A method for automatically identifying the start and end times of loading operations and the shipment quantity based on tank liquid level curves

Abstract

The application discloses a kind of tank liquid level curve automatic identification shipment operation start-stop time and shipment quantity method, the method uses liquid level mean difference to identify the subinterval where operation start-stop time point is in in liquid level curve, interval is detected start-stop time point position by difference method, obtain shipment operation start time and end time, and obtain liquid level difference and shipment quantity in combination with liquid level data and tank capacity table.Compared with prior art, the method uses difference method to eliminate the influence of fluctuation of liquid level data, avoids the misidentification problem of shipment operation start-stop time, and accurately calculates the shipment quantity of enterprise shipment.This improves the detection accuracy of shipment operation start-stop time and shipment quantity, improves the efficiency of customs supervision, realizes automatic loading confirmation, and has important value.

Description

Technical Field

[0001] This invention relates to the field of customs supervision, specifically to a method for automatically identifying the start and end of loading operations and the shipment volume and time based on the liquid level curve of a storage tank. Background Technology

[0002] my country's customs authorities need to efficiently supervise the import and export of liquid chemicals. During port loading operations, liquid chemicals are transported from storage tanks to chemical carriers via pipelines and ship-to-shore connection pipelines. Currently, customs supervision of loading and unloading still requires manual review of tank level curves to obtain the start and end times and shipment volumes of the loading operations, verification of loading plans, and confirmation of loading, resulting in low efficiency. Therefore, customs is gradually shifting towards automated loading confirmation to achieve real-time and efficient monitoring of loading and unloading operations.

[0003] Conventional automated detection methods for identifying the start and end times of loading operations and the volume of goods shipped often suffer from low accuracy and susceptibility to fluctuations in the data curve. They struggle to accurately identify the actual start and end times of loading operations within the tank level curve and are easily affected by external interference and noise. Particularly for tanks located near the coast, factors such as strong winds often cause fluctuations in tank level data, leading to misidentification during level curve analysis using existing methods. This results in incorrect start and end times of loading operations and incorrect volumes of goods shipped, impacting customs supervision.

[0004] It is evident that there is an urgent need for a detection method that can accurately identify the start and end times of loading operations and the amount of goods shipped, improve the accuracy of identifying the start and end points in the liquid level curve, and distinguish between tank liquid level fluctuations and the loading process, so as to improve the efficiency and accuracy of customs supervision. Summary of the Invention

[0005] This invention addresses the problems existing in the background technology by proposing a method for automatically identifying the start and end times of loading operations and the shipment volume based on the liquid level curve of a storage tank. The method uses the difference in the average liquid level value to identify sub-intervals within the liquid level curve containing the start and end times of loading operations, and uses a difference method to detect the start and end times of the loading operations. The shipment volume is then obtained by combining the liquid level data with the tank capacity table. The method includes the following steps:

[0006] 1) Based on the loading plan submitted manually by the company's personnel, collect and record the tank liquid level data during the loading operation period to obtain the tank liquid level data sequence. T is the data length;

[0007] 2) The data sequence is preprocessed by moving median denoising and noise filling to obtain the denoised data sequence F = (F1, F2, ..., F...). T ), and divide F into N equal intervals [m k ,n kLet F = k = 1, 2, ..., N, and let the length of each interval be r. N Given a set of N intervals, [m k ,n k ]∈F N The number of intervals N is obtained by the following formula:

[0008]

[0009] r is usually taken as 20 to 40. If the data length T is not divisible by a whole number, it is padded at the end of the data.

[0010] 3) Calculate F N Average liquid level within N intervals Calculate the difference between the means Search from left to right, if ΔFA i >0 and

[0011] ΔFA i+u >ΔFA i u = 1, 2, ..., U

[0012] Therefore, it is assumed that the starting time of the curved loading operation exists within the interval [m]. i+1 ,n i+1 In the formula, U is rounded down, i.e. Search from right to left, if ΔFA j >0 and

[0013] ΔFA j-u >ΔFA j u = 1, 2, ..., U

[0014] Therefore, it is assumed that the end time of the curved loading operation exists within the interval [m]. j ,n j ]middle;

[0015] 4) Interval [m] i+1 ,n i+1 Internal liquid level data is set up Differences from each data point If at q1 And difference sequence If there are one or more differences greater than zero in R1, then q1 is taken as the start time of the loading operation, where q1∈{m i+1 +1,m i+1 +2,…,n i+1}; where R1 is rounded down, obtained from the following formula:

[0016]

[0017] α is usually taken as 0.7 to 0.9, and the interval [m] is calculated similarly.j ,n j Internal liquid level data and The difference s 2,q =(F q -F nj ),q=n j -1,n j -2,…,m j If at q2 And difference sequence If there are two or more differences greater than zero in R2, then q2 is taken as the end time of the loading operation, where q2∈{n} j -1,n j -2,…,m j}; where R2 is obtained from the following formula:

[0018]

[0019] q1 and q2 are the detection results of the start and end time points of the liquid level curve loading operation.

[0020] 5) Generate a loading confirmation form, including the monitoring start time (planned loading start time), monitoring end time (planned loading end time), tank code, and identification of loading start time q1, loading end time q2, and liquid level difference from the loading plan. The shipment quantity M is identified by combining the identified liquid level difference with the tank capacity table.

[0021] Beneficial effects:

[0022] This invention discloses a method for automatically identifying the start and end times of loading operations and the shipment volume based on the liquid level curve of a storage tank. This method accurately identifies the interval containing the start and end times of the operation within the liquid level curve. Within this interval, a differential method is used to detect the location of the start and end times of the loading operation. Then, by combining the liquid level data with the tank capacity table, the start and end times of the loading operation and the shipment volume are obtained. Compared to traditional methods, this method eliminates the influence of liquid level data fluctuations through the differential method, thereby avoiding misidentification of the start and end times of the loading operation and accurately calculating the enterprise's shipment volume. This is of great significance for improving the accuracy of detecting the start and end times of loading operations, enhancing customs supervision efficiency, and achieving automatic loading confirmation. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for automatically identifying the start and end times of loading operations and the shipment volume of a storage tank liquid level curve according to the present invention.

[0024] Figure 2 This is the result of noise reduction preprocessing of the tank level curve in the embodiment of the present invention;

[0025] Figure 3 This is the calculation result of the average value of the tank liquid level curve interval in the embodiment of the present invention;

[0026] Figure 4 The data represents the detection results of the start and end times of the loading operation on the tank level curve in this embodiment of the invention. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The specific operational process illustrates the effectiveness of this method in detecting the start and end times of loading operations and the volume of goods shipped. This embodiment is implemented based on the technical solution of the present invention, but the scope of protection of the present invention is not limited to the following embodiments.

[0028] This invention uses a customs office's real-time monitoring of shipment operations as an example. The duration of a single loading operation for a company typically ranges from 2 to 3 days, depending on the size of the cargo ship. Before loading, company personnel submit a loading plan, including the ship's name, port storage tank code, planned loading time, planned completion time, and shipment volume. During the loading operation, customs collects and records tank liquid level data at 5-minute intervals. After the operation, the customs office compares the liquid level curve analysis results with the loading plan to achieve automatic loading confirmation. Using a customs office's monitoring of a company's loading operation from November 16th to 18th, 2020, as an example, this invention illustrates the role of this method in detecting the start and end times of loading operations and the shipment volume.

[0029] The implementation process of this method is as follows: Figure 1 As shown, the specific process is as follows:

[0030] 1) The loading plan submitted manually by the company's personnel is shown in Table 1, which includes the planned start and end times of loading and the tank code. Based on the plan, the tank liquid level data is collected and recorded during the loading operation period, with a collection cycle of 5 minutes, to obtain the tank liquid level data sequence. T is the data length; in this embodiment, T = 624.

[0031] Table 1. Partial Shipment Plan Submission by a Certain Company

[0032]

[0033] 2) The data sequence is preprocessed by moving median denoising and noise filling to obtain the denoised data sequence F = (F1, F2, ..., F...). T The noise-reduced liquid level curve is as follows: Figure 2 As shown. Divide F into N equal intervals [m k ,n k Let F = k = 1, 2, ..., N, and let the length of each interval be r. N Given a set of N intervals, [m k ,n k ]∈F NThe number of intervals N increases as T increases, specifically as shown by the following formula:

[0034]

[0035] r is typically taken as 20 to 40. In this embodiment, r = 20. By adding 16 data points to the end of the liquid level data to make T divisible, T = 640.

[0036] 3) Calculate F N Average liquid level within N intervals The results are as follows Figure 3 As shown. Calculate the difference between the means. The calculation results are shown in Table 2:

[0037] Table 2. Calculation results of mean and difference liquid levels in the interval (unit: meters)

[0038] [1.20] 21.02 -0.10 [321,340] 11.08 0.78 [21,40] 21.12 0.16 [341,360] 10.30 0.83 [41,60] 20.96 -0.03 [361,380] 9.47 0.77 [61,80] 20.99 0.32 [381,400] 8.69 0.81 [81,100] 20.67 0.77 [401,420] 7.89 0.76 [101,120] 19.90 0.80 [421,440] 7.12 0.82 [121,140] 19.10 0.80 [441,460] 6.31 0.83 [141,160] 18.30 0.80 [461,480] 5.47 0.76 [161,180] 17.49 0.81 [481,500] 4.71 0.80 [181,200] 16.68 0.79 [501,520] 3.91 0.22 [201,220] 15.89 0.79 [521,540] 3.70 -0.04 [221,240] 15.10 0.82 [541,560] 3.73 0.02 [241,260] 14.28 0.78 [561,580] 3.72 -0.02 [261,280] 13.51 0.79 [581,600] 3.74 -0.01 [281,300] 12.72 0.83 [601,620] 3.74 0.06 [301,320] 11.89 0.80 [621,640] 3.68 \

[0039] The interval [81, 100] from 17:49 to 19:24 on November 16th is denoted as [m]. i+1 ,n i+1 As can be seen from Table 2, ΔFA i =0.32>0 and

[0040] ΔFA i+u >ΔFA i u = 1, 2, ..., U

[0041] In the formula, U is rounded down, that is It is assumed that the starting time of the curved loading operation exists within the interval [m] i+1 ,n i+1 In the range [81, 100], the interval [501, 520] from 4:49 to 6:24 on November 18th is denoted as [m]. j ,n j ], there is ΔFA j =0.22>0 and

[0042] ΔFA j-u >ΔFA j u = 1, 2, ..., U

[0043] It is assumed that the end time of the curved loading operation exists within the interval [m] j ,n j In [501,520];

[0044] 4) Interval [m] i+1 ,n i+1 The liquid level data within [81, 100] is F. 81 ,F82 ,…,F 100 Let F 81 The difference s between each data point 1,q =(F 81 -F q ), q=82,83,…,100, the difference calculation results within this interval are shown in Table 3:

[0045] Table 3 Interval [m] i+1 ,n i+1 Inner difference calculation results

[0046] 20.92 -0.968 0.071 0.135 -0.832 0.208 0.241 0.298 0.335 0.373 <![CDATA[s 1,91 ]]> <![CDATA[s 1,92 ]]> <![CDATA[s 1,93 ]]> <![CDATA[s 1,94 ]]> <![CDATA[s 1,95 ]]> <![CDATA[s 1,96 ]]> <![CDATA[s 1,97 ]]> <![CDATA[s 1,98 ]]> <![CDATA[s 1,99 ]]> <![CDATA[s 1,100 ]]> 0.418 0.434 0.351 0.482 0.499 0.488 0.520 0.548 0.583 0.636

[0047] At q1 = 83, s 1,83 =0.071>0 and the difference sequence s 1,83 ,s 1,84 ,…,s 1,100 There are 17 (≥R1) differences that are greater than zero, where R1 is rounded down, obtained from the following formula:

[0048]

[0049] α is typically taken as 0.7 to 0.9; in this embodiment, α = 0.9. Therefore, q1 = 83, meaning 11-16 15:59 is the start time of the loading operation. Similarly, the interval [m] is calculated. j ,n j [501, 520] Internal liquid level data and F 520 The difference s 2,q =(F q -F 520 ), q=519,518,…,501, the difference calculation results within this interval are shown in Table 4:

[0050] Table 4 Interval [m] j ,n j Inner difference calculation results

[0051] 3.69 0.007 -0.067 0.050 -0.007 0.079 -0.064 0.130 -0.071 0.235 <![CDATA[s 2,510 ]]> <![CDATA[s 2,509 ]]> <![CDATA[s 2,508 ]]> <![CDATA[s 2,507 ]]> <![CDATA[s 2,506 ]]> <![CDATA[s 2,505 ]]> <![CDATA[s 2,504 ]]> <![CDATA[s 2,503 ]]> <![CDATA[s 2,502 ]]> <![CDATA[s 2,501 ]]> 0.341 0.357 0.395 0.409 0.412 0.400 0.440 0.495 0.548 0.641

[0052] At q2 = 515, s 2,515 =0.079>0 and the difference sequence s 2,515 ,s 2,514 ,…,s 2,501 There are 13 (≥R²) differences that are greater than zero, where R² is rounded down and obtained from the following formula:

[0053]

[0054] Therefore, q2 = 515, i.e., 11-18 5:59 is the end time of the loading operation. The detection results of the liquid level curve at the start and end time points of the loading operation are as follows: Figure 4 As shown;

[0055] 5) Generate a loading confirmation table, as shown in Table 5, including the monitoring start time (planned loading start time), monitoring end time (planned loading end time), tank code, and identification of loading start time q1, loading end time q2, and liquid level difference from the loading plan. The shipment quantity M is identified by combining the identified liquid level difference ΔF with the tank capacity table.

[0056] Table 5 Loading Operation Confirmation Form

[0057]

[0058] Depend on Figure 4 As can be seen, this method can accurately locate the interval between the start and end times of the operation, detect the start and end time points of the loading operation on the liquid level curve in this embodiment, and avoid misidentification of the start and end times of the loading operation caused by fluctuations in liquid level data. It can accurately calculate the enterprise's shipment volume, check it against the loading plan, and realize automatic loading confirmation.

Claims

1. A method for automatically identifying the start and end times of loading operations and the shipment volume of a storage tank based on its liquid level curve, characterized in that... By analyzing the tank level curve, the start and end times of the loading operation are identified, and the loading volume is calculated based on the tank level data, thereby enabling customs to monitor the shipment in real time. The process involves the following steps: 1) Based on the loading plan submitted manually by the company's personnel, collect and record the tank liquid level data during the loading operation period to obtain the tank liquid level data sequence. T is the data length; 2) Perform denoising preprocessing on the data sequence to obtain the denoised data sequence F = (F1, F2, ..., F...). T ), and divide F into N equal intervals [m k ,n k Let F be an integer k = 1, 2, ..., N. N Given a set of N intervals, [m k ,n k ]∈F N ; 3) Calculate F N Average liquid level within N intervals Calculate the difference between the means Search from left to right, if ΔFA i >0 and ΔFA i+u >ΔFA i ,u=1,2,…,U Therefore, it is assumed that the starting time of the loading operation exists within the interval [m]. i+1 ,n i+1 In the context of [the setting], U is the set value; Search from right to left, if ΔFA j >0 and ΔFA j-u >ΔFA j ,u=1,2,…,U Therefore, it is assumed that the end time of the loading operation exists within the interval [m]. j ,n j In the context of [the setting], U is the set value; 4) Interval [m] i+1 ,n i+1 Internal liquid level data is set up Difference from each data point q If at q1 And difference sequence If there are one or more differences greater than zero in R1, then q1 is taken as the start time of the loading operation, where q1∈{m i+1 +1,m i+1 +2,…,n i+1 Similarly, calculate the interval [m] j ,n j Internal liquid level data and difference If at q2 And difference sequence If there are two or more differences greater than zero in R2, then q2 is taken as the end time of the loading operation, where q2∈{n} j -1,n j -2,…,m j }; q1 and q2 are the detection results of the start and end time points of the loading operation of the liquid level curve; 5) Generate a loading confirmation form, including the monitoring start time, monitoring end time, and tank code from the loading plan, as well as identifying the loading start time q1, loading end time q2, and liquid level difference. Identify the shipment volume M.

2. The method for automatically identifying the start and end times of loading operations and the shipment volume of a storage tank based on the liquid level curve according to claim 1, characterized in that... The storage tank liquid level data sequence is subjected to denoising preprocessing, including moving median denoising and noise filling.

3. The method for automatically identifying the start and end times of loading operations and the shipment volume of a storage tank based on the liquid level curve according to claim 1, characterized in that... The number of intervals N increases as T increases. r represents the length of each interval data.

4. The method for automatically identifying the start and end times of loading operations and the shipment volume of a storage tank based on the liquid level curve according to claim 1, characterized in that... r is between 20 and 40. If the data length T is not divisible by a number, padding is added to the end of the data.

5. The method for automatically identifying the start and end times of loading operations and the shipment volume of a storage tank based on the liquid level curve according to claim 1, characterized in that... In step 3), U is rounded down, that is... r represents the length of each interval data.

6. The method for automatically identifying the start and end times of loading operations and the shipment volume of a storage tank based on the liquid level curve according to claim 1, characterized in that... In step 4), R1 and R2 are rounded down, i.e. α is taken as 0.7 to 0.

9.

7. The method for automatically identifying the start and end times of loading operations and the shipment volume of a storage tank based on the liquid level curve according to claim 1, characterized in that... The shipment volume M is identified by combining the liquid level difference with the tank capacity table.

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