the gas station.

[0046] The comparison and data pairing process involves the following steps: Using a specific oil delivery data point from the tank's oil delivery dataset as the center, the oil level data before and after that delivery is combined to form a corresponding data pair. This data pair includes: the tank number, and the start and end times of each delivery, the number of liters delivered, the oil level before delivery, and the oil level after delivery, etc. Combining these data pairs from each delivery creates a data set, the specific format of which is shown in Table 4 below.

[0047] Table 4 - Data sets corresponding to fuel delivery

[0048]

[0049] The initial data processing involves: removing data from abnormal periods within the entire oil dispensing period; dividing the data set into multiple smaller segments; analyzing each segment separately; and adding auxiliary columns. Abnormal periods include: periods with missing level gauge data and periods of oil inflow. Periods with missing level gauge data refer to times where the difference between two adjacent level gauge data points is greater than 10 minutes; periods of oil inflow refer to times when the level gauge height increases over time. The auxiliary columns include: a height difference column and a scale average volume column. The generated data format is shown in Table 5.

[0050] Table 5 - Data sets corresponding to fuel delivery after preliminary data processing

[0051]

[0052] The specific steps of the data depth processing are as follows:

[0053] A. Merge data:

[0054] A1. To match the 1mm accuracy of the oil tank level gauge, the data in the row where the height difference is less than 1.2mm are merged in chronological order. That is, if the height difference of the oil tank level before and after this oil delivery is less than 1.2mm, the height difference statistics are combined with the height difference before and after the next oil delivery in chronological order, as shown in Tables 6 and 7.

[0055] Table 6 - Datasets before high-level merging

[0056]

[0057] Table 7 - Highly Merged Datasets

[0058]

[0059] A2. Merge data where the time interval between the end time of the previous fuel dispensing and the start time of the next fuel dispensing is less than 1 minute. The data before and after merging are shown in Tables 8 and 9.

[0060] Table 8 - Datasets before time merging

[0061]

[0062] Table 9 - Dataset after time merging

[0063]

[0064] B. Discarding Data: A moving average is calculated based on the average volume of the most recent oil delivery data. If the average volume fluctuates excessively beyond a predetermined threshold, the data is merged and the standard deviation is recalculated. This means that if the current data shows excessively fluctuating average volume, the data volume is increased, and a new set of data is added for calculation. If the fluctuation still exceeds the predetermined threshold, the current data is discarded, and a new data segment is started for subsequent data. The following example uses data from Table 10.

[0065] Table 10 - Dataset before dynamic merging and cleaning

[0066]

[0067] (1) For the average volume column of the scale in the table above, the calculation is carried out one by one starting from the third value. First, the difference between the average value of 13.33, 13.67, and 7 and the last value 7 is calculated as (13.33+13.67+7) / 3-7=4.333, which is greater than the currently set threshold. Therefore, the data is merged as shown in Table 11 below:

[0068] Table 11 - Result set after the first data merge

[0069]

[0070]

[0071] After merging, continue calculating the next group (13.33, 10.33, 8).

[0072] (2) The final merge result is shown in Table 12:

[0073] Table 12 - Final merged data set

[0074]

[0075] According to the standard deviation formula The "Average Volume at Scale" column was calculated, and the result was 1.996, which is higher than the set threshold, so this dataset was discarded. The subsequent calculation and statistical analysis of the oil volume table for the underground tanks followed these steps:

[0076] C. Intermediate Standard Tank: After preliminary and in-depth data processing, an intermediate standard tank capacity table is formed according to the order of the tank level height scale values. It specifically includes the tank number, scale value, and one or more average volume values ​​under that scale value.

[0077] The above steps resulted in an intermediate standard tank capacity table, as detailed in Table 13.

[0078] Table 13 - Intermediate Standard Tank Capacity Table

[0079]

[0080] D. Final Standard Tank: Analyze and calculate the multiple average volume values ​​corresponding to each scale value in the intermediate standard tank capacity table in step C of the previous step, select the most likely value, and compile the final standard oil height and volume table of the ground tank.

[0081] The logical method for analyzing, calculating, and selecting the most likely value is as follows:

[0082] D1. If there is only one average volume value corresponding to the scale value of the container, then directly select that average volume value. For example, as shown in Table 13, the scale value 29 has only one average volume value of 2.21, so it is directly selected.

[0083] D2. If there are two average volume values ​​corresponding to a given scale value for the container capacity, select the value in the column with the longest row span; if the column spans are equal, select the first value. For example, as shown in Table 13, scale value 31 has two average volume values, and the one with an average volume of 2.3 spans the longest row, so it is selected. Scale value 35 also has two average volume values, but they span the same number of columns, so the first value, 2.32, is ultimately selected.

[0084] D3. If there are more than two average volume values ​​corresponding to this scale value of the can's capacity, then calculate the variance based on the cumulative values:

[0085] D3.1 If the calculated variance is less than 2.5, then take the value from the column with the most rows. For example, as shown in Table 13, the scale value 33 has 5 average volume values. According to the standard deviation calculation formula... The standard deviation of these 5 values ​​is calculated to be 0.02 < 2.5. The value of the column with the most rows is 2.31, so it is selected.

[0086] D3.2 If the calculated variance is ≥2.5, continue to judge:

[0087] D3.21. If the value in the column with the most rows spans is within the mean ± 1 * standard deviation, then select that value. For example, as shown in Table 13, the scale value is 34, and its average volume value has 4 rows. The calculated standard deviation is 2.6 > 2.5. The value in the column with the most rows spans is 2.31, which is within the mean ± 1 * standard deviation, so it is directly selected.

[0088] D3.2.2 If the value in the column with the most rows is not within the mean ± 1 standard deviation, then take all the values ​​within the mean ± 1 standard deviation and calculate the average, then select the value closest to the mean.

[0089] For example, as shown in Table 13, the scale value is 35, and there are 4 average volume values. The calculated standard deviation is 2.89 > 2.5. The value of the column with the most rows is 7.8, which is not within the mean ± 1 * standard deviation. Then, all values ​​within the mean ± 1 * standard deviation are taken, the average is calculated, and the value closest to the mean is selected.

[0090] The final standard oil height and volume table for the underground tanks was compiled, and part of the oil height and volume table is as follows: Figure 1 What is seen. As... Figure 1 As shown, this volume table clearly and intuitively displays the real-time oil level of the calibrated tank and its corresponding actual capacity.

[0091] The present invention provides a method for calibrating the capacity of underground tanks at gas stations. This method can effectively calibrate the actual capacity of the underground tanks, taking into account the angular deviation when the tanks are actually placed at the gas station and the deformation of the tanks during transportation or use. It can also effectively establish a standard oil level volume table, which facilitates the intuitive display of the actual capacity of the oil tanks in the later stages. The method is simple, efficient, accurate, and easy to use, and is conducive to industrial application.

[0092] The above is an exemplary description of the invention. Obviously, the specific implementation of the invention is not limited to the above-described manner. Any non-substantial improvement made using the inventive concept and technical solution of the invention, or the direct application of the inventive concept and technical solution to other situations without modification, is within the protection scope of the invention.

Claims

1. A method for calibrating the capacity of underground tanks at gas stations, characterized in that, Includes the following steps: S1. Through the refueling control system of the gas station and the oil tank level gauge installed in the underground tank of the gas station, the oil level data in the underground tank is recorded at regular intervals to form the timed oil level dataset of the underground tank. S2. Record each fuel dispensing data of multiple fuel nozzles connected to the gas station's underground tank in a timely manner through the gas station's refueling control system, including the time of nozzle lifting, the time of nozzle hanging, the amount of fuel dispensed in liters, and the nozzle number; for several fuel dispensing data, establish a fuel dispensing dataset for the underground tank according to the principle of merging fuel dispensing data with overlapping times, including the start time of each fuel dispensing, the end time of each fuel dispensing, and the corresponding amount of fuel dispensed in liters. S3. The timed oil level dataset obtained in step S1 and the oil dispensing dataset obtained in step S2 are compared and a data pair set is formed. Then, the data in the data pair set is subjected to preliminary data processing and in-depth data processing, and the oil level volume table of the underground tank is calculated and statistically analyzed, thereby completing the entire process of calibrating the underground tank capacity of the gas station. In step S3, the specific steps for calculating and compiling the oil volume table of the underground tank are as follows: C. Intermediate Standard Tank: After preliminary and in-depth data processing, an intermediate standard tank capacity table is formed according to the order of the tank level height scale values. The intermediate standard tank capacity table includes the tank number, scale value, and one or more average volume values ​​under that scale value. D. Final Standard Tank: Analyze and calculate the multiple average volume values ​​corresponding to each scale value in the intermediate standard tank capacity table in step C of the previous step, select the most likely value, and compile the final standard oil height volume table for the ground tank. In step D, the logical method for analyzing, calculating, and selecting the most likely value is as follows: D1. If there is only one average volume value corresponding to the scale value of the can capacity, then directly select that average volume value. D2. If there are two average volume values ​​corresponding to the scale value of the can capacity, select the value of the column with the longest span; if the span of columns is equal, select the first one. D3. If there are more than two average volume values ​​corresponding to this scale value of the can's capacity, then calculate the variance based on the cumulative values: D3.1 If the calculated variance is less than 2.5, then take the value of the column with the most rows. D3.2 If the calculated variance > 2.5, continue to determine: D3.

21. If the value in the column with the most rows is within the mean ± 1 standard deviation, then select that value; D3.2.2 If the value in the column with the most rows is not within the mean ± 1 standard deviation, then take all the values ​​within the mean ± 1 standard deviation and calculate the average, then select the value closest to the mean.

2. The method for calibrating the capacity of a gas station underground tank according to claim 1, characterized in that, In step S3, the specific operation of comparing and forming data pairs is as follows: taking a certain oil delivery data in the oil delivery data of the tank as the center, and combining the oil level data of the tank before and after the oil delivery, a data pair corresponding to the oil delivery is formed; by combining the data pairs corresponding to each oil delivery, a data pair set can be formed.

3. A method for calibrating the capacity of a gas station underground tank according to claim 1 or 2, characterized in that, The data pairs include: tank number, and the start time, end time, volume of oil dispensed, oil level before and after each dispensing.

4. The method for calibrating the capacity of a gas station underground tank according to claim 1, characterized in that, In step S3, the preliminary data processing specifically involves: removing data from abnormal periods within the entire fuel dispensing period, and dividing the entire data set into multiple smaller segments, analyzing each segment separately, and adding auxiliary columns. The abnormal time periods include: the time period when the level gauge data is missing and the time period when oil is introduced; the time period when the level gauge data is missing refers to the time period when the time difference between two adjacent level gauge data is greater than 10 minutes, and the time period when oil is introduced refers to the time period when the level gauge height increases over time; the auxiliary columns include: height difference column and scale average volume column.

5. The method for calibrating the capacity of underground tanks at gas stations according to claim 1, characterized in that, In step S3, the data depth processing specifically includes the following steps: A. Merge data: A1. To match the 1mm accuracy of the oil tank level gauge, merge the data in rows with height differences less than 1.2mm in chronological order. A2. Merge data where the time interval between the end time of the previous fuel dispensing and the start time of the next fuel dispensing is less than 1 minute. B. Discard data: Calculate the variance based on the average volume of the scale readings from several consecutive oil delivery data points. If the average volume fluctuates too much and exceeds the predetermined threshold, merge the data and recalculate the variance. If the fluctuation is still too large and exceeds the predetermined threshold, the current data will be discarded, and the subsequent data will start a new data segment.

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