A liquid bulk cargo management method and system based on big data

Abstract

The invention relates to the technical field of cargo management, and discloses a liquid bulk cargo management method and system based on big data, which are used to obtain multiple historical liquid cargo pick-up time nodes and multiple historical liquid cargo pick-up information of a port liquid cargo control point; calculate the single-dimensional historical liquid cargo variation factor of the historical liquid cargo pick-up time node; divide the historical liquid cargo variation factor cluster into factor data slices, and calculate the multi-dimensional historical liquid cargo variation factor; determine multiple multi-dimensional historical liquid cargo variation coefficients, and calculate the liquid bulk cargo control management coefficient of the port liquid cargo control point; generate a cargo control management reminder for the port liquid cargo control point based on the liquid bulk cargo control management coefficient and a preset liquid bulk cargo control management coefficient, deeply analyze the historical cargo management data, improve the accuracy and real-time performance of the cargo control reminder, optimize inventory planning and resource allocation, effectively avoid liquid bulk cargo backlog or shortage, and improve the efficiency and reliability of dynamic cargo control management of the port.

Description

Technical Field

[0001] The present invention relates to the technical field of cargo management, and in particular to a liquid bulk cargo management method and system based on big data. Background Art

[0002] With the rapid development of global trade, the importance of ports as logistics hubs has become increasingly prominent. Port dynamic cargo control management is the core link in port operations, which directly affects the flow efficiency of goods, port throughput capacity and overall operating costs.

[0003] Traditional methods of dynamic cargo control management in ports mainly rely on manual operations. Experienced staff generate cargo control reminders and notify cargo control personnel based on the set safety values ​​of liquid bulk inventory and statistical values ​​of daily operations. However, the existing methods of cargo control reminders are still subject to human subjectivity in setting various parameters and safety values. Therefore, the existing methods have problems such as single function, limited data integration capabilities, and lack of in-depth analysis of historical data. They are unable to make inventory planning and resource allocation in advance, which easily leads to backlogs or shortages of liquid bulk cargo. Summary of the invention

[0004] The embodiments of the present invention provide a liquid bulk cargo management method and system based on big data. The present invention can deeply analyze historical cargo management data, reduce human subjective influence, improve the accuracy and real-time performance of cargo control reminders, optimize inventory planning and resource allocation, effectively avoid liquid bulk cargo backlog or shortage, and improve the efficiency and reliability of dynamic cargo control management in ports.

[0005] In order to achieve the above object, the present invention provides a liquid bulk cargo management method based on big data, comprising: Determine a liquid cargo control point at the port, obtain multiple historical liquid cargo pick-up time nodes of the liquid cargo control point at the port, and obtain multiple historical liquid cargo pick-up information corresponding to each historical liquid cargo pick-up time node; Analyze each historical liquid cargo pickup information, and calculate the single-dimensional historical liquid cargo variable wave factor corresponding to each historical liquid cargo pickup time node based on the analysis result; Clustering all single-dimensional historical liquid cargo wave variation factors to determine a historical liquid cargo wave variation factor cluster, dividing the historical liquid cargo wave variation factor cluster into a plurality of factor data slices, and calculating the multi-dimensional historical liquid cargo wave variation factor of the historical liquid cargo pickup time node according to the factor data slices; Processing all multidimensional historical liquid cargo wave variation factors, determining a plurality of multidimensional historical liquid cargo wave variation coefficients based on the processing results, and calculating the liquid bulk cargo control management coefficient of the liquid cargo control point of the port according to all the multidimensional historical liquid cargo wave variation coefficients; Based on the relationship between the liquid bulk cargo control management coefficient and a preset liquid bulk cargo control management coefficient, a cargo control management reminder is generated for the liquid cargo control point of the port.

[0006] Furthermore, when analyzing each historical liquid cargo pickup information and calculating the single-dimensional historical liquid cargo variable wave factor corresponding to each historical liquid cargo pickup time node based on the analysis result, it includes: Determine the historical liquid cargo pick-up quantity and the historical liquid cargo inventory corresponding to each historical liquid cargo pick-up information; A single-dimensional historical liquid cargo variation factor corresponding to the historical liquid cargo pickup time node is calculated according to the historical liquid cargo pickup quantity and the historical liquid cargo inventory.

[0007] Further, when calculating the single-dimensional historical liquid cargo variation factor corresponding to the historical liquid cargo delivery time node according to the historical liquid cargo delivery volume and the historical liquid cargo inventory, it includes: The one-dimensional historical liquid cargo variable wave factor corresponding to the historical liquid cargo delivery time node is calculated according to the following formula: ; Among them, q is the one-dimensional historical liquid cargo variable wave factor corresponding to the historical liquid cargo pick-up time node, w is the calculation coefficient corresponding to the historical liquid cargo pick-up time node, and the value range is [0.1,1.9], e is a constant, r is the historical liquid cargo pick-up volume, and r1 is the historical liquid cargo inventory.

[0008] Furthermore, when the historical liquid cargo variation factor cluster is divided into a plurality of factor data slices, and the multi-dimensional historical liquid cargo variation factor of the historical liquid cargo pickup time node is calculated according to the factor data slices, it includes: Calculate the wave factor mean of all single-dimensional historical liquid cargo wave factor variations, divide the historical liquid cargo wave factor clusters according to the wave factor mean, and when the single-dimensional historical liquid cargo wave factor variation is less than the wave factor mean, divide the corresponding single-dimensional historical liquid cargo wave factor variation into a low wave factor data slice; Calculate the low-wave factor data slice coefficient of the historical liquid cargo pickup time node according to the low-wave factor data slice; ; Among them, t1 is the coefficient of the low-wave factor data slice of the historical liquid cargo delivery time node, y1 is the number of single-dimensional historical liquid cargo variable wave factors in the low-wave factor data slice, p is the mean value of the variable wave factor, and a u is the u-th single-dimensional historical liquid cargo variable wave factor in the low wave factor data slice, For all The minimum value in ; When the single-dimensional historical liquid cargo wave variation factor is greater than or equal to the wave variation factor mean value, the corresponding single-dimensional historical liquid cargo wave variation factor is divided into a high wave factor data slice; Calculating the high wave factor data slice coefficient of the historical liquid cargo delivery time node according to the high wave factor data slice; ; Among them, t2 is the coefficient of the high-wave factor data slice of the historical liquid cargo delivery time node, y2 is the number of single-dimensional historical liquid cargo variable wave factors in the high-wave factor data slice, and s i is the i-th single-dimensional historical liquid cargo variable wave factor in the high wave factor data slice, For all The minimum value in ; Randomly cross-pair the single-dimensional historical liquid cargo wave variation factors on the low wave factor data slice and the high wave factor data slice to obtain high and low historical liquid cargo wave variation factor groups, wherein each high and low historical liquid cargo wave variation factor group includes two single-dimensional historical liquid cargo wave variation factors; Calculate the cross high and low factor coefficients of the historical liquid cargo delivery time node according to the high and low historical liquid cargo variable wave factor group; ; Among them, t3 is the cross-high and low factor coefficient of the historical liquid cargo delivery time node, y3 is the number of high and low historical liquid cargo variable wave factor groups, and f d is the high unidimensional historical liquid cargo variable wave factor in the dth high and low historical liquid cargo variable wave factor group, g d is the low single-dimensional historical liquid cargo variable wave factor in the dth high and low historical liquid cargo variable wave factor group; The multi-dimensional historical liquid cargo variable wave factor of the historical liquid cargo pick-up time node is calculated according to the low wave factor data slice coefficient, the high wave factor data slice coefficient and the cross high and low factor coefficient.

[0009] Further, when the multi-dimensional historical liquid cargo variable wave factor of the historical liquid cargo delivery time node is calculated according to the low wave factor data slice coefficient, the high wave factor data slice coefficient and the cross high and low factor coefficient, it includes: The multi-dimensional historical liquid cargo variation factor of the historical liquid cargo pick-up time node is calculated according to the following formula: ; Among them, h is the multi-dimensional historical liquid cargo variable wave factor of the historical liquid cargo pick-up time node, k1 is the first calculation coefficient, k2 is the second calculation coefficient, k1>0, k2>0.

[0010] Furthermore, when all multi-dimensional historical liquid cargo variable wave factors are processed and a plurality of multi-dimensional historical liquid cargo variable wave coefficients are determined based on the processing results, it includes: Combining each c multi-dimensional historical liquid cargo wave variation factors to obtain a multi-dimensional historical liquid cargo wave variation factor set; The multi-dimensional historical liquid cargo wave variation factors in each multi-dimensional historical liquid cargo wave variation factor set are summed up to obtain a corresponding multi-dimensional historical liquid cargo wave variation factor sum value; Determine the maximum multidimensional historical liquid cargo variation factor in the multidimensional historical liquid cargo variation factor set, and determine the ratio of the multidimensional historical liquid cargo variation factor sum to the maximum multidimensional historical liquid cargo variation factor as the multidimensional historical liquid cargo variation coefficient of the multidimensional historical liquid cargo variation factor set.

[0011] Furthermore, when calculating the liquid bulk cargo control management coefficient of the port liquid cargo control point according to all multi-dimensional historical liquid cargo variable wave coefficients, it includes: Randomly extracting the first multidimensional historical liquid cargo variable wave factor and value and the first multidimensional historical liquid cargo variable wave coefficient corresponding to the first multidimensional historical liquid cargo variable wave factor set; Randomly extract the second multidimensional historical liquid cargo variable wave factor and value corresponding to the second multidimensional historical liquid cargo variable wave factor set and the second multidimensional historical liquid cargo variable wave coefficient; Calculate the absolute value of the multidimensional historical liquid cargo variation factor difference between the first multidimensional historical liquid cargo variation factor sum value and the second multidimensional historical liquid cargo variation factor sum value; Extracting the maximum multidimensional historical liquid cargo wave variation factor and value and the minimum multidimensional historical liquid cargo wave variation factor and value from all multidimensional historical liquid cargo wave variation factor and value, and calculating the multidimensional historical liquid cargo wave variation factor extreme difference value between the maximum multidimensional historical liquid cargo wave variation factor and value and the minimum multidimensional historical liquid cargo wave variation factor and value; Calculate the ratio of the absolute value of the multi-dimensional historical liquid cargo wave variation factor difference and the extreme difference of the multi-dimensional historical liquid cargo wave variation factor as the first coefficient to be calculated; Calculate the absolute value of the difference between the first multidimensional historical liquid cargo wave coefficient and the second multidimensional historical liquid cargo wave coefficient; Extracting the maximum multidimensional historical liquid cargo wave coefficient and the minimum multidimensional historical liquid cargo wave coefficient from all multidimensional historical liquid cargo wave coefficients, and calculating the multidimensional historical liquid cargo wave coefficient extreme difference between the maximum multidimensional historical liquid cargo wave coefficient and the minimum multidimensional historical liquid cargo wave coefficient; Calculate the ratio of the absolute value of the multi-dimensional historical liquid cargo wave coefficient difference to the multi-dimensional historical liquid cargo wave coefficient extreme difference as the second coefficient to be calculated; Determine a sum of the first coefficient to be calculated and the second coefficient to be calculated as a comprehensive coefficient to be calculated; Extract and calculate the remaining multi-dimensional historical liquid cargo variable wave factor set to obtain multiple comprehensive coefficients to be calculated; The liquid bulk cargo control management coefficient of the liquid cargo control point of the port is calculated based on all the comprehensive coefficients to be calculated.

[0012] Further, when calculating the liquid bulk cargo control management coefficient of the port liquid cargo control point based on all the comprehensive coefficients to be calculated, it includes: Determine the maximum comprehensive coefficient to be calculated and the minimum comprehensive coefficient to be calculated from all comprehensive coefficients to be calculated; Calculate the liquid bulk cargo control management coefficient of the port liquid cargo control point according to the maximum comprehensive coefficient to be calculated and the minimum comprehensive coefficient to be calculated; ; Where v is the liquid bulk cargo control management coefficient of the port liquid cargo control point, j is the number of comprehensive coefficients to be calculated, and n b is the bth comprehensive coefficient to be calculated, n min is the minimum comprehensive coefficient to be calculated, n max is the maximum comprehensive coefficient to be calculated.

[0013] Further, when a cargo control management reminder is generated for the port liquid cargo control point based on the relationship between the liquid bulk cargo control management coefficient and a preset liquid bulk cargo control management coefficient, it includes: When the liquid bulk cargo control management coefficient is less than the preset liquid bulk cargo control management coefficient, a replenishment reminder is generated for the port liquid cargo control point; When the liquid bulk cargo control management coefficient is greater than or equal to the preset liquid bulk cargo control management coefficient, a stop replenishment reminder is generated for the port liquid cargo control point.

[0014] In order to achieve the above object, the present invention also provides a liquid bulk cargo management system based on big data, comprising: An information acquisition module is used to determine a port liquid cargo control point, obtain a plurality of historical liquid cargo pick-up time nodes of the port liquid cargo control point, and obtain a plurality of historical liquid cargo pick-up information corresponding to each historical liquid cargo pick-up time node; A single-dimensional calculation module is used to analyze each historical liquid cargo pickup information, and calculate the single-dimensional historical liquid cargo variable wave factor corresponding to each historical liquid cargo pickup time node based on the analysis result; A multi-dimensional calculation module, for clustering all single-dimensional historical liquid cargo wave variation factors, determining a historical liquid cargo wave variation factor cluster, dividing the historical liquid cargo wave variation factor cluster into a plurality of factor data slices, and calculating the multi-dimensional historical liquid cargo wave variation factor of the historical liquid cargo pick-up time node according to the factor data slices; A coefficient calculation module, for processing all multi-dimensional historical liquid cargo variable wave factors, determining a plurality of multi-dimensional historical liquid cargo variable wave coefficients based on the processing results, and calculating the liquid bulk cargo control management coefficient of the liquid cargo control point of the port according to all the multi-dimensional historical liquid cargo variable wave coefficients; The cargo management module is used to generate a cargo control management reminder for the liquid cargo control point of the port based on the relationship between the liquid bulk cargo control management coefficient and a preset liquid bulk cargo control management coefficient.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a liquid bulk cargo management method and system based on big data, which obtain multiple historical liquid cargo pick-up time nodes and multiple historical liquid cargo pick-up information of a port liquid cargo control point; calculate the single-dimensional historical liquid cargo variable wave factor of the historical liquid cargo pick-up time node; divide the historical liquid cargo variable wave factor cluster into factor data slices, and calculate the multi-dimensional historical liquid cargo variable wave factor; determine multiple multi-dimensional historical liquid cargo variable wave coefficients, and calculate the liquid bulk cargo control management coefficient of the port liquid cargo control point; generate a cargo control management reminder for the port liquid cargo control point based on the liquid bulk cargo control management coefficient and the preset liquid bulk cargo control management coefficient, deeply analyze the historical cargo management data, improve the accuracy and real-time performance of the cargo control reminder, optimize inventory planning and resource allocation, effectively avoid liquid bulk cargo backlog or shortage, and improve the efficiency and reliability of dynamic cargo control management of the port. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A schematic diagram of the process of a liquid bulk cargo management method based on big data in an embodiment of the present invention is shown; Figure 2 A schematic structural diagram of a liquid bulk cargo management system based on big data in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0017] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0018] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0021] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0022] like Figure 1 As shown, an embodiment of the present invention discloses a liquid bulk cargo management method based on big data, comprising: S110: Determine a port liquid cargo control point, obtain multiple historical liquid cargo pick-up time nodes of the port liquid cargo control point, and obtain multiple historical liquid cargo pick-up information corresponding to each historical liquid cargo pick-up time node; In this embodiment, the port liquid cargo control point refers to a location where liquid bulk cargo needs to be controlled and managed.

[0023] In this embodiment, the historical liquid cargo pick-up time node refers to the set collection time days, such as the first day in the past, the second day in the past, the third day in the past, etc. Here, the historical liquid cargo pick-up time node is preferably the past 10 days, that is, there are 10 historical liquid cargo pick-up time nodes, and each day represents a historical liquid cargo pick-up time node, which can be adjusted according to actual conditions.

[0024] In this embodiment, if several pick-up orders are received on the first day in the past, each pick-up order will have corresponding pick-up information, that is, each historical liquid cargo pick-up time node corresponds to one or more historical liquid cargo pick-up information.

[0025] S120: Analyze each historical liquid cargo pick-up information, and calculate the single-dimensional historical liquid cargo variable wave factor corresponding to each historical liquid cargo pick-up time node based on the analysis result; In some embodiments of the present application, when analyzing each historical liquid cargo pickup information and calculating the single-dimensional historical liquid cargo variable wave factor corresponding to each historical liquid cargo pickup time node based on the analysis result, it includes: Determine the historical liquid cargo pick-up quantity and the historical liquid cargo inventory corresponding to each historical liquid cargo pick-up information; A single-dimensional historical liquid cargo variation factor corresponding to the historical liquid cargo pickup time node is calculated according to the historical liquid cargo pickup quantity and the historical liquid cargo inventory.

[0026] In this embodiment, the historical liquid cargo delivery quantity refers to the quantity of liquid cargo displayed on the delivery note, and the historical liquid cargo inventory refers to the remaining quantity of liquid cargo corresponding to the liquid cargo control point of the port.

[0027] The beneficial effect of the above technical solution is that the present invention calculates the single-dimensional historical liquid cargo variation factor corresponding to the historical liquid cargo pickup time node according to the historical liquid cargo pickup volume and the historical liquid cargo inventory, thereby ensuring the calculation accuracy of the single-dimensional historical liquid cargo variation factor. The single-dimensional historical liquid cargo variation factor can reflect the degree of deviation between the historical liquid cargo pickup volume and the historical liquid cargo inventory.

[0028] In some embodiments of the present application, when calculating the single-dimensional historical liquid cargo variation factor corresponding to the historical liquid cargo pickup time node according to the historical liquid cargo pickup quantity and the historical liquid cargo inventory, it includes: The one-dimensional historical liquid cargo variable wave factor corresponding to the historical liquid cargo delivery time node is calculated according to the following formula: ; Among them, q is the one-dimensional historical liquid cargo variable wave factor corresponding to the historical liquid cargo pick-up time node, w is the calculation coefficient corresponding to the historical liquid cargo pick-up time node, and the value range is [0.1,1.9], e is a constant, r is the historical liquid cargo pick-up volume, and r1 is the historical liquid cargo inventory.

[0029] S130: clustering all single-dimensional historical liquid cargo wave variation factors to determine a historical liquid cargo wave variation factor cluster, dividing the historical liquid cargo wave variation factor cluster into a plurality of factor data slices, and calculating a multi-dimensional historical liquid cargo wave variation factor of the historical liquid cargo pick-up time node according to the factor data slices; In this embodiment, each cluster includes a corresponding factor data slice, and the factor data slice is used to store data.

[0030] In some embodiments of the present application, when the historical liquid cargo variation factor cluster is divided into a plurality of factor data slices, and the multi-dimensional historical liquid cargo variation factor of the historical liquid cargo pickup time node is calculated according to the factor data slices, it includes: Calculate the wave factor mean of all single-dimensional historical liquid cargo wave factor variations, divide the historical liquid cargo wave factor clusters according to the wave factor mean, and when the single-dimensional historical liquid cargo wave factor variation is less than the wave factor mean, divide the corresponding single-dimensional historical liquid cargo wave factor variation into a low wave factor data slice; Calculate the low-wave factor data slice coefficient of the historical liquid cargo pickup time node according to the low-wave factor data slice; ; Among them, t1 is the coefficient of the low-wave factor data slice of the historical liquid cargo delivery time node, y1 is the number of single-dimensional historical liquid cargo variable wave factors in the low-wave factor data slice, p is the mean value of the variable wave factor, and a u is the u-th single-dimensional historical liquid cargo variable wave factor in the low wave factor data slice, For all The minimum value in ; When the single-dimensional historical liquid cargo wave variation factor is greater than or equal to the wave variation factor mean value, the corresponding single-dimensional historical liquid cargo wave variation factor is divided into a high wave factor data slice; Calculating the high wave factor data slice coefficient of the historical liquid cargo delivery time node according to the high wave factor data slice; ; Among them, t2 is the coefficient of the high-wave factor data slice of the historical liquid cargo delivery time node, y2 is the number of single-dimensional historical liquid cargo variable wave factors in the high-wave factor data slice, and s i is the i-th single-dimensional historical liquid cargo variable wave factor in the high wave factor data slice, For all The minimum value in ; Randomly cross-pair the single-dimensional historical liquid cargo wave variation factors on the low wave factor data slice and the high wave factor data slice to obtain high and low historical liquid cargo wave variation factor groups, wherein each high and low historical liquid cargo wave variation factor group includes two single-dimensional historical liquid cargo wave variation factors; Calculate the cross high and low factor coefficients of the historical liquid cargo delivery time node according to the high and low historical liquid cargo variable wave factor group; ; Among them, t3 is the cross-high and low factor coefficient of the historical liquid cargo delivery time node, y3 is the number of high and low historical liquid cargo variable wave factor groups, and f d is the high unidimensional historical liquid cargo variable wave factor in the dth high and low historical liquid cargo variable wave factor group, g d is the low single-dimensional historical liquid cargo variable wave factor in the dth high and low historical liquid cargo variable wave factor group; The multi-dimensional historical liquid cargo variable wave factor of the historical liquid cargo pick-up time node is calculated according to the low wave factor data slice coefficient, the high wave factor data slice coefficient and the cross high and low factor coefficient.

[0031] In this embodiment, the average of all single-dimensional historical liquid cargo variable wave factors is calculated as the variable wave factor average.

[0032] In this embodiment, the one-dimensional historical liquid cargo variable wave factors on the low wave factor data slice and the high wave factor data slice are randomly cross-paired, which means that the one-dimensional historical liquid cargo variable wave factors on the low wave factor data slice and the high wave factor data slice are randomly paired in pairs. Therefore, each high and low historical liquid cargo variable wave factor group includes two one-dimensional historical liquid cargo variable wave factors, one from the low wave factor data slice and the other from the high wave factor data slice. It should be noted that if there is a separate one-dimensional historical liquid cargo variable wave factor after random pairing, it can be deleted and does not participate in the matching calculation.

[0033] In this embodiment, in order to facilitate distinction, the single-dimensional historical liquid cargo variable wave factor from the high-wave factor data slice is used as the high single-dimensional historical liquid cargo variable wave factor, and the single-dimensional historical liquid cargo variable wave factor from the low-wave factor data slice is used as the low single-dimensional historical liquid cargo variable wave factor.

[0034] The beneficial effect of the above technical solution is: the present invention realizes the refined division of all single-dimensional historical liquid cargo variable wave factors, obtains low wave factor data slices and high wave factor data slices, and calculates the low wave factor data slice coefficients and the high wave factor data slice coefficients respectively, thereby ensuring the calculation accuracy and avoiding the subjectivity of the calculation. The low wave factor data slice and the high wave factor data slice can be calculated separately, and the low wave factor data slice and the high wave factor data slice can also be cross-matched. The calculation is further guaranteed. Comprehensiveness and avoidance of calculation errors.

[0035] In some embodiments of the present application, when calculating the multidimensional historical liquid cargo variable wave factor of the historical liquid cargo delivery time node according to the low wave factor data slice coefficient, the high wave factor data slice coefficient and the cross high and low factor coefficient, it includes: The multi-dimensional historical liquid cargo variation factor of the historical liquid cargo pick-up time node is calculated according to the following formula: ; Among them, h is the multi-dimensional historical liquid cargo variable wave factor of the historical liquid cargo pick-up time node, k1 is the first calculation coefficient, k2 is the second calculation coefficient, k1>0, k2>0.

[0036] The beneficial effect of the above technical solution is: the present invention calculates the multi-dimensional historical liquid cargo variable wave factor of the historical liquid cargo pick-up time node according to the low wave factor data slice coefficient, the high wave factor data slice coefficient and the cross high and low factor coefficient, realizes the multi-faceted calculation of the multi-dimensional historical liquid cargo variable wave factor, effectively ensures the calculation accuracy of the multi-dimensional historical liquid cargo variable wave factor, and lays the foundation for the cargo control management of the port liquid cargo control point.

[0037] S140: Processing all multidimensional historical liquid cargo wave variation factors, determining a plurality of multidimensional historical liquid cargo wave variation coefficients based on the processing results, and calculating the liquid bulk cargo control management coefficient of the liquid cargo control point of the port according to all the multidimensional historical liquid cargo wave variation coefficients; In some embodiments of the present application, when all multi-dimensional historical liquid cargo variable wave factors are processed and multiple multi-dimensional historical liquid cargo variable wave coefficients are determined based on the processing results, it includes: Combining each c multi-dimensional historical liquid cargo wave variation factors to obtain a multi-dimensional historical liquid cargo wave variation factor set; The multi-dimensional historical liquid cargo wave variation factors in each multi-dimensional historical liquid cargo wave variation factor set are summed up to obtain a corresponding multi-dimensional historical liquid cargo wave variation factor sum value; Determine the maximum multidimensional historical liquid cargo variation factor in the multidimensional historical liquid cargo variation factor set, and determine the ratio of the multidimensional historical liquid cargo variation factor sum to the maximum multidimensional historical liquid cargo variation factor as the multidimensional historical liquid cargo variation coefficient of the multidimensional historical liquid cargo variation factor set.

[0038] In this embodiment, c is preferably 3, that is, every 3 multidimensional historical liquid cargo variable wave factors are combined to obtain a multidimensional historical liquid cargo variable wave factor set, and the sum is calculated to obtain the corresponding multidimensional historical liquid cargo variable wave factor sum value.

[0039] In this embodiment, a plurality of multi-dimensional historical liquid cargo wave variation factor sets may be obtained, and then a plurality of multi-dimensional historical liquid cargo wave variation coefficients may be obtained.

[0040] The beneficial effect of the above technical solution is that the present invention processes all multidimensional historical liquid cargo variable wave factors, determines multiple multidimensional historical liquid cargo variable wave coefficients, provides a prerequisite basis for calculating the liquid bulk cargo control management coefficient of the port liquid cargo control point, and provides reliable calculation support.

[0041] In some embodiments of the present application, when calculating the liquid bulk cargo control management coefficient of the port liquid cargo control point according to all multi-dimensional historical liquid cargo variable wave coefficients, it includes: Randomly extracting the first multidimensional historical liquid cargo variable wave factor and value and the first multidimensional historical liquid cargo variable wave coefficient corresponding to the first multidimensional historical liquid cargo variable wave factor set; Randomly extract the second multidimensional historical liquid cargo variable wave factor and value corresponding to the second multidimensional historical liquid cargo variable wave factor set and the second multidimensional historical liquid cargo variable wave coefficient; Calculate the absolute value of the multidimensional historical liquid cargo variation factor difference between the first multidimensional historical liquid cargo variation factor sum value and the second multidimensional historical liquid cargo variation factor sum value; Extracting the maximum multidimensional historical liquid cargo wave variation factor and value and the minimum multidimensional historical liquid cargo wave variation factor and value from all multidimensional historical liquid cargo wave variation factor and value, and calculating the multidimensional historical liquid cargo wave variation factor extreme difference value between the maximum multidimensional historical liquid cargo wave variation factor and value and the minimum multidimensional historical liquid cargo wave variation factor and value; Calculate the ratio of the absolute value of the multi-dimensional historical liquid cargo wave variation factor difference and the extreme difference of the multi-dimensional historical liquid cargo wave variation factor as the first coefficient to be calculated; Calculate the absolute value of the difference between the first multidimensional historical liquid cargo wave coefficient and the second multidimensional historical liquid cargo wave coefficient; Extracting the maximum multidimensional historical liquid cargo wave coefficient and the minimum multidimensional historical liquid cargo wave coefficient from all multidimensional historical liquid cargo wave coefficients, and calculating the multidimensional historical liquid cargo wave coefficient extreme difference between the maximum multidimensional historical liquid cargo wave coefficient and the minimum multidimensional historical liquid cargo wave coefficient; Calculate the ratio of the absolute value of the multi-dimensional historical liquid cargo wave coefficient difference to the multi-dimensional historical liquid cargo wave coefficient extreme difference as the second coefficient to be calculated; Determine a sum of the first coefficient to be calculated and the second coefficient to be calculated as a comprehensive coefficient to be calculated; Extract and calculate the remaining multi-dimensional historical liquid cargo variable wave factor set to obtain multiple comprehensive coefficients to be calculated; The liquid bulk cargo control management coefficient of the liquid cargo control point of the port is calculated based on all the comprehensive coefficients to be calculated.

[0042] In this embodiment, in order to facilitate distinction, the multidimensional historical liquid cargo wave variation factor sum value and the multidimensional historical liquid cargo wave variation coefficient corresponding to the first randomly extracted multidimensional historical liquid cargo wave variation factor set are used as the first multidimensional historical liquid cargo wave variation factor sum value and the first multidimensional historical liquid cargo wave variation coefficient.

[0043] In this embodiment, in order to facilitate distinction, the multidimensional historical liquid cargo wave variation factor sum and the multidimensional historical liquid cargo wave variation coefficient corresponding to the randomly extracted second multidimensional historical liquid cargo wave variation factor set are used as the second multidimensional historical liquid cargo wave variation factor sum and the second multidimensional historical liquid cargo wave variation coefficient.

[0044] In this embodiment, the difference between the first multidimensional historical liquid cargo variable wave factor sum and the second multidimensional historical liquid cargo variable wave factor sum is calculated, and then the absolute value is taken as the absolute value of the multidimensional historical liquid cargo variable wave factor difference.

[0045] In this embodiment, the difference between the maximum multidimensional historical liquid cargo wave variation factor sum value and the minimum multidimensional historical liquid cargo wave variation factor sum value is calculated as the multidimensional historical liquid cargo wave variation factor extreme difference value.

[0046] In this embodiment, the difference between the first multidimensional historical liquid cargo variable wave coefficient and the second multidimensional historical liquid cargo variable wave coefficient is calculated, and then the absolute value is taken as the absolute value of the multidimensional historical liquid cargo variable wave coefficient difference.

[0047] In this embodiment, the difference between the maximum multi-dimensional historical liquid cargo wave coefficient and the minimum multi-dimensional historical liquid cargo wave coefficient is calculated as the multi-dimensional historical liquid cargo wave coefficient extreme difference value.

[0048] In this embodiment, all remaining multidimensional historical liquid cargo variable wave factor sets are extracted and calculated in the above manner to obtain multiple comprehensive coefficients to be calculated. It should be noted that if there is a separate multidimensional historical liquid cargo variable wave factor set, it can be deleted and does not participate in the calculation.

[0049] The beneficial effect of the above technical solution is: the present invention extracts and calculates the remaining multi-dimensional historical liquid cargo variable wave factor set, obtains multiple comprehensive coefficients to be calculated, and calculates the liquid bulk cargo control management coefficient of the port liquid cargo control point, thereby ensuring the calculation accuracy and efficiency of the liquid bulk cargo control management coefficient. The liquid cargo management situation of the port liquid cargo control point can be accurately and quickly predicted through the liquid bulk cargo control management coefficient, without the need for manual participation in calculation and management, effectively eliminating liquid cargo management errors, and avoiding problems such as single function, limited data integration capabilities, and lack of in-depth analysis of historical data.

[0050] In some embodiments of the present application, when calculating the liquid bulk cargo control management coefficient of the port liquid cargo control point based on all comprehensive coefficients to be calculated, it includes: Determine the maximum comprehensive coefficient to be calculated and the minimum comprehensive coefficient to be calculated from all comprehensive coefficients to be calculated; Calculate the liquid bulk cargo control management coefficient of the port liquid cargo control point according to the maximum comprehensive coefficient to be calculated and the minimum comprehensive coefficient to be calculated; ; Where v is the liquid bulk cargo control management coefficient of the port liquid cargo control point, j is the number of comprehensive coefficients to be calculated, and n b is the bth comprehensive coefficient to be calculated, n min is the minimum comprehensive coefficient to be calculated, n max is the maximum comprehensive coefficient to be calculated.

[0051] S150: Based on the relationship between the liquid bulk cargo control management coefficient and a preset liquid bulk cargo control management coefficient, generating a cargo control management reminder for the liquid cargo control point of the port.

[0052] In some embodiments of the present application, when a cargo control management reminder is generated for the port liquid cargo control point based on the relationship between the liquid bulk cargo control management coefficient and a preset liquid bulk cargo control management coefficient, it includes: When the liquid bulk cargo control management coefficient is less than the preset liquid bulk cargo control management coefficient, a replenishment reminder is generated for the port liquid cargo control point; When the liquid bulk cargo control management coefficient is greater than or equal to the preset liquid bulk cargo control management coefficient, a stop replenishment reminder is generated for the port liquid cargo control point.

[0053] In this embodiment, the preset liquid bulk cargo control management coefficient is preferably 0.8, and can be adaptively adjusted according to actual needs.

[0054] The beneficial effect of the above technical solution is: based on the relationship between the liquid bulk cargo control management coefficient and the preset liquid bulk cargo control management coefficient, the present invention generates a replenishment reminder or a stop replenishment reminder for the port liquid cargo control point, improves the accuracy and real-time performance of the control reminder, optimizes inventory planning and resource allocation, effectively avoids liquid bulk cargo backlog or shortage, and improves the efficiency and reliability of the port's dynamic cargo control management.

[0055] In order to further explain the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0056] Correspondingly, such as Figure 2 As shown, the present application also provides a liquid bulk cargo management system based on big data, including: An information acquisition module is used to determine a port liquid cargo control point, obtain a plurality of historical liquid cargo pick-up time nodes of the port liquid cargo control point, and obtain a plurality of historical liquid cargo pick-up information corresponding to each historical liquid cargo pick-up time node; A single-dimensional calculation module is used to analyze each historical liquid cargo pickup information, and calculate the single-dimensional historical liquid cargo variable wave factor corresponding to each historical liquid cargo pickup time node based on the analysis result; A multi-dimensional calculation module, for clustering all single-dimensional historical liquid cargo wave variation factors, determining a historical liquid cargo wave variation factor cluster, dividing the historical liquid cargo wave variation factor cluster into a plurality of factor data slices, and calculating the multi-dimensional historical liquid cargo wave variation factor of the historical liquid cargo pick-up time node according to the factor data slices; A coefficient calculation module, for processing all multi-dimensional historical liquid cargo variable wave factors, determining a plurality of multi-dimensional historical liquid cargo variable wave coefficients based on the processing results, and calculating the liquid bulk cargo control management coefficient of the liquid cargo control point of the port according to all the multi-dimensional historical liquid cargo variable wave coefficients; The cargo management module is used to generate a cargo control management reminder for the liquid cargo control point of the port based on the relationship between the liquid bulk cargo control management coefficient and a preset liquid bulk cargo control management coefficient.

[0057] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0058] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0059] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A liquid bulk cargo management method based on big data, characterized in that: include: Determine a liquid cargo control point at the port, obtain multiple historical liquid cargo pick-up time nodes of the liquid cargo control point at the port, and obtain multiple historical liquid cargo pick-up information corresponding to each historical liquid cargo pick-up time node; Analyze each historical liquid cargo pickup information, and calculate the single-dimensional historical liquid cargo variable wave factor corresponding to each historical liquid cargo pickup time node based on the analysis result; Clustering all single-dimensional historical liquid cargo wave variation factors to determine a historical liquid cargo wave variation factor cluster, dividing the historical liquid cargo wave variation factor cluster into a plurality of factor data slices, and calculating the multi-dimensional historical liquid cargo wave variation factor of the historical liquid cargo pickup time node according to the factor data slices; Processing all multidimensional historical liquid cargo wave variation factors, determining a plurality of multidimensional historical liquid cargo wave variation coefficients based on the processing results, and calculating the liquid bulk cargo control management coefficient of the liquid cargo control point of the port according to all the multidimensional historical liquid cargo wave variation coefficients; Based on the relationship between the liquid bulk cargo control management coefficient and a preset liquid bulk cargo control management coefficient, a cargo control management reminder is generated for the liquid cargo control point of the port.

2. The method for managing liquid bulk cargo based on big data according to claim 1, characterized in that: When analyzing each historical liquid cargo pickup information and calculating the single-dimensional historical liquid cargo variable wave factor corresponding to each historical liquid cargo pickup time node based on the analysis result, it includes: Determine the historical liquid cargo pick-up quantity and the historical liquid cargo inventory corresponding to each historical liquid cargo pick-up information; A single-dimensional historical liquid cargo variation factor corresponding to the historical liquid cargo pickup time node is calculated according to the historical liquid cargo pickup quantity and the historical liquid cargo inventory.

3. The method for managing liquid bulk cargo based on big data according to claim 2, characterized in that: When calculating the single-dimensional historical liquid cargo variable wave factor corresponding to the historical liquid cargo delivery time node according to the historical liquid cargo delivery volume and the historical liquid cargo inventory, it includes: The one-dimensional historical liquid cargo variable wave factor corresponding to the historical liquid cargo delivery time node is calculated according to the following formula: ； Among them, q is the one-dimensional historical liquid cargo variable wave factor corresponding to the historical liquid cargo pick-up time node, w is the calculation coefficient corresponding to the historical liquid cargo pick-up time node, and the value range is [0.1,1.9], e is a constant, r is the historical liquid cargo pick-up volume, and r1 is the historical liquid cargo inventory.

4. The method for managing liquid bulk cargo based on big data according to claim 1, characterized in that: When the historical liquid cargo variation factor cluster is divided into a plurality of factor data slices, and the multi-dimensional historical liquid cargo variation factor of the historical liquid cargo pickup time node is calculated according to the factor data slices, it includes: Calculate the wave factor mean of all single-dimensional historical liquid cargo wave factor variations, divide the historical liquid cargo wave factor clusters according to the wave factor mean, and when the single-dimensional historical liquid cargo wave factor variation is less than the wave factor mean, divide the corresponding single-dimensional historical liquid cargo wave factor variation into a low wave factor data slice; Calculate the low-wave factor data slice coefficient of the historical liquid cargo pickup time node according to the low-wave factor data slice; ； Among them, t1 is the coefficient of the low-wave factor data slice of the historical liquid cargo delivery time node, y1 is the number of single-dimensional historical liquid cargo variable wave factors in the low-wave factor data slice, p is the mean value of the variable wave factor, and a u is the u-th single-dimensional historical liquid cargo variable wave factor in the low wave factor data slice, For all The minimum value in ; When the single-dimensional historical liquid cargo wave variation factor is greater than or equal to the wave variation factor mean value, the corresponding single-dimensional historical liquid cargo wave variation factor is divided into a high wave factor data slice; Calculating the high wave factor data slice coefficient of the historical liquid cargo delivery time node according to the high wave factor data slice; ； Among them, t2 is the coefficient of the high-wave factor data slice of the historical liquid cargo delivery time node, y2 is the number of single-dimensional historical liquid cargo variable wave factors in the high-wave factor data slice, and s i is the i-th single-dimensional historical liquid cargo variable wave factor in the high wave factor data slice, For all The minimum value in ; Randomly cross-pair the single-dimensional historical liquid cargo wave variation factors on the low wave factor data slice and the high wave factor data slice to obtain high and low historical liquid cargo wave variation factor groups, wherein each high and low historical liquid cargo wave variation factor group includes two single-dimensional historical liquid cargo wave variation factors; Calculate the cross high and low factor coefficients of the historical liquid cargo delivery time node according to the high and low historical liquid cargo variable wave factor group; ； Among them, t3 is the cross-high and low factor coefficient of the historical liquid cargo delivery time node, y3 is the number of high and low historical liquid cargo variable wave factor groups, and f d is the high unidimensional historical liquid cargo variable wave factor in the dth high and low historical liquid cargo variable wave factor group, g d is the low single-dimensional historical liquid cargo variable wave factor in the dth high and low historical liquid cargo variable wave factor group; The multi-dimensional historical liquid cargo variable wave factor of the historical liquid cargo pick-up time node is calculated according to the low wave factor data slice coefficient, the high wave factor data slice coefficient and the cross high and low factor coefficient.

5. The method for managing liquid bulk cargo based on big data according to claim 4, characterized in that: When the multi-dimensional historical liquid cargo variable wave factor of the historical liquid cargo delivery time node is calculated according to the low wave factor data slice coefficient, the high wave factor data slice coefficient and the cross high and low factor coefficient, it includes: The multi-dimensional historical liquid cargo variation factor of the historical liquid cargo pick-up time node is calculated according to the following formula: ； Among them, h is the multi-dimensional historical liquid cargo variable wave factor of the historical liquid cargo pick-up time node, k1 is the first calculation coefficient, k2 is the second calculation coefficient, k1>0, k2>0.

6. The method for managing liquid bulk cargo based on big data according to claim 1, characterized in that: When all multi-dimensional historical liquid cargo variable wave factors are processed and multiple multi-dimensional historical liquid cargo variable wave coefficients are determined based on the processing results, it includes: Combining each c multi-dimensional historical liquid cargo wave variation factors to obtain a multi-dimensional historical liquid cargo wave variation factor set; The multi-dimensional historical liquid cargo wave variation factors in each multi-dimensional historical liquid cargo wave variation factor set are summed up to obtain a corresponding multi-dimensional historical liquid cargo wave variation factor sum value; Determine the maximum multidimensional historical liquid cargo variation factor in the multidimensional historical liquid cargo variation factor set, and determine the ratio of the multidimensional historical liquid cargo variation factor sum to the maximum multidimensional historical liquid cargo variation factor as the multidimensional historical liquid cargo variation coefficient of the multidimensional historical liquid cargo variation factor set.

7. The method for managing liquid bulk cargo based on big data according to claim 6, characterized in that: When calculating the liquid bulk cargo control management coefficient of the liquid cargo control point of the port based on all multi-dimensional historical liquid cargo variable wave coefficients, it includes: Randomly extracting the first multidimensional historical liquid cargo variable wave factor and value and the first multidimensional historical liquid cargo variable wave coefficient corresponding to the first multidimensional historical liquid cargo variable wave factor set; Randomly extract the second multidimensional historical liquid cargo variable wave factor and value corresponding to the second multidimensional historical liquid cargo variable wave factor set and the second multidimensional historical liquid cargo variable wave coefficient; Calculate the absolute value of the multidimensional historical liquid cargo variation factor difference between the first multidimensional historical liquid cargo variation factor sum value and the second multidimensional historical liquid cargo variation factor sum value; Extracting the maximum multidimensional historical liquid cargo wave variation factor and value and the minimum multidimensional historical liquid cargo wave variation factor and value from all multidimensional historical liquid cargo wave variation factor and value, and calculating the multidimensional historical liquid cargo wave variation factor extreme difference value between the maximum multidimensional historical liquid cargo wave variation factor and value and the minimum multidimensional historical liquid cargo wave variation factor and value; Calculate the ratio of the absolute value of the multi-dimensional historical liquid cargo wave variation factor difference and the extreme difference of the multi-dimensional historical liquid cargo wave variation factor as the first coefficient to be calculated; Calculate the absolute value of the difference between the first multidimensional historical liquid cargo wave coefficient and the second multidimensional historical liquid cargo wave coefficient; Extracting the maximum multidimensional historical liquid cargo wave coefficient and the minimum multidimensional historical liquid cargo wave coefficient from all multidimensional historical liquid cargo wave coefficients, and calculating the multidimensional historical liquid cargo wave coefficient extreme difference between the maximum multidimensional historical liquid cargo wave coefficient and the minimum multidimensional historical liquid cargo wave coefficient; Calculate the ratio of the absolute value of the multi-dimensional historical liquid cargo wave coefficient difference to the multi-dimensional historical liquid cargo wave coefficient extreme difference as the second coefficient to be calculated; Determine a sum of the first coefficient to be calculated and the second coefficient to be calculated as a comprehensive coefficient to be calculated; Extract and calculate the remaining multi-dimensional historical liquid cargo variable wave factor set to obtain multiple comprehensive coefficients to be calculated; The liquid bulk cargo control management coefficient of the liquid cargo control point of the port is calculated based on all the comprehensive coefficients to be calculated.

8. The method for managing liquid bulk cargo based on big data according to claim 7, characterized in that: When calculating the liquid bulk cargo control management coefficient of the liquid cargo control point of the port based on all the comprehensive coefficients to be calculated, it includes: Determine the maximum comprehensive coefficient to be calculated and the minimum comprehensive coefficient to be calculated from all comprehensive coefficients to be calculated; Calculate the liquid bulk cargo control management coefficient of the port liquid cargo control point according to the maximum comprehensive coefficient to be calculated and the minimum comprehensive coefficient to be calculated; ； Where v is the liquid bulk cargo control management coefficient of the port liquid cargo control point, j is the number of comprehensive coefficients to be calculated, and n b is the bth comprehensive coefficient to be calculated, n min is the minimum comprehensive coefficient to be calculated, n max is the maximum comprehensive coefficient to be calculated.

9. The method for managing liquid bulk cargo based on big data according to claim 1, characterized in that: When a cargo control management reminder is generated for the liquid cargo control point of the port based on the relationship between the liquid bulk cargo control management coefficient and the preset liquid bulk cargo control management coefficient, it includes: When the liquid bulk cargo control management coefficient is less than the preset liquid bulk cargo control management coefficient, a replenishment reminder is generated for the port liquid cargo control point; When the liquid bulk cargo control management coefficient is greater than or equal to the preset liquid bulk cargo control management coefficient, a stop replenishment reminder is generated for the port liquid cargo control point.

10. A liquid bulk cargo management system based on big data, applied to the liquid bulk cargo management method based on big data as claimed in any one of claims 1 to 9, characterized in that: include: An information acquisition module is used to determine a port liquid cargo control point, obtain a plurality of historical liquid cargo pick-up time nodes of the port liquid cargo control point, and obtain a plurality of historical liquid cargo pick-up information corresponding to each historical liquid cargo pick-up time node; A single-dimensional calculation module is used to analyze each historical liquid cargo pickup information, and calculate the single-dimensional historical liquid cargo variable wave factor corresponding to each historical liquid cargo pickup time node based on the analysis result; A multi-dimensional calculation module, for clustering all single-dimensional historical liquid cargo wave variation factors, determining a historical liquid cargo wave variation factor cluster, dividing the historical liquid cargo wave variation factor cluster into a plurality of factor data slices, and calculating the multi-dimensional historical liquid cargo wave variation factor of the historical liquid cargo pick-up time node according to the factor data slices; A coefficient calculation module, for processing all multi-dimensional historical liquid cargo variable wave factors, determining a plurality of multi-dimensional historical liquid cargo variable wave coefficients based on the processing results, and calculating the liquid bulk cargo control management coefficient of the liquid cargo control point of the port according to all the multi-dimensional historical liquid cargo variable wave coefficients; The cargo management module is used to generate a cargo control management reminder for the liquid cargo control point of the port based on the relationship between the liquid bulk cargo control management coefficient and a preset liquid bulk cargo control management coefficient.

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