Gasoline and diesel oil marketing method and device based on wholesale and zero integration

Abstract

The invention provides a gasoline and diesel oil marketing method and device based on wholesale and zero integration. The corresponding method comprises the following steps: acquiring historical marketing data of gasoline and diesel oil; predicting future prices of gasoline and diesel oil according to the historical marketing data and a pre-generated time sequence marketing model; wherein the future price comprises a wholesale price and a retail price of gasoline and diesel oil; the endogenous variables of the time sequence marketing model comprise crude oil properties, processing capacity and market supply and demand; and determining a marketing scheme of gasoline and diesel according to the future price of gasoline and diesel. According to the method, in the process of establishing the time sequence marketing model, the change rule of the variable is considered, and the influence of the exogenous variable on the variable is also increased, so that the accuracy of a prediction result can be improved; besides, according to the method, the wholesale volume and the retail sales volume of the diesel oil serve as explained variables respectively, the purpose of optimizing the wholesale structure is achieved, and the method has higher operability in actual business work.

Description

Technical Field

[0001] This application belongs to the field of data mining technology, specifically relating to a method and apparatus for marketing gasoline and diesel based on wholesale and retail integration. Background Technology

[0002] Existing methods for gasoline and diesel marketing have several shortcomings. First, they fail to consider all explanatory variables, neglecting competitor sales, macroeconomic conditions, and market environment within the relevant province or region. Second, the predictive models used in these methods lack precision, relying solely on the inherent patterns of variable changes. Third, all existing methods use overall diesel sales volume as the explained variable, offering limited guidance for practical business operations. Summary of the Invention

[0003] One objective of this invention is to provide a wholesale-retail integrated gasoline and diesel marketing method. In addition to considering the changing patterns of the variables affecting gasoline and diesel prices themselves, this method also considers the influence of exogenous variables on these variables, thereby improving the accuracy of predicting gasoline and diesel prices.

[0004] Another object of the present invention is to provide a gasoline and diesel marketing device based on wholesale and retail integration. A further object of the present invention is to provide an electronic device comprising a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the aforementioned gasoline and diesel marketing method based on wholesale and retail integration. A further object of the present invention is to provide a readable medium storing a computer program thereon, the computer program being executed by a processor to implement the steps of the aforementioned gasoline and diesel marketing method based on wholesale and retail integration.

[0005] To address the technical problems in the background section of this application, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a gasoline and diesel marketing method based on wholesale and retail integration, comprising:

[0007] Obtain historical marketing data for gasoline and diesel;

[0008] The future prices of gasoline and diesel are predicted based on the historical marketing data and a pre-generated time-series marketing model; wherein the future prices include the wholesale and retail prices of gasoline and diesel; and the endogenous variables of the time-series marketing model include: crude oil properties, processing capacity, and market supply and demand; the number of autoregressive terms in the time-series marketing model is determined by a pre-generated autocorrelation function, which is:

[0009] δ(k)=cov(X t ,X t+k ) / var(X t )

[0010] Among them, X t Let be the time series of crude oil properties, processing capacity, and market supply and demand in period t; δ(k) is the autocorrelation coefficient with a lag of k periods; cov(X) t ,X t+k ) is X t and X t+k covariance, var(X) t ) is X t The variance;

[0011] The number of moving average terms in the time series marketing model is determined by a pre-generated partial autocorrelation function, which is:

[0012]

[0013] in, For X t The predicted value obtained by performing linear regression; For X t+k The predicted value obtained by linear regression; η(k) is the partial autocorrelation coefficient with a lag of k periods;

[0014] The marketing plan for the gasoline and diesel will be determined based on the future prices of the gasoline and diesel.

[0015] In some embodiments of the present invention, a gasoline and diesel marketing method based on wholesale and retail integration further includes:

[0016] Solve the characteristic equation of the time series;

[0017] Determine whether the characteristic equation contains a unit root to generate a determination result;

[0018] Based on the determination results, determine whether the time series needs to be differencing.

[0019] In some embodiments of the present invention, determining whether the time series needs differencing based on the determination result includes:

[0020] When the characteristic equation contains the unit root, the time series is subjected to first-order difference.

[0021] When the characteristic equation corresponding to the first-order difference contains a unit root, the time series after the first-order difference is subjected to a second-order difference.

[0022] In some embodiments of the present invention, the exogenous variables of the time series marketing model include: the sales region of the gasoline and diesel, the sales price, the factors affecting the sales price, the sales prices of competitors, and the sales volume of competitors.

[0023] Secondly, the present invention provides a gasoline and diesel marketing device based on wholesale and retail integration, the device comprising:

[0024] The historical marketing data acquisition module is used to acquire historical marketing data for gasoline and diesel.

[0025] The price prediction module is used to predict the future prices of gasoline and diesel based on the historical marketing data and a pre-generated time-series marketing model. The future prices include both wholesale and retail prices of the gasoline and diesel. The endogenous variables of the time-series marketing model include crude oil properties, processing capacity, and market supply and demand. The number of autoregressive terms in the time-series marketing model is determined by a pre-generated autocorrelation function, which is:

[0026] δ(k)=cov(X t ,X t+k ) / var(X t )

[0027] Among them, X t Let be the time series of crude oil properties, processing capacity, and market supply and demand in period t; δ(k) is the autocorrelation coefficient with a lag of k periods; cov(X) t ,X t+k ) is X t and X t+k covariance, var(X) t ) is X t The variance;

[0028] The number of moving average terms in the time series marketing model is determined by a pre-generated partial autocorrelation function, which is:

[0029]

[0030] in, For X t The predicted value obtained by performing linear regression; For X t+k The predicted value obtained by linear regression; η(k) is the partial autocorrelation coefficient with a lag of k periods;

[0031] The marketing plan determination module is used to determine the marketing plan for the gasoline and diesel based on the future prices of the gasoline and diesel.

[0032] In some embodiments of the present invention, a gasoline and diesel marketing device based on wholesale and retail integration further includes:

[0033] The characteristic equation solving module is used to solve the characteristic equation of the time series.

[0034] The result generation module is used to determine whether the characteristic equation contains a unit root, so as to generate a determination result;

[0035] The difference determination module is used to determine whether the time series needs to be differencing based on the determination result.

[0036] In some embodiments of the present invention, the difference determination module includes:

[0037] A first-order difference unit is used to perform first-order difference on the time series when the characteristic equation contains the unit root;

[0038] The second-order difference unit is used to perform second-order differencing on the time series after first-order differencing when the characteristic equation corresponding to the first-order differencing contains a unit root.

[0039] In some embodiments of the present invention, the exogenous variables of the time series marketing model include: the sales region of the gasoline and diesel, the sales price, the factors affecting the sales price, the sales prices of competitors, and the sales volume of competitors.

[0040] Thirdly, the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of a wholesale-retail integrated gasoline and diesel marketing method.

[0041] Fourthly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a wholesale-retail integrated gasoline and diesel marketing method.

[0042] Fifthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a wholesale-retail integrated gasoline and diesel marketing method.

[0043] As described above, embodiments of the present invention provide a method and apparatus for marketing gasoline and diesel based on wholesale and retail integration. The corresponding method for marketing gasoline and diesel based on wholesale and retail integration includes: acquiring historical marketing data of gasoline and diesel; predicting future prices of gasoline and diesel based on historical marketing data and a pre-generated time series marketing model; wherein, the future prices include wholesale and retail prices of gasoline and diesel; and the endogenous variables of the time series marketing model include: crude oil properties, processing capacity, and market supply and demand; the number of autoregressive terms in the time series marketing model is determined by a pre-generated autocorrelation function, the number of moving average terms in the time series marketing model is determined by a pre-generated partial autocorrelation function, and a marketing plan for gasoline and diesel is determined based on the future prices of gasoline and diesel.

[0044] In establishing a time-series marketing model, this invention considers not only the inherent variation patterns of variables but also the influence of exogenous variables, thus improving the accuracy of prediction results. Furthermore, this invention uses wholesale and retail sales of diesel fuel as the explained variables, aiming to optimize the wholesale-retail structure, making it more practical for actual business operations. Attached Figure Description

[0045] 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.

[0046] Figure 1 This is a flowchart illustrating the gasoline and diesel marketing method based on wholesale and retail integration, as described in an embodiment of the present invention. Figure 1 ;

[0047] Figure 2 This is a flowchart illustrating the gasoline and diesel marketing method based on wholesale and retail integration, as described in an embodiment of the present invention. Figure 2 ;

[0048] Figure 3 This is a flowchart illustrating step 600 of the wholesale-retail integrated gasoline and diesel marketing method in an embodiment of the present invention.

[0049] Figure 4 This is a flowchart illustrating the gasoline and diesel marketing method based on wholesale and retail integration in a specific embodiment of the present invention.

[0050] Figure 5 This is a logical diagram illustrating a wholesale-retail integrated gasoline and diesel marketing method according to a specific embodiment of the present invention.

[0051] Figure 6 This is a schematic diagram illustrating the changes in oil price prediction error in a specific embodiment of the present invention;

[0052] Figure 7 This is a schematic diagram illustrating the specific decomposition of the gross profit maximization function in a specific embodiment of the present invention;

[0053] Figure 8 This is a schematic diagram illustrating the relationship between the retail prices of Company C and Company D in a specific embodiment of the present invention;

[0054] Figure 9 The block shown is a gasoline and diesel marketing device based on wholesale and retail integration in an embodiment of the present invention. Figure 1 ;

[0055] Figure 10The block shown is a gasoline and diesel marketing device based on wholesale and retail integration in an embodiment of the present invention. Figure 2 ;

[0056] Figure 11 This is a block diagram of the difference determination module 60 in an embodiment of the present invention;

[0057] Figure 12 This is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. Detailed Implementation

[0058] 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.

[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] It should be noted that the terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses. Without conflict, the embodiments and features in the embodiments of this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0061] The acquisition, storage, use, and processing of data in this application comply with relevant laws and regulations.

[0062] An embodiment of the present invention provides a specific implementation of a gasoline and diesel marketing method based on wholesale and retail integration. See [link to relevant documentation]. Figure 1 The method specifically includes the following:

[0063] Step 100: Obtain historical marketing data for gasoline and diesel;

[0064] Step 200: Predict the future prices of gasoline and diesel based on the historical marketing data and the pre-generated time series marketing model; wherein the future prices include the wholesale and retail prices of gasoline and diesel; and the endogenous variables of the time series marketing model include: crude oil properties, processing capacity, and market supply and demand; the number of autoregressive terms in the time series marketing model is determined by the pre-generated autocorrelation function, which is:

[0065] δ(k)=cov(X t ,X t+k ) / var(X t )

[0066] Among them, X t Let be the time series of crude oil properties, processing capacity, and market supply and demand in period t; δ(k) is the autocorrelation coefficient with a lag of k periods; cov(X) t ,X t+k ) is X t and X t+k covariance, var(X) t ) is X t The variance;

[0067] The number of moving average terms in the time series marketing model is determined by a pre-generated partial autocorrelation function, which is:

[0068]

[0069] in, For X t The predicted value obtained by performing linear regression; For X t+k The predicted value obtained by linear regression; η(k) is the partial autocorrelation coefficient with a lag of k periods;

[0070] Step 300: Determine the marketing plan for the gasoline and diesel based on the future prices of the gasoline and diesel.

[0071] As described above, this invention provides a gasoline and diesel marketing method based on wholesale and retail integration, comprising: acquiring historical marketing data of gasoline and diesel; predicting future prices of gasoline and diesel based on the historical marketing data and a pre-generated time series marketing model; wherein, the future prices include wholesale and retail prices of gasoline and diesel; and the endogenous variables of the time series marketing model include: crude oil properties, processing capacity, and market supply and demand; the number of autoregressive terms in the time series marketing model is determined by a pre-generated autocorrelation function, the number of moving average terms in the time series marketing model is determined by a pre-generated partial autocorrelation function, and a marketing plan for gasoline and diesel is determined based on the future prices of gasoline and diesel.

[0072] This invention considers a comprehensive range of explanatory variables, including competitors' sales, the impact of social resources, and the macroeconomic situation and market environment of the region. It also has predictive capabilities, obtaining the sales volume corresponding to the price based solely on historical patterns. Furthermore, this invention introduces a time series forecasting model to predict the market environment and competitors, thereby deriving the corresponding sales volume of the company.

[0073] For step 100, the historical marketing data for gasoline and diesel includes the following:

[0074] 1. Sales volume data:

[0075] Daily sales volume: The total amount of gasoline and diesel sold each day, categorized by region and channel (gas stations, wholesale, retail, etc.).

[0076] Monthly / Quarterly / Annual Sales Volume: Sales data summarized by month, quarter, or year, making it easy to observe trends and seasonal changes.

[0077] By product category: Sales volume of different types of gasoline and diesel (such as 92-octane gasoline, 95-octane gasoline, 0-grade diesel, etc.).

[0078] 2. Price data:

[0079] Retail price: The price that gas stations sell to consumers, usually divided into different grades of gasoline and diesel.

[0080] Wholesale price: The price at which refineries or distributors sell to downstream wholesalers or large customers.

[0081] Historical price trends: Price fluctuations over a period of time, which helps in analyzing market trends.

[0082] International oil price comparison: Comparing domestic gasoline and diesel prices with international crude oil prices, and analyzing the correlation between the two.

[0083] 3. Market share

[0084] Brand market share: The market share of different brands (such as Company C, Company D, Shell, etc.), calculated by region or nationwide.

[0085] Competitor analysis: Sales performance and market share changes of major competitors.

[0086] Product market share: The percentage of different grades of gasoline or diesel in the market.

[0087] 4. Marketing campaign data

[0088] Promotional activities: the implementation time and effects (increased sales) of promotional measures such as discounts, points programs, and free gifts.

[0089] Advertising investment: The amount of advertising and its impact on sales.

[0090] Customer loyalty programs: the implementation and effectiveness of programs such as membership cards and fuel point programs.

[0091] 5. Consumer behavior data

[0092] Demand elasticity: Sensitivity analysis of gasoline and diesel demand to price changes.

[0093] Consumer preferences: The preferences of different regions or groups for different grades of gasoline and diesel.

[0094] Purchase channels: How consumers purchase gasoline and diesel (such as gas stations, mobile refueling trucks, etc.).

[0095] 6. Supply chain and inventory data

[0096] Inventory levels: The inventory levels of refineries, wholesalers, and retailers at different times.

[0097] Supply chain disruptions: Supply chain problems caused by factors such as natural disasters and international situations, and their impact on the market.

[0098] 8. Macroeconomic data

[0099] Economic growth and consumption: the relationship between GDP growth rate, inflation rate and gasoline and diesel consumption.

[0100] Transportation data: The relationship between changes in vehicle ownership, traffic flow, and gasoline and diesel demand.

[0101] 9. Environmental and External Impacts

[0102] Seasonal effects: such as the difference in demand between summer and winter, due to the influence of seasonal use (such as winter heating demand, peak tourist season).

[0103] Natural disasters and emergencies, such as typhoons and earthquakes, can impact the supply chain and cause fluctuations in sales volume.

[0104] Regarding step 200, the invention uses wholesale and retail sales of diesel fuel as the explained variables to optimize the wholesale-retail structure, making it more feasible in actual business operations. Furthermore, the core of optimizing the time-series marketing model lies in optimizing the wholesale-retail structure and the wholesale-retail price difference.

[0105] It is understandable that the time series marketing model in step 200 is a data set arranged in chronological order. This time series appears to be a random process, but its underlying logic has the following relationship: the retail and wholesale prices of diesel and gasoline are necessarily related to various dependent variables. After discovering this necessary relationship, the optimal retail and wholesale prices of diesel and gasoline in the future can be accurately predicted. This is the purpose of constructing the time series marketing model in this application.

[0106] For step 300, specifically, the optimal solution of the time series marketing model is sought with the goal of maximizing the gross profit of diesel and gasoline retail and wholesale.

[0107] In some embodiments of the present invention, see Figure 2 A gasoline and diesel marketing method based on wholesale and retail integration also includes:

[0108] Step 400: Solve the characteristic equation of the time series;

[0109] First, solve the characteristic equation of the autoregressive component in the time series. Then, solve the characteristic equation of the moving average component in the time series. Finally, combine the characteristic equations of the autoregressive component and the moving average component to generate the characteristic equation of the time series.

[0110] Step 500: Determine whether the characteristic equation contains a unit root to generate a determination result;

[0111] Let the characteristic equation be:

[0112] P(λ)=λ n +a n-1 λ n-1 +a n-2 λ n-2 +...+a1λ+a0=0

[0113] A unit root is a complex root with a modulus of 1, denoted as λ = e^(-1 / 2). iθ , where θ is a real number.

[0114] First, try to solve the characteristic equation directly to find its roots. Then use the Routh-Hurwitz criterion: for higher-order polynomials, the Routh-Hurwitz criterion can be used to determine the stability of the system, and also indirectly determine whether a unit root exists. This can be expressed as λ = e^(-λ / 2). iθ Substitute the values ​​into the characteristic equation and check if there exists a value of θ that satisfies the equation. If the coefficients of the characteristic equation satisfy certain characteristics (e.g., all are real numbers), it can be inferred whether a root of unity exists.

[0115] Step 600: Determine whether the time series needs to be differencing based on the determination result.

[0116] In some embodiments of the present invention, see Figure 3 Step 600 includes:

[0117] Step 601: When the characteristic equation contains the unit root, perform a first-order difference on the time series;

[0118] The first-order difference refers to the difference between each data point in a sequence and the previous data point.

[0119] Step 602: When the characteristic equation corresponding to the first-order difference contains a unit root, perform a second-order difference on the time series after the first-order difference.

[0120] Second-order differencing refers to the difference between first-order differencing sequences. It can be understood that first-order differencing removes linear trends, while second-order differencing further removes non-linear trends.

[0121] In some embodiments of the present invention, the exogenous variables of the time series marketing model include: the sales region of the gasoline and diesel, the sales price, the factors affecting the sales price, the sales prices of competitors, and the sales volume of competitors.

[0122] The discussion on the sales regions, prices, and influencing factors of gasoline and diesel involves multiple aspects. The following is a brief analysis of these aspects:

[0123] The sales regions for gasoline and diesel are mainly determined based on the following factors:

[0124] Geographical location: Sales areas typically cover different provinces, cities, towns, and other regions within a country or region. Supply and demand dynamics, transportation costs, and tax policies in different regions all influence gasoline and diesel sales.

[0125] Market type: Sales regions can be subdivided into urban markets, rural markets, industrial areas, etc. Urban markets have high demand and fierce competition, while rural markets have relatively lower demand, but prices fluctuate more due to supply chain factors.

[0126] Transportation and Logistics: The sales area of ​​petroleum products is also closely related to transportation routes and logistics facilities. Areas near refineries or with good port facilities typically have lower prices.

[0127] The selling price of gasoline and diesel is a dynamic factor, influenced by a variety of variables:

[0128] International oil prices: This forms the basis for gasoline and diesel pricing. Fluctuations in international crude oil prices directly affect the wholesale and retail prices of gasoline and diesel.

[0129] Supply chain costs include costs associated with refining, transportation, storage, and distribution. Transportation costs are closely related to factors such as geographical location, mode of transport, and energy prices.

[0130] Market demand: Seasonal changes in demand (such as increased demand for heating in winter) and socio-economic activities (such as peak traffic during holidays) can all affect market demand, thereby affecting prices.

[0131] Competition: In a free market economy, competition among multiple suppliers affects the price of gasoline and diesel. Competition among gas stations, as well as between local and multinational corporations, all influence prices.

[0132] Factors affecting sales prices: The main factors influencing the sales prices of gasoline and diesel can be summarized as follows:

[0133] Currency exchange rate: International crude oil transactions are mostly settled in US dollars. Exchange rate fluctuations affect import costs, which in turn affect domestic gasoline and diesel prices.

[0134] National policies: Adjustments to policies such as fuel tax, environmental protection policies, and energy subsidies will directly affect the final sales price.

[0135] Supply and demand: Tight or surplus supply within a region, as well as changes in consumer demand, will affect the pricing of gasoline and diesel.

[0136] Seasonal factors, such as increased demand for diesel in winter (for heating) or increased demand for gasoline in summer (peak travel season), can cause price fluctuations.

[0137] Market structure: including the degree of market monopoly and the state of competition, which affects the formation of gasoline and diesel prices.

[0138] Inventory levels: Inventory levels at refineries and gas stations also affect prices. High inventory levels and ample supply may lead to lower prices; conversely, low inventory levels may lead to higher prices.

[0139] Transportation and logistics costs: Especially in some remote areas or islands, transportation and logistics costs are high, which directly drives up the retail prices of gasoline and diesel.

[0140] In one specific embodiment, the present invention also provides a specific implementation of a gasoline and diesel marketing method based on wholesale and retail integration, see [link to implementation details]. Figure 4 as well as Figure 5 Specifically, it includes the following:

[0141] Overall, the specific implementation of the wholesale-retail integrated gasoline and diesel marketing method consists of three steps: First, based on relevant historical data of the region, a model is established to depict the interaction between wholesale and retail volume and price, laying the foundation for the work. Second, mathematical methods are used to find the composition logic of local market sales and the price formation mechanism, establishing a key model to predict competitors' prices and the range of volume and price for the company itself, and determining the possible achievable range of volume and price. Third, based on the company's own market share target, a function of gross profit and wholesale-retail volume and price is established, seeking the optimal solution for wholesale-retail volume and price with the goal of maximizing gross profit. Specifically, this includes the following steps:

[0142] Step S1: Establish a simultaneous equation model;

[0143] Characterizing the interaction between wholesale and retail volume and price is mainly achieved by establishing a set of equations to find the influence relationship between volume and price. The refined oil market can be divided into wholesale and retail markets according to differences in sales models, and there is a relatively close connection between the two markets. Simulating a single market presents certain problems: the model contains multiple endogenous variables, and using least squares regression on each one leads to inconsistent parameter estimates; the relationships between variables cannot be simulated using simple linear regression, requiring the establishment of a holistic system and parameter estimation.

[0144] Simultaneous equation modeling, based on economic theory and certain assumptions, distinguishes various economic variables and establishes a set of equations to describe the simultaneous relationships between them. This model describes the causal relationships between variables bidirectionally, and can more comprehensively and realistically reflect the operation of the economic system.

[0145] The process of establishing a system of simultaneous equations involves three steps: First, based on market characteristics, determine the factors influencing the quantity and price in the wholesale and retail markets, and based on factors such as the economy, supply and demand, policies, market structure, and enterprise status in different provinces, identify and adjust specific relevant variables. Second, establish four models for the influence of quantity and price. Third, introduce data for empirical calculations.

[0146] Step S2: Explore the principles of volume and price formation and establish a time-series marketing model.

[0147] Sales fluctuations are influenced by both their own inherent volatility and external and random factors. Understanding sales volume primarily involves grasping the impact of endogenous, exogenous, and random factors. From a mathematical perspective, endogenous factors are mainly understood through time series analysis, while exogenous variables are primarily understood through transfer functions.

[0148] By identifying patterns in internal and external factors, different methods are used to predict relevant variables in the time-series marketing model. First, macroeconomic variables are predicted, primarily because the release of current macroeconomic data is often delayed, failing to meet practical forecasting needs. A time-series marketing model is established based on the cyclical and seasonal fluctuations of macroeconomic variables. Second, competitor prices are predicted using econometric models based on changes in international oil prices for wholesale and retail prices. Third, competitor wholesale and retail volumes are predicted using econometric models based on macroeconomic variables and international oil prices.

[0149] Unlike traditional demand forecasting models that employ regression analysis, scenario analysis, and industry decomposition frameworks, the time-series marketing model presented in this application innovatively forms a dual-period dynamic model based on the transfer function model. This model establishes the cross-correlation relationship between independent and dependent variables through regression analysis, quantifying the impact of changes in independent variables at any given historical period on the current dependent variable. On one hand, traditional models or methods often require determining the values ​​of independent variables, meaning they first need to predict the independent variables. This prediction itself is potentially more complex than predicting the dependent variable. This application effectively solves this problem by identifying leading variables, resulting in more accurate and convenient predictions. On the other hand, traditional models can only provide static interpretations of the relationships between variables, while the time-series marketing model reflects the dynamic relationship between dependent and independent variables, fully demonstrating the process of interaction between variables and avoiding the assumption that short-term fluctuations represent the entire impact. Furthermore, through scientific parameter settings, the time-series marketing model focuses on observing changes in core variables such as "economic development indicators" and "industry representative indicators," absorbing the influence of numerous unpredictable factors and accurately predicting changes in independent variables over the next 1-2 months. Key technical indicators include leading indicators of economic development and industry prosperity indicators. For example, "leading indicators of economic development" can indicate the cyclical changes of independent variables such as fixed asset investment preceding those of the dependent variable; "industry prosperity indicators" can, based on relevant industry data, indicate the degree of prosperity or decline in related industries at present and in the future. From a technical perspective, the time-series marketing model boasts high predictive accuracy, exceeding 95%; it also has broad application and can be used for research related to refined oil demand forecasting. In terms of prediction accuracy, the average absolute error rate between the predicted values ​​of the time-series marketing model and the actual apparent consumption is less than 2%. (See [link to relevant documentation]). Figure 6 .

[0150] Step S3: Solve the nonlinear programming problem.

[0151] This application addresses the problem of maximizing the overall benefits of two markets (wholesale and retail), which cannot be solved by theoretical analysis of a single market. In addition to analyzing the market situation, it is necessary to constrain the model based on specific operational conditions and objectives. The model is further optimized based on the market competition model, and mathematical programming methods from operations research are applied for calculation.

[0152] See Figure 7 The objective function of this model is to maximize the gross profit of the wholesale and retail integration of each regional company. 1) Gross profit = Sales revenue - Purchase cost; 2) Depending on the sales company's business policies, the regional companies will receive different subsidies depending on their plan completion status; 3) The revised gross profit formula is: Gross profit = Sales revenue - Purchase cost + Subsidy.

[0153] The model's constraints are as follows: 1) The wholesale price, retail price, wholesale volume, and retail volume of diesel fuel follow the boundary conditions of the quantity-price relationship; 2) The relative market share of diesel retail and wholesale is not lower than the level of the same period last year; 3) All wholesale and retail quantity-price variables are non-negative. The solution approach is equilibrium solution: based on the constraints, find the point in the set of points that satisfy the relevant constraints in space that maximizes the objective function.

[0154] Application effect:

[0155] Taking Region A as an example, by combining the correlation between factors and the sales entity's requirements for market share, constraints are set, and the optimal wholesale and retail sales structure, optimal price difference structure, and maximum gross profit of the sales company in Region A in May 2023 are calculated through a programming solution model.

[0156] It is recommended that the sales company in Region A increase its retail and wholesale prices by RMB 256 / ton and RMB 35 / ton respectively in May 2023 compared to April, widening the wholesale-retail price difference to around RMB 638 / ton. The optimal values ​​for diesel retail volume, diesel wholesale volume, diesel retail price, and diesel wholesale price are 191,000 tons, 141,000 tons, RMB 8,426 / ton, and RMB 7,788 / ton respectively. Sales based on this wholesale-retail volume and price combination can achieve a gross profit of RMB 210 million. See Table 1.

[0157] Table 1. Optimization Solution Results (May)

[0158] variable name name unit April actual value May Recommended Value π gross profit Ten thousand yuan -- 21493 qcry Company C's retail volume ton 225478 190612 pcry Company C retail price Yuan / ton 8170 8426 qcwy Company C wholesale volume ton 179582 141294 pcwy Company C wholesale price Yuan / ton 7753 7788

[0159] The sales company in Region A, referring to the suggested wholesale-retail volume-price sales combination, arranged its wholesale-retail structure and wholesale-retail price difference for May. In terms of actual implementation, in May, the sales company in Region A followed the model's recommendations to lower both retail and wholesale prices, ultimately achieving an average price close to the model's price, realizing a gross profit of 190 million yuan, achieving 89% of the optimal gross profit (see Table 2).

[0160] Table 2. Comparison of Actual Operation and Model of Inner Mongolia Sales Company in May 2023

[0161]

[0162] Specific application examples:

[0163] Using data from January 2016 to April 2023, the independent variables were screened using the methods provided in this application. The explanatory power for the wholesale and retail market reached 96%. The final results of the diesel wholesale and retail volume-price relationship for the sales company in Region B are as follows.

[0164] A basic assessment of the refined oil market for sales companies in Region B was conducted. Combining national macroeconomic variables and the situation of oil-consuming industries, a time-series marketing model for sales companies in Region B was established using data from January 2013 to April 2023, achieving a 96% explanatory power for the wholesale and retail market. Specifically:

[0165] Company C's retail diesel price is affected by Company C's retail volume and Company D's (Company C's competitor) wholesale diesel price. The time-series marketing model has an explanatory power of 98.2%. The model shows that for every 100 tons / month decrease in Company C's retail volume, Company C's average monthly retail price increases by 12 yuan / ton; for every 100 yuan / ton increase in Company D's wholesale price, Company C's average monthly retail price increases by 91 yuan / ton.

[0166] Company C's wholesale diesel price is affected by its wholesale volume and Brent crude oil price. The wholesale price model explains 96.9% of the data. The model shows that for every 1000 tons / month decrease in Company C's wholesale volume, its average monthly wholesale price increases by 17 yuan / month; for every 10 USD / barrel increase in Brent crude oil price, Company C's average monthly wholesale price increases by 549 yuan / ton.

[0167] Company C's diesel retail volume is affected by Company C's retail price, Company D's retail volume, local refinery prices, and fleet truck discount rates. The retail volume model explains 96.4% of the total volume. For every 100 yuan / ton decrease in Company C's diesel retail price, Company C's retail volume increases by 265 tons / month; for every 100 tons / month increase in Company D's diesel retail volume, Company C's retail volume increases by 47 tons / month; for every 100 yuan / ton increase in local refinery prices, Company C's retail volume increases by 310 tons / month; and for every 0.1% increase in the fleet truck discount rate, Company C's retail volume increases by 152 tons / month.

[0168] Company C's diesel wholesale volume is affected by Company C's wholesale price, Company D's wholesale volume, Brent crude oil price, the number of Company C's self-operated gas stations, and customer inventory. The wholesale volume model explains 92.0% of the data. The model shows the following correlations: for every 10 yuan / ton decrease in Company C's wholesale price, Company C's wholesale volume increases by 101 tons / month; for every 100 yuan / ton increase in Company D's wholesale volume, Company C's wholesale volume increases by 27 tons / month; for every 1 USD / barrel increase in Brent crude oil price, Company C's wholesale volume increases by 683 tons / month; for every additional self-operated gas station in Company C's fleet, Company C's wholesale volume increases by 172 tons / month; and for every 100 tons / month increase in customer inventory, Company C's wholesale volume increases by 15 tons / month. (See [link to relevant documentation]). Figure 8 .

[0169] In summary, the influencing factors and fit of the diesel volume-price model for the sales company in Region B are shown in Figure 0.

[0170] Table 3B: Factors Influencing Diesel Quantity-Price Model and Fit of Regional Sales Companies

[0171]

[0172] Prediction of factors influencing volume and price: Before calculation, it is necessary to predict some independent variables. In the sales model of sales company in region B, the independent variables that need to be predicted are: wholesale volume of diesel from company D, retail volume of diesel from company D, local refinery price, wholesale price of diesel from company D, Brent crude oil price, and customer inventory.

[0173] Taking Company D's wholesale price as an example, a time series marketing model was used to identify a strong correlation between Company D's wholesale price and Brent crude oil prices. The wholesale price of diesel from Company D in May was then predicted. The relationship between the two is shown in [the original text]. Figure 7 .

[0174] Based on the actual and forecast data for May, input the following variable values. The forecast model results are shown in Table 4.

[0175] Table 4. Input variables for the mathematical programming model (May)

[0176]

[0177] Establishment of the optimization solution model: Appropriate market share constraints must be set during model operation. Specifically, the relative market share of diesel retail sales by Company B in May (retail volume of Company C / retail volume of the two major groups) must not be lower than the market share in April (27.4%); the relative market share of diesel wholesale by Company C in May (wholesale volume of Company C / wholesale volume of the two major groups) must not be lower than the market share in April (30.4%). This application considers the scenario without planning constraints.

[0178] The objective function set in this model is to maximize the gross profit of the wholesale and retail sales of the sales company in region B.

[0179] That is, maximum gross profit = retail volume * retail price + wholesale volume * wholesale price - retail plan × (average monthly retail price of diesel - 700) - (retail volume + wholesale volume - retail plan) × market price.

[0180] The application of the time series marketing model, combined with the correlation between factors and the sales entity's requirements for market share, sets constraints and uses a programming solution model to calculate the optimal wholesale and retail sales structure, optimal price difference structure, and maximum gross profit for the sales company in Region B in May 2023, assuming that the diesel sales share does not decrease.

[0181] It is recommended that the sales company in Region B increase the retail price by RMB 11.3 / ton and decrease the wholesale price by RMB 540.6 / ton in May 2023 compared to April, widening the wholesale-retail price difference to approximately RMB 894.9 / ton. The optimal values ​​for diesel retail volume, diesel wholesale volume, diesel retail price, and diesel wholesale price are 17,000 tons, 15,000 tons, RMB 8,153.3 / ton, and RMB 7,258.4 / ton, respectively. Based on this combination of wholesale and retail volume and price, a gross profit of RMB 8.73 million can be achieved (see Table 5).

[0182] Table 5. Optimization Solution Results (May)

[0183]

[0184] As described above, the specific embodiments of the present invention provide a gasoline and diesel marketing method based on wholesale and retail integration, including: acquiring historical marketing data of gasoline and diesel; predicting future prices of gasoline and diesel based on the historical marketing data and a pre-generated time series marketing model; wherein, the future prices include wholesale and retail prices of gasoline and diesel; and the endogenous variables of the time series marketing model include: crude oil properties, processing capacity, and market supply and demand; the number of autoregressive terms in the time series marketing model is determined by a pre-generated autocorrelation function, the number of moving average terms in the time series marketing model is determined by a pre-generated partial autocorrelation function, and a marketing plan for gasoline and diesel is determined based on the future prices of gasoline and diesel. The present invention has the following beneficial effects:

[0185] (1) Explore a method that can comprehensively calculate the optimal combination of wholesale and retail volume and price. Using this method, quantitative pricing strategies and sales targets can be given, and the maximum gross profit level can be predicted.

[0186] (2) The analysis reveals the factors affecting the wholesale and retail sales and prices of gasoline and diesel in various regions, providing assistance to companies in each region to grasp market characteristics and respond to market changes;

[0187] (3) Establish a marketing volume and price optimization model for diesel wholesale and retail in each region. The data frequency is monthly, which provides a basis for the head office and regional companies to dynamically formulate marketing strategies.

[0188] Based on the same inventive concept, this application also provides a wholesale-retail integrated gasoline and diesel marketing device, which can be used to implement the method described in the above embodiments, as shown in the following embodiments. Since the principle of solving the problem based on the wholesale-retail integrated gasoline and diesel marketing device is similar to that of the wholesale-retail integrated gasoline and diesel marketing method, the implementation of the wholesale-retail integrated gasoline and diesel marketing device can refer to the implementation of the wholesale-retail integrated gasoline and diesel marketing method, and repeated details will not be repeated. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0189] The embodiments of the present invention provide a specific implementation of a gasoline and diesel marketing device based on a wholesale-retail integration method, wherein, see [link to relevant documentation]. Figure 9 A wholesale-retail integrated gasoline and diesel marketing device includes:

[0190] Historical Marketing Data Acquisition Module 10 is used to acquire historical marketing data for gasoline and diesel.

[0191] Price prediction module 20 is used to predict the future prices of gasoline and diesel based on the historical marketing data and a pre-generated time series marketing model; wherein the future prices include the wholesale and retail prices of gasoline and diesel; and the endogenous variables of the time series marketing model include: crude oil properties, processing capacity, and market supply and demand; the number of autoregressive terms in the time series marketing model is determined by a pre-generated autocorrelation function, which is:

[0192] δ(k)=cov(X t ,X t+k ) / var(X t )

[0193] Among them, X t Let be the time series of crude oil properties, processing capacity, and market supply and demand in period t; δ(k) is the autocorrelation coefficient with a lag of k periods; cov(X) t ,X t+k ) is X t and X t+k covariance, var(X) t ) is X t The variance;

[0194] The number of moving average terms in the time series marketing model is determined by a pre-generated partial autocorrelation function, which is:

[0195]

[0196] in, For X t The predicted value obtained by performing linear regression; For X t+k The predicted value obtained by linear regression; η(k) is the partial autocorrelation coefficient with a lag of k periods;

[0197] Marketing plan determination module 30 is used to determine the marketing plan for the gasoline and diesel based on the future prices of the gasoline and diesel.

[0198] In some embodiments of the present invention, see Figure 10 A wholesale-retail integrated gasoline and diesel marketing device also includes:

[0199] Characteristic equation solving module 40 is used to solve the characteristic equation of the time series;

[0200] The result generation module 50 is used to determine whether the characteristic equation contains a unit root, so as to generate a determination result;

[0201] The difference determination module 60 is used to determine whether the time series needs to be differencing based on the determination result.

[0202] In some embodiments of the present invention, see Figure 11 The difference determination module 60 includes:

[0203] A first-order difference unit 60a is used to perform first-order difference on the time series when the characteristic equation contains the unit root;

[0204] The second-order difference unit 60b is used to perform second-order difference on the time series after first-order difference when the characteristic equation corresponding to the first-order difference contains a unit root.

[0205] In some embodiments of the present invention, the exogenous variables of the time series marketing model include: the sales region of the gasoline and diesel, the sales price, the factors affecting the sales price, the sales prices of competitors, and the sales volume of competitors.

[0206] As described above, embodiments of the present invention provide a wholesale-retail integrated gasoline and diesel marketing device. The corresponding wholesale-retail integrated gasoline and diesel marketing device (adapted to single-phase flow production profile) includes: a response reference generation module, used to generate an acoustic response reference under static liquid surface conditions in the wellbore based on multiple sensors preset in the distributed optical fiber in the wellbore; a response data acquisition module, used to acquire distributed optical fiber response data corresponding to each layer based on multiple sensors; and a production volume generation module, used to generate the production volume of the corresponding layer based on the acoustic response reference and the distributed optical fiber response data.

[0207] The embodiments of this application also provide a specific implementation of an electronic device capable of implementing all steps in the wholesale-retail integrated gasoline and diesel marketing method described in the above embodiments. See [link to relevant documentation]. Figure 12 The electronic devices specifically include the following:

[0208] Processor 1201, memory 1202, communications interface 1203, and bus 1204;

[0209] The processor 1201, memory 1202, and communication interface 1203 communicate with each other via bus 1204; the communication interface 1203 is used to realize information transmission between server-side devices and client-side devices and other related devices.

[0210] The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, it implements all the steps in the wholesale-retail integrated gasoline and diesel marketing method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0211] Step 100: Obtain historical marketing data for gasoline and diesel;

[0212] Step 200: Predict the future prices of gasoline and diesel based on the historical marketing data and the pre-generated time series marketing model; wherein the future prices include the wholesale and retail prices of gasoline and diesel; and the endogenous variables of the time series marketing model include: crude oil properties, processing capacity, and market supply and demand; the number of autoregressive terms in the time series marketing model is determined by the pre-generated autocorrelation function, which is:

[0213] δ(k)=cov(X t ,X t+k ) / var(X t )

[0214] Among them, X t Let be the time series of crude oil properties, processing capacity, and market supply and demand in period t; δ(k) is the autocorrelation coefficient with a lag of k periods; cov(X) t ,X t+k ) is X t and X t+k covariance, var(X) t ) is X t The variance;

[0215] The number of moving average terms in the time series marketing model is determined by a pre-generated partial autocorrelation function, which is:

[0216]

[0217] in, For X t The predicted value obtained by performing linear regression; For X t+k The predicted value obtained by linear regression; η(k) is the partial autocorrelation coefficient with a lag of k periods;

[0218] Step 300: Determine the marketing plan for the gasoline and diesel based on the future prices of the gasoline and diesel.

[0219] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the wholesale-retail integrated gasoline and diesel marketing method in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the wholesale-retail integrated gasoline and diesel marketing method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0220] Step 100: Obtain historical marketing data for gasoline and diesel;

[0221] Step 200: Predict the future prices of gasoline and diesel based on the historical marketing data and the pre-generated time series marketing model; wherein the future prices include the wholesale and retail prices of gasoline and diesel; and the endogenous variables of the time series marketing model include: crude oil properties, processing capacity, and market supply and demand; the number of autoregressive terms in the time series marketing model is determined by the pre-generated autocorrelation function, which is:

[0222] δ(k)=cov(X t ,X t+k ) / var(X t )

[0223] Among them, X t Let be the time series of crude oil properties, processing capacity, and market supply and demand in period t; δ(k) is the autocorrelation coefficient with a lag of k periods; cov(X) t ,X t+k ) is X t and X t+k covariance, var(X) t ) is X t The variance;

[0224] The number of moving average terms in the time series marketing model is determined by a pre-generated partial autocorrelation function, which is:

[0225]

[0226] in, For X t The predicted value obtained by performing linear regression; For X t+k The predicted value obtained by linear regression; η(k) is the partial autocorrelation coefficient with a lag of k periods;

[0227] Step 300: Determine the marketing plan for the gasoline and diesel based on the future prices of the gasoline and diesel.

[0228] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, hardware + program embodiments are relatively simple in description because they are fundamentally similar to method embodiments; relevant parts can be referred to the descriptions in the method embodiments.

[0229] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0230] While this application provides method operation steps as shown in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the method can be executed sequentially as shown in the embodiments or drawings, or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0231] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing the embodiments of this specification, the functions of each module can be implemented in one or more software and / or hardware components, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0232] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0233] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0234] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0235] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, system embodiments are basically similar to method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. In the description of this specification, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments in this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0236] The above description is merely an embodiment of the present specification and is not intended to limit the embodiments of the present specification. For those skilled in the art, various modifications and variations can be made to the embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of the present specification should be included within the scope of the claims of the embodiments of the present specification.

Claims

1. A method for marketing gasoline and diesel oil based on a bulk sale integration, characterized by, The method comprises: obtaining historical marketing data of gasoline and diesel oil; predicting future prices of the gasoline and diesel oil according to the historical marketing data and a pre-generated time series marketing model; wherein the future prices comprise wholesale prices and retail prices of the gasoline and diesel oil; and endogenous variables of the time series marketing model comprise crude oil properties, processing capacity and market supply and demand; a number of autoregressive terms of the time series marketing model is determined by a pre-generated autocorrelation function, the autocorrelation function being: delta(k) = cov(x t , x t+k ) / var(x t ) wherein X t is a time series consisting of the crude oil properties, processing capacity and market supply and demand in the tth period; δ(k) is an autocorrelation coefficient of a lag k period; cov(X t ,X t+k ) is a covariance of X t and X t+k ; and var(X t ) is a variance of X t . a number of moving average terms of the time series marketing model is determined by a pre-generated partial autocorrelation function, the partial autocorrelation function being: wherein X is t the predicted value from linear regression; X is t+k the predicted value from linear regression; η(k) is the lag-kth partial autocorrelation coefficient; determining a marketing scheme of the gasoline and diesel oil according to the future prices of the gasoline and diesel oil.

2. The gasoline-diesel marketing method according to claim 1, characterized by, The method further comprises: solving a characteristic equation of the time series; determining whether the characteristic equation contains a unit root to generate a determination result; determining whether the time series needs to be differenced according to the determination result.

3. The gasoline-diesel marketing method according to claim 2, characterized by, The determining whether the time series needs to be differenced according to the determination result comprises: performing first-order differencing on the time series when the characteristic equation contains the unit root; performing second-order differencing on the time series after first-order differencing when a characteristic equation corresponding to the time series after first-order differencing contains a unit root.

4. The gasoline-diesel marketing method according to claim 1, characterized by, Exogenous variables of the time series marketing model comprise sales regions, sales prices, sales price influencing factors, sales prices of competitors and sales volumes of competitors of the gasoline and diesel oil.

5. A gasoline and diesel oil marketing device based on a bulk sale integration, characterized by, The method comprises: a historical marketing data obtaining module configured to obtain historical marketing data of gasoline and diesel oil; a price predicting module configured to predict future prices of the gasoline and diesel oil according to the historical marketing data and a pre-generated time series marketing model; wherein the future prices comprise wholesale prices and retail prices of the gasoline and diesel oil; and endogenous variables of the time series marketing model comprise crude oil properties, processing capacity and market supply and demand; a number of autoregressive terms of the time series marketing model is determined by a pre-generated autocorrelation function, the autocorrelation function being: delta(k) = cov(x t , x t+k ) / var(x t ) wherein X t is the time series consisting of the crude oil properties, processing capacity and market supply and demand at the tth period; δ(k) is the autocorrelation coefficient of the lag k period; cov(X t , X t+k ) is the covariance of X t and X t+k ; var(X t ) is the variance of X t . a number of moving average terms of the time series marketing model is determined by a pre-generated partial autocorrelation function, the partial autocorrelation function being: wherein is X t the predicted value from linear regression; is X t+k the predicted value from linear regression; η(k) is the lag-kth partial autocorrelation coefficient; a marketing scheme determining module configured to determine a marketing scheme of the gasoline and diesel oil according to the future prices of the gasoline and diesel oil.

6. The gasoline-diesel marketing device according to claim 5, wherein The method further comprises: a characteristic equation solving module configured to solve a characteristic equation of the time series; a determination result generating module configured to determine whether the characteristic equation contains a unit root to generate a determination result; a differencing determining module configured to determine whether the time series needs to be differenced according to the determination result.

7. The gasoline-diesel marketing device according to claim 6, characterized by The differencing determining module comprises: a first-order differencing unit configured to perform first-order differencing on the time series when the characteristic equation contains the unit root; a second-order differencing unit configured to perform second-order differencing on the time series after first-order differencing when a characteristic equation corresponding to the time series after first-order differencing contains a unit root.

8. The gasoline-diesel marketing device according to claim 5, wherein Exogenous variables of the time series marketing model comprise sales regions, sales prices, sales price influencing factors, sales prices of competitors and sales volumes of competitors of the gasoline and diesel oil.

9. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions, when executed by the processor, implement the steps of the method for marketing gasoline and diesel oil based on the integration of batch and zero according to any one of claims 1 to 4.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor, when executing the program, implements the steps of the method for marketing gasoline and diesel oil based on the integration of batch and zero according to any one of claims 1 to 4.

11. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method for marketing gasoline and diesel oil based on the integration of batch and zero according to any one of claims 1 to 4.

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