Method and system for constructing flow direction and flow analysis model of product oil resources

Abstract

The invention relates to a method and a system for constructing a flow direction and flow analysis model of product oil resources, and belongs to the technical field of flow direction data analysis of product oil. The method comprises the following steps: collecting supply and demand data, freight distance and freight data of product oil in each unit area in a target area; constructing a resource flow direction and flow analysis preliminary model based on supply and demand data, freight distance and freight data of product oil in each unit area in the target area, the target function and a first constraint condition; based on the analysis preliminary model, supply and demand data, freight distance and freight data of the product oil in each unit area in the target area and a second constraint condition, completing optimization of the product oil resource flow direction and flow analysis preliminary model; and obtaining a flow direction and flow analysis model of the product oil resource based on an optimization result. According to the analysis model disclosed by the invention, the limitation of transportation and transportation cost of the product oil across the regions is considered in the construction process, and the requirement limitation inside and outside the target region is met, so that the constructed analysis model is more scientific and systematic.

Description

Technical Field

[0001] This invention belongs to the field of refined oil flow direction data analysis technology, and specifically relates to the construction method and system of refined oil resource flow direction and flow analysis model. Background Technology

[0002] Currently, there are some studies on the flow direction and volume analysis of refined oil resources both domestically and internationally, but most of them focus on the analysis of single factors or local areas, lacking comprehensiveness and systematicity. Existing technical solutions mostly adopt classical optimization methods such as linear programming and network flow, but when dealing with large-scale, multi-variable, and multi-constraint complex systems, they suffer from problems such as large computational load and low solution efficiency.

[0003] Although scholars both domestically and internationally have conducted extensive research on issues such as supply and demand and price fluctuations in the refined oil market, most studies rely on qualitative judgments or rough estimates by researchers, resulting in limited reliability in terms of methodology and data accuracy. Furthermore, the analyses lack constraints on cross-regional refined oil production, sales volume, transportation methods, and costs, further reducing the reliability and practical applicability of the research and conclusions.

[0004] Therefore, constructing a scientific and systematic model of the flow direction and volume of refined oil resources is an urgent problem to be solved in the field of refined oil flow data analysis technology. Summary of the Invention

[0005] To address the above problems, this invention provides a method and system for constructing a refined oil resource flow direction and flow analysis model.

[0006] The first objective of this invention is to provide a method for constructing a refined oil resource flow direction and flow analysis model, comprising:

[0007] Collect supply and demand data, transportation distance and freight data for refined oil products in each unit area within the target region;

[0008] Based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil in each unit area of ​​the target region, a preliminary model for the analysis of refined oil resource flow direction and flow rate is constructed.

[0009] Based on the preliminary model of refined oil resource flow direction and flow analysis, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target region and the second constraint, the preliminary model of refined oil resource flow direction and flow analysis was optimized.

[0010] Based on the optimization results, an analysis model for the flow direction and flow rate of refined oil resources was obtained.

[0011] In a specific embodiment of the present invention, the collection of supply and demand data for refined oil products in each unit area of ​​the target region includes:

[0012] Collect historical production and demand data for refined oil products in each unit area within the target region for a given period of time.

[0013] Based on the historical production and demand data of refined oil products in each unit area of ​​the target region, analyze and determine whether each unit area in the target region is a resource exporter or importer in the historical time period.

[0014] Based on the analysis and judgment results, supply and demand data for each unit area in the target region are obtained within the historical time period.

[0015] In a specific embodiment of the present invention, the collection of supply and demand data for refined oil products in each unit area of ​​the target region includes:

[0016] Collect historical production and demand data for refined oil products in each unit area within the target region for a given period of time.

[0017] Based on the historical production and demand data of refined oil products in each unit area of ​​the target region, predict the production and demand data of refined oil products in each unit area of ​​the target region during the target time period.

[0018] Based on the production and demand data of refined oil products in each unit area of ​​the target region during the target time period, analyze and determine whether each unit area in the target region is a resource output or input area during the target time period.

[0019] Based on the analysis and judgment results, supply and demand data for each unit area in the target region within the target time period are obtained.

[0020] In a specific embodiment of the present invention, the preliminary model for analyzing the flow direction and volume of refined oil resources, based on the supply and demand data, transportation distance and freight data, objective function, and first constraint conditions of refined oil in each unit area of ​​the target region, includes:

[0021] Number each unit area within the target area;

[0022] Define model symbols for the supply and demand data, transportation distance and freight data of refined oil products in each unit area of ​​the target region;

[0023] Based on the unit area number of each unit area in the target area, the supply and demand data of refined oil products in each unit area of ​​the target area, the model symbols of transportation distance and freight data, the objective function and the first constraint, establish the expressions of the objective function and the first constraint;

[0024] Based on the expressions of the objective function and the first constraint, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target region, a preliminary model for the analysis of the flow direction and flow rate of refined oil resources is constructed and solved by the solver.

[0025] In a specific embodiment of the present invention, the objective function is to minimize the total transportation cost of refined oil products;

[0026] The first constraint is that the dispatch volume of refined oil in each unit area of ​​the target area must be greater than or equal to 0, and the dispatch volume of each area itself must be 0.

[0027] In a specific embodiment of the present invention, the second constraint includes constraints on the transportation pipeline route and transportation capacity, constraints on the amount of refined oil to meet cross-regional demand, and constraints on the scheduling of refined oil between different regional units.

[0028] In a specific embodiment of the present invention, the optimization of the preliminary model for analyzing the flow direction and volume of refined oil resources, based on the preliminary model for analyzing the flow direction and volume of refined oil resources, as well as the supply and demand data, transportation distance and freight data of refined oil resources in each unit area of ​​the target region, and the second constraint conditions, includes:

[0029] Establish mathematical expressions for the constraints on transportation pipeline routes and transportation capacity, the constraints on the amount of refined oil products needed to meet cross-regional demand, and the constraints on the inter-regional scheduling of refined oil products.

[0030] Based on the expressions of the constraints of the transportation pipeline route and transportation capacity, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target region, the preliminary model of refined oil resource flow direction and flow rate analysis is optimized by the solver.

[0031] Based on the expression of the constraint condition for the refined oil to meet the cross-regional demand, and the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area, the solver performs a second solution optimization on the model after the first solution optimization;

[0032] Based on the expression of the constraints of the scheduling of refined oil products between different regions, and the supply and demand data, transportation distance and freight data of refined oil products in each region of the target area, the solver performs a third solution optimization on the model after the second solution optimization.

[0033] Based on the results of the third solution optimization, the preliminary model for the analysis of the flow direction and flow rate of refined oil resources was optimized.

[0034] In a specific embodiment of the present invention, the constraints on the transportation pipeline route and transportation capacity are the limits on the maximum transportation capacity of each transportation pipeline under the condition that each unit area is the starting point, ending point or transit point of the transportation pipeline route.

[0035] The constraint condition for the refined oil to meet the cross-regional demand is to meet the demand limits of the target region and the third region.

[0036] The constraints for the scheduling of refined oil products between different regions include the limitations on the demand gap and surplus of each region in the exporting area, as well as the limitations on the targeted radiation and supply methods of refined oil resources to each region under the transportation conditions within the target area.

[0037] The second objective of this invention is to provide a system for constructing a model for analyzing the flow direction and volume of refined oil resources, comprising:

[0038] Data collection module: used to collect supply and demand data, transportation distance and freight data of refined oil products in each unit area of ​​the target region;

[0039] Model building module: used to construct a preliminary model for the flow direction and flow analysis of refined oil resources based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil in each unit area of ​​the target region;

[0040] Model optimization module: It is used to optimize the preliminary model of refined oil resource flow direction and flow analysis based on the preliminary model, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area and the second constraint conditions; it is also used to obtain the refined oil resource flow direction and flow analysis model based on the optimization results.

[0041] The third objective of this invention is to provide a method for analyzing the flow direction and volume of refined oil resources, including:

[0042] Collect supply and demand data, transportation distance and freight data for refined oil products in each unit area within the target region;

[0043] Based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil in each unit area of ​​the target region, a preliminary model for the analysis of refined oil resource flow direction and flow rate is constructed.

[0044] Based on the preliminary model of refined oil resource flow direction and flow analysis, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target region and the second constraint, the preliminary model of refined oil resource flow direction and flow analysis was optimized.

[0045] Based on the optimization results, an analysis model for the flow direction and flow rate of refined oil resources was obtained;

[0046] Based on the refined oil resource flow direction and flow analysis model, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area, the refined oil resource flow direction and flow in the target area are analyzed.

[0047] The fourth objective of this invention is to provide a refined oil resource flow direction and flow analysis system, comprising:

[0048] Data collection module: used to collect supply and demand data, transportation distance and freight data of refined oil products in each unit area of ​​the target region;

[0049] Model building module: used to construct a preliminary model for the flow direction and flow analysis of refined oil resources based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil in each unit area of ​​the target region;

[0050] Model optimization module: Based on the preliminary model of refined oil resource flow direction and flow analysis, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area, and the second constraint, the module optimizes the preliminary model of refined oil resource flow direction and flow analysis; it also obtains the refined oil resource flow direction and flow analysis model based on the optimization results.

[0051] Analysis module: Used to analyze the flow direction and volume of refined oil resources in the target area based on the refined oil resource flow direction and flow analysis model, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area.

[0052] A fifth objective of the present invention is to provide an electronic device, characterized in that it comprises: a processor coupled to a memory;

[0053] The memory is used to store computer programs;

[0054] The processor is configured to execute the computer program stored in the memory, so that the electronic device performs the construction method described above.

[0055] A sixth objective of the present invention is to provide a computer-readable storage medium, characterized in that the computer-readable storage medium stores a program or instructions that, when the program or instructions are run on a computer, cause the computer to perform the construction method as described above.

[0056] The beneficial effects of this invention are:

[0057] The present invention discloses a method and system for constructing a refined oil resource flow direction and flow analysis model. This model considers the transportation of refined oil across regions, transportation cost constraints, and the need to meet demand constraints both within and outside the target region. Based on the supply and demand data, transportation distance, and freight data of refined oil in each unit area within the target region, an objective function reflecting transportation costs is constructed. Furthermore, it incorporates various transportation restrictions across regions within the target region, as well as first and second constraints related to meeting demand constraints both within and outside the target region. This makes the constructed refined oil resource flow direction and flow analysis model more scientific and systematic. Using this model for flow direction and flow analysis within the target region yields accurate and reliable results, providing a fundamental reference for refined oil resource allocation and regional industrial layout optimization. It also provides a scientific basis for optimizing and adjusting the oil transportation, distribution, and sales networks within the target region.

[0058] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 A flowchart illustrating a method for constructing a refined oil resource flow direction and flow analysis model according to an embodiment of the present invention is shown;

[0061] Figure 2 A schematic diagram illustrating the solution approach according to an embodiment of the present invention is shown;

[0062] Figure 3 A framework diagram of a system for constructing a refined oil resource flow direction and flow analysis model according to an embodiment of the present invention is shown;

[0063] Figure 4 A flowchart of a method for analyzing the flow direction and volume of refined oil resources according to an embodiment of the present invention is shown;

[0064] Figure 5 A framework diagram of a refined oil resource flow direction and flow analysis system according to an embodiment of the present invention is shown;

[0065] Figure 6A frame diagram of an electronic device according to an embodiment of the present invention is shown;

[0066] In the diagram: Receipt collection module 10; Model building module 20; Model optimization module 30; Analysis module 40; Electronic device 300; Processor 301; Memory 302. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] like Figure 1 As shown, a method for constructing a refined oil resource flow direction and flow analysis model according to an embodiment of the present invention includes:

[0069] S1. Collect supply and demand data, transportation distance and freight data of refined oil products in each unit area of ​​the target area;

[0070] S2. Based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil in each unit area of ​​the target area, construct a preliminary model for the analysis of the flow direction and flow rate of refined oil resources;

[0071] S3. Based on the preliminary model of refined oil resource flow direction and flow analysis, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area and the second constraint, optimize the preliminary model of refined oil resource flow direction and flow analysis.

[0072] S4. Based on the optimization results, obtain the refined oil resource flow direction and flow analysis model.

[0073] The method for constructing the refined oil resource flow direction and flow analysis model provided in this invention embodiment is constructed after fully analyzing the actual situation of refined oil cross-regional resource flow and understanding the complexity of refined oil cross-regional resource flow. Analysis shows that the conditions for refined oil cross-regional resource flow mainly include three aspects:

[0074] The flow of refined oil products is driven by three main factors: First, regional resource imbalances. Influenced by factors such as resource endowment and market size, there are significant differences in the supply and demand balance of refined oil products across provinces in China. Some provinces have resource surpluses, while others face shortages. Provinces with surpluses intentionally sell across regions to maximize production efficiency, while provinces with shortages intentionally import resources to meet their living and development needs. This regional imbalance is a necessary condition for resource flow. Second, the availability of transportation infrastructure between regions objectively enables resource circulation, which is also a necessary condition. Third, the economic viability of cross-regional circulation is crucial. For the market to spontaneously drive resource circulation, the sales price in shortage areas must cover production and distribution costs; this is a sufficient condition for resource flow. Therefore, based on these two conditions (sufficient and necessary), quantitative analysis of flow direction and volume can be achieved from three aspects: regional balance, transportation methods, and the economic viability of circulation, thus constructing a quantitative tool.

[0075] When analyzing the flow of refined oil resources, a quantitative analysis tool is constructed based on sufficient and necessary conditions, considering three dimensions: regional balance, transportation mode, and circulation economy. This is combined with the actual situation of the refined oil market in the target region (example: the whole country). Ex-factory price limits are consistent across regions, and maximum price limits are also basically the same. The overall economic efficiency of circulation is determined by transportation mode, transportation distance, and transportation cost. Therefore, when resources from several resource-rich areas flow to areas with market demand, the resource with the lowest transportation cost has the highest economic efficiency. The most economical flow pattern is when the overall transportation cost in the target region is the lowest.

[0076] Therefore, analyzing the flow of refined oil resources can be performed through the following steps: First, analyze the supply and demand balance of each unit area (e.g., provinces) within the target region, identifying all resource-supplying unit areas and all resource-receiving unit areas. Second, investigate the transportation methods between unit areas and determine the transportation costs under each method based on transportation distance. Third, using resource unit areas as the starting point, calculate the optimal cost flow for each resource unit area. Fourth, using receiving unit areas as the starting point, calculate the optimal resource flow for each unit area. Fifth, based on the balance gap of the receiving unit area and the supply from the supplying province to that unit area, rank them by economic efficiency to determine the resource sources. Sixth, calculate step by step to form a global freight calculation equation and find the lowest total freight cost.

[0077] From an operations research perspective, this has transformed into a single-objective optimization problem aimed at minimizing total transportation costs. Multi-point-to-multi-point problems with a single-objective optimization can typically be solved using linear programming. Starting with the meta-model of linear programming, the research group, after clarifying the decision variables, objective function, and constraints, progressively constructed the model and obtained the optimal solution for each option. The specific solution approach is as follows: Figure 2 As shown.

[0078] The refined oil resource flow and flow analysis model of this invention can be constructed entirely based on the supply and demand data of refined oil in each unit area of ​​the target area within a historical time period, or it can be constructed based on the supply and demand data of refined oil in each unit area of ​​the target area within a historical time period and the supply and demand data of refined oil in each unit area of ​​the target area within the target time period (future time, obtained through prediction).

[0079] Both models can be used for analyzing the flow and volume of refined oil resources in historical periods, as well as for predicting the flow and volume of refined oil resources in future periods. However, compared to the former, the latter model is more accurate and reliable in predicting the flow and volume of refined oil resources in future periods.

[0080] Therefore, in this embodiment of the invention, the supply and demand data in step S1 can be obtained through two methods.

[0081] In some embodiments of the present invention, the collection of supply and demand data for refined oil products in each unit area of ​​the target region in step S-1 includes:

[0082] A1. Collect historical production and demand data for refined oil products in each unit area within the target region over a specific time period;

[0083] A2. Based on the historical production and demand data of refined oil products for each unit area within the target region, analyze and determine whether each unit area within the target region was a resource exporter or importer in the historical time period. Specifically:

[0084] If the total output of refined oil products in a unit area is greater than or equal to the total demand in a historical period, then the unit area is an exporting region of refined oil products in that historical period.

[0085] If the total output of refined oil products in a unit area is less than the total demand in a historical period, then the unit area is considered an importer of refined oil products in that historical period.

[0086] A3. Based on the analysis and judgment results, obtain the supply and demand data of each unit area in the target area within the historical time period.

[0087] In some embodiments of the present invention, the step S-1 of collecting supply and demand data of refined oil products in each unit area of ​​the target region includes:

[0088] B1. Collect historical production and demand data for refined oil products in each unit area within the target region over a specific time period;

[0089] B2. Based on the historical production and demand data of refined oil products in each unit area of ​​the target region over a specific time period, predict the production and demand data of refined oil products in each unit area of ​​the target region during the target time period.

[0090] B3. Based on the production and demand data of refined oil products in each unit area of ​​the target region during the target time period, analyze and determine whether each unit area in the target region is a resource exporter or importer during the target time period. Specifically:

[0091] If the total output of refined oil products in a unit area is greater than or equal to the total demand in the target time period, then the unit area is considered an exporting region for refined oil products in the target time period.

[0092] If the total output of refined oil products in a unit area is less than the total demand in a given time period, then the unit area is considered an importer of refined oil products in that time period.

[0093] B4. Based on the analysis and judgment results, obtain the supply and demand data of each unit area in the target area within the target time period.

[0094] In this embodiment of the invention, the prediction in step B2 is performed using Arimax, also known as the transfer function model. Its fundamental principle is to add consideration of the dynamic relationship of exogenous variables to the Arima model, extract information from the residuals of many exogenous variables, and combine Arima and regression analysis models to effectively improve the prediction accuracy. Therefore, the transfer function model is also called the dynamic regression model.

[0095] The modeling process using the Arimax method mainly involves the following steps.

[0096] First, the selection of independent variables. Taking into account the economic situation, industry characteristics, and oil consumption closely related to refined oil consumption, independent variables related to the dependent variable were selected. On average, each province selected 26 indicators to establish the relationship between the independent and dependent variables.

[0097] Second, the processing and prediction of independent variables. An ARIMA model is established for the independent variables, the difference order, the order of autoregression and moving average are determined, and the values ​​of the independent variables for the next four periods are predicted.

[0098] Thirdly, cross-correlation analysis between the dependent and independent variables is performed. A key feature of Arimax is its ability to extract information from the residuals to determine the correlation between the independent and dependent variables. This step is crucial in the Arimax modeling process, essentially finding the relationship between the residuals of the independent and dependent variables. In addition to the same ACF and other parameters as in univariate modeling, SAS output will also display a graph of the cross-correlation function of the variables.

[0099] Fourthly, estimation and prediction using transfer function models. After determining the correlation between variables, an Arimax model can be established, and information can be extracted from the residuals to improve model accuracy. The estimation method is still completed using the ESTIMATE statement, and the relationship between the residuals of the independent and dependent variables can be checked after running the program.

[0100] Fifth, the analysis of the prediction results. Once the relationship between the independent and dependent variables is established in Arimax, predictions can be made.

[0101] In some embodiments of the present invention, step S2 includes:

[0102] S2-1. Number each unit area in the target area;

[0103] S2-2, Define model symbols for the supply and demand data, transportation distance and freight data of refined oil products in each unit area of ​​the target region;

[0104] S2-3. Based on the unit area number of each unit area in the target area, the supply and demand data of refined oil in each unit area of ​​the target area, the model symbols of transportation distance and freight data, the objective function and the first constraint condition, establish the expression of the objective function and the first constraint condition;

[0105] S2-4. Based on the expression of the objective function and the first constraint condition, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target region, the preliminary model of refined oil resource flow direction and flow analysis is constructed and solved by the solver.

[0106] In some embodiments of the present invention, the objective function is to minimize the total transportation cost of refined oil; the first constraint is that the dispatch volume of refined oil in each unit area of ​​the target region must be greater than or equal to 0, and the dispatch volume of each region itself is 0.

[0107] In some embodiments of the present invention, the second constraint includes constraints on the transportation pipeline route and transportation capacity, constraints on the amount of refined oil to meet cross-regional demand, and constraints on the scheduling of refined oil between different regional units.

[0108] in:

[0109] The constraints on the transportation pipeline routes and transportation capacity are the limits on the maximum transportation capacity of each transportation pipeline, provided that each unit area is the starting point, ending point, or transit point of the transportation pipeline route.

[0110] The constraint condition for the refined oil to meet the cross-regional demand is to meet the demand limits of the target region and the third region.

[0111] The constraints for the scheduling of refined oil products between different regions include the limitations on the demand gap and surplus of each region in the output area, as well as the limitations on the targeted radiation and supply methods of refined oil resources in each region under the transportation conditions within the target area.

[0112] The third region mentioned above refers to the area outside the target region.

[0113] In some embodiments of the present invention, step S3 includes:

[0114] S3-1. Mathematical expressions for establishing the constraints on transportation pipeline routes and transportation capacity, the constraints on meeting cross-regional demand for refined oil products, and the constraints on meeting the inter-regional scheduling of refined oil products.

[0115] S3-2. Based on the expressions of the constraints of the transportation pipeline route and transportation capacity, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area, the first solution optimization of the preliminary model of refined oil resource flow direction and flow rate analysis is performed by the solver.

[0116] S3-3. Based on the expression of the constraint condition for the refined oil to meet the cross-regional demand, and the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area, the solver performs a second solution optimization on the model after the first solution optimization.

[0117] S3-5. Based on the expression of the constraints of the scheduling of refined oil products between different units and regions, as well as the supply and demand data, transportation distance and freight data of refined oil products in each unit of the target region, the solver performs a third solution optimization on the model after the third solution optimization.

[0118] S3-6. Based on the results of the third solution optimization, the preliminary model for the analysis of the flow direction and flow rate of refined oil resources is optimized.

[0119] The solver is the lp_solve solver in the yalmip toolbox of MATLAB. Specifically, the algorithm principles involved in the establishment and optimization of the refined oil resource flow direction and flow analysis model are as follows:

[0120] 1. Linear Programming

[0121] Linear programming refers to a class of optimization problems where both the objective function and constraints are linear expressions. Linear optimization theory is an early, rapidly developing, widely applied, and mature branch of operations research. It is a mathematical method to assist in scientific management, and it is the mathematical theory and methodology for studying the extrema of linear objective functions under linear constraints. This theory is currently widely used in economic analysis, business management, and engineering technology, providing a scientific basis for making optimal decisions using limited human, material, and financial resources.

[0122] A linear programming model mainly consists of three elements: (1) Objective function: The objective function is an expression used to represent what needs to be optimized. In this study, we set the objective function to minimize the transportation cost of refined oil. (2) Variables: Variables are quantities that can be controlled and selected. The purpose of optimization is to determine the values ​​of the model variables under the condition of satisfying the constraints on the variables so that the objective function reaches the optimal result. (3) Constraints: The model imposes certain restrictions on the values ​​of the variables, and these restrictions are called constraints.

[0123] The meta-model of linear programming is shown in equation (1).

[0124]

[0125] When faced with a specific problem, it is necessary to concretize the above meta-model, that is, to complete the specific modeling process. Generally speaking, the optimization model for each specific problem is unique.

[0126] Historically, various industries have developed a variety of models to solve linear programming problems.

[0127] Lindo and Lingo are software packages developed by Lindo Systems, Inc. in the United States, specifically designed for solving optimization problems. Lindo is used to solve linear and quadratic programming problems, while Lingo, in addition to possessing all the functionalities of Lindo, can also be used to solve nonlinear programming problems, as well as some linear and nonlinear equations (systems). The most significant feature of Lindo and Lingo software is that it allows decision variables in the optimization model to be integers (i.e., integer programming), and it executes very quickly. However, this method is suitable for solving single-point to multi-point problems, limiting its application scope.

[0128] WinQSB can generally compute most non-large problems and can demonstrate intermediate computation processes for smaller problems. It is suitable for solving linear and goal programming problems, and is commonly used in transportation and allocation problems, shortest path and minimum number problems, network maximum flow and decision analysis problems. Its core features include support for function analysis and viewing a glossary, providing sensitivity analysis, and more convenient data import. However, this method requires manual input of data one by one. When encountering multi-point multi-point problems, adjusting parameters each time wastes a significant amount of time, impacting research efficiency.

[0129] MATLAB's optimization toolbox provides a large number of optimization functions for solving linear programming, quadratic programming, and nonlinear programming problems under both constrained and unconstrained conditions. It also integrates parallel computing capabilities, effectively reducing computation time on multi-core processors. For linear programming problems, various mature algorithm packages are available; however, the content of these packages needs to be adjusted according to the research content. Considering the characteristics of this problem—numerous parameters requiring multiple adjustments, multi-point to multi-point calculations, and high efficiency requirements—relevant algorithms in MATLAB can be used for computation.

[0130] 2. Particle Swarm Optimization Algorithm

[0131] The basic idea of ​​particle swarm optimization is to find the optimal solution through cooperation and information sharing among individuals in the swarm.

[0132] This algorithm originates from the simulation of predation behaviors of birds and schools of fish. In the swarm behavior of birds preying on each other, each bird is considered a particle, and each particle represents a solution to the optimization problem. A particle swarm can be considered as the self-organizing behavior of particles in D-dimensional space, transmitting information according to certain rules and changing their own state based on changes in information. The position of the i-th particle in D-dimensional space is represented by the vector Xi = (xi, 1, xi, 2, ..., xi, D), and its flight speed is represented by the vector Vi = (vi, 1, vi, 2, ..., vi, D). Each particle has a fitness value determined by the optimization function and knows its best position Pi = (pi, 1, pi, 2, ..., pi, D) found so far, which is the individual particle optimal point, as well as the best position found by its neighboring particles, which is the global optimal point. In the (t+1)-th iteration, the particle determines its next state based on its own experience and the experience of its companion particles.

[0133] Particle swarm optimization (PSO) is well-suited for solving this problem. Similar to other evolutionary algorithms, PSO employs the concepts of "population" and "evolution," simulating the process based on the fitness values ​​of particles. However, it differs in that it is simpler and more operational, finding the global optimum by following the currently found optimal value. With further development and evolution, PSO, due to its ease of implementation, high accuracy, fast convergence, and minimal parameter adjustments, is well-suited for solving resource allocation problems requiring multiple adjustments, multi-point to multi-point operations, repeated calculations, fast search speeds, and high efficiency, thus improving operator productivity.

[0134] The particle swarm optimization algorithm is used to solve this problem. The solution process is as follows:

[0135] (1) Data preprocessing. The data is preprocessed and sorted in the same order.

[0136] (2) Initialize particle swarm parameters. It is necessary to set the population size S, evolution number G, inertia weight parameters w1 and w2, and learning factors c1 and c2.

[0137] (3) Initialize the particle swarm positions. Randomly generate a population POP as the initial solution to the problem. Each particle is represented by a matrix, where columns represent the resource input of the city and rows represent the resource output of the city.

[0138] (4) Calculate the fitness of the particles. Substitute the current position of the particle swarm into the formula to calculate the minimum cost required to complete the transportation, which is taken as the fitness of the current particle swarm. Also, save the population best (gbest) and individual best (pbest) in the particle swarm. The fitness calculation formula is shown in formula (2):

[0139] Q = POP (i,j) *min(p (i,j) *C p ,t (i,j) *C t ,r (i,j) *C r ,s (i,j) *C s (2)

[0140] (5) Calculate the particle swarm velocity and update the position. Calculate the particle swarm's movement velocity, as shown in equation (3):

[0141] V i =w*rand*(pop) k -pop i )+c1*rand*(pbest i -pop i )+c2*rand*(gbest-pop i (3)

[0142] Where i represents the i-th particle; popk represents a random particle; the inertial weight w is defined as shown in equation (4):

[0143]

[0144] Where i represents the i-th iteration, and this inertial parameter decreases with the number of iterations. The particle swarm velocity and position are updated as shown in equation (5):

[0145] POP = POP + V(5)

[0146] (6) Update the population fitness, population optimum, and individual optimum. Repeat step 4 to calculate the particle swarm fitness, population optimum, and individual optimum.

[0147] (7) Determine if the search is complete. That is, determine if the best result has been obtained or the number of iterations has been reached. Otherwise, repeat steps 4, 5, and 6 until the stopping condition is met. Find the population optimum in the last population as the solution and return it to end the program.

[0148] Particle swarm optimization (PSO) is used to solve linear optimization models, significantly improving computational efficiency. PSO eliminates crossover and mutation operations, relying on particle velocity for the search. In iterative evolution, only the optimal particle transmits information to other particles, resulting in rapid search speed. It also possesses memory, remembering and passing on the best historical position of the particle swarm to other particles, making it easier to find the optimal solution. Furthermore, it requires fewer parameters to be adjusted, has a simple structure, and is easy to implement in engineering. Therefore, applying PSO within a linear optimization framework can approximate the optimal solution relatively quickly, providing an optimized path for the regional logistics and distribution mechanism of refined oil products.

[0149] Example: To illustrate the entire process of constructing the refined oil resource flow and volume analysis model according to an embodiment of the present invention, the following steps are shown as an example. The target research area is the entire country, and the third area is the domestic area. The historical time period is 2016-2023, the target time period is 2025, and the refined oil is diesel.

[0150] I. Data Collection:

[0151] (I) Collect diesel production and demand data for all provinces in China from 2016 to 2023, and collect refined oil production and demand data for all provinces from 2016 to 2023 from the WIND database;

[0152] (II) Based on the production and demand data of all provinces in China from 2016 to 2023, predict the production and demand data of refined oil products in all provinces in China in 2025. The prediction will be conducted using a forecasting model constructed using Arimax. The steps for constructing the Arimax forecasting model include:

[0153] First, the selection of independent variables. Taking into account the economic situation, industry characteristics, and fuel consumption closely related to gasoline and diesel consumption, independent variables related to the dependent variable were selected. On average, each province selected 26 indicators to establish the relationship between the independent and dependent variables.

[0154] Second, the processing and prediction of independent variables. An ARIMA model is established for the independent variables, the difference order, the order of autoregression and moving average are determined, and the values ​​of the independent variables for the next four periods are predicted.

[0155] Thirdly, cross-correlation analysis between the dependent and independent variables is performed. A key feature of Arimax is its ability to extract information from the residuals to determine the correlation between the independent and dependent variables. This step is crucial in the Arimax modeling process, essentially finding the relationship between the residuals of the independent and dependent variables. This relationship is identified using the "Identify" statement. In addition to the same ACF and other parameters as in univariate analysis, SAS output will also display a graph of the cross-correlation function of the variables.

[0156] Fourthly, estimation and prediction using transfer function models. After determining the correlation between variables, an Arimax model can be established, and information can be extracted from the residuals to improve model accuracy. The estimation method is still completed using the ESTIMATE statement, and the relationship between the residuals of the independent and dependent variables can be checked after running the program.

[0157] Fifth, the analysis of the prediction results. Once the relationship between the independent and dependent variables is established in Arimax, predictions can be made.

[0158] The prediction of gasoline and diesel resources in various regions using the above prediction model includes:

[0159] Gasoline and diesel production is mainly affected by refinery primary processing capacity, operating load, and gasoline and diesel yield. Future new projects are mostly integrated refining and chemical projects, generally characterized by large primary processing capacity, stable operating load, and lower gasoline and diesel yields than traditional refineries. Since primary processing capacity is fixed, the increased gasoline and diesel production from new refinery operations can be obtained by predicting operating load and gasoline and diesel yield, and then multiplying these three factors.

[0160] From 2022 to 2025, China is expected to add five new oil refining capacity projects. These projects generally feature large-scale primary processing capacity, stable operating rates, and relatively low gasoline and diesel yields. The steady growth in non-state-owned crude oil import quotas has provided strong support for domestic refined oil supply. Although actual quota allocations have fluctuated, the trend towards large-scale private refining projects is evident, reflecting the requirements of energy structure transformation and compliant operation.

[0161] (III) Based on the production and demand data of refined oil products in all provinces of the country in 2025, analyze and determine whether the refined oil products in the target region will be a resource exporter or importer in 2025.

[0162] (IV) Collecting data on diesel transport distances and costs across all provinces in China. Regarding transport methods and distances, the data was compiled by connecting various provinces based on the availability of highway, railway, waterway, and pipeline infrastructure. Ideal routes lacking infrastructure support were excluded, ensuring the model was grounded in reality and guided by practical considerations. Transport distance data primarily came from highway bureaus, railway bureaus, and WIND data. Unit freight costs were obtained from surveys conducted by sales companies in the east, west, and north of China.

[0163] II. Model Construction and Solution:

[0164] 1. Number all provinces in the country;

[0165] 2. Define model symbols for the supply and demand data, transportation distance and freight data of refined oil products in all provinces of China, as detailed in Table 1;

[0166] Table 1

[0167]

[0168] 3. Based on the model symbols of the province numbers, the supply and demand data of refined oil products in each province, the transportation distance and freight data, the objective function, and the first constraint, establish the expressions for the objective function and the first constraint. Specifically, the expression for the objective function that minimizes the total freight cost of refined oil products is detailed in equation (6). The expression for the first constraint that the dispatch volume of refined oil products in each unit area of ​​the target region must be greater than or equal to 0, and the dispatch volume of each region itself must be 0 is detailed in equation (7).

[0169]

[0170] In equation (6), x p,i,j p i,j c p x represents the pipeline transportation cost from city i to city j. t,i,j t i,j c t x represents the railway transportation cost from city i to city j. p,i,j r i,j c r x represents the road transport cost from city i to city j. p,i,j s i,j c s The cost of waterway transportation from city i to city j.

[0171] 4. Based on the expressions of the objective function and the first constraint condition, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target region, the preliminary model for the analysis of the flow direction and flow rate of refined oil resources is constructed and solved using the lp_solve solver in the yalmip toolbox of MATLAB.

[0172] III. Model Optimization Solution:

[0173] 1. Establish mathematical expressions for the constraints of transportation pipeline routes and transportation capacity, the constraints of refined oil meeting cross-regional demand, and the constraints of refined oil meeting inter-regional dispatching among various units.

[0174] The constraints on the pipeline routes and transport capacity are that each unit region is the starting point, ending point, or transit point of the pipeline route, which causes changes in the pipeline routes, but all are limited by the maximum transport capacity of each pipeline; that is, some provinces are both the input and output points of pipeline resources. There are two situations that cause this phenomenon: one is that the province is both the starting point of a pipeline and the ending point of another pipeline; the other is that the province is a transit point of a pipeline, but can both receive and store oil from the pipeline and input oil into the pipeline. In order to consider the above two situations, it is necessary to add constraints to the model. In the first stage, constraints (16) to (26) are added. In the second stage, a detailed verification is carried out based on the national refined oil allocation situation, and constraints (27) to (36) are added again.

[0175] The constraint condition for the refined oil to meet the cross-regional demand is to meet the restrictions on domestic and foreign demand, and to increase the consideration of gasoline and diesel export volume. The specific expression is shown in equation (9).

[0176] The constraints on the inter-regional scheduling of refined oil products include the limitations on the demand gap and surplus of each unit region in the exporting region, and the limitations on the directional radiation and supply methods of refined oil resources in each unit region under the transportation conditions in the target region. That is, in terms of regional scheduling, on the one hand, it is necessary to exclude the demand gap and surplus problem in non-exporting regions, and on the other hand, it is necessary to comprehensively consider the national transportation conditions and constrain the directional radiation and supply methods of resources in some regions. The surplus resources of some provinces can only be supplied to a limited number of provinces in a specific way, rather than being selected for flow scheduling according to the calculation results. Therefore, in the adjustment of this stage, equations (10)-(15) are added to constrain the supply of some provinces to some provinces in a specific way; in the final stage, based on the dependence of water transport on the objective environment and the limitations of infrastructure, equation (38) is supplemented to exclude the maritime scheduling problem of neighboring provinces that will not occur in reality.

[0177] The above equations (9) to (38) are shown below:

[0178] If k∈E, then Y k +I k ≥O k +S k Otherwise Y k +I k =O k +S k (9)

[0179] Equation (9) shows that, except for the 10 regions with export capabilities, the sum of production and shipments in other regions must be equal to the sum of sales and imports (i.e. there can be no demand gap or surplus).

[0180] like Then x p,6,j =0,x r,6,j =0 (10)

[0181] like Then x s,6,j =0 (11)

[0182] like Then x t,6,j =0 (12)

[0183] If i∈NW and but

[0184] x p,i,j =x t,i,j =x r,i,j =x s,i,j =0 (13)

[0185] If i∈{9,10,12,14} and but

[0186] x p,i,j =x t,i,j =x r,i,j =x s,i,j =0 (14)

[0187] If i∈{19,20} and Then x p,i,j =x t,i,j =x r,i,j =x s,i,j =0 (15)

[0188] x p,30,27 ≤1000 (16)

[0189] Equation (16) indicates that the pipeline transport volume from region 30 to region 27 cannot exceed 10 million tons / year.

[0190] x p,3,4 ≤340 (17)

[0191] Equation (17) indicates that the pipeline transport volume from region 3 to region 4 cannot exceed 3.4 million tons / year.

[0192] x p,2,12 ≤650 (18)

[0193] Equation (18) indicates that the pipeline transport volume from region 2 to region 12 cannot exceed 6.5 million tons / year.

[0194] x p,19,20 ≤1000 (19)

[0195] xp,20,24 ≤1000 (20)

[0196] x p,24,25 ≤1000 (21)

[0197] x p,19,20 +x p,20,24 +x p,24,25 ≤1000 (22)

[0198] Equations (19)(20)(21)(22) together indicate that the pipeline transport volume from region 19 to region 20, then from region 20 to region 24, and then from region 24 to region 25 cannot exceed 10 million tons / year.

[0199] x p,27,26 ≤2200 (23)

[0200] Equation (23) indicates that the pipeline transport volume from region 27 to region 26 cannot exceed 22 million tons / year.

[0201] x p,26,23 ≤700 (24)

[0202] x p,23,22 ≤700 (25)

[0203] x p,27,23 +x p,23,22 ≤700 (26)

[0204] Equations (24), (25), and (26) together indicate that the pipeline transport volume from region 27 to region 23, and then from region 23 to region 22, cannot exceed 7 million tons per year.

[0205] x p,26,16 ≤1500(27)

[0206] x p,16,17 ≤1500(28)

[0207] x p,17,18 ≤1500(29)

[0208] x p,27,26 +x p,26,16 +x p,16,17 +x p,17,18 ≤1500(30)

[0209] Equations (27)(28)(29)(30) together indicate that the pipeline transport volume from region 26 to region 16, then from region 16 to region 17, and then from region 17 to region 18 cannot exceed 15 million tons / year.

[0210] x p,6,2 ≤1300(31)

[0211] x p,2,3 ≤1585(32)

[0212] x p,3,16 ≤1300(33)

[0213] x p,3,15 ≤285(34)

[0214] x p,6,2 +x p,6,3 +x p,3,16 ≤1300(35)

[0215] x p,2,3 +x p,3,15 ≤285(36)

[0216] Equations (31)(32)(33)(34)(35)(36) together indicate that the pipeline transport capacity constraints for the two pipelines from region 6 to region 2, then from region 2 to region 3, then from region 3 to region 16, and from region 2 to region 3, then from region 3 to region 15 are 13 million and 2.85 million tons / year, respectively.

[0217] like

[0218] x p,23,22 ,x p,26,16 ,x p,16,17 ,x p,17,18 ,x p,6,2 ,x p,2,3 ,x p,3,16 ,x p,3,15}, then x p,i,j =0(37)

[0219] Equation (37) shows that only two adjacent regions connected by a pipeline can transport goods directly through the pipeline (two non-adjacent regions can only transport goods indirectly through an intermediary province, even if there is a pipeline).

[0220] x s,6,2 =x s,2,3 =x s,15,10 =x s,15,11 =0(38)

[0221] Equation (38) indicates that waterways are not used between region 6 and region 2, between region 2 and region 3, between region 15 and region 10, and between region 15 and region 11.

[0222] 2. Based on the expression of the constraints of the transportation pipeline route and transportation capacity, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area, the preliminary model of refined oil resource flow direction and flow analysis is optimized by the solver. That is, in the first optimization process, the expression is added in two stages. The first stage adds the constraints of equation (16)-equation (26), and the second stage adds the constraints of equation (27)-equation (36).

[0223] 3. Based on the expression of the constraint condition for the cross-regional demand of refined oil, and the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area, the model after the first solution optimization is optimized by the solver, that is, the constraint of equation (9) is added in the second optimization process.

[0224] 4. Based on the expression of the constraint conditions of the refined oil satisfying the scheduling between each unit area, and the supply and demand data, transportation distance and freight data of refined oil in each unit area in the target area, the solver performs the third solution optimization on the model after the second solution optimization. That is, in the third optimization process, the constraints are added in two stages. The first stage adds the constraints of equation (10)-equation (15), and the second stage adds the constraints of equation (37)-equation (38).

[0225] 5. Based on the results of the third solution optimization, the preliminary model for the analysis of the flow direction and flow rate of refined oil resources was optimized.

[0226] like Figure 3 As shown, a system for constructing a refined oil resource flow direction and flow analysis model according to an embodiment of the present invention includes:

[0227] Data collection module 10: Used to collect supply and demand data, transportation distance and freight data of refined oil products in each unit area of ​​the target area;

[0228] Model building module 20: Used to construct a preliminary model for the analysis of refined oil resource flow direction and flow rate based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil in each unit area of ​​the target area;

[0229] Model optimization module 30: It is used to optimize the preliminary model of refined oil resource flow direction and flow analysis based on the preliminary model of refined oil resource flow direction and flow analysis, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area and the second constraint conditions; it is also used to obtain the refined oil resource flow direction and flow analysis model based on the optimization results.

[0230] like Figure 4 As shown, according to certain embodiments of the present invention, a method for analyzing the flow direction and volume of refined oil resources includes:

[0231] X1. Collect supply and demand data, transportation distance and freight data of refined oil products in each unit area of ​​the target region;

[0232] X2. Based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil in each unit area of ​​the target region, a preliminary model for the analysis of the flow direction and flow of refined oil resources is constructed.

[0233] X3. Based on the preliminary model of refined oil resource flow direction and flow analysis, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area and the second constraint, optimize the preliminary model of refined oil resource flow direction and flow analysis.

[0234] X4. Based on the optimization results, obtain the analysis model of refined oil resource flow direction and flow rate;

[0235] X5. Based on the refined oil resource flow direction and flow analysis model, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area, analyze the refined oil resource flow direction and flow in the target area.

[0236] like Figure 5 As shown, a refined oil resource flow direction and flow analysis system according to an embodiment of the present invention includes:

[0237] Data collection module 10: Used to collect supply and demand data, transportation distance and freight data of refined oil products in each unit area of ​​the target area;

[0238] Model building module 20: Used to construct a preliminary model for the analysis of refined oil resource flow direction and flow rate based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil in each unit area of ​​the target area;

[0239] Model optimization module 30: It is used to optimize the preliminary model of refined oil resource flow direction and flow analysis based on the preliminary model of refined oil resource flow direction and flow analysis, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area and the second constraint conditions; it is also used to obtain the refined oil resource flow direction and flow analysis model based on the optimization results.

[0240] Analysis Module 40: Used to analyze the flow direction and flow of refined oil resources in the target area based on the refined oil resource flow direction and flow analysis model, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area.

[0241] like Figure 6 As shown, in some embodiments of the present invention, an electronic device is provided, the electronic device 300 including: a processor 301 coupled to a memory 302;

[0242] The memory 302 is used to store computer programs;

[0243] The processor 301 is configured to execute the computer program stored in the memory 302, so that the electronic device performs the method described in the above embodiments.

[0244] In some embodiments of the present invention, a computer-readable storage medium is provided that stores a program or instructions that, when executed on a computer, cause the computer to perform the methods described in the above embodiments.

[0245] According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, electronic device, or apparatus.

[0246] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a refined oil resource flow direction and flow analysis model, characterized in that, include: Collect supply and demand data, transportation distance and freight data for refined oil products in each unit area within the target region; Based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil in each unit area of ​​the target region, a preliminary model for the analysis of refined oil resource flow direction and flow rate is constructed. Based on the preliminary model of refined oil resource flow direction and flow analysis, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target region and the second constraint, the preliminary model of refined oil resource flow direction and flow analysis was optimized. Based on the optimization results, an analysis model for the flow direction and flow rate of refined oil resources was obtained.

2. The method for constructing a refined oil resource flow direction and flow analysis model according to claim 1, characterized in that, The collection of supply and demand data for refined oil products in each unit area within the target region includes: Collect historical production and demand data for refined oil products in each unit area within the target region for a given period of time. Based on the historical production and demand data of refined oil products in each unit area of ​​the target region, analyze and determine whether each unit area in the target region is a resource exporter or importer in the historical time period. Based on the analysis and judgment results, supply and demand data for each unit area in the target region are obtained within the historical time period.

3. The method for constructing a refined oil resource flow direction and flow analysis model according to claim 1, characterized in that, The collection of supply and demand data for refined oil products in each unit area within the target region includes: Collect historical production and demand data for refined oil products in each unit area within the target region for a given period of time. Based on the historical production and demand data of refined oil products in each unit area of ​​the target region, predict the production and demand data of refined oil products in each unit area of ​​the target region during the target time period. Based on the production and demand data of refined oil products in each unit area of ​​the target region during the target time period, analyze and determine whether each unit area in the target region is a resource output or input area during the target time period. Based on the analysis and judgment results, supply and demand data for each unit area in the target region within the target time period are obtained.

4. The method for constructing a refined oil resource flow direction and flow analysis model according to claim 1, characterized in that, Based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil products in each unit area of ​​the target region, a preliminary model for the analysis of refined oil resource flow direction and flow rate is constructed, including: Number each unit area within the target area; Define model symbols for the supply and demand data, transportation distance and freight data of refined oil products in each unit area of ​​the target region; Based on the unit area number of each unit area in the target area, the supply and demand data of refined oil products in each unit area of ​​the target area, the model symbols of transportation distance and freight data, the objective function and the first constraint, establish the expressions of the objective function and the first constraint; Based on the expressions of the objective function and the first constraint, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target region, a preliminary model for the analysis of the flow direction and flow rate of refined oil resources is constructed and solved by the solver.

5. The method for constructing a refined oil resource flow direction and flow analysis model according to claim 1, characterized in that, The objective function is to minimize the total transportation cost of refined oil products. The first constraint is that the dispatch volume of refined oil in each unit area of ​​the target area must be greater than or equal to 0, and the dispatch volume of each area itself must be 0.

6. A method for constructing a refined oil resource flow direction and flow analysis model according to any one of claims 1-5, characterized in that, The second constraint includes constraints on the transportation pipeline route and transportation capacity, constraints on the amount of refined oil products needed to meet cross-regional demand, and constraints on the availability of refined oil products for inter-regional dispatching among various units.

7. The method for constructing a refined oil resource flow direction and flow analysis model according to claim 6, characterized in that, The preliminary model for analyzing the flow direction and volume of refined oil resources, along with supply and demand data, transportation distance and freight data for each unit area in the target region, and the second constraint, is used to optimize the preliminary model for analyzing the flow direction and volume of refined oil resources. This optimization includes: Establish mathematical expressions for the constraints on transportation pipeline routes and transportation capacity, the constraints on the amount of refined oil products needed to meet cross-regional demand, and the constraints on the inter-regional scheduling of refined oil products. Based on the expressions of the constraints of the transportation pipeline route and transportation capacity, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target region, the preliminary model of refined oil resource flow direction and flow rate analysis is optimized by the solver. Based on the expression of the constraint condition for the refined oil to meet the cross-regional demand, and the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area, the solver performs a second solution optimization on the model after the first solution optimization; Based on the expression of the constraints of the scheduling of refined oil products between different regions, and the supply and demand data, transportation distance and freight data of refined oil products in each region of the target area, the solver performs a third solution optimization on the model after the second solution optimization. Based on the results of the third solution optimization, the preliminary model for the analysis of the flow direction and flow rate of refined oil resources was optimized.

8. The method for constructing a refined oil resource flow direction and flow analysis model according to claim 6, characterized in that, The constraints on the transportation pipeline routes and transportation capacity are the limits on the maximum transportation capacity of each transportation pipeline, provided that each unit area is the starting point, ending point, or transit point of the transportation pipeline route. The constraint condition for the refined oil to meet the cross-regional demand is to meet the demand limits of the target region and the third region. The constraints for the scheduling of refined oil products between different regions include the limitations on the demand gap and surplus of each region in the exporting area, as well as the limitations on the targeted radiation and supply methods of refined oil resources to each region under the transportation conditions within the target area.

9. A system for constructing a refined oil resource flow direction and flow rate analysis model, characterized in that, include: Data collection module: used to collect supply and demand data, transportation distance and freight data of refined oil products in each unit area of ​​the target region; Model building module: used to construct a preliminary model for the flow direction and flow analysis of refined oil resources based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil in each unit area of ​​the target region; Model optimization module: It is used to optimize the preliminary model of refined oil resource flow direction and flow analysis based on the preliminary model, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area and the second constraint conditions; it is also used to obtain the refined oil resource flow direction and flow analysis model based on the optimization results.

10. A method for analyzing the flow direction and volume of refined oil resources, characterized in that, include: Collect supply and demand data, transportation distance and freight data for refined oil products in each unit area within the target region; Based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil in each unit area of ​​the target region, a preliminary model for the analysis of refined oil resource flow direction and flow rate is constructed. Based on the preliminary model of refined oil resource flow direction and flow analysis, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target region and the second constraint, the preliminary model of refined oil resource flow direction and flow analysis was optimized. Based on the optimization results, an analysis model for the flow direction and flow rate of refined oil resources was obtained; Based on the refined oil resource flow direction and flow analysis model, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area, the refined oil resource flow direction and flow in the target area are analyzed.

11. A refined oil resource flow direction and flow analysis system, characterized in that, include: Data collection module: used to collect supply and demand data, transportation distance and freight data of refined oil products in each unit area of ​​the target region; Model building module: used to construct a preliminary model for the flow direction and flow analysis of refined oil resources based on the supply and demand data, transportation distance and freight data, objective function and first constraint conditions of refined oil in each unit area of ​​the target region; Model optimization module: Based on the preliminary model of refined oil resource flow direction and flow analysis, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area, and the second constraint, the module optimizes the preliminary model of refined oil resource flow direction and flow analysis; it also obtains the refined oil resource flow direction and flow analysis model based on the optimization results. Analysis module: Used to analyze the flow direction and volume of refined oil resources in the target area based on the refined oil resource flow direction and flow analysis model, as well as the supply and demand data, transportation distance and freight data of refined oil in each unit area of ​​the target area.

12. An electronic device, characterized in that, include: Processor, the processor being coupled to memory; The memory is used to store computer programs; The processor is configured to execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 8.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 8.

Images