Petroleum transportation and production plan collaborative optimization method and device and medium

Abstract

The invention discloses a method for collaborative optimization of petroleum transportation and production plans. The method comprises the following steps: acquiring basic data of an oil product storage tank related to oil product transportation; establishing a scheduling optimization model for cooperatively scheduling the oil product storage tank and the oil product transportation; inputting the basic data into the scheduling optimization model to obtain a transportation tool arrangement strategy and an oil storage tank scheduling strategy; and based on the transportation tool arrangement strategy and the oil storage tank scheduling strategy, a scheduling instruction meeting a constraint condition function is formed for the transportation tools and is issued to the corresponding transportation tools, so that the cost target is realized when the transportation tools execute the scheduling instruction, the cost targets comprise the penalty cost of the difference value between the minimum blending proportion and the optimal proportion and the penalty cost of the number of tank reversing times. The invention further discloses a petroleum transportation and production plan collaborative optimization device, electronic equipment and a storage medium, and maximization of petroleum production load and economic benefits is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent collaboration in petroleum transportation and production, and in particular to a method, device, electronic equipment and computer storage medium for collaborative optimization of petroleum transportation and production plans. Background Art

[0002] Oil refineries are the core link in the oil industry supply chain, responsible for processing crude oil into a variety of downstream products (such as gasoline, diesel, jet fuel and oil, etc.), of which crude oil transportation and production processing are the two main links in refinery operations. The supply of crude oil usually involves a variety of transportation methods (such as tankers, vehicles, pipelines, etc.). By optimizing the raw material transportation plan, it can be ensured that the refinery obtains the required type of crude oil supply at the time required for production. Different types of crude oil (light, heavy, different sulfur content, etc.) have different production process requirements for refineries. Refineries need to flexibly arrange production processes (such as upgrading, catalytic cracking, hydrogenation, etc.) according to crude oil characteristics and market demand to maximize the production of high value-added products. Therefore, integrating crude oil transportation and production planning as a global optimization problem can improve the overall efficiency and economic benefits of refineries from a system perspective. This application considers the problem of coordinated scheduling optimization of oil refineries from ship arrival to port to tank filling production. After the raw material ship arrives at the dock, based on the tank inventory information and refinery production conditions, with the optimization goal of minimizing the difference between the blending ratio and the optimal ratio and the penalty cost of tank emptying times, decisions are made on the order and ratio of raw materials transported to each tank through pipelines, the tank emptying conditions, and the specific order of tank dispatching devices, thereby maximizing the refinery production load and economic benefits, and providing technical support for the digital transformation and intelligent decision-making of the oil industry.

[0003] To sum up, the production and transportation scale of petrochemicals is huge, often measured in millions of tons, and there are many restrictions on the coordination of production and transportation. The oil transportation operation is highly mobile and requires comprehensive supervision of the transportation situation. Storage tanks are important storage facilities at petrochemical terminals and need to be fully coordinated with generation and raw material blending. Summary of the invention

[0004] The embodiments of the present application provide a method, device, electronic device and computer storage medium for collaborative optimization of oil transportation and production plans. The method utilizes the collaborative optimization of oil transportation and production plans, and combines the actual production conditions of the refinery to finely characterize constraints such as tank blending and backfilling operations, so as to maximize the oil production load and economic benefits, and provide technical support for the digital transformation and intelligent decision-making of the oil industry.

[0005] On the one hand, an embodiment of the present application provides a method for collaborative optimization of oil transportation and production plans, including: acquiring basic data related to oil storage tanks and oil transportation; establishing a scheduling optimization model for collaborative scheduling of the oil storage tanks and the oil transportation; inputting the basic data into the scheduling optimization model to obtain a transportation tool scheduling strategy and an oil storage tank scheduling strategy; based on the transportation tool scheduling strategy and the oil storage tank scheduling strategy, forming a scheduling instruction that satisfies a constraint condition function for the transportation tool and issuing it to the corresponding transportation tool, so that the transportation tool achieves a cost target when executing the scheduling instruction, wherein the cost target includes: minimizing the penalty cost of the difference between the blending ratio and the optimal ratio, and the penalty cost of the number of tank reversals.

[0006] In a possible embodiment, the scheduling optimization model is a nonlinear mathematical model established based on the objective function corresponding to the cost target, and the input parameters of the scheduling optimization model include:

[0007] Storage tank information, raw material ratio information, ship and raw material information, pipeline transportation information and processing information;

[0008] The output parameters of the scheduling optimization model include: the total amount of raw materials received by the storage tank in each round, the inventory of raw materials at the end of each round of the storage tank, whether the storage tank in this round is a blending tank or a storage tank, the order of the equipment dispatching of the storage tank in each round, the start time of the equipment dispatching of each storage tank in each round, the end time of the equipment dispatching of each storage tank in each round, the amount of tanks unloaded between the storage tanks in each round, and the total number of tank unloads.

[0009] In a possible embodiment, the basic data is input into the scheduling optimization model to obtain a transportation tool scheduling strategy and an oil storage tank scheduling strategy, including the following steps: determining the input parameters according to an application scenario; matching the input parameters with corresponding constraint function to obtain a constraint function of the objective function; and determining the transportation tool scheduling strategy and the oil storage tank scheduling strategy respectively according to the objective function and the constraint function.

[0010] In a possible embodiment, the transport tool arrangement strategy and the oil storage tank scheduling strategy are determined respectively according to the objective function and the constraint condition function, further comprising:

[0011] Under the conditions of satisfying the upper and lower limit constraints of storage tank inventory, the constraint function that each delivery device sequence in each round corresponds to a storage tank, the constraint function that each storage tank in each round must correspond to a delivery device sequence, the tank emptying operation constraint function and the constraint function that the tank emptying amount in each round is not greater than the inventory amount in the previous round, the oil storage tank strategy is obtained through the scheduling optimization model; and,

[0012] The transportation tool arrangement strategy is obtained through the scheduling optimization model under the conditions of satisfying the basic constraint function of the relationship between raw material inventory and total inventory, the raw material storage constraint function, the inventory balance constraint function, the relationship constraint function between the start time and the end time of receiving goods, the constraint function that the shipment start time is greater than or equal to the shipment end time plus the reconciliation time, the constraint function that the shipment end time is equal to the shipment start time plus the raw material processable time, the constraint function that the time of previous and subsequent shipments must be continuous, and the constraint function of tank emptying operation.

[0013] In a possible embodiment, the constraint condition function is divided into hard constraints and soft constraints.

[0014] In a possible embodiment, the method further includes: matching the corresponding constraint condition function according to the application scenario, establishing the scheduling optimization model in a general algebraic modeling system, and then solving the model using a solver.

[0015] In a possible embodiment, the method further includes: updating the basic data according to a predetermined period.

[0016] On the one hand, an embodiment of the present application provides a device for collaborative optimization of oil transportation and production planning, comprising:

[0017] Data receiving module, used to obtain basic data related to oil storage tanks and oil transportation;

[0018] A model building module, used to build a scheduling optimization model for coordinated scheduling of the oil storage tank and the oil transportation;

[0019] A strategy generation module, used for inputting the basic data into the scheduling optimization model to obtain a transportation tool scheduling strategy and an oil storage tank scheduling strategy;

[0020] A processing module is used to form a scheduling instruction that satisfies the constraint condition function for the transportation tool based on the transportation tool arrangement strategy and the oil storage tank scheduling strategy, and issue it to the corresponding transportation tool, so that the transportation tool achieves the cost target when executing the scheduling instruction, wherein the cost target includes: minimizing the blending ratio, the optimal ratio difference and the penalty cost of the number of tank reversals.

[0021] On the one hand, an embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes any one of the above-mentioned methods for collaborative optimization of oil transportation and production plans.

[0022] On the one hand, the present application provides a computer-readable storage medium, which includes a program code. When the storage medium is run on an electronic device, the program code is used to enable the electronic device to execute any of the above-mentioned methods for collaborative optimization of oil transportation and production plans.

[0023] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least one of the following:

[0024] 1. The oil transportation and production planning collaborative optimization method disclosed in this application can take into account both oil transportation and production planning and perform collaborative scheduling optimization.

[0025] 2. The oil transportation and production planning collaborative optimization method disclosed in this application is adopted, with minimizing the difference between the blending ratio and the optimal ratio and the penalty cost of the number of tank turnovers as the optimization goal.

[0026] 3. In order to adapt to the industry characteristics of oil transportation, the collaborative optimization method of oil transportation and production planning disclosed in this application can also be combined with several constraint function, for example: the upper and lower limits of storage tank inventory, raw material ratio, number of storage tank launch devices, storage tank inventory balance, tank emptying operation, storage tank status and time transfer and other business constraints are restricted at the same time, and the mathematical programming solver is called to solve and output multiple feasible solutions and plans.

[0027] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0029] Figure 1 A schematic diagram of an application scenario of the method for collaborative optimization of oil transportation and production planning provided in an embodiment of the present application;

[0030] Figure 2 A schematic diagram of a process for collaborative optimization of oil transportation and production planning provided in an embodiment of the present application;

[0031] Figure 3 A module diagram of a device for collaborative optimization of oil transportation and production planning provided in an embodiment of the present application;

[0032] Figure 4 The figure is a schematic diagram of the hardware structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be arbitrarily combined with each other. In addition, although the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in an order different from that here.

[0034] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0035] The following is a brief introduction to the design concept of the embodiment of the present application:

[0036] In today's oil production, the scale of petrochemical production and transportation is huge, often measured in millions of tons, and there are many production and transportation coordination restrictions. The oil transportation operation is highly mobile and requires comprehensive supervision of the transportation situation. Storage tanks are important storage facilities at petrochemical terminals. This application considers the problem of coordinated scheduling optimization of oil refineries from ship arrival to port to tank filling production. After the raw material ship arrives at the terminal, based on the tank inventory information and the refinery production situation, the optimization goal is to minimize the difference between the blending ratio and the optimal ratio and the penalty cost of the number of tank dumping. The order and ratio of raw materials transported to each tank through pipelines, the tank dumping situation, and the specific order of the tank launch device are decided, thereby maximizing the refinery production load and economic benefits.

[0037] In view of this, an embodiment of the present application provides a method for collaborative optimization of oil transportation and production plans, which is used for the coordinated scheduling of oil storage tanks and the management and control optimization of oil transportation, including: obtaining basic data related to oil storage tanks and oil transportation; establishing a scheduling optimization model for collaborative scheduling of the oil storage tanks and the oil transportation; inputting the basic data into the scheduling optimization model to obtain a transportation tool scheduling strategy and an oil storage tank scheduling strategy; based on the transportation tool scheduling strategy and the oil storage tank scheduling strategy, forming a scheduling instruction that satisfies a constraint condition function for the transportation tool and issuing it to the corresponding transportation tool, so that the transportation tool achieves a cost target when executing the scheduling instruction, wherein the cost target includes: minimizing the penalty cost of the difference between the blending ratio and the optimal ratio, and the penalty cost of the number of tank reversals.

[0038] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application may be combined with each other if there is no conflict.

[0039] like Figure 1 1 is a schematic diagram of an application scenario of the oil transportation and production planning collaborative optimization method provided in an embodiment of the present application. In the schematic diagram of the application scenario, a terminal device 101 and a server 102 are included. The terminal device 101 and the server 102 communicate with each other through a communication network.

[0040] The terminal device 101 is an electronic device used by the target object, and the electronic device may be a personal computer, a mobile phone, a tablet computer, a notebook, an e-book reader, a vehicle-mounted terminal, etc. In addition, a client related to the method for collaborative optimization of oil transportation and production plans may be installed on the terminal device 101, and the client may be software (for example, an APP, a browser, etc.), or a web page, a small program, etc. By implementing the method for collaborative optimization of oil transportation and production plans on the terminal device 101, related operations of collaborative optimization of oil transportation and production plans are performed.

[0041] Server 102 can be an independent physical server or an edge device in the field of cloud computing. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, cloud functions, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (English name: Content Delivery Network, abbreviated as CDN), as well as big data and artificial intelligence platforms.

[0042] There is no restriction on the number of the terminal devices 101 and / or servers 102 .

[0043] It should be noted that the method for collaborative optimization of oil transportation and production planning in the embodiment of the present application can be executed by the terminal device 101 or the server 102 alone, or can be executed by the terminal device 101 and the server 102 together. For example, when executed by the terminal device 101 and the server 102 together, the server 102 can provide the terminal device 101 with a scheduling instruction based on the transportation tool arrangement strategy and the oil storage tank scheduling strategy, and form a scheduling instruction that satisfies the constraint condition function for the transportation tool and issue it to the corresponding transportation tool, so that the transportation tool achieves the cost target when executing the scheduling instruction, wherein the cost target includes: minimizing the penalty cost of the difference between the blending ratio and the optimal ratio, and the penalty cost of the number of tank flips. The terminal device 101 can provide the server 102 with basic data related to oil storage tanks and oil transportation for the scheduling optimization model. The server 102 establishes a scheduling optimization model for collaborative scheduling of oil storage tanks and oil transportation when the constraint condition function is satisfied; the basic data is input into the scheduling optimization model to obtain the transportation tool arrangement strategy and the oil storage tank scheduling strategy.

[0044] The following describes the method for collaborative optimization of oil transportation and production plans provided by an exemplary embodiment of the present application in combination with the above-mentioned application scenarios and with reference to the accompanying drawings. It should be noted that the above-mentioned application scenarios are only shown to facilitate understanding of the spirit and principles of the present application, and the implementation methods of the present application are not subject to any limitations in this regard.

[0045] like Figure 2 , which is a schematic diagram of a method 200 for collaborative optimization of oil transportation and production plans provided in an embodiment of the present application. In the schematic diagram of the method for collaborative optimization of oil transportation and production plans, the following steps are shown:

[0046] Step S201, obtaining basic data, that is, obtaining basic data related to oil storage tanks and oil transportation.

[0047] In a possible embodiment, the scheduling optimization model is a nonlinear mathematical model established based on the objective function corresponding to the cost target, and the input parameters of the scheduling optimization model include: storage tank information, raw material ratio information, ship and raw material information, pipeline transportation information and processing process information; the output parameters of the scheduling optimization model include: the total amount of raw materials received by the storage tank in each round, the raw material inventory at the end of each round of the storage tank, whether the storage tank is a blending tank or a storage tank in this round, the order of the launch device of each tank in each round, the start time of each tank launch device in each round, the end time of each tank launch device in each round, the amount of tank emptying between tanks in each round and the total number of tank emptying.

[0048] More specifically, the tank information includes: the number of tanks, the inventory information of each raw material in the tank at the initial moment, the upper limit of the tank capacity, and the lower limit of the tank capacity; the raw material ratio information includes: the raw material mixing ratio requirements; the ship and raw material information includes: the ship load, the quantity of each raw material; the pipeline transportation information includes: the pipeline transportation rate or unloading time of the tank receiving status, the pipeline transportation rate of the tank emptying status, and the pipeline transportation rate of the tank launching device status; and the processing process information includes: the blending time. For example, Table 1 below shows a list of basic data that can be input into the scheduling optimization model. For another example: Table 2 shows a list of output data of the scheduling optimization model.

[0049] Table 1: Basic data for input scheduling optimization model

[0050]

[0051] Table 2: Output data of the scheduling optimization model

[0052]

[0053]

[0054] Step S202, establishing a scheduling optimization model, that is, establishing a scheduling optimization model for coordinated scheduling of oil storage tanks and oil transportation.

[0055] In a possible embodiment, a scheduling optimization model for coordinated scheduling of oil storage tanks and oil transportation is established, including: combining the objective function and the constraint function according to the basic data, thereby establishing the scheduling optimization model. The setting of the constraint function is to limit the decision under certain conditions when calculating the mathematical model, so that the final result conforms to the business logic, and the business logic is mainly divided into facility capacity restriction constraints and business process constraints.

[0056] In a possible embodiment, the constraint function may include at least one of the following:

[0057] The first constraint condition function: the basic constraint function of the relationship between raw material inventory and total inventory; the function expression is as follows:

[0058]

[0059] Among them, WA it represents the inventory of raw material A in tank i at the end of round t, which is an integer variable; WB it represents the inventory of raw material B in tank i at the end of round t, which is an integer variable; W it represents the total raw material inventory of tank i at the end of round t, which is an integer variable; I represents the set of storage tanks, i represents a specific tank; T represents the set of ship rounds, and t represents a specific ship round.

[0060] The second constraint condition function: the upper and lower limit constraint function of the tank inventory; the function expression is as follows:

[0061]

[0062] Among them, w min Represents the lower limit of tank inventory; W it represents the total raw material inventory of tank i at the end of round t, which is an integer variable; w max Represents the upper limit of tank inventory; I represents the tank set, i represents a specific tank; T represents the ship round set, t represents a specific ship round.

[0063] The third constraint condition function: the total amount of raw material allocation constraint function; the function expression is as follows:

[0064]

[0065] Among them, XA it represents the tons of raw material A received by tank i from the ship in round t, which is an integer variable; XB it Represents the tons of raw material B received by tank i from the ship in round t, which is an integer variable; MA t MB represents the arrival quantity of raw materials of ship A on the tth ship; t represents the arrival quantity of raw material B on the tth ship; i represents a specific tank; T represents the set of ship rounds, and t represents a specific ship round.

[0066] The fourth constraint function: raw material blending ratio constraint function; if tank i is used for blending in round t, the ratio of raw materials A and B should be greater than or equal to the ratio rate. The function expression is as follows:

[0067]

[0068] Among them, XA it represents the tons of raw material A received by tank i from the ship in round t, which is an integer variable; XB it represents the tons of raw material B received by tank i from the ship in round t, which is an integer variable; YA ii′t represents the tons of raw material A received by tank i from tank i' in round t, which is an integer variable; YB ii′t represents the amount of tons of raw material B that tank i receives from tank i' in round t, which is an integer variable; YA i′it represents the tons of raw material A that tank i' receives from tank i in round t, which is an integer variable; YB i′it represents the amount of tons of raw material B that tank i' receives from tank i in round t, which is an integer variable; WA it-1 represents the inventory of raw material A in tank i at the end of round t-1, which is an integer variable; WBit-1 represents the inventory of raw material B in tank i at the end of round t-1, which is an integer variable; R it Represents the proportion of tank i blended in round t, which is a continuous variable; α it represents the slack variable of the blending ratio of tank i in the tth round, which is a continuous variable; I represents the set of storage tanks, i represents a specific tank; T represents the set of ship rounds, t represents a specific ship round; rate represents the optimal ratio of the two raw materials A and B.

[0069] The fifth constraint function: raw material storage constraint function; if tank i is used for storage in round t, it can only receive one type of raw material. The function expression is as follows:

[0070]

[0071] Among them, XA it represents the tons of raw material A received by tank i from the ship in round t, which is an integer variable; XB it represents the tons of raw material B received by tank i from the ship in round t, which is an integer variable; Z it Indicates whether tank i is used for blending and launching in round t, otherwise for storage, a Boolean variable of 0 or 1; M represents a predetermined extreme value constant; I represents a storage tank set, i represents a specific tank; T represents a ship round set, t represents a specific ship round.

[0072] The sixth constraint function: inventory balance constraint function; if tank i is used for blending in round t, the inventory at the end of the round is the lower limit of inventory, otherwise, the inventory remains unchanged. The function expression is as follows:

[0073]

[0074] Among them, W it represents the total raw material inventory of tank i at the end of round t, which is an integer variable; M represents a predetermined extreme value constant; Z it Indicates whether tank i is used for blending and dispensing in round t, otherwise it is used for storage, a Boolean variable of 0 or 1; W it represents the total raw material inventory of tank i at the end of round t, which is an integer variable; w min Represents the lower limit of tank inventory; W it-1 represents the total raw material inventory of tank i at the end of round t-1, which is an integer variable; I represents the set of storage tanks, i represents a specific tank; T represents the set of ship rounds, and t represents a specific ship round.

[0075] The seventh constraint condition function: the relationship constraint function between the start time and the end time of receiving goods; the function expression is as follows:

[0076]

[0077] Among them, SL it represents the start time of tank i receiving raw materials in round t, which is a continuous variable; XA it represents the tons of raw material A received by tank i from the ship in round t, which is an integer variable; XB it represents the tons of raw material B received by tank i from the ship in round t, which is an integer variable; YA ii′t represents the tons of raw material A received by tank i from tank i' in round t, which is an integer variable; YB ii′t represents the amount of tons of raw material B that tank i receives from tank i' in round t, which is an integer variable; YA i′it represents the tons of raw material A that tank i' receives from tank i in round t, which is an integer variable; YB i′it represents the amount of tons of raw material B that tank i′ receives from tank i in round t, which is an integer variable; v in Represents the pipeline transportation rate for receiving and unloading tanks; EL it Represents the end time of tank i receiving raw materials in round t, which is a continuous variable.

[0078] The eighth constraint function: the delivery start time is greater than or equal to the pickup end time plus the reconciliation time constraint function; the function expression is as follows:

[0079]

[0080] Among them, EL it represents the end time of tank i receiving raw materials in round t, which is a continuous variable; mt represents the blending time; Z it Indicates whether tank i is used for blending and sending in round t, otherwise it is used for storage, a Boolean variable of 0 or 1; SP it represents the start time of the launch device of tank i in the tth round, which is a continuous variable; I represents the storage tank set, i represents a specific tank; T represents the ship round set, and t represents a specific ship round.

[0081] Ninth constraint condition function: the delivery end time is equal to the delivery start time plus the raw material processing time constraint function; the function expression is as follows:

[0082]

[0083] Among them, SP it represents the start time of the launch device of tank i in the tth round, which is a continuous variable; Z it Indicates whether tank i is used for blending and dispensing in round t, otherwise it is used for storage, a Boolean variable of 0 or 1; W it represents the total raw material inventory of tank i at the end of round t, which is an integer variable; w minRepresents the lower limit of tank inventory; EP it represents the end time of the launch device of tank i in the tth round, which is a continuous variable; v out represents the refinery processing rate; I represents the storage tank set, i represents a specific tank; T represents the ship round set, t represents a specific ship round.

[0084] The tenth constraint function: the time of the previous and next shipments must be continuous. The function expression is as follows:

[0085]

[0086]

[0087] Among them, EP it represents the end time of the tank i in the tth round of the device, which is a continuous variable; M represents a predetermined extreme value constant; O itn Represents the nth sequential device of tank i in round t, a Boolean variable of 0 or 1; i′tn-1 represents the n-1th sequential device of tank i′ in round t, a Boolean variable of 0 or 1; it1 Represents that tank i is the first sequential device in round t, a Boolean variable of 0 or 1; i′t-1N Represents tank i′ as the Nth sequential launch device in the t-1th round, which is a Boolean variable of 0 or 1; n represents the sequential launch device; N represents a positive integer; I represents the tank set, i, i′ represent specific tanks; T represents the ship round set, and t represents a specific ship round.

[0088] Eleventh constraint condition function: Each delivery device sequence in each round corresponds to a constraint function of a storage tank; the function expression is as follows:

[0089]

[0090] Among them, O itn Represents the nth sequential launch device of tank i in the tth round, which is a Boolean variable of 0 or 1; n represents the sequential launch device; N represents a positive integer; T represents the set of ship rounds, and t represents a specific ship round.

[0091] The twelfth constraint condition function: Each tank in each round must correspond to a constraint function of the order of delivery of the device; if the device is not actually delivered, the time will remain unchanged. The function expression is as follows:

[0092]

[0093] Among them, O itnRepresents tank i in the nth sequential launch device in the tth round, a Boolean variable of 0 or 1; n represents the sequential launch device; I represents the tank set, i represents a specific tank; T represents the ship round set, t represents a specific ship round.

[0094] Thirteenth constraint condition function: can-inverting operation constraint function; the function expression is as follows:

[0095]

[0096] Among them, YA ii′t represents the tons of raw material A received by tank i from tank i' in round t, which is an integer variable; YB ii′t represents the tons of raw material B received by tank i from tank i' in round t, which is an integer variable; M represents a predetermined extreme constant; K ii′t Indicates whether tank i receives raw materials from tank i′ in round t, a Boolean variable of 0 or 1; I represents the set of storage tanks, i represents a specific tank; T represents the set of ship rounds, t represents a specific ship round.

[0097] The fourteenth constraint condition function: The amount of tank dumping in each round is not greater than the inventory in the previous round. The function expression is as follows:

[0098]

[0099] Among them, YA ii′t represents the tons of raw material A received by tank i from tank i' in round t, which is an integer variable; YB ii′t represents the tons of raw material B received by tank i from tank i' in round t, which is an integer variable; M represents a predetermined extreme constant; K ii′t WA represents whether tank i receives raw materials from tank i′ in round t, a Boolean variable of 0 or 1; i′t-1 represents the inventory of raw material A in tank i′ at the end of round t-1, which is an integer variable; WB i′t-1 represents the inventory of raw material B in tank i′ at the end of round t-1, which is an integer variable; I represents the set of storage tanks, i′ represents a specific tank; T represents the set of ship rounds, and t represents a specific ship round.

[0100] Step S203, generating corresponding strategies, that is, inputting the basic data into the scheduling optimization model to obtain a transportation tool scheduling strategy and an oil storage tank scheduling strategy.

[0101] In a possible embodiment, the basic data is input into the scheduling optimization model to obtain a transportation tool scheduling strategy and an oil storage tank scheduling strategy, which may include the following steps: determining the input parameters according to an application scenario; matching the input parameters with corresponding constraint function to obtain a constraint function of the objective function; and determining the transportation tool scheduling strategy and the oil storage tank scheduling strategy respectively according to the objective function and the constraint function.

[0102] In a possible embodiment, the transport tool arrangement strategy and the oil tank scheduling strategy are determined respectively according to the objective function and the constraint condition function, including: obtaining the oil tank strategy through the scheduling optimization model under the conditions of satisfying the upper and lower limit constraint functions of the tank inventory, the constraint function that each delivery device sequence in each round corresponds to a tank, the constraint function that each tank in each round must correspond to a delivery device sequence, the tank dumping operation constraint function and the tank dumping quantity in each round is not greater than the inventory quantity in the previous round constraint function; and obtaining the transport tool arrangement strategy through the scheduling optimization model under the conditions of satisfying the basic constraint function of the relationship between the raw material inventory and the total inventory, the raw material storage constraint function, the inventory balance constraint function, the relationship constraint function between the receiving start time and the end time, the delivery start time is greater than or equal to the receiving end time plus the reconciliation time constraint function, the delivery end time is equal to the delivery start time plus the raw material processable time constraint function, the constraint function that the time of the previous and subsequent deliveries must be continuous and the tank dumping operation constraint function.

[0103] More specifically, in a possible embodiment, the constraint function is divided into hard constraints and soft constraints. The so-called soft constraint means that slack variables are set in the constraint function and optimized in the objective; in contrast, the so-called hard constraint means that slack variables are not set in the constraint function, and strict restrictions are required to prevent the constraint from being broken. For example: the raw material blending ratio in the fourth constraint function is allowed not to strictly follow the prescribed ratio, so slack variables are set and optimized in the objective, so it is a soft constraint. In addition, other constraint functions are hard constraints. In specific application scenarios, the corresponding constraint functions can be matched as needed, and hard constraints and soft constraints can be added to the objective. Table 4 shows the description of the constraint function on hard constraints and soft constraints:

[0104]

[0105]

[0106] Step S204, achieving the cost target, that is, based on the transportation tool arrangement strategy and the oil storage tank scheduling strategy, a scheduling instruction that satisfies the constraint condition function is formed for the transportation tool and issued to the corresponding transportation tool, so that the transportation tool achieves the cost target when executing the scheduling instruction, wherein the cost target includes: minimizing the penalty cost of the difference between the blending ratio and the optimal ratio, and the penalty cost of the number of tank reversals.

[0107] In a possible embodiment, the penalty cost of the difference between the reconciliation ratio and the optimal ratio is minimized, and the function expression is as follows:

[0108]

[0109] Among them, obj1 represents the penalty cost of minimizing the difference between the reconciliation ratio and the optimal ratio; c1 represents the reconciliation ratio penalty cost; α it The slack variable representing the harmonic ratio of tank i in round t is a continuous variable; i represents a specific tank; t represents a specific ship round.

[0110] In a possible embodiment, the penalty cost of the number of can flipping is minimized, and the function expression is as follows:

[0111]

[0112] Among them, obj2 represents the penalty cost of minimizing the number of can-turning times; c2 represents the penalty cost of can-turning operation; K ii′t Indicates whether tank i receives raw materials from tank i' in round t, a Boolean variable of 0 or 1; i, i' represent specific tanks; t represents a specific ship round.

[0113] In a possible embodiment, it may also include: matching the corresponding constraint function according to the application scenario, establishing the scheduling optimization model in the general algebraic modeling system, and then using the solver to solve. Common general algebraic modeling systems include: LINDO, DOT and MATLAB. The method disclosed in the present application can be implemented in a corresponding program according to the programming language syntax / lexical specifications specified by the general algebraic modeling system, and then using the system's solver to solve and improve efficiency.

[0114] In a possible embodiment, the basic data may be updated at a predetermined period. In an application scenario, input parameters of the scheduling optimization model, such as storage tank information, ship and raw material information, need to be updated at a predetermined period to ensure accuracy.

[0115] refer to Figure 3 , which is a module diagram of a petroleum transportation and production planning collaborative optimization device 300 provided in an embodiment of the present application, referring to Figure 3The device includes: a data receiving module 302, a model building module 304, a strategy generating module 306, and a processing module 308. Among them:

[0116] The data receiving module 302 is used to obtain basic data related to oil storage tanks and oil transportation.

[0117] The model building module 304 is used to build a scheduling optimization model for coordinated scheduling of the oil storage tanks and the oil transportation.

[0118] The strategy generation module 306 is used to input the basic data into the scheduling optimization model to obtain a transportation tool scheduling strategy and an oil storage tank scheduling strategy.

[0119] The processing module 308 is used to form a scheduling instruction that satisfies the constraint condition function for the transportation tool based on the transportation tool arrangement strategy and the oil storage tank scheduling strategy and send it to the corresponding transportation tool, so that the transportation tool achieves the cost target when executing the scheduling instruction, wherein the cost target includes: minimizing the penalty cost of the difference between the blending ratio and the optimal ratio, and the penalty cost of the number of tank reversals.

[0120] Based on the same inventive concept, an electronic device is also provided in the embodiment of the present application, and the electronic device can realize the functions of the aforementioned oil transportation and production plan collaborative optimization device, referring to Figure 4 , the electronic device comprises:

[0121] At least one processor 401, and a memory 402 connected to the at least one processor 401. The specific connection medium between the processor 401 and the memory 402 is not limited in the embodiment of the present application. Figure 4 In the example, the processor 401 and the memory 402 are connected via the bus 400. The bus 400 is Figure 4 The connection between other components is shown by bold lines, and is not intended to be limiting. The bus 400 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. Alternatively, the processor 401 can also be called a controller, and there is no limitation on the name.

[0122] In the embodiment of the present application, the memory 402 stores instructions that can be executed by at least one processor 501. The at least one processor 401 can execute the method for collaborative optimization of oil transportation and production planning discussed above by executing the instructions stored in the memory 402. The processor 401 can implement Figure 4 The functions of each module in the device shown.

[0123] Among them, the processor 401 is the control center of the device, and can use various interfaces and lines to connect the various parts of the entire control device. By running or executing instructions stored in the memory 402 and calling the data stored in the memory 402, the various functions of the device and process data, the device can be monitored as a whole.

[0124] In one possible design, the processor 401 may include one or more processing units, and the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 401. In some embodiments, the processor 401 and the memory 402 may be implemented on the same chip, and in some embodiments, they may also be implemented separately on separate chips.

[0125] Processor 401 can be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method for collaborative optimization of oil transportation and production planning disclosed in the embodiments of the present application can be directly embodied as a hardware processor, or can be executed by a combination of hardware and software modules in the processor.

[0126] The memory 402 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 402 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 402 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 402 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0127] By programming the processor 401, the code corresponding to the method for collaborative optimization of oil transportation and production planning described in the above embodiment can be fixed into the chip, so that the chip can execute the code when running. Figure 2 The steps of the method for coordinated optimization of oil transportation and production plan in the illustrated embodiment are as follows: How to design and program the processor 401 is a technique known to those skilled in the art and will not be described in detail here.

[0128] Based on the same inventive concept, an embodiment of the present application also provides a storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes the method for collaborative optimization of oil transportation and production plans discussed above.

[0129] In some possible implementations, various aspects of the method for collaborative optimization of oil transportation and production plans provided in the present application may also be implemented in the form of a program product, which includes a program code. When the program product is run on an apparatus, the program code is used to enable the control device to execute the steps of the method for collaborative optimization of oil transportation and production plans according to various exemplary embodiments of the present application described above in this specification.

[0130] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0131] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0134] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for collaborative optimization of oil transportation and production planning, characterized in that: include: Obtain basic data related to oil storage tanks and oil transportation; Establishing a scheduling optimization model for coordinated scheduling of the oil storage tanks and the oil transportation; Inputting the basic data into the scheduling optimization model to obtain a transportation tool scheduling strategy and an oil storage tank scheduling strategy; Based on the transport tool arrangement strategy and the oil storage tank scheduling strategy, a scheduling instruction that satisfies the constraint condition function is formed for the transport tool and issued to the corresponding transport tool, so that the transport tool achieves the cost target when executing the scheduling instruction, wherein the cost target includes: minimizing the penalty cost of the difference between the blending ratio and the optimal ratio, and the penalty cost of the number of tank reversals.

2. The method for collaborative optimization of oil transportation and production planning as claimed in claim 1, characterized in that: The scheduling optimization model is a nonlinear mathematical model established based on the objective function corresponding to the cost target; and the input parameters of the scheduling optimization model include: storage tank information, raw material ratio information, ship and raw material information, pipeline transportation information and processing process information; The output parameters of the scheduling optimization model include: the total amount of raw materials received by the storage tank in each round, the inventory of raw materials at the end of each round of the storage tank, whether the storage tank in this round is a blending tank or a storage tank, the order of the equipment dispatching of the storage tank in each round, the start time of the equipment dispatching of each storage tank in each round, the end time of the equipment dispatching of each storage tank in each round, the amount of tanks unloaded between the storage tanks in each round, and the total number of tank unloads.

3. The method for collaborative optimization of oil transportation and production planning as claimed in claim 2, characterized in that: Inputting the basic data into the scheduling optimization model to obtain a transportation tool arrangement strategy and an oil storage tank scheduling strategy includes the following steps: Determine the input parameters according to the application scenario; Matching the input parameters with corresponding constraint function to obtain the constraint function of the objective function; The transportation tool arrangement strategy and the oil storage tank scheduling strategy are determined respectively according to the objective function and the constraint condition function.

4. The method for collaborative optimization of oil transportation and production planning as claimed in claim 3, characterized in that: According to the objective function and the constraint condition function, the transportation tool arrangement strategy and the oil storage tank scheduling strategy are determined respectively, and further comprising: Under the conditions of satisfying the upper and lower limit constraints of storage tank inventory, the constraint function that each delivery device sequence in each round corresponds to a storage tank, the constraint function that each storage tank in each round must correspond to a delivery device sequence, the tank emptying operation constraint function and the constraint function that the tank emptying amount in each round is not greater than the inventory amount in the previous round, the oil storage tank strategy is obtained through the scheduling optimization model; and, The transportation tool arrangement strategy is obtained through the scheduling optimization model under the conditions of satisfying the basic constraint function of the relationship between raw material inventory and total inventory, the raw material storage constraint function, the inventory balance constraint function, the relationship constraint function between the start time and the end time of receiving goods, the constraint function that the shipment start time is greater than or equal to the shipment end time plus the reconciliation time, the constraint function that the shipment end time is equal to the shipment start time plus the raw material processable time, the constraint function that the time of previous and subsequent shipments must be continuous, and the constraint function of tank emptying operation.

5. The method for collaborative optimization of oil transportation and production planning as claimed in claim 4, characterized in that: The constraint condition function is divided into hard constraints and soft constraints.

6. The method for collaborative optimization of oil transportation and production planning according to any one of claims 1 to 5, characterized in that it also includes: The corresponding constraint condition function is matched according to the application scenario, the scheduling optimization model is established in the general algebraic modeling system, and then the solver is used to solve it.

7. The method for collaborative optimization of oil transportation and production planning as claimed in claim 6, characterized in that it also includes: The basic data is updated according to a predetermined period.

8. A device for collaborative optimization of oil transportation and production planning, characterized in that: include: Data receiving module, used to obtain basic data related to oil storage tanks and oil transportation; A model building module, used to build a scheduling optimization model for coordinated scheduling of the oil storage tank and the oil transportation; A strategy generation module, used for inputting the basic data into the scheduling optimization model to obtain a transportation tool scheduling strategy and an oil storage tank scheduling strategy; A processing module is used to form a scheduling instruction that satisfies the constraint condition function for the transportation tool based on the transportation tool arrangement strategy and the oil storage tank scheduling strategy, and issue it to the corresponding transportation tool, so that the transportation tool achieves the cost target when executing the scheduling instruction, wherein the cost target includes: minimizing the penalty cost of the difference between the blending ratio and the optimal ratio, and the penalty cost of the number of tank reversals.

9. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor executes the method for collaborative optimization of oil transportation and production planning as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: It includes program code, and when the storage medium is run on an electronic device, the program code is used to enable the electronic device to execute the method for collaborative optimization of oil transportation and production planning as described in any one of claims 1 to 5.

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