Method and device for allocating and transporting petroleum products and storage medium

Abstract

The invention discloses a petroleum product dispatching method and device and a storage medium, and the method comprises the steps: determining the sales prediction data of petroleum products in a target time period according to the historical sales data of the petroleum products of all terminal stations in a to-be-detected region; substituting the parameter data of the petroleum product and the sales volume prediction data of the petroleum product in the target time period into the matching function to obtain library station matching data; generating an initial transportation plan based on the parameter data of the petroleum product, the sales volume prediction data of the petroleum product in the target time period and the library station matching data; genetic operation is conducted on the initial transportation plan, the transportation plan with the minimum cost is determined as a target transportation plan, and the target transportation plan comprises the target oil refinery number, the target oil depot number, the target petroleum product number, the replenishment time, the transportation mode, the replenishment amount and the arrival time. According to the method, the supply stability of the petroleum products in the oil depots and the gas stations in the target area can be ensured while the cost is minimized.

Description

Technical Field

[0001] This application relates to the technical field of oil product transportation and distribution, and particularly relates to a method, device, and storage medium for transporting and distributing oil products. Background Art

[0002] Since the prices and demands of oil products will constantly change, oil companies need to plan the distribution and transportation of oil products to ensure the stable supply of oil depots and gas stations in the target area, and at the same time determine the inventory level according to price changes to reduce inventory costs.

[0003] In the related art, oil companies usually distribute and transport oil products to each gas station and oil depot based on the personal experience of relevant personnel. However, this method relies too much on personal experience, has poor accuracy, cannot ensure the supply stability of oil depots and gas stations in the target area, and cannot minimize the cost of oil product inventory while meeting the demand. Summary of the Invention

[0004] In view of this, this application provides a method, device, and storage medium for transporting and distributing oil products, which can ensure the supply stability of oil products in oil depots and gas stations in the target area while minimizing costs.

[0005] Specifically, the following technical solutions are included:

[0006] In a first aspect, an embodiment of this application provides a method for transporting and distributing oil products, and the method includes:

[0007] Determine the sales forecast data of oil products in the target time period according to the historical sales data of oil products at each terminal station in the area to be measured;

[0008] Substitute the parameter data of the oil products and the sales forecast data of the oil products in the target time period into a matching function to obtain depot-station matching data;

[0009] Generate an initial transportation plan based on the parameter data of the oil products, the sales forecast data of the oil products in the target time period, and the depot-station matching data;

[0010] Perform a genetic operation on the initial transportation plan to determine the transportation plan with the minimum cost as the target transportation plan, and the target transportation plan includes the target refinery number, target oil depot number, target oil product number, replenishment time, transportation method, replenishment quantity, and arrival time.

[0011] In some embodiments, the determining the sales forecast data of oil products in the target time period according to the historical sales data of oil products at each terminal station in the area to be measured includes:

[0012] Determine the sales volume parameter based on the historical sales volume data of petroleum products at each terminal station within the area to be measured;

[0013] Substitute the sales volume parameters into the predicted sales volume formula and the calculation formula for the smoothed value and trend term respectively, and calculate the predicted sales volume data of petroleum products within the target time period.

[0014] In some embodiments, the generating the initial transportation plan based on the parameter data of the petroleum products, the predicted sales volume data of the petroleum products within the target time period, and the depot-station matching data includes:

[0015] Determine the plan generation parameters based on the parameter data of the petroleum products, the predicted sales volume data of the petroleum products within the target time period, and the depot-station matching data;

[0016] Construct a multi-layer chromosome framework for the plan generation parameters to obtain the initial transportation plan.

[0017] In some embodiments, the plan generation parameters include:

[0018] The set of refineries L = {1, 2,..., L}, l ∈ L; the set of depots with demand M = {1, 2,..., M}, m ∈ M; the set of gas stations with demand N = {1, 2,..., N}, n ∈ N; the set of petroleum product types P = {1, 2,..., P}, p ∈ P; the planning period D, a specific day is represented by d, D = {1, 2,..., D}, d ∈ D; the demand R of gas station n for petroleum product p on the dth day npd ; the unit secondary transportation cost E of petroleum product p from depot m to gas station n mnp ; the unit transportation cost E of petroleum product p from refinery l to gas station n lnp ; variable If the petroleum product p of gas station n can be delivered by depot m, then the variable is 1, otherwise it is 0; variable If the petroleum product p of gas station n can be directly delivered by refinery l, then the variable is 1, otherwise it is 0; the maximum delivery volume of petroleum product p of depot m The minimum delivery volume of petroleum product p of depot m The unit secondary shipping cost F of petroleum product p of depot m mp ; the delivery volume limit DF of petroleum product p of refinery l on the dth day lpd ; the minimum turnover volume of petroleum product p of depot m The maximum turnover volume of petroleum product p of depot m The unit storage cost C of petroleum product p of depot m mp ; the unit primary shipping cost F of petroleum product p of refinery llp ; The unit in-transit freight for the refinery l to ship the petroleum product p to the oil depot m via transportation mode a The unit one-time receiving cost S of the petroleum product p at the oil depot m mp ; The oil depot opening cost K of the petroleum product p at the oil depot m mp ; At the oil depot m on the d th day, the direct sales volume XL of the petroleum product p mpd ; At the oil depot m on the dth day in the future, the out-of-storage volume V of the petroleum product p mpd ; The initial inventory I of the petroleum product p at the oil depot m mp0 , that is, the inventory in the early morning of the first day of this period; The safety tank capacity of the petroleum product p at the oil depot m The non-payable volume b of the petroleum product p at the oil depot m mp ; The minimum ending inventory of the petroleum product p at the oil depot m The maximum ending inventory of the petroleum product p at the oil depot m The reasonable inventory I of the petroleum product p at the oil depot m on the dth day mpd ; The minimum unloading volume for the oil depot m to unload the petroleum product p under transportation mode a The maximum unloading volume for the oil depot m to unload the petroleum product p under transportation mode a The minimum batch quantity of the petroleum product p transported from the refinery l to the oil depot m by railway The maximum batch quantity of the petroleum product p transported from the refinery l to the oil depot m by railway The in-transit time for the refinery l to ship the petroleum product p to the oil depot m via transportation mode a The shipment volume of the petroleum product p transported from the refinery l to the oil depot m via transportation mode a in the previous period The time window [t ls , t le during which the refinery l can ship; The time window [t ms , t me during which the oil depot m can unload; Transportation mode a, when a = 0, represents railway transportation, and when a = 1, represents pipeline transportation; The shipment volume Q of the petroleum product p from the oil depot m to the gas station n on the dth day mnpd ; The shipment volume O of the petroleum product p from the refinery l to the gas station n on the dth day lnpd ; Variable, when the petroleum product p is shipped from the refinery l to the oil depot m, the variable is 1, otherwise 0; Variable, if the petroleum product p transported from the refinery l to the oil depot m arrives at the depot on the dth day, then the variable is 1, otherwise 0; Variable, if the petroleum product p transported from the refinery l to the oil depot m in the previous planning period and not arriving at the depot in the current period arrives at the depot on the dth day of this planning period, then the variable is 1, otherwise 0; The replenishment volume of the petroleum product p at the oil depot m via transportation mode a on the dth day

[0019] In some embodiments, the genetic operation includes a fitness function, the fitness function includes a cost minimization function and cost minimization constraint conditions, and the step of performing genetic operations on the initial transportation plan to determine the transportation plan with the minimum cost as the target transportation plan includes:

[0020] Performing genetic operations on the initial transportation plan according to the roulette method, two-point crossover method, and binary mutation method to obtain a plurality of intermediate transportation plans;

[0021] Based on the cost minimization function and the cost minimization constraint conditions, determining the intermediate transportation plan with the minimum cost as the target transportation plan.

[0022] In some embodiments, the method further includes:

[0023] Generating an initial inventory based on the parameter data of the petroleum products, the sales volume prediction data of the petroleum products within the target time period, and the depot-station matching data;

[0024] Performing genetic operations on the initial inventory to determine the inventory with the minimum cost as the target inventory.

[0025] In some embodiments, the genetic operation includes a fitness function, the fitness function includes a cost minimization function and cost minimization constraint conditions, and the step of performing genetic operations on the initial inventory to determine the inventory with the minimum cost as the target inventory includes:

[0026] Performing genetic operations on the initial inventory according to the roulette method, two-point crossover method, and binary mutation method to obtain a plurality of intermediate inventories;

[0027] Based on the cost minimization function and the cost minimization constraint conditions, determining the intermediate inventory with the minimum cost as the target inventory.

[0028] In some embodiments, the cost minimization function includes a gas station cost minimization function and an oil depot cost minimization function. The gas station cost minimization function is:

[0029]

[0030] The oil depot cost minimization function is:

[0031]

[0032] Where Z 1 is the gas station cost, Z 2Let \(C\) be the oil depot cost, \(L\) be the set of refineries, \(L = \{1, 2, \ldots, L\}\), \(l\in L\), \(M\) be the set of oil depots with demand, \(M = \{1, 2, \ldots, M\}\), \(m\in M\), \(N\) be the set of gas stations with demand, \(N = \{1, 2, \ldots, N\}\), \(n\in N\), \(P\) be the set of petroleum product types, \(P = \{1, 2, \ldots, P\}\), \(p\in P\), \(D\) be the planning period, and a specific day be denoted by \(d\), \(D = \{1, 2, \ldots, D\}\), \(d\in D\). It is \(1\) when the petroleum product \(p\) at gas station \(n\) can be delivered by oil depot \(m\), and \(0\) otherwise. It is \(1\) when the petroleum product \(p\) at gas station \(n\) can be directly delivered by refinery \(l\), and \(0\) otherwise, \(E\) mnp , \(E\) is the unit secondary transportation cost of petroleum product \(p\) from oil depot \(m\) to gas station \(n\). lnp , \(F\) is the unit transportation cost of petroleum product \(p\) from refinery \(l\) to gas station \(n\). mp , \(F\) is the unit secondary shipping cost of petroleum product \(p\) at oil depot \(m\). lp , is the unit primary shipping cost of petroleum product \(p\) at refinery \(l\). , \(S\) is the unit in-transit freight of shipping petroleum product \(p\) from refinery \(l\) to oil depot \(m\) by transportation mode \(a\). mp , \(I\) is the unit primary receiving cost of petroleum product \(p\) at oil depot \(m\). mpd , \(C\) is the reasonable inventory of petroleum product \(p\) at oil depot \(m\) on day \(d\). mp , \(Q\) is the unit storage cost of petroleum product \(p\) at oil depot \(m\). mnpd , \(O\) is the shipping volume of petroleum product \(p\) from oil depot \(m\) to gas station \(n\) on day \(d\). lnpd , is the shipping volume of petroleum product \(p\) from refinery \(l\) to gas station \(n\) on day \(d\). It is \(1\) when petroleum product \(p\) is shipped from refinery \(l\) to oil depot \(m\), and \(0\) otherwise. , is the replenishment volume of petroleum product \(p\) at oil depot \(m\) by transportation mode \(a\) on day \(d\).

[0033] The cost minimization function is as follows:

[0034] \(\min Z=Z\) 1 +Z 2 ;

[0035] where \(Z\) represents the sum of the gas station cost \(Z\) 1 and the oil depot cost \(Z\) 2 .

[0036] In some embodiments, the cost minimization constraint conditions are as follows:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] or 1,

[0049] or 1,

[0050]

[0051]

[0052]

[0053] or 1,

[0054] where L is the set of refineries, L = {1, 2,..., L}, l ∈ L; M is the set of depots with demands, M = {1, 2,..., M}, m ∈ M; N is the set of gas stations with demands, N = {1, 2,..., N}, n ∈ N; P is the set of petroleum product types, P = {1, 2,..., P}, p ∈ P; D is the planning period, and a specific day is represented by d, D = {1, 2,..., D}, d ∈ D, R npd is the demand of gas station n for petroleum product p on day d, is 1 when the petroleum product p at gas station n is directly delivered by refinery l, otherwise 0, is the maximum delivery volume of petroleum product p at depot m, is the minimum delivery volume of petroleum product p at depot m, DF lp is the delivery volume limit of petroleum product p at refinery l, XL mpd is the direct sales volume of petroleum product p at depot m on day d, V mpdis the outbound volume of petroleum product p from oil depot m on the d-th day in the future, I mp0 is the initial inventory of petroleum product p in oil depot m, that is, the inventory at the beginning of the current period on the morning of the first day of this period, is the safety tank capacity of petroleum product p in oil depot m, b mp is the non-payable volume of petroleum product p in oil depot m, is the minimum ending inventory of petroleum product p in oil depot m, is the maximum ending inventory of petroleum product p in oil depot m, I mpd is the reasonable inventory of petroleum product p in oil depot m on the d-th day, is the minimum unloading volume of petroleum product p received by oil depot m in transportation mode a, is the maximum unloading volume of petroleum product p received by oil depot m in transportation mode a, is the minimum batch volume of petroleum product p transported from refinery l to oil depot m by railway, is the maximum batch volume of petroleum product p transported from refinery l to oil depot m by railway, is the in-transit time of petroleum product p shipped from refinery l to oil depot m by transportation mode a, is the shipment volume of petroleum product p shipped from refinery l to oil depot m by transportation mode a in the previous period, [t ls ,t le is the time window during which refinery l can ship goods, and are the actual start time and end time of refinery l's oil shipment respectively, [t ms ,t me is the time window during which oil depot m can receive goods, and are the actual start time and end time of oil depot m's oil unloading respectively, a is the transportation mode. When a = 0, it represents railway transportation. When a = 1, it represents pipeline transportation, Q mnpd is the shipment volume of petroleum product p from oil depot m to gas station n on the d-th day, O lnpd is the shipment volume of petroleum product p from refinery l to gas station n on the d-th day, is 1 when petroleum product p is shipped from refinery l to oil depot m, otherwise it is 0, is 1 when the petroleum product p transported from refinery l to oil depot m arrives at the depot on the d-th day, otherwise it is 0; if the petroleum product p transported from refinery l to oil depot m in the previous planning period and not arriving at the depot in the current period arrives at the depot on the d-th day of this planning period is 1, otherwise it is 0; is the replenishment volume of petroleum product p in oil depot m on the d-th day through transportation mode a.

[0055] Second, the embodiments of the present application also provide a dispatching device for petroleum products. The device includes:

[0056] A first determination module, configured to determine predicted sales data of petroleum products in a target time period according to historical sales data of petroleum products of each terminal station in a region to be measured.

[0057] An obtaining module, configured to substitute parameter data of petroleum products and the predicted sales data of petroleum products in the target time period into a matching function to obtain depot-station matching data.

[0058] A generating module, configured to generate an initial transportation plan based on the parameter data of the petroleum products, the predicted sales data of the petroleum products in the target time period, and the depot-station matching data.

[0059] A second determination module, configured to perform genetic operations on the initial transportation plan to determine that the transportation plan with the minimum cost is the target transportation plan, where the target transportation plan includes a target refinery number, a target oil depot number, a target petroleum product number, a replenishment time, a transportation mode, a replenishment quantity, and an arrival time.

[0060] In a third aspect, an embodiment of the present application further provides a non-volatile readable storage medium, in which at least one program is stored, and the at least one program is loaded and executed by a processor to implement the petroleum product transportation method according to any embodiment of the first aspect.

[0061] The beneficial effects of the technical solution provided by the embodiment of the present application at least include:

[0062] For the petroleum product transportation method provided by the embodiment of the present application, first, the predicted sales data of petroleum products in the target time period is determined through the historical sales data of each terminal station in the region to be measured, then the parameter data of the petroleum products and the predicted sales data of the petroleum products in the target time period are substituted into the matching function to obtain the depot-station matching data, and then an initial transportation plan is generated according to the parameter data of the petroleum products, the predicted sales data, and the depot-station matching data, and genetic operations are performed on the initial transportation plan to determine that the transportation plan with the minimum cost is the target transportation plan. This method can obtain a target transportation plan including a target refinery, a target oil depot, a target petroleum product, a replenishment time, a transportation mode, a replenishment quantity, and an arrival time, replacing the method in the prior art of relying on the personal experience of relevant personnel to allocate the transportation of petroleum products to each gas station and oil depot, ensuring the supply stability of petroleum products in the oil depot and gas station in the target area while minimizing the cost. Description of the Drawings

[0063] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0064] Figure 1 It is a flowchart of a method for transporting petroleum products provided by an embodiment of the present application;

[0065] Figure 2 It is a flowchart of another method for transporting petroleum products provided by an embodiment of the present application;

[0066] Figure 3 It is a flowchart of a method for determining the predicted sales volume data of petroleum products within a target time period according to the historical sales volume data of petroleum products at each terminal station in a method for transporting petroleum products provided by an embodiment of the present application;

[0067] Figure 4 It is a flowchart of a method for generating an initial transportation plan based on the parameter data of petroleum products, the predicted sales volume data of petroleum products within a target time period, and the depot-station matching data in a method for transporting petroleum products provided by an embodiment of the present application;

[0068] Figure 5 It is a schematic diagram of a multi-layer chromosome framework provided by an embodiment of the present application;

[0069] Figure 6 It is a flowchart of an algorithm for genetic operations provided by an embodiment of the present application;

[0070] Figure 7 It is a structural diagram of a population chromosome feature matrix provided by an embodiment of the present application;

[0071] Figure 8 It is a flowchart of a method for performing genetic operations on an initial transportation plan to determine the transportation plan with the minimum cost as the target transportation plan in a method for transporting petroleum products provided by an embodiment of the present application;

[0072] Figure 9 It is a flowchart of an algorithm for performing genetic operations on an initial transportation plan according to the roulette wheel method, the two-point crossover method, and the binary mutation method provided by an embodiment of the present application;

[0073] Figure 10 It is a flowchart of a method for performing genetic operations on an initial inventory to determine the inventory with the minimum cost as the target inventory in a method for transporting petroleum products provided by an embodiment of the present application;

[0074] Figure 11Schematic structural diagram of a transportation device for petroleum products provided by an embodiment of the present application.

[0075] Through the above-mentioned drawings, specific embodiments of the present application have been shown, and more detailed descriptions will be given hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed implementation manners

[0076] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without making creative efforts shall fall within the protection scope of the present application.

[0077] Unless otherwise defined, all technical terms used in the embodiments of the present application have the same meaning as commonly understood by those of ordinary skill in the art.

[0078] To make the technical solutions and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0079] Since the prices and demands of petroleum products will change continuously, petroleum companies need to plan the distribution and transportation of petroleum products. Through direct distribution from refineries and transfer distribution through oil depots, the supply stability of oil depots and gas stations in the target area can be ensured. At the same time, the inventory level is determined according to price changes to reduce inventory costs and maximize economic benefits.

[0080] In the related art, petroleum companies usually formulate transportation plans for distributing and transporting petroleum products to each gas station and oil depot based on the personal experience of relevant personnel. However, this method of formulating transportation plans relies too much on personal experience, has poor accuracy, cannot ensure the supply stability of oil depots and gas stations in the target area, and cannot minimize the cost of the inventory of petroleum products while meeting the demand.

[0081] To solve the technical problems existing in the related art, the embodiments of the present application provide a transportation method for petroleum products, which can ensure the supply stability of petroleum products in oil depots and gas stations in the target area while minimizing costs.

[0082] Figure 1 Flowchart of a transportation method for petroleum products provided by an embodiment of the present application. Refer to Figure 1 The method is applied to a terminal installed with a planning system for the transportation method of petroleum products. The method includes the following steps:

[0083] Step 101: Determine the sales forecast data of petroleum products within the target time period based on the historical sales data of petroleum products at each terminal station in the area to be measured.

[0084] Step 102: Substitute the parameter data of petroleum products and the sales forecast data of petroleum products within the target time period into the matching function to obtain the depot-station matching data.

[0085] Step 103: Generate an initial transportation plan based on the parameter data of petroleum products, the sales forecast data of petroleum products within the target time period, and the depot-station matching data.

[0086] Step 104: Perform genetic operations on the initial transportation plan to determine that the transportation plan with the minimum cost is the target transportation plan. The target transportation plan includes the target refinery number, the target oil depot number, the target petroleum product number, the replenishment time, the transportation method, the replenishment quantity, and the arrival time.

[0087] Therefore, for the petroleum product transportation method provided in the embodiments of the present application, first, the sales forecast data of petroleum products within the target time period is determined through the historical sales data of each terminal station in the area to be measured. Then, the parameter data of petroleum products and the sales forecast data of petroleum products within the target time period are substituted into the matching function to obtain the depot-station matching data. Next, an initial transportation plan is generated based on the parameter data of petroleum products, the sales forecast data, and the depot-station matching data, and genetic operations are performed on the initial transportation plan to determine that the transportation plan with the minimum cost is the target transportation plan. This method can obtain a target transportation plan including the target refinery, the target oil depot, the target petroleum product, the replenishment time, the transportation method, the replenishment quantity, and the arrival time, replacing the method in the prior art that relies on the personal experience of relevant personnel to allocate the transportation of petroleum products to each gas station and oil depot, ensuring the supply stability of petroleum products in the oil depots and gas stations within the target area while minimizing costs.

[0088] In some embodiments, determining the sales forecast data of petroleum products within the target time period based on the historical sales data of petroleum products at each terminal station in the area to be measured includes:

[0089] Determine the sales parameters based on the historical sales data of petroleum products at each terminal station in the area to be measured;

[0090] Substitute the sales parameters into the predicted sales formula and the smoothing value and trend term calculation formula respectively to calculate the sales forecast data of petroleum products within the target time period.

[0091] In some embodiments, generating an initial transportation plan based on the parameter data of petroleum products, the sales forecast data of petroleum products within the target time period, and the depot-station matching data includes:

[0092] Based on the parameter data of petroleum products, the sales volume prediction data of petroleum products within the target time period, and the depot-station matching data, determine the parameter for generating the plan;

[0093] Construct a multi-layer chromosome framework for the parameter for generating the plan to obtain the initial transportation plan.

[0094] In some embodiments, the parameter for generating the plan includes:

[0095] The set of refineries L = {1, 2,..., L}, l ∈ L; the set of depots with demand M = {1, 2,..., M}, m ∈ M; the set of gas stations with demand N = {1, 2,..., N}, n ∈ N; the set of petroleum product types P = {1, 2,..., P}, p ∈ P; the planning period D, a specific day is represented by d, D = {1, 2,..., D}, d ∈ D; the demand R of gas station n for petroleum product p on the d-th day npd ; the unit secondary transportation cost E of petroleum product p from depot m to gas station n mnp ; the unit transportation cost E of petroleum product p from refinery l to gas station n lnp ; variables If the petroleum product p of gas station n can be delivered by depot m, then the variable is 1, otherwise 0; variable If the petroleum product p of gas station n can be directly delivered by refinery l, then the variable is 1, otherwise 0; the maximum delivery volume of petroleum product p of depot m The minimum delivery volume of petroleum product p of depot m The unit secondary shipping cost F of petroleum product p of depot m mp ; the delivery volume limit DF of petroleum product p of refinery l on the d-th day lpd ; the minimum turnover volume of petroleum product p of depot m The maximum turnover volume of petroleum product p of depot m The unit storage cost C of petroleum product p of depot m mp ; the unit primary shipping cost F of petroleum product p of refinery l lp ; the unit in-transit freight of transporting petroleum product p from refinery l to depot m by transportation mode a The unit primary receiving cost S of petroleum product p of depot m mp ; the depot opening cost K of petroleum product p of depot m mp ; the direct sales volume XL of petroleum product p of depot m on the d-th day mpd ; the outbound volume V of petroleum product p of depot m on the future d-th day mpd ; the initial inventory I of petroleum product p of depot m mp0 , that is, the inventory in the morning of the first day of this period; the safety tank capacity of petroleum product p of depot m Unpayable quantity b of petroleum product p in oil depot m mp ; Minimum ending inventory of petroleum product p in oil depot m Maximum ending inventory of petroleum product p in oil depot m Reasonable inventory I of petroleum product p in oil depot m on the d-th day mpd ; Minimum unloading quantity of petroleum product p received by oil depot m under transportation mode a Maximum unloading quantity of petroleum product p received by oil depot m under transportation mode a Minimum batch quantity of petroleum product p transported from refinery l to oil depot m by rail Maximum batch quantity of petroleum product p transported from refinery l to oil depot m by rail In-transit time of petroleum product p shipped from refinery l to oil depot m under transportation mode a Shipped quantity of petroleum product p shipped from refinery l to oil depot m by transportation mode a in the previous period Time window [t ls , t le during which refinery l can ship; Time window [t ms , t me during which oil depot m can receive; Transportation mode a, when a = 0, represents rail transportation, and when a = 1, represents pipeline transportation; Shipment quantity Q of petroleum product p from oil depot m to gas station n on the d-th day mnpd ; Shipment quantity O of petroleum product p from refinery l to gas station n on the d-th day lnpd ; Variable, when petroleum product p is shipped from refinery l to oil depot m, the variable is 1, otherwise 0; Variable, if the petroleum product p transported from refinery l to oil depot m arrives at the depot on the d-th day, the variable is 1, otherwise 0; Variable, if the petroleum product p transported from refinery l to oil depot m in the previous planning period that did not arrive in the current period arrives at the depot on the d-th day of this planning period, the variable is 1, otherwise 0; Replenishment quantity of petroleum product p in oil depot m on the d-th day through transportation mode a

[0096] In some embodiments, the genetic operation includes a fitness function, and the fitness function includes a cost minimization function and cost minimization constraint conditions. Conducting genetic operations on the initial transportation plan and determining the transportation plan with the minimum cost as the target transportation plan includes:

[0097] Conducting genetic operations on the initial transportation plan according to the roulette wheel method, two-point crossover method, and binary mutation method to obtain multiple intermediate transportation plans;

[0098] Based on the cost minimization function and cost minimization constraint conditions, determining the intermediate transportation plan with the minimum cost as the target transportation plan.

[0099] In some embodiments, the method further includes:

[0100] generating an initial inventory based on the parameter data of the petroleum product, the sales volume prediction data of the petroleum product within the target time period, and the depot-station matching data;

[0101] performing a genetic operation on the initial inventory, and determining the inventory with the minimum cost as the target inventory.

[0102] In some embodiments, the genetic operation includes a fitness function, the fitness function includes a cost minimization function and a cost minimization constraint condition, and performing a genetic operation on the initial inventory and determining the inventory with the minimum cost as the target inventory includes:

[0103] performing a genetic operation on the initial inventory according to the roulette wheel method, the two-point crossover method, and the binary mutation method to obtain multiple intermediate inventories;

[0104] determining the intermediate inventory with the minimum cost as the target inventory based on the cost minimization function and the cost minimization constraint condition.

[0105] In some embodiments, the cost minimization function includes a gas station cost minimization function and an oil depot cost minimization function, and the gas station cost minimization function is:

[0106]

[0107] The oil depot cost minimization function is:

[0108]

[0109] where Z 1 is the gas station cost, Z 2 is the oil depot cost, L is the set of refineries, L = {1, 2,..., L}, l ∈ L, M is the set of oil depots with demand, M = {1, 2,..., M}, m ∈ M, N is the set of gas stations with demand, N = {1, 2,..., N}, n ∈ N, P is the set of petroleum product types, P = {1, 2,..., P}, p ∈ P, D is the planning period, a specific day is represented by d, D = {1, 2,..., D}, d ∈ D, is 1 when the petroleum product p at gas station n can be delivered by oil depot m, otherwise 0, is 1 when the petroleum product p at gas station n can be directly delivered by refinery l, otherwise 0, E mnp , is the unit secondary transportation cost of petroleum product p from oil depot m to gas station n, E lnp , is the unit transportation cost of petroleum product p from refinery l to gas station n, F mpis the unit secondary delivery cost of petroleum product p at oil depot m, F lp is the unit delivery cost of petroleum product p from refinery l, S is the unit in-transit freight cost of transporting petroleum product p from refinery l to oil depot m by means of transportation a. mp I is the unit one-time delivery cost of petroleum product p in oil depot m, mpd is the reasonable inventory of petroleum product p at oil depot m on day d, C mp is the unit storage cost of petroleum product p in tank m, Q mnpd is the shipment volume of petroleum product p from oil depot m to gas station n on day d, O lnpd is the shipment volume of petroleum product p from refinery l to gas station n on day d, It is 1 when the petroleum product p is shipped from refinery l to oil depot m, otherwise it is 0. is the replenishment quantity of petroleum product p of tank depot m by transport mode a on day d;

[0110] The cost minimization function is:

[0111] minZ=Z 1 +Z 2 ;

[0112] Among them, Z represents the gas station cost Z 1 and the tank depot cost Z 2 sum.

[0113] In some embodiments, the cost minimization constraint is:

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] or 1,

[0126] or 1,

[0127]

[0128]

[0129]

[0130] or 1,

[0131] where L is the set of refineries, L = {1, 2,..., L}, l ∈ L; M is the set of depots with demands, M = {1, 2,..., M}, m ∈ M; N is the set of gas stations with demands, N = {1, 2,..., N}, n ∈ N; P is the set of petroleum product types, P = {1, 2,..., P}, p ∈ P; D is the planning period, and a specific day is represented by d, D = {1, 2,..., D}, d ∈ D, R npd is the demand of gas station n for petroleum product p on day d, is 1 when the petroleum product p at gas station n is directly delivered by refinery l, otherwise 0, is the maximum delivery volume of petroleum product p at depot m, is the minimum delivery volume of petroleum product p at depot m, DF lp is the delivery volume limit of petroleum product p at refinery l, XL mpd is the direct sales volume of petroleum product p at depot m on day d, V mpd is the outbound volume of petroleum product p at depot m on day d in the future, I mp0 is the initial inventory of petroleum product p at depot m, i.e., the inventory at the beginning of the first day of this period, is the safety tank capacity of petroleum product p at depot m, b mp is the non - deliverable volume of petroleum product p at depot m, is the minimum ending inventory of petroleum product p at depot m, is the maximum ending inventory of petroleum product p at depot m, I mpd is the reasonable inventory of petroleum product p at depot m on day d, is the minimum unloading volume of petroleum product p received by depot m under transportation mode a, is the maximum unloading volume of petroleum product p received by depot m under transportation mode a, The minimum batch quantity of petroleum product p transported from refinery l to oil depot m by rail The maximum batch quantity of petroleum product p transported from refinery l to oil depot m by rail The transit time for petroleum product p shipped from refinery l to oil depot m by transportation mode a The shipped quantity of petroleum product p from refinery l to oil depot m by transportation mode a in the previous period, [t ls ,t le The time window during which refinery l can ship goods and The actual start time and end time of oil shipment from refinery l, [t ms ,t me The time window during which oil depot m can receive and unload and The actual start time and end time of oil unloading at oil depot m respectively. a is the transportation mode. When a = 0, it represents rail transportation. When a = 1, it represents transportation through pipelines, Q mnpd The shipped quantity of petroleum product p from oil depot m to gas station n on the dth day, O lnpd The shipped quantity of petroleum product p from refinery l to gas station n on the dth day Is 1 when petroleum product p is shipped from refinery l to oil depot m, otherwise 0 Is 1 when the petroleum product p transported from refinery l to oil depot m arrives at the depot on the dth day, otherwise 0; For the petroleum product p transported from refinery l to oil depot m in the previous planning period that did not arrive at the depot in the current period, if it arrives at the depot on the dth day of this planning period Is 1, otherwise 0 The replenishment quantity of petroleum product p at oil depot m on the dth day by transportation mode a

[0132] Figure 2 Is the flowchart of another petroleum product transportation and allocation method provided by the embodiment of the present application. Refer to Figure 2 , This method is applied to a terminal, and a planning system for the petroleum product transportation and allocation method is installed on the terminal. This method includes the following steps:

[0133] Step 201, Determine the sales forecast data of petroleum products within the target time period according to the historical sales data of petroleum products at each terminal station within the area to be measured

[0134] Determining the sales forecast data of petroleum products within the future target time period according to the historical sales data of petroleum products at each terminal station can provide a reference for the determination of the transportation plan in the subsequent steps

[0135] In some embodiments, refer to Figure 3 , Step 201 includes the following sub-steps:

[0136] Step 2011: Determine the sales volume parameters based on the historical sales volume data of petroleum products at each terminal station within the area to be measured.

[0137] Step 2012: Substitute the sales volume parameters into the predicted sales volume formula and the calculation formula for the smoothed value and trend term respectively, and calculate the predicted sales volume data of petroleum products within the target time period.

[0138] In some embodiments, the calculation of the predicted sales volume data is implemented by the double exponential smoothing method.

[0139] It can be understood that both the predicted sales volume formula and the calculation formula for the smoothed value and trend term in Step 2012 belong to the double exponential smoothing method. Specifically, the calculation formula for the smoothed value and trend term is as follows:

[0140]

[0141] The predicted sales volume formula is as follows:

[0142] y’ t+T =l t +b t T;

[0143] Where y’ t is the predicted sales volume for day y t is the actual sales volume for day t, l t is the single exponential smoothed value for day t, l’ t is the double exponential smoothed value for day t. b t is the predicted trend for day t, T is the number of predicted days, α is the horizontal smoothing coefficient, and β is the trend smoothing coefficient.

[0144] That is to say, by setting the value of T, the calculation of the predicted sales volume data of petroleum products within the future target time period can be achieved.

[0145] In some embodiments, the target time period can be 7 days.

[0146] In some embodiments, α can be set to 0.8 and β can be set to 0.2.

[0147] Step 202: Substitute the parameter data of petroleum products and the predicted sales volume data of petroleum products within the target time period into the matching function to obtain the depot-station matching data.

[0148] By substituting the parameter data of petroleum products and the predicted sales volume data of petroleum products within the target time period into the matching function, the depot-station matching data can be obtained, which is convenient for formulating subsequent transportation plans.

[0149] In some embodiments, the depot-station matching data includes the matching data between oil depots and gas stations and the matching data between refineries and gas stations. That is to say, some gas stations need to be supplied with petroleum products by oil depots, and another part of gas stations are directly transported with petroleum products by refineries.

[0150] In some embodiments, the parameter data of petroleum products can be the preliminary depot-station matching data, the density data of petroleum products, the classification relationship data of petroleum products, and the blending data of petroleum products. Among them, the density data of petroleum products is used to convert the sales volume data of gas stations from tons to liters according to the density of different petroleum products.

[0151] In some embodiments, the matching function is set as follows: screen and merge all oil depots that can match the demand of a certain petroleum product at a certain gas station; sort the merged depot-station relationships in ascending order of the freight per ton of oil; based on the optimal freight, perform depot-station matching for the oil depots whose available oil volume meets the demand; update the available oil volume of the oil depots. That is to say, the function that meets the above four requirements is the matching function.

[0152] In some embodiments, for example, the depot-station matching data obtained through step 202 is shown in Table 1 below:

[0153] Table 1 Depot-Station Matching Data Table

[0154] Gas Station Number Oil Product Number Oil Depot Number Gas Station Number Oil Product Number Oil Depot Number … 908710423 300644 457A 908710423 300644 ZA4A … 908710482 300668 468A 908710423 300668 458A …

[0155] Through the numbers of the gas stations, oil depots, and petroleum products in Table 1, the depot-station matching relationship can be obtained.

[0156] In some embodiments, for example, through step 202, the estimated outbound volume data of each oil depot for each petroleum product within the target time period can also be obtained. The estimated outbound volume data is shown in Table 2 below:

[0157] Table 2 Estimated Outbound Volume Data Table of Each Oil Depot for Each Petroleum Product within the Target Time Period

[0158] Oil Depot Number Oil Product Number Outbound Quantity … Outbound Quantity 457A 300644 1684 … 2425 457A 300668 2231 … 1557

[0159] Step 203: Generate an initial transportation plan based on the parameter data of petroleum products, the sales volume prediction data of petroleum products within the target time period, and the depot-station matching data.

[0160] Determine the initial transportation plan according to the parameter data of petroleum products, the sales volume prediction data of petroleum products within the target time period, and the depot-station matching data, which is convenient for using the genetic algorithm to optimize the transportation plan in subsequent steps.

[0161] In some embodiments, referring to Figure 4 , step 203 includes the following sub-steps:

[0162] Step 2031: Determine the plan generation parameters based on the parameter data of the petroleum products, the sales volume prediction data of the petroleum products within the target time period, and the depot-station matching data.

[0163] That is to say, in order to obtain the initial transportation plan, it is necessary to first determine the plan generation parameters.

[0164] In some embodiments, the parameter data of the petroleum products further includes the petroleum product classification relationship data, the basic information data of the oil depot, the oil depot unloading capacity data, the basic information data of the refinery, the railway transportation restriction data, the primary in-transit data, and the petroleum product density data.

[0165] In some embodiments, the plan generation parameters include:

[0166] The set of refineries L = {1, 2,..., L}, l ∈ L; the set of oil depots with demand M = {1, 2,..., M}, m ∈ M; the set of gas stations with demand N = {1, 2,..., N}, n ∈ N; the set of petroleum product types P = {1, 2,..., P}, p ∈ P; the planning period D, and a specific day is represented by d, D = {1, 2,..., D}, d ∈ D; the demand R of gas station n for petroleum product p on the d-th day npd ; the unit secondary transportation cost E of petroleum product p from oil depot m to gas station n mnp ; the unit transportation cost E of petroleum product p from refinery l to gas station n lnp ; variables If the petroleum product p of gas station n can be delivered by oil depot m, then the variable is 1, otherwise it is 0; variable If the petroleum product p of gas station n can be directly delivered by refinery l, then the variable is 1, otherwise it is 0; the maximum delivery volume of petroleum product p in oil depot m The minimum delivery volume of petroleum product p in oil depot m The unit secondary shipping cost F of petroleum product p in oil depot m mp ; the delivery volume limit DF of petroleum product p in refinery l on the d-th day lpd ; the minimum turnover volume of petroleum product p in oil depot m The maximum turnover volume of petroleum product p in oil depot m The unit storage cost C of petroleum product p in oil depot m mp ; the unit primary shipping cost F of petroleum product p in refinery l lp ; the unit in-transit freight of refinery l shipping petroleum product p to oil depot m by transportation mode a The unit primary receiving cost S of petroleum product p in oil depot m mp ; the oil depot opening cost K of petroleum product p in oil depot m mp; The direct sales volume XL of petroleum product p in oil depot m on the d-th day mpd ; The out - storage volume V of petroleum product p in oil depot m on the future d-th day mpd ; The initial inventory I of petroleum product p in oil depot m mp0 , that is, the inventory in the early morning of the first day of this period; The safety tank capacity of petroleum product p in oil depot m The non - payable quantity b of petroleum product p in oil depot m mp ; The minimum ending inventory of petroleum product p in oil depot m The maximum ending inventory of petroleum product p in oil depot m The reasonable inventory I of petroleum product p in oil depot m on the d-th day mpd ; The minimum unloading quantity of petroleum product p received by oil depot m in transportation mode a The maximum unloading quantity of petroleum product p received by oil depot m in transportation mode a The minimum batch quantity of petroleum product p transported from refinery l to oil depot m by railway The maximum batch quantity of petroleum product p transported from refinery l to oil depot m by railway The in - transit time of petroleum product p shipped from refinery l to oil depot m by transportation mode a The shipment volume of petroleum product p shipped from refinery l to oil depot m by transportation mode a in the previous period The time window [t ls , t le during which refinery l can ship; The time window [t ms , t me during which oil depot m can receive; Transportation mode a, when a = 0, represents railway transportation, and when a = 1, represents pipeline transportation; The shipment volume Q of petroleum product p from oil depot m to gas station n on the d-th day mnpd ; The shipment volume O of petroleum product p from refinery l to gas station n on the d-th day lnpd ; Variable, when petroleum product p is shipped from refinery l to oil depot m, the variable is 1, otherwise 0; Variable, if the petroleum product p transported from refinery l to oil depot m arrives at the depot on the d-th day, the variable is 1, otherwise 0; Variable, if the petroleum product p transported from refinery l to oil depot m in the previous planning period and not arriving at the depot in the current period arrives at the depot on the d-th day of this planning period, the variable is 1, otherwise 0; The replenishment quantity of petroleum product p in oil depot m on the d-th day through transportation mode a

[0167] Step 2032, construct a multi - layer chromosome framework for the plan generation parameters to obtain the initial transportation plan.

[0168] In some embodiments, the parameters required to construct the multi - layer chromosome framework are shown in Table 3 below:

[0169] Table 3 Chromosome Framework Construction Parameter Table

[0170]

[0171]

[0172] Among them, the daily expected out - storage volume of the oil depot is the sum of the gun - sales volume and the direct - sales volume.

[0173] It can be understood that the transportation variables (refinery number + receiving oil depot number + petroleum product number + date + transportation mode) and the oil depot inventory variables can be set through the parameters in Table 3 above, which is convenient for subsequent calculations.

[0174] In some embodiments, the solution() function is defined by programming. The constructed multi - layer chromosome framework is as Figure 5 shown, see Figure 5 . The first layer of the multi - layer chromosome framework contains information such as refineries, oil depots, petroleum products, time, transportation modes, etc. The second to fourth layers respectively represent the replenishment volume of the oil depot, the expected delivery date of the petroleum product, the end - of - period inventory information of the oil depot, etc. Among them, the replenishment volume of the oil depot is randomly generated on the premise of meeting the restrictions of the oil depot's unloading capacity and the minimum and maximum end - of - period inventory restrictions. The chromosome length is the number of oil depots to be replenished multiplied by the number of days in the target time period.

[0175] Step 204, perform genetic operations on the initial transportation plan, and determine the transportation plan with the minimum cost as the target transportation plan. The target transportation plan includes the target refinery number, target oil depot number, target petroleum product number, replenishment time, transportation mode, replenishment volume, and arrival time.

[0176] By performing genetic operations on the initial transportation plan, further determine the transportation plan with the minimum cost as the target transportation plan. The supply stability of petroleum products in oil depots and gas stations within the target area is ensured while minimizing the cost through the target refinery number, target oil depot number, target petroleum product number, replenishment time, transportation mode, replenishment volume, and arrival time included in the target transportation plan.

[0177] In some embodiments, the algorithm flow chart of the genetic operation is as Figure 6 shown, where K represents the oil depots to be replenished, Kmax represents the maximum number of oil depots to be replenished, and the replenishment plan is the transportation plan.

[0178] See Figure 6, first, fill in the information parameters of the oil depots into the chromosome in the order of the oil depots that need to be replenished. Then, traverse each oil depot to be replenished. For example, if it is an oil depot to be replenished on Monday, first calculate the ending inventory on Monday. Since there is no situation where the replenished oil arrives on the same day in the oil depot replenishment, the ending inventory on Monday is: the beginning inventory on Monday + the in-transit quantity of petroleum products on Monday - the out-of-stock quantity of petroleum products on Monday. Then, calculate the inventory-sales ratio of the oil depot to be replenished. If the sales volume on the next day is 0, set the inventory-sales ratio to 9999. Otherwise, the inventory-sales ratio = (the ending inventory of the previous day - the minimum ending inventory) / the out-of-stock quantity on the next day. When the inventory-sales ratio is greater than 7, the petroleum products in this oil depot do not need to be replenished. When the inventory-sales ratio is less than 7, randomly generate a replenishment quantity within the unloading capacity of the oil depot. Then, calculate the ending inventory when the goods arrive under this replenishment quantity. When the ending inventory meets the minimum and maximum ending inventory limits, this replenishment quantity is valid and filled into the chromosome; if the calculated ending inventory does not meet the constraint limits, take this replenishment quantity as the upper or lower limit of the unloading capacity of the oil depot replenishment quantity, further narrow the range of the replenishment quantity, randomly generate a replenishment quantity again, and repeat the above steps until the randomly generated replenishment quantity meets the ending inventory limit when the goods arrive at this oil depot.

[0179] In some embodiments, set the population size to sizepop, and randomly generate sizepop chromosomes that meet the constraint conditions according to the coding rules, and use this as the initial population for genetic operations. The population chromosome characteristic matrix structure is as Figure 7 shown.

[0180] In some embodiments, use a decoding matrix (a matrix describing the characteristics of the population chromosomes) to decode the population chromosomes to obtain the population gene phenotype matrix.

[0181] In some embodiments, the genetic operation includes a fitness function, and the fitness function includes a cost minimization function and cost minimization constraint conditions. See Figure 8 , step 204 includes the following sub-steps:

[0182] Step 2041, perform genetic operations on the initial transportation plan according to the roulette wheel method, two-point crossover method, and binary mutation method to obtain multiple intermediate transportation plans.

[0183] Here, the fitness is the standard for evaluating the quality of chromosomes in the genetic algorithm. Therefore, in the embodiments of the present application, the cost minimization function is selected as the fitness function to ensure that the output target transportation plan is a transportation plan with minimized cost.

[0184] Step 2042, based on the cost minimization function and cost minimization constraint conditions, determine the intermediate transportation plan with the minimum cost as the target transportation plan.

[0185] In some embodiments, the cost minimization function includes a gas station cost minimization function and an oil depot cost minimization function. The gas station cost minimization function is:

[0186]

[0187] The oil depot cost minimization function is:

[0188]

[0189] where Z 1 is the gas station cost, Z 2 is the oil depot cost, L is the set of refineries, L = {1, 2,..., L}, l ∈ L, M is the set of oil depots with demand, M = {1, 2,..., M}, m ∈ M, N is the set of gas stations with demand, N = {1, 2,..., N}, n ∈ N, P is the set of petroleum product types, P = {1, 2,..., P}, p ∈ P, D is the planning period, and a specific day is represented by d, D = {1, 2,..., D}, d ∈ D. It is 1 when the petroleum product p at gas station n can be delivered by oil depot m, otherwise it is 0. It is 1 when the petroleum product p at gas station n can be directly delivered by refinery l, otherwise it is 0. E mnp , is the unit secondary transportation cost of petroleum product p from oil depot m to gas station n, E lnp , is the unit transportation cost of petroleum product p from refinery l to gas station n, F mp is the unit secondary shipping cost of petroleum product p at oil depot m, F lp is the unit primary shipping cost of petroleum product p at refinery l. is the unit in-transit freight of shipping petroleum product p from refinery l to oil depot m by transportation mode a, S mp is the unit primary receiving cost of petroleum product p at oil depot m, I mpd is the reasonable inventory of petroleum product p at oil depot m on day d, C mp is the unit storage cost of petroleum product p at oil depot m, Q mnpd is the shipping volume of petroleum product p from oil depot m to gas station n on day d, O lnpd is the shipping volume of petroleum product p from refinery l to gas station n on day d. It is 1 when petroleum product p is shipped from refinery l to oil depot m, otherwise it is 0. is the replenishment volume of petroleum product p at oil depot m by transportation mode a on day d;

[0190] The cost minimization function is:

[0191] minZ = Z 1 + Z 2 ;

[0192] Among them, Z represents the gas station cost Z 1 and the oil depot cost Z 2 and their sum.

[0193] In some embodiments, the cost minimization constraint is:

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203]

[0204]

[0205] or 1,

[0206] or 1,

[0207]

[0208]

[0209]

[0210] or 1,

[0211] Among them, \(L\) is the set of refineries, \(L = \{1, 2, \cdots, L\}\), \(l\in L\); \(M\) is the set of oil depots with demand, \(M=\{1, 2, \cdots, M\}\), \(m\in M\); \(N\) is the set of gas stations with demand, \(N = \{1, 2, \cdots, N\}\), \(n\in N\); \(P\) is the set of petroleum product types, \(P=\{1, 2, \cdots, P\}\), \(p\in P\); \(D\) is the planning period, and a specific day is represented by \(d\), \(D=\{1, 2, \cdots, D\}\), \(d\in D\); \(R\) npd is the demand of gas station \(n\) for petroleum product \(p\) on the \(d\)-th day, is \(1\) when the petroleum product \(p\) at gas station \(n\) is directly delivered by refinery \(l\), otherwise it is \(0\), is the maximum delivery volume of petroleum product \(p\) in oil depot \(m\), is the minimum delivery volume of petroleum product \(p\) in oil depot \(m\), \(DF\) lp is the delivery volume limit of petroleum product \(p\) in refinery \(l\), \(XL\) mpd is the direct sales volume of petroleum product \(p\) in oil depot \(m\) on the \(d\)-th day, \(V\) mpd is the outbound volume of petroleum product \(p\) in oil depot \(m\) on the \(d\)-th day in the future, \(I\) mp0 is the initial inventory of petroleum product \(p\) in oil depot \(m\), that is, the inventory in the morning of the first day of this period, is the safety tank capacity of petroleum product \(p\) in oil depot \(m\), \(b\) mp is the non-delivery volume of petroleum product \(p\) in oil depot \(m\), is the minimum ending inventory of petroleum product \(p\) in oil depot \(m\), is the maximum ending inventory of petroleum product \(p\) in oil depot \(m\), \(I\) mpd is the reasonable inventory of petroleum product \(p\) in oil depot \(m\) on the \(d\)-th day, is the minimum unloading volume of petroleum product \(p\) received by oil depot \(m\) under transportation mode \(a\), is the maximum unloading volume of petroleum product \(p\) received by oil depot \(m\) under transportation mode \(a\), is the minimum batch volume of petroleum product \(p\) transported from refinery \(l\) to oil depot \(m\) by railway, is the maximum batch volume of petroleum product \(p\) transported from refinery \(l\) to oil depot \(m\) by railway, is the in-transit time of petroleum product \(p\) shipped from refinery \(l\) to oil depot \(m\) by transportation mode \(a\), is the shipment volume of petroleum product \(p\) shipped from refinery \(l\) to oil depot \(m\) by transportation mode \(a\) in the previous period, \([t ls ,t le is the time window during which refinery \(l\) can ship goods, and are the actual start time and end time of refinery \(l\) for oil delivery, \([t ms ,t me is the time window during which oil depot \(m\) can receive goods, and are respectively the actual start time and end time for unloading oil at oil depot m, a is the transportation mode. When a = 0, it represents rail transportation. When a = 1, it represents pipeline transportation, Q mnpd is the shipment volume of petroleum product p from oil depot m to gas station n on the d-th day, O lnpd is the shipment volume of petroleum product p from refinery l to gas station n on the d-th day, is 1 when petroleum product p is shipped from refinery l to oil depot m, otherwise it is 0, is 1 when the petroleum product p transported from refinery l to oil depot m arrives at the depot on the d-th day, otherwise it is 0; for the petroleum product p transported from refinery l to oil depot m in the previous planning period that did not arrive at the depot in the current period, if it arrives at the depot on the d-th day of this planning period is 1, otherwise it is 0; is the replenishment volume of petroleum product p at oil depot m on the d-th day by transportation mode a.

[0212] In some embodiments, according to the roulette wheel method, two-point crossover method, and binary mutation method, the algorithm flowchart for performing genetic operations on the initial transportation plan is as Figure 9 shown, where gen represents the number of iterations of population evolution, gen max represents the maximum number of iterations of population evolution, and the replenishment plan is the transportation plan.

[0213] In some embodiments, the roulette wheel method is implemented by defining the selecting() function. Specifically, first, the fitness of individuals is calculated. The probability of an individual in the population being selected is related to its fitness value, that is, the larger the fitness value, the higher the probability of being selected. Then, according to the different fitness values of individuals, their selection probabilities are set. Finally, the roulette wheel is randomly rotated to select two parent individuals.

[0214] In some embodiments, the two-point crossover method is implemented by the jiaocha() function. Specifically, for two individuals that need to be paired, first, a random number is generated between [0, 1]. If the value of the random number is less than the specified crossover probability, then a number of 0.01 times the chromosome length is randomly selected between the two chromosome individuals. The same positions of the two chromosome individuals are used as crossover points, and the genes within the crossover points of the two individuals are exchanged. When the selected gene segments are exchanged, if the exchanged segments do not meet the end-of-period inventory limit, then the segment exchange is cancelled, so as to ensure that the offspring individuals are all feasible solutions of the replenishment plan.

[0215] In some embodiments, the binary mutation method is implemented through the bianyi() function. Specifically, a chromosome is randomly selected, and whether it undergoes mutation is determined by a set mutation probability. Similar to the two-point crossover method, a random number between [0, 1] is generated for each individual in the population. When the mutation probability is not less than this number, the individual is selected for mutation. During the mutation process, a number of 0.01 times the chromosome length is randomly selected from a chromosome as the mutation point, and the replenishment quantity at the mutation point is randomly generated again within a certain range. It is determined whether the point can successfully mutate according to the existing constraints. When the selected gene segment mutates, if the mutated segment does not meet the end-of-period inventory limit, then the mutation of this segment is cancelled, so as to ensure that the offspring individual is a feasible solution for the replenishment plan.

[0216] In some embodiments, the maximum number of iterations is set as the termination condition of the genetic algorithm. When the number of iterations is less than the maximum number of iterations, operations such as calculating the fitness value of the population individuals, selection, crossover, and mutation are repeated; when the termination condition is met, the algorithm program stops running, and the optimal chromosome is found through the findbest() function.

[0217] In some embodiments, for example, the target transportation plan is as shown in Table 4 below:

[0218] Table 4 Target Transportation Plan Table

[0219] Refinery Number Oil Depot Number Oil Product Number Replenishment Time Transportation Mode Number Replenishment Quantity Expected Arrival Time ZZZ5 457A 300644 1 t1 778 3 ZZZ5 457A 300668 1 t1 693 3

[0220] Step 205, based on the parameter data of the petroleum products, the sales forecast data of the petroleum products within the target time period, and the depot-station matching data, generate the initial inventory.

[0221] The initial inventory is obtained by a method similar to that for generating the initial transportation plan described above.

[0222] Step 206, perform genetic operations on the initial inventory, and determine the inventory with the minimum cost as the target inventory.

[0223] The target inventory is obtained by a method similar to that for generating the target transportation plan described above.

[0224] In some embodiments, the genetic operation includes a fitness function, and the fitness function includes a cost minimization function and cost minimization constraint conditions. Refer to Figure 10 , Step 206 includes the following sub-steps:

[0225] Step 2061, perform genetic operations on the initial inventory according to the roulette wheel method, the two-point crossover method, and the binary mutation method to obtain multiple intermediate inventories.

[0226] Step 2062: Based on the cost minimization function and cost minimization constraint conditions, determine the intermediate inventory level with the minimum cost as the target inventory level.

[0227] In some embodiments, for example, the target inventory levels are shown in Table 5 below:

[0228] Table 5 Target Inventory Table

[0229] Oil Depot Number Oil Product Number Date Reasonable Inventory Reasonable Inventory Reasonable Inventory ZA4A 300668 1 1120 1064 1176 ZA4A 300667 2 4021 3819 4222

[0230] In some embodiments, there are also the following considerations related to the enterprise business scenario for the oil product transportation method provided in the embodiments of the present application:

[0231] According to the actual enterprise business and modeling requirements, the following business assumptions are made:

[0232] (1) The demand of each gas station is met, that is, the shortage cost is not considered.

[0233] (2) The demand for oil products at the gas station is equal to the known predicted sales volume.

[0234] (3) The inventory problems of refineries and gas stations are not considered.

[0235] (4) The daily out - bound volume of the oil depot consists only of the direct sales volume (wholesale volume) and the pump sales volume. The pump sales volume represents the total quantity of oil products shipped to gas stations.

[0236] (5) The replenishment volume of the oil depot is equal to the shipment volume of the corresponding refinery. The time difference between replenishment and transportation can be offset through prediction and reasonable advance operations.

[0237] (6) The demand of a single oil depot within a planning period is met by one refinery.

[0238] (7) Speculative factors caused by changes in oil product prices are not considered.

[0239] (8) The customer inventory factor is not considered. Customer inventory, also known as sold but not picked up, may account for a large proportion in refined oil inventories, especially for product numbers mainly engaged in wholesale business. Not considering the impact of customer inventory mainly means not considering the sudden increase in out - bound volume caused by short - term market factors. If the out - bound volume is used as the demand source for oil depot inventory management, without special market condition changes, the customer inventory factor does not affect this method either.

[0240] Therefore, for the oil product transportation method provided in the embodiments of the present application, first, the sales forecast data of oil products within the target time period is determined based on the historical sales data of each terminal station in the area to be measured. Then, the parameter data of the oil products and the sales forecast data of the oil products within the target time period are substituted into the matching function to obtain the depot-station matching data. Next, an initial transportation plan is generated based on the parameter data of the oil products, the sales forecast data, and the depot-station matching data, and a genetic operation is performed on the initial transportation plan to determine the transportation plan with the minimum cost as the target transportation plan. This method can obtain a target transportation plan including the target refinery, the target oil depot, the target oil product, the replenishment time, the transportation method, the replenishment quantity, and the arrival time, replacing the method in the prior art that relies on the personal experience of relevant personnel to allocate the transportation of oil products to each gas station and oil depot, ensuring the supply stability of oil products in the oil depot and gas station within the target area while minimizing the cost.

[0241] Figure 11 FIG. is a schematic structural diagram of an oil product transportation device provided in an embodiment of the present application. Refer to Figure 11 , the device 1100 includes:

[0242] A first determination module 1101, configured to determine the sales forecast data of oil products within the target time period according to the historical sales data of oil products of each terminal station in the area to be measured.

[0243] An obtaining module 1102, configured to substitute the parameter data of the oil products and the sales forecast data of the oil products within the target time period into the matching function to obtain the depot-station matching data.

[0244] A generating module 1103, configured to generate an initial transportation plan based on the parameter data of the oil products, the sales forecast data of the oil products within the target time period, and the depot-station matching data.

[0245] A second determination module 1104, configured to perform a genetic operation on the initial transportation plan to determine the transportation plan with the minimum cost as the target transportation plan. The target transportation plan includes the target refinery number, the target oil depot number, the target oil product number, the replenishment time, the transportation method, the replenishment quantity, and the arrival time.

[0246] In some embodiments, the first determination module includes:

[0247] A first determination sub-module, configured to determine the sales parameters based on the historical sales data of oil products of each terminal station in the area to be measured;

[0248] A first obtaining sub-module, configured to substitute the sales parameters into the predicted sales formula and the smoothing value and trend item calculation formula respectively to calculate the sales forecast data of the oil products within the target time period.

[0249] In some embodiments, the generation module includes:

[0250] A second determination sub-module, configured to determine the planned generation parameters based on the parameter data of the petroleum product, the sales volume prediction data of the petroleum product within the target time period, and the depot-station matching data;

[0251] A second obtaining sub-module, configured to construct a multi-layer chromosome framework for the planned generation parameters to obtain an initial transportation plan.

[0252] In some embodiments, the planned generation parameters include:

[0253] The set of refineries L = {1, 2,..., L}, l ∈ L; the set of depots with demand M = {1, 2,..., M}, m ∈ M; the set of gas stations with demand N = {1, 2,..., N}, n ∈ N; the set of petroleum product types P = {1, 2,..., P}, p ∈ P; the planning period D, a specific day is represented by d, D = {1, 2,..., D}, d ∈ D; the demand R of gas station n for petroleum product p on the d-th day npd ; the unit secondary transportation cost E of petroleum product p from depot m to gas station n mnp ; the unit transportation cost E of petroleum product p from refinery l to gas station n lnp ; variables If the petroleum product p of gas station n can be delivered by depot m, then the variable is 1, otherwise 0; variable If the petroleum product p of gas station n can be directly delivered by refinery l, then the variable is 1, otherwise 0; the maximum delivery volume of petroleum product p in depot m The minimum delivery volume of petroleum product p in depot m The unit secondary shipping cost F of petroleum product p in depot m mp ; the delivery volume limit DF of petroleum product p from refinery l on the d-th day lpd ; the minimum turnover volume of petroleum product p in depot m The maximum turnover volume of petroleum product p in depot m The unit storage cost C of petroleum product p in depot m mp ; the unit first-time shipping cost F of petroleum product p in refinery l lp ; the unit in-transit freight of petroleum product p shipped from refinery l to depot m by transportation mode a The unit first-time receiving cost S of petroleum product p in depot m mp ; the depot opening cost K of petroleum product p in depot m mp ; the direct sales volume XL of petroleum product p in depot m on the d-th day mpd ; the outbound volume V of petroleum product p in depot m on the future d-th day mpd; Initial inventory I of petroleum product p in oil depot m mp0 , that is, the inventory at the beginning of the first day of this period; Safe tank capacity of petroleum product p in oil depot m Unpayable quantity b of petroleum product p in oil depot m mp ; Minimum ending inventory of petroleum product p in oil depot m Maximum ending inventory of petroleum product p in oil depot m Reasonable inventory I of petroleum product p in oil depot m on the dth day mpd ; Minimum unloading quantity of petroleum product p unloaded by oil depot m in transportation mode a Maximum unloading quantity of petroleum product p unloaded by oil depot m in transportation mode a Minimum batch quantity of petroleum product p transported from refinery l to oil depot m by railway Maximum batch quantity of petroleum product p transported from refinery l to oil depot m by railway Transit time of petroleum product p shipped from refinery l to oil depot m by transportation mode a Shipment volume of petroleum product p shipped from refinery l to oil depot m by transportation mode a in the previous period Time window [t ls , t le during which refinery l can ship; Time window [t ms , t me during which oil depot m can unload; Transportation mode a, when a = 0, represents railway transportation, and when a = 1, represents pipeline transportation; Shipment volume Q of petroleum product p from oil depot m to gas station n on the dth day mnpd ; Shipment volume O of petroleum product p from refinery l to gas station n on the dth day lnpd ; Variable, when petroleum product p is shipped from refinery l to oil depot m, the variable is 1, otherwise 0; Variable, if the petroleum product p transported from refinery l to oil depot m arrives at the depot on the dth day, then the variable is 1, otherwise 0; Variable, if the petroleum product p transported from refinery l to oil depot m in the previous planning period that did not arrive at the depot in the current period arrives at the depot on the dth day of this planning period, then the variable is 1, otherwise 0; Replenishment quantity of petroleum product p in oil depot m on the dth day by transportation mode a

[0254] In some embodiments, the genetic operation includes a fitness function, the fitness function includes a cost minimization function and cost minimization constraints, and the second determination module includes:

[0255] A third obtaining sub-module, configured to perform genetic operations on the initial transportation plan according to the roulette wheel method, the two-point crossover method, and the binary mutation method to obtain a plurality of intermediate transportation plans;

[0256] A third determination sub-module, configured to determine, based on a cost minimization function and cost minimization constraint conditions, the intermediate transportation plan with the minimum cost as the target transportation plan.

[0257] In some embodiments, the apparatus further includes:

[0258] An initial generation module, configured to generate an initial inventory based on parameter data of petroleum products, sales forecast data of petroleum products within a target time period, and depot-station matching data;

[0259] A target determination module, configured to perform a genetic operation on the initial inventory and determine the inventory with the minimum cost as the target inventory.

[0260] In some embodiments, the genetic operation includes a fitness function, the fitness function includes a cost minimization function and cost minimization constraint conditions, and the target determination module includes:

[0261] A fourth obtaining sub-module, configured to perform a genetic operation on the initial inventory according to the roulette wheel method, two-point crossover method, and binary mutation method to obtain multiple intermediate inventories;

[0262] A fourth determination sub-module, configured to determine, based on the cost minimization function and cost minimization constraint conditions, the intermediate inventory with the minimum cost as the target inventory.

[0263] In some embodiments, the cost minimization function includes a gas station cost minimization function and an oil depot cost minimization function. The gas station cost minimization function is:

[0264]

[0265] The oil depot cost minimization function is:

[0266]

[0267] Where Z 1 is the gas station cost, Z 2 is the oil depot cost, L is the set of refineries, L = {1, 2,..., L}, l ∈ L, M is the set of oil depots with demand, M = {1, 2,..., M}, m ∈ M, N is the set of gas stations with demand, N = {1, 2,..., N}, n ∈ N, P is the set of petroleum product types, P = {1, 2,..., P}, p ∈ P, D is the planning period, and a specific day is represented by d, D = {1, 2,..., D}, d ∈ D. Is 1 when the petroleum product p at gas station n can be delivered by oil depot m, otherwise 0. Is 1 when the petroleum product p at gas station n can be directly delivered by refinery l, otherwise 0, E mnp, is the unit secondary transportation cost of petroleum product p from oil depot m to gas station n, E lnp , is the unit transportation cost of petroleum product p from refinery l to gas station n, F mp is the unit secondary delivery cost of petroleum product p at oil depot m, F lp is the unit delivery cost of petroleum product p from refinery l, S is the unit in-transit freight cost of transporting petroleum product p from refinery l to oil depot m by means of transportation a. mp I is the unit one-time delivery cost of petroleum product p in oil depot m, mpd is the reasonable inventory of petroleum product p at oil depot m on day d, C mp is the unit storage cost of petroleum product p in tank m, Q mnpd is the shipment volume of petroleum product p from oil depot m to gas station n on day d, O lnpd is the shipment volume of petroleum product p from refinery l to gas station n on day d, It is 1 when the petroleum product p is shipped from refinery l to oil depot m, otherwise it is 0. is the replenishment quantity of petroleum product p of tank depot m by transport mode a on day d;

[0268] The cost minimization function is:

[0269] minZ=Z 1 +Z 2 ;

[0270] Among them, Z represents the gas station cost Z 1 and the tank depot cost Z 2 sum.

[0271] In some embodiments, the cost minimization constraint is:

[0272]

[0273]

[0274]

[0275]

[0276]

[0277]

[0278]

[0279]

[0280]

[0281]

[0282]

[0283] or 1,

[0284] or 1,

[0285]

[0286]

[0287]

[0288] or 1,

[0289] where L is the set of refineries, L = {1, 2,..., L}, l ∈ L; M is the set of depots with demands, M = {1, 2,..., M}, m ∈ M; N is the set of gas stations with demands, N = {1, 2,..., N}, n ∈ N; P is the set of petroleum product types, P = {1, 2,..., P}, p ∈ P; D is the planning period, and a specific day is represented by d, D = {1, 2,..., D}, d ∈ D, R npd is the demand of gas station n for petroleum product p on day d, is 1 when the petroleum product p at gas station n is directly delivered by refinery l, otherwise 0, is the maximum delivery volume of petroleum product p at depot m, is the minimum delivery volume of petroleum product p at depot m, DF lp is the delivery volume limit of petroleum product p at refinery l, XL mpd is the direct sales volume of petroleum product p at depot m on day d, V mpd is the outbound volume of petroleum product p at depot m on day d in the future, I mp0 is the initial inventory of petroleum product p at depot m, that is, the inventory in the morning of the first day of this period, is the safety tank capacity of petroleum product p at depot m, b mp is the non - deliverable volume of petroleum product p at depot m, is the minimum ending inventory of petroleum product p at depot m, is the maximum ending inventory of petroleum product p at depot m, I mpd is the reasonable inventory of petroleum product p at depot m on day d, is the minimum unloading volume of petroleum product p received by depot m under transportation mode a, is the maximum unloading volume of petroleum product p unloaded by oil depot m under transportation mode a, is the minimum batch quantity of petroleum product p transported from refinery l to oil depot m by rail, is the maximum batch quantity of petroleum product p transported from refinery l to oil depot m by rail, is the in-transit time for petroleum product p shipped from refinery l to oil depot m under transportation mode a, is the shipped volume of petroleum product p shipped from refinery l to oil depot m under transportation mode a in the previous period, [t ls ,t le is the time window during which refinery l can ship goods, and are the actual start time and end time of oil shipment from refinery l, [t ms ,t me is the time window during which oil depot m can unload, and are the actual start time and end time of oil unloading at oil depot m respectively. a is the transportation mode. When a = 0, it represents rail transportation. When a = 1, it represents pipeline transportation. Q mnpd is the shipped volume of petroleum product p from oil depot m to gas station n on the d-th day, O lnpd is the shipped volume of petroleum product p from refinery l to gas station n on the d-th day, is 1 when petroleum product p is shipped from refinery l to oil depot m, otherwise 0, is 1 when the petroleum product p transported from refinery l to oil depot m arrives at the depot on the d-th day, otherwise 0; for the petroleum product p transported from refinery l to oil depot m in the previous planning period that did not arrive at the depot in the current period, if it arrives at the depot on the d-th day of this planning period is 1, otherwise 0; is the replenishment volume of petroleum product p at oil depot m on the d-th day under transportation mode a.

[0290] Therefore, for the oil product transportation device provided in the embodiments of the present application, first, the sales prediction data of oil products within a target time period is determined based on the historical sales data of each terminal station in the area to be measured. Then, the parameter data of the oil products and the sales prediction data of the oil products within the target time period are substituted into a matching function to obtain depot-station matching data. Next, an initial transportation plan is generated based on the parameter data of the oil products, the sales prediction data, and the depot-station matching data, and a genetic operation is performed on the initial transportation plan to determine that the transportation plan with the minimum cost is the target transportation plan. This method can obtain a target transportation plan including the target refinery, the target oil depot, the target oil products, the replenishment time, the transportation mode, the replenishment quantity, and the arrival time, replacing the method in the prior art that relies on the personal experience of relevant personnel to allocate the transportation of oil products to each gas station and oil depot. While minimizing the cost, it ensures the supply stability of the oil products in the oil depots and gas stations within the target area.

[0291] The embodiments of the present application also provide a non-volatile readable storage medium, in which at least one program is stored. The at least one program is loaded and executed by a processor to implement the oil product transportation method in any of the above embodiments.

[0292] In the present application, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance. The term "plural" means two or more, unless otherwise clearly defined.

[0293] After considering the specification and practicing the present application disclosed herein, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include the common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only regarded as exemplary.

[0294] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A method for transporting and distributing petroleum products, characterized in that, the method includes: Determine the sales forecast data of petroleum products within the target time period according to the historical sales data of petroleum products at each terminal station in the area to be measured; Substitute the parameter data of the petroleum products and the sales forecast data of the petroleum products within the target time period into the matching function to obtain the depot-station matching data; Generate an initial transportation plan based on the parameter data of the petroleum products, the sales forecast data of the petroleum products within the target time period, and the depot-station matching data; Perform a genetic operation on the initial transportation plan to determine that the transportation plan with the minimum cost is the target transportation plan, and the target transportation plan includes the target refinery number, the target oil depot number, the target petroleum product number, the replenishment time, the transportation method, the replenishment quantity, and the arrival time.

2. The method for transporting and distributing petroleum products according to claim 1, characterized in that, the determining the sales forecast data of petroleum products within the target time period according to the historical sales data of petroleum products at each terminal station in the area to be measured includes: Determine the sales parameters based on the historical sales data of petroleum products at each terminal station in the area to be measured; Substitute the sales parameters into the predicted sales formula and the smoothing value and trend term calculation formula respectively to calculate the sales forecast data of the petroleum products within the target time period.

3. The method for transporting and distributing petroleum products according to claim 1, characterized in that, the generating an initial transportation plan based on the parameter data of the petroleum products, the sales forecast data of the petroleum products within the target time period, and the depot-station matching data includes: Determine the plan generation parameters based on the parameter data of the petroleum products, the sales forecast data of the petroleum products within the target time period, and the depot-station matching data; Construct a multi-layer chromosome framework for the plan generation parameters to obtain the initial transportation plan.

4. The method for transporting and distributing petroleum products according to claim 3, characterized in that, the plan generation parameters include: The set of refineries \(L = \{1, 2, \ldots, L\}\), where \(l\in L\); the set of depots with demands \(M=\{1, 2, \ldots, M\}\), where \(m\in M\); the set of gas stations with demands \(N = \{1, 2, \ldots, N\}\), where \(n\in N\); the set of petroleum product types \(P=\{1, 2, \ldots, P\}\), where \(p\in P\); the planning period \(D\), and a specific day is denoted by \(d\), \(D = \{1, 2, \ldots, D\}\), where \(d\in D\); the demand \(R\) of gas station \(n\) for petroleum product \(p\) on the \(d\)-th day npd ; the unit quadratic transportation cost \(E\) of petroleum product \(p\) from depot \(m\) to gas station \(n\) mnp ; the unit transportation cost \(E\) of petroleum product \(p\) from refinery \(l\) to gas station \(n\) lnp ; variables If the petroleum product \(p\) of gas station \(n\) can be distributed by depot \(m\), then the variable is 1, otherwise 0; variable If the petroleum product \(p\) of gas station \(n\) can be directly distributed by refinery \(l\), then the variable is 1, otherwise 0; the maximum delivery volume the minimum delivery volume of petroleum product \(p\) of depot \(m\) the unit secondary shipping cost \(F\) of petroleum product \(p\) of depot \(m\) mp ; the delivery volume limit \(DF\) of petroleum product \(p\) of refinery \(l\) on the \(d\)-th day lpd ; the minimum turnover volume the maximum turnover volume of petroleum product \(p\) of depot \(m\) the unit storage cost \(C\) of petroleum product \(p\) of depot \(m\) mp ; the unit primary shipping cost \(F\) of petroleum product \(p\) of refinery \(l\) lp ; the unit in-transit freight of shipping petroleum product \(p\) from refinery \(l\) to depot \(m\) by transportation mode \(a\) the unit primary receiving cost \(S\) of petroleum product \(p\) of depot \(m\) mp ; the depot opening cost \(K\) of petroleum product \(p\) of depot \(m\) mp ; depot \(m\) on the d th day, the direct sales volume \(XL\) of petroleum product \(p\) mpd ; the out - warehouse volume \(V\) of petroleum product \(p\) of depot \(m\) on the future \(d\)-th day mpd ; the initial inventory \(I\) of petroleum product \(p\) of depot \(m\) mp0 , that is, the inventory in the early morning of the first day of this period; the safety tank capacity the non - deliverable volume \(b\) of petroleum product \(p\) of depot \(m\) mp ; the minimum ending inventory the maximum ending inventory of petroleum product \(p\) of depot \(m\) the reasonable inventory \(I\) of petroleum product \(p\) of depot \(m\) on the \(d\)-th day mpd ; the minimum unloading volume of receiving petroleum product \(p\) by depot \(m\) in transportation mode \(a\) The maximum unloading volume of petroleum product p unloaded by oil depot m under transportation mode a The minimum batch quantity of petroleum product p transported from refinery l to oil depot m by rail The maximum batch quantity of petroleum product p transported from refinery l to oil depot m by rail The in-transit time of petroleum product p shipped from refinery l to oil depot m by transportation mode a The shipment volume of petroleum product p shipped from refinery l to oil depot m by transportation mode a in the previous period The time window [t ls , t le during which refinery l can ship; the time window [t ms , t me during which oil depot m can unload; transportation mode a, when a = 0, represents rail transportation, and when a = 1, represents pipeline transportation; the shipment volume Q of petroleum product p from oil depot m to gas station n on the d-th day mnpd ; the shipment volume O of petroleum product p from refinery l to gas station n on the d-th day lnpd ; variable, when petroleum product p is shipped from refinery l to oil depot m, the variable is 1, otherwise 0; variable, if the petroleum product p transported from refinery l to oil depot m arrives at the depot on the d-th day, the variable is 1, otherwise 0; variable, if the petroleum product p transported from refinery l to oil depot m in the previous planning period and not arriving at the depot in the current period arrives at the depot on the d-th day of this planning period, the variable is 1, otherwise 0; the replenishment volume of petroleum product p at oil depot m by transportation mode a on the d-th day 5. The method for transporting and distributing petroleum products according to claim 1, characterized in that, the genetic operation includes a fitness function, and the fitness function includes a cost minimization function and a cost minimization constraint condition. The performing a genetic operation on the initial transportation plan to determine that the transportation plan with the minimum cost is the target transportation plan includes: Perform a genetic operation on the initial transportation plan according to the roulette wheel method, the two-point crossover method, and the binary mutation method to obtain multiple intermediate transportation plans; Based on the cost minimization function and the cost minimization constraint condition, determine that the intermediate transportation plan with the minimum cost is the target transportation plan.

6. The method for transporting and distributing petroleum products according to claim 1, characterized in that, the method further includes: Generate an initial inventory based on the parameter data of the petroleum products, the sales forecast data of the petroleum products within the target time period, and the depot-station matching data; Perform a genetic operation on the initial inventory to determine that the inventory with the minimum cost is the target inventory.

7. The method for transporting and distributing petroleum products according to claim 6, characterized in that, The genetic operation includes a fitness function, and the fitness function includes a cost minimization function and cost minimization constraint conditions. The genetic operation on the initial inventory quantity to determine the inventory quantity with the minimum cost as the target inventory quantity includes: Performing a genetic operation on the initial inventory quantity according to the roulette wheel method, two-point crossover method, and binary mutation method to obtain a plurality of intermediate inventory quantities; Based on the cost minimization function and the cost minimization constraint conditions, determining the intermediate inventory quantity with the minimum cost as the target inventory quantity.

8. The oil product transportation method according to claim 5 or 7, characterized in that The cost minimization function includes a gas station cost minimization function and an oil depot cost minimization function. The gas station cost minimization function is: The oil depot cost minimization function is: Among them, Z 1 is the cost of the gas station, Z 2 is the cost of the oil depot, L is the set of refineries, L = {1, 2,..., L}, l ∈ L, M is the set of oil depots with demand, M = {1, 2,..., M}, m ∈ M, N is the set of gas stations with demand, N = {1, 2,..., N}, n ∈ N, P is the set of petroleum product types, P = {1, 2,..., P}, p ∈ P, D is the planning period, and a specific day is represented by d, D = {1, 2,..., D}, d ∈ D. It is 1 when the petroleum product p at the gas station n can be delivered by the oil depot m, otherwise it is 0. It is 1 when the petroleum product p at the gas station n can be directly delivered by the refinery l, otherwise it is 0, E mnp , is the unit secondary transportation cost of the petroleum product p from the oil depot m to the gas station n, E lnp , is the unit transportation cost of the petroleum product p from the refinery l to the gas station n, F mp is the unit secondary shipping cost of the petroleum product p of the oil depot m, F lp is the unit primary shipping cost of the petroleum product p of the refinery l. is the unit in-transit freight of the petroleum product p shipped from the refinery l to the oil depot m by transportation mode a, S mp is the unit primary receiving cost of the petroleum product p of the oil depot m, I mpd is the reasonable inventory of the petroleum product p of the oil depot m on the d-th day, C mp is the unit storage cost of the petroleum product p of the oil depot m, Q mnpd is the shipping volume of the petroleum product p from the oil depot m to the gas station n on the d-th day, O lnpd is the shipping volume of the petroleum product p from the refinery l to the gas station n on the d-th day. It is 1 when the petroleum product p is shipped from the refinery l to the oil depot m, otherwise it is 0. is the replenishment volume of the petroleum product p of the oil depot m by transportation mode a on the d-th day; The cost minimization function is: min Z = Z 1 + Z 2 ; Among them, Z represents the gas station cost Z 1 and the tank depot cost Z 2 sum.

9. The oil product transportation method according to claim 5 or 7, characterized in that The cost minimization constraint conditions are: or or or Among them, \(L\) is the set of refineries, \(L = \{1, 2, \cdots, L\}\), \(l\in L\); \(M\) is the set of oil depots with demands, \(M=\{1, 2, \cdots, M\}\), \(m\in M\); \(N\) is the set of gas stations with demands, \(N = \{1, 2, \cdots, N\}\), \(n\in N\); \(P\) is the set of petroleum product types, \(P=\{1, 2, \cdots, P\}\), \(p\in P\); \(D\) is the planning period, and a specific day is represented by \(d\), \(D=\{1, 2, \cdots, D\}\), \(d\in D\), \(R\) npd is the demand of gas station \(n\) for petroleum product \(p\) on the \(d\)-th day, is \(1\) when the petroleum product \(p\) at gas station \(n\) is directly delivered by refinery \(l\), otherwise it is \(0\), is the maximum delivery volume of petroleum product \(p\) in oil depot \(m\), is the minimum delivery volume of petroleum product \(p\) in oil depot \(m\), \(DF\) lp is the delivery volume limit of petroleum product \(p\) in refinery \(l\), \(XL\) mpd is the direct sales volume of petroleum product \(p\) in oil depot \(m\) on the \(d\)-th day, \(V\) mpd is the outbound volume of petroleum product \(p\) in oil depot \(m\) on the future \(d\)-th day, \(I\) mp0 is the initial inventory of petroleum product \(p\) in oil depot \(m\), that is, the inventory in the early morning of the first day of this period, is the safety tank capacity of petroleum product \(p\) in oil depot \(m\), \(b\) mp is the non-delivery volume of petroleum product \(p\) in oil depot \(m\), is the minimum ending inventory of petroleum product \(p\) in oil depot \(m\), is the maximum ending inventory of petroleum product \(p\) in oil depot \(m\), \(I\) mpd is the reasonable inventory of petroleum product \(p\) in oil depot \(m\) on the \(d\)-th day, is the minimum unloading volume of petroleum product \(p\) received by oil depot \(m\) in transportation mode \(a\), is the maximum unloading volume of petroleum product \(p\) received by oil depot \(m\) in transportation mode \(a\), is the minimum batch volume of petroleum product \(p\) transported from refinery \(l\) to oil depot \(m\) by railway, is the maximum batch volume of petroleum product \(p\) transported from refinery \(l\) to oil depot \(m\) by railway, is the in-transit time of petroleum product \(p\) shipped from refinery \(l\) to oil depot \(m\) in transportation mode \(a\), is the shipment volume of petroleum product \(p\) shipped from refinery \(l\) to oil depot \(m\) in transportation mode \(a\) in the previous period, \([t ls ,t le is the time window during which refinery \(l\) can ship goods, and are the actual start time and end time of oil shipment from refinery \(l\), \([t ms ,t me is the time window during which oil depot \(m\) can receive goods, and are the actual start time and end time for unloading oil at oil depot m, a is the transportation mode. When a = 0, it represents rail transportation; when a = 1, it represents pipeline transportation, Q mnpd is the shipment volume of petroleum product p from oil depot m to gas station n on the d-th day, O lnpd is the shipment volume of petroleum product p from refinery l to gas station n on the d-th day, is 1 when petroleum product p is shipped from refinery l to oil depot m, otherwise it is 0, is 1 when the petroleum product p transported from refinery l to oil depot m arrives at the depot on the d-th day, otherwise it is 0; for the petroleum product p transported from refinery l to oil depot m in the previous planning period that did not arrive at the depot in the current period, if it arrives at the depot on the d-th day of this planning period is 1, otherwise it is 0; is the replenishment volume of petroleum product p at oil depot m on the d-th day by transportation mode a.

10. An oil product transportation device, characterized in that The device includes: A first determination module, configured to determine sales prediction data of oil products in a target time period according to historical sales data of oil products of each terminal station in a to-be-detected area; An obtaining module, configured to substitute parameter data of oil products and the sales prediction data of oil products in the target time period into a matching function to obtain depot-station matching data; A generating module, configured to generate an initial transportation plan based on the parameter data of the oil products, the sales prediction data of the oil products in the target time period, and the depot-station matching data; A second determination module, configured to perform a genetic operation on the initial transportation plan to determine the transportation plan with the minimum cost as the target transportation plan, where the target transportation plan includes a target refinery number, a target oil depot number, a target oil product number, a replenishment time, a transportation mode, a replenishment quantity, and an arrival time.

11. A non-volatile readable storage medium, characterized in that At least one program is stored in the non-volatile readable storage medium, and the at least one program is loaded and executed by a processor to implement the oil product transportation method according to any one of claims 1 to 9.

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