Intelligent multi-zone fuel collaborative dispatching method and system

Abstract

The invention discloses an intelligent multi-region fuel collaborative dispatching method and system, and the method comprises the steps: obtaining and preprocessing the fuel consumption data of each region, building a fuel consumption prediction model of each region based on the preprocessed fuel consumption data, and carrying out the prediction according to the corresponding fuel consumption prediction model, obtaining fuel consumption prediction data of each region; acquiring fuel inventory data of each region, and determining residual fuel data characteristics of each region based on the fuel consumption prediction data and the fuel inventory data of each region; analyzing and evaluating the inventory condition of each region based on the remaining fuel data features to obtain an inventory condition evaluation value of each region; and determining the fuel dispatching priority of each area according to the inventory condition evaluation value, and carrying out cooperative dispatching on the fuel according to the fuel dispatching priority. According to the method, fuel allocation and transportation can be preferentially obtained in an accurate fuel area, the resource utilization efficiency can be improved, and the stability and sustainability of fuel supply are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel transportation, and in particular to an intelligent multi-regional coordinated fuel transportation method and system. Background Art

[0002] In modern society, fuel, as an important component of energy resources, plays a vital role in the normal operation of various industries and the development of society. However, due to the imbalance of energy demand between regions and the complexity of the energy supply chain, how to achieve intelligent coordinated transportation of fuel in multiple regions has become an urgent problem to be solved.

[0003] However, traditional multi-regional fuel coordinated transportation methods usually rely on experience and static rules, and lack intelligent analysis and utilization of large amounts of fuel consumption data and inventory data. This makes traditional methods limited in effectiveness when dealing with dynamic changes and complex situations. At the same time, the planning and decision-making of traditional methods often rely on the experience and subjective judgment of professionals, lack scientific data support and systematic decision-making models, and are easily affected by subjective factors. In addition, when faced with dynamic changes in fuel consumption and inventory, traditional methods lack real-time optimization and adjustment mechanisms, making it difficult to flexibly respond to emergencies and market fluctuations. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides an intelligent multi-regional fuel coordinated transportation method and system, comprising: Obtaining fuel consumption data of each area and preprocessing the fuel consumption data of each area; Building a fuel consumption prediction model for each area based on the preprocessed fuel consumption data of each area, and performing predictions based on the fuel consumption prediction model of each area to obtain fuel consumption prediction data for each area; Obtaining fuel inventory data for each region, and determining remaining fuel data characteristics for each region based on fuel consumption forecast data and fuel inventory data for each region; Analyze and evaluate the inventory status of each region based on the remaining fuel data characteristics to obtain an inventory status evaluation value of each region; The fuel transportation priority for each region is determined based on the inventory status assessment value, and the fuel for each region is coordinated and transported based on the fuel transportation priority.

[0005] Furthermore, the preprocessing of the fuel consumption data of each region includes: The fuel consumption data of each region is preprocessed, which includes removing abnormal data and overloaded data in the fuel consumption data of each region and filling in missing data, and standardizing the fuel consumption data of each region.

[0006] Furthermore, the fuel consumption prediction model of each area is constructed based on the preprocessed fuel consumption data of each area, and prediction is performed according to the fuel consumption prediction model of each area to obtain the fuel consumption prediction data of each area, including: A data set is constructed based on the preprocessed fuel consumption data of each region, and the data set is input into a preset neural network model to construct an initial fuel consumption prediction model for each region; The data set is divided into a training set and a test set according to a certain ratio, and the training set and the test set are input into the corresponding initial model for fuel consumption prediction; Training and testing each initial fuel consumption prediction model until the initial fuel consumption prediction model meets a preset convergence condition, thereby obtaining a fuel consumption prediction model for each region; The fuel consumption forecast data of each area in the future is predicted based on the obtained fuel consumption forecast model of each area.

[0007] Further, the obtaining of fuel inventory data of each region and determining the remaining fuel data characteristics of each region based on the fuel consumption forecast data and the fuel inventory data of each region include: Obtain the fuel inventory data of each area, and perform a difference calculation between the fuel consumption forecast data of each area and the fuel inventory data to obtain the remaining fuel data of each area; Plotting the remaining fuel data into a remaining fuel curve according to the time sequence, and determining the peak value and the trough value from the remaining fuel curve; Based on the peak value and the trough value, a descending curve segment corresponding to each peak value to the trough value and an ascending curve segment corresponding to each trough value to the peak value are determined, and the slope of each descending curve segment and the ascending curve segment are calculated to obtain a descending slope corresponding to each descending curve segment and an ascending slope corresponding to each ascending curve segment; respectively calculating a first average value of the descending slopes corresponding to all descending curve segments and a second average value of the ascending slopes corresponding to all ascending curve segments in each remaining fuel curve; Determine a first average value of the descending slopes corresponding to all the descending curve segments as a first data feature, and determine a second average value of the ascending slopes corresponding to all the ascending curve segments as a second data feature; The remaining fuel data feature of each region is constituted by the first data feature and the second data feature.

[0008] Furthermore, the inventory status of each region is analyzed and evaluated based on the remaining fuel data characteristics to obtain an inventory status evaluation value of each region, including: Obtaining a first data feature and a second data feature corresponding to each region, and respectively determining a first average value corresponding to the first data feature and a second average value corresponding to the second data feature; Subtracting a first average value corresponding to the first data feature from a preset first threshold value to obtain a first difference value, and subtracting a second average value corresponding to the second data feature from a preset second threshold value to obtain a second difference value; The first data feature is evaluated and valued according to the size of the first difference to obtain a first evaluation value, and the second data feature is evaluated and valued according to the size of the second difference to obtain a second evaluation value; The inventory status evaluation value of each region is obtained by performing calculation based on the first evaluation value and the second evaluation value of each region and the preset weight corresponding to each evaluation value.

[0009] Furthermore, the calculation based on the first evaluation value and the second evaluation value of each region and the preset weight corresponding to each evaluation value to obtain the inventory status evaluation value of each region includes: An evaluation calculation is performed based on the first evaluation value and the second evaluation value of each region and the preset weight corresponding to each evaluation value to obtain the inventory status evaluation value of each region, wherein the calculation formula of the inventory status evaluation value of each region is: ; Among them, S is the inventory status evaluation value of each region, represents a natural exponential function, A is a first evaluation value, α is a preset weight corresponding to the first evaluation value, B is a second evaluation value, and β is a preset weight corresponding to the second evaluation value.

[0010] Furthermore, determining the fuel transportation priority of each region according to the inventory status evaluation value includes: A fuel transfer priority-inventory status assessment value interval correspondence relationship is preset, and the fuel transfer priority-inventory status assessment value interval correspondence relationship is associated with a corresponding fuel transfer priority for each inventory status assessment value interval; The inventory status assessment value is obtained, and based on the mapping relationship between the inventory status assessment value interval to which the inventory status assessment value belongs and the fuel transfer priority-inventory status assessment value interval correspondence relationship, the fuel transfer priority corresponding to the inventory status assessment value interval is selected and determined as the fuel transfer priority for each area.

[0011] The present invention also provides an intelligent multi-regional fuel coordinated transportation system, comprising: An acquisition module, used for acquiring the fuel consumption data of each area and preprocessing the fuel consumption data of each area; A prediction module is used to construct a fuel consumption prediction model for each area based on the preprocessed fuel consumption data of each area, and to perform prediction according to the fuel consumption prediction model of each area to obtain fuel consumption prediction data for each area; A determination module, used to obtain fuel inventory data of each area, and determine the remaining fuel data characteristics of each area based on the fuel consumption forecast data and the fuel inventory data of each area; An evaluation module, used to analyze and evaluate the inventory status of each area based on the remaining fuel data characteristics, and obtain an inventory status evaluation value of each area; The transportation module is used to determine the fuel transportation priority of each area according to the inventory status assessment value, and to coordinate the transportation of fuel to each area according to the fuel transportation priority.

[0012] Compared with the prior art, the intelligent multi-regional fuel coordinated transportation method and system according to the embodiment of the present invention has the following beneficial effects: By constructing a fuel consumption prediction model, the present invention can accurately predict the fuel consumption of each area, which helps to rationally plan fuel supply and transportation; Based on the fuel consumption forecast data and fuel inventory data, the present invention can carry out refined management of the remaining fuel in each area, minimize the situation of excess or insufficient inventory, and achieve inventory optimization; The present invention can realize intelligent dispatching and coordinated dispatching by determining the priority of fuel dispatching in each area, thereby ensuring the effective utilization and reasonable allocation of fuel resources; The present invention can achieve supply and demand balance by analyzing and evaluating the inventory status of each area, avoiding problems caused by insufficient or excessive fuel supply, and ensuring normal production and living needs in each area; The present invention can realize data-driven decision-making and improve the scientificity and accuracy of decision-making by analyzing a large amount of fuel consumption and inventory data. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the process structure of the intelligent multi-regional fuel coordinated transportation method in an embodiment of the present invention; Figure 2 It is a schematic diagram of the composition of the intelligent multi-region fuel coordinated transportation system in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0015] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0016] The terms "second" and "second" are used for descriptive purposes only and should not be understood as indicating or implying a relative degree of importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined with "second" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "multiple" means two or more.

[0017] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technical personnel in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0018] like Figure 1 As shown, in an embodiment of the present application, an intelligent multi-regional coordinated fuel transportation method is provided, including: S100: obtaining fuel consumption data of each region, and preprocessing the fuel consumption data of each region; S200: constructing a fuel consumption prediction model for each region based on the preprocessed fuel consumption data of each region, and predicting according to the fuel consumption prediction model of each region to obtain fuel consumption prediction data for each region; S300: obtaining the fuel inventory data of each region, and determining the remaining fuel data characteristics of each region based on the fuel consumption prediction data and the fuel inventory data of each region; S400: analyzing and evaluating the inventory status of each region based on the remaining fuel data characteristics to obtain an inventory status evaluation value of each region; S500: determining the fuel transportation priority of each region according to the inventory status evaluation value, and coordinatedly transporting the fuel of each region according to the fuel transportation priority.

[0019] Furthermore, by constructing a fuel consumption prediction model, the present invention can accurately predict the fuel consumption of each area, which is helpful for rationally planning fuel supply and transportation; based on the fuel consumption prediction data and fuel inventory data, the present invention can finely manage the remaining fuel in each area, minimize the situation of excess or insufficient inventory, and achieve inventory optimization; by determining the fuel transportation priority of each area, the present invention can realize intelligent scheduling and coordinated transportation, and ensure the effective utilization and reasonable allocation of fuel resources; by analyzing and evaluating the inventory status of each area, the present invention can achieve supply and demand balance, avoid problems caused by insufficient or excessive fuel supply, and ensure normal production and living needs of each area; by analyzing a large amount of fuel consumption and inventory data, the present invention can realize data-driven decision-making and improve the scientificity and accuracy of decision-making.

[0020] In an embodiment of the present application, an intelligent multi-regional fuel coordinated transportation method is provided, wherein the preprocessing of the fuel consumption data of each region includes: preprocessing the fuel consumption data of each region, the preprocessing includes removing abnormal data and overloaded data in the fuel consumption data of each region and filling in missing data, and standardizing the fuel consumption data of each region.

[0021] Specifically, it is very important to preprocess the fuel consumption data of each region, which helps to ensure the accuracy and reliability of the data, provide a good data foundation for subsequent analysis and modeling, and identify and process abnormal data, such as extreme values ​​beyond the normal range, which may be caused by recording errors or equipment failures. Statistical methods or visualization methods can be used to identify outliers and process them according to business logic. Outliers can be deleted or filled with appropriate values; for missing fuel consumption data, you can choose to fill missing values, such as using the mean, median, or interpolation to ensure the integrity and continuity of the data; sometimes there may be duplicate records in the data set, and the duplicate data needs to be identified and deleted to ensure the uniqueness and accuracy of the data; the fuel consumption data is standardized by centering and scaling the data so that the data conforms to the standard normal distribution or a specific distribution to facilitate subsequent modeling and analysis.

[0022] In an embodiment of the present application, an intelligent multi-regional coordinated fuel transportation method is provided, wherein a fuel consumption prediction model for each region is constructed based on the fuel consumption data of each region after preprocessing, and prediction is performed according to the fuel consumption prediction model of each region to obtain the fuel consumption prediction data of each region, including: constructing data sets based on the fuel consumption data of each region after preprocessing, and inputting the data sets into a preset neural network model, and constructing an initial fuel consumption prediction model for each region; dividing the data sets into a training set and a test set according to a certain ratio, and inputting the training set and the test set into the corresponding initial fuel consumption prediction model; training and testing each initial fuel consumption prediction model until the initial fuel consumption prediction model meets the preset convergence conditions to obtain the fuel consumption prediction model of each region; and predicting the fuel consumption prediction data of each region in the future period according to the obtained fuel consumption prediction model of each region.

[0023] Specifically, according to the preprocessed fuel consumption data of each area, a data set required for training the neural network model is constructed to ensure that the data set includes input features and corresponding output labels; the data sets are respectively input into the preset neural network model to respectively construct the initial fuel consumption prediction model for each area; the data sets are divided into training set and test set according to a certain ratio, with 80% of the data used as the training set and 20% of the data used as the test set; the training set is input into the corresponding initial fuel consumption prediction model to perform model training, and the model parameters are optimized by the back propagation algorithm until the model meets the preset convergence conditions; the trained model is evaluated using the test set, and the evaluation indicators may include mean square error; root mean square error; etc., to evaluate the prediction performance of the model; the trained fuel consumption prediction model is used to predict the fuel consumption of each area in the future to obtain prediction data.

[0024] In an embodiment of the present application, an intelligent multi-regional fuel coordinated transportation method is provided, wherein the fuel inventory data of each region is obtained, and the remaining fuel data characteristics of each region are determined based on the fuel consumption forecast data and the fuel inventory data of each region, including: obtaining the fuel inventory data of each region, and performing a difference calculation between the fuel consumption forecast data and the fuel inventory data of each region to obtain the remaining fuel data of each region; plotting the remaining fuel data into a remaining fuel curve according to time sequence, and determining the peak value and the trough value from the remaining fuel curve; determining the descending curve segment corresponding to each peak value to the trough value and the rising curve segment corresponding to each trough value to the peak value based on the peak value and the trough value, and calculating the slope of each descending curve segment and the rising curve segment to obtain the descending slope corresponding to each descending curve segment and the rising slope corresponding to each rising curve segment respectively; calculating the first average value of the descending slopes corresponding to all descending curve segments and the second average value of the rising slopes corresponding to all rising curve segments in each remaining fuel curve respectively; determining the first average value of the descending slopes corresponding to all descending curve segments as the first data feature, and determining the second average value of the rising slopes corresponding to all rising curve segments as the second data feature; the remaining fuel data characteristics of each region are constituted by the first data feature and the second data feature.

[0025] Specifically, the fuel inventory data of each area is obtained to ensure the accuracy and completeness of the data, and the fuel consumption forecast data of each area is subtracted from the fuel inventory data to obtain the remaining fuel data of each area; the remaining fuel data is plotted into a remaining fuel curve in chronological order, and the peak value and trough value are determined from the remaining fuel curve, which can be determined by signal processing methods or extreme points of mathematical functions; based on the peak value and trough value, the descending curve segment corresponding to each peak value to the trough value and the rising curve segment corresponding to each trough value to the peak value are determined, and the slope of each descending curve segment and rising curve segment is calculated; the first average value of the descending slopes corresponding to all the descending curve segments and the second average value of the rising slopes corresponding to all the rising curve segments in each remaining fuel curve are calculated respectively; the first average value of the descending slopes corresponding to all the descending curve segments is determined as the first data feature, and the second average value of the rising slopes corresponding to all the rising curve segments is determined as the second data feature, thereby constituting the remaining fuel data feature of each area.

[0026] In an embodiment of the present application, an intelligent multi-regional coordinated fuel transportation method is provided, wherein the inventory status of each region is analyzed and evaluated based on the remaining fuel data feature to obtain an inventory status evaluation value of each region, including: obtaining the first data feature and the second data feature corresponding to each region, and respectively determining the first average value corresponding to the first data feature and the second average value corresponding to the second data feature; performing a difference calculation between the first average value corresponding to the first data feature and a preset first threshold value to obtain a first difference value, and performing a difference calculation between the second average value corresponding to the second data feature and a preset second threshold value to obtain a second difference value; evaluating and valuing the first data feature according to the size of the first difference value to obtain a first evaluation value, and evaluating and valuing the second data feature according to the size of the second difference value to obtain a second evaluation value; and performing a calculation based on the first evaluation value and the second evaluation value of each region and the preset weight corresponding to each evaluation value to obtain an inventory status evaluation value of each region.

[0027] Specifically, the first data feature and the second data feature corresponding to each region are obtained from the previous steps, and the first average value corresponding to the first data feature and the second average value corresponding to the second data feature are determined respectively; the first average value corresponding to the first data feature is calculated by difference with the preset first threshold value to obtain the first difference value, and the second average value corresponding to the second data feature is calculated by difference with the preset second threshold value to obtain the second difference value; the first data feature is evaluated and valued according to the size of the first difference value to obtain the first evaluation value. The second data feature is evaluated and valued according to the size of the second difference value to obtain the second evaluation value; the inventory status evaluation value of each region is calculated based on the first evaluation value and the second evaluation value of each region and the preset weight corresponding to each evaluation value.

[0028] In an embodiment of the present application, an intelligent multi-regional fuel coordinated transportation method is provided, wherein the method is based on the first evaluation value and the second evaluation value of each region and the preset weight corresponding to each evaluation value to obtain the inventory status evaluation value of each region, including: based on the first evaluation value and the second evaluation value of each region and the preset weight corresponding to each evaluation value, the evaluation calculation is performed to obtain the inventory status evaluation value of each region, wherein the calculation formula of the inventory status evaluation value of each region is: ; Among them, S is the inventory status evaluation value of each region, represents a natural exponential function, A is a first evaluation value, α is a preset weight corresponding to the first evaluation value, B is a second evaluation value, and β is a preset weight corresponding to the second evaluation value.

[0029] In an embodiment of the present application, an intelligent multi-regional coordinated fuel transportation method is provided, wherein the fuel transportation priority of each region is determined according to the inventory status assessment value, including: pre-setting a fuel transportation priority-inventory status assessment value interval correspondence relationship, wherein the fuel transportation priority-inventory status assessment value interval correspondence relationship is associated with a corresponding fuel transportation priority for each inventory status assessment value interval; obtaining the inventory status assessment value, and based on a mapping relationship between the inventory status assessment value interval to which the inventory status assessment value belongs within the fuel transportation priority-inventory status assessment value interval correspondence relationship, selecting the fuel transportation priority corresponding to the inventory status assessment value interval as the fuel transportation priority for each region.

[0030] Specifically, the pre-set correspondence between the fuel transportation priority and the inventory status assessment value interval is a pre-defined mapping table, which maps different inventory status assessment value intervals to corresponding fuel transportation priorities; these values ​​are obtained based on the inventory status assessment values ​​of each area calculated previously, and based on the mapping relationship between the inventory status assessment value interval to which the inventory status assessment value belongs within the fuel transportation priority-inventory status assessment value interval correspondence, the fuel transportation priority corresponding to the inventory status assessment value interval is selected to determine the fuel transportation priority for each area.

[0031] like Figure 2 As shown, in an embodiment of the present application, an intelligent multi-regional fuel coordinated transportation system is provided, including: an acquisition module, used to acquire fuel consumption data of each region, and preprocess the fuel consumption data of each region; a prediction module, used to construct a fuel consumption prediction model for each region based on the preprocessed fuel consumption data of each region, and predict according to the fuel consumption prediction model of each region to obtain fuel consumption prediction data for each region; a determination module, used to acquire fuel inventory data of each region, and determine the remaining fuel data characteristics of each region based on the fuel consumption prediction data and the fuel inventory data of each region; an evaluation module, used to analyze and evaluate the inventory status of each region based on the remaining fuel data characteristics, and obtain an inventory status evaluation value of each region; a transportation module, used to determine the fuel transportation priority of each region according to the inventory status evaluation value, and coordinately transport the fuel of each region according to the fuel transportation priority.

[0032] In summary, an embodiment of the present invention provides an intelligent multi-regional coordinated fuel transportation method and system, which includes: acquiring and preprocessing the fuel consumption data of each region, constructing a fuel consumption prediction model for each region based on the preprocessed fuel consumption data, and predicting according to the fuel consumption prediction model of each region to obtain the fuel consumption prediction data of each region; acquiring the fuel inventory data of each region, and determining the remaining fuel data characteristics of each region based on the fuel consumption prediction data and the fuel inventory data of each region; analyzing and evaluating the inventory status of each region based on the remaining fuel data characteristics to obtain an inventory status evaluation value of each region; determining the fuel transportation priority of each region according to the inventory status evaluation value, and collaboratively transporting fuel according to the fuel transportation priority. The present invention determines the transportation priority by analyzing and evaluating the fuel consumption and inventory conditions, so that the region with accurate fuel can obtain fuel transportation first, which helps to improve resource utilization efficiency and ensure the stability and sustainability of fuel supply.

[0033] Finally, it should be noted that: Obviously, a person skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, the present invention is also intended to include these modifications and variations.

[0034] The above is only an example of implementation of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be regarded as falling within the scope of protection of the present invention and being restricted. Technical personnel in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the platform described above can refer to the corresponding process in the aforementioned platform embodiment, and will not be repeated here.

[0035] The term "comprises" or any other similar term is intended to cover a non-exclusive inclusion such that a process, platform, article, or apparatus / platform that includes a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, platform, article, or apparatus / platform.

[0036] So far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings. However, it is easy for a person skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, a person skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0037] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. An intelligent multi-regional fuel coordinated transportation method, characterized in that: include: Obtaining fuel consumption data of each area and preprocessing the fuel consumption data of each area; Building a fuel consumption prediction model for each area based on the preprocessed fuel consumption data of each area, and performing predictions based on the fuel consumption prediction model of each area to obtain fuel consumption prediction data for each area; Obtaining fuel inventory data for each region, and determining remaining fuel data characteristics for each region based on fuel consumption forecast data and fuel inventory data for each region; Analyze and evaluate the inventory status of each region based on the remaining fuel data characteristics to obtain an inventory status evaluation value of each region; The fuel transportation priority for each region is determined based on the inventory status assessment value, and the fuel for each region is coordinated and transported based on the fuel transportation priority.

2. An intelligent multi-regional fuel coordinated transportation method according to claim condition 1, characterized in that: The preprocessing of the fuel consumption data of each region includes: The fuel consumption data of each region is preprocessed, which includes removing abnormal data and overloaded data in the fuel consumption data of each region and filling in missing data, and standardizing the fuel consumption data of each region.

3. An intelligent multi-regional fuel coordinated transportation method according to claim condition 2, characterized in that: The fuel consumption prediction model of each area is constructed based on the pre-processed fuel consumption data of each area, and prediction is performed according to the fuel consumption prediction model of each area to obtain the fuel consumption prediction data of each area, including: A data set is constructed based on the preprocessed fuel consumption data of each region, and the data set is input into a preset neural network model to construct an initial fuel consumption prediction model for each region; The data set is divided into a training set and a test set according to a certain ratio, and the training set and the test set are input into the corresponding initial model for fuel consumption prediction; Training and testing each initial fuel consumption prediction model until the initial fuel consumption prediction model meets a preset convergence condition, thereby obtaining a fuel consumption prediction model for each region; The fuel consumption forecast data of each area in the future is predicted based on the obtained fuel consumption forecast model of each area.

4. An intelligent multi-regional fuel coordinated transportation method according to claim condition 3, characterized in that: The obtaining of the fuel inventory data of each region and determining the remaining fuel data characteristics of each region based on the fuel consumption forecast data and the fuel inventory data of each region include: Obtain the fuel inventory data of each area, and perform a difference calculation between the fuel consumption forecast data of each area and the fuel inventory data to obtain the remaining fuel data of each area; Plotting the remaining fuel data into a remaining fuel curve according to the time sequence, and determining the peak value and the trough value from the remaining fuel curve; Based on the peak value and the trough value, a descending curve segment corresponding to each peak value to the trough value and an ascending curve segment corresponding to each trough value to the peak value are determined, and the slope of each descending curve segment and the ascending curve segment are calculated to obtain a descending slope corresponding to each descending curve segment and an ascending slope corresponding to each ascending curve segment; respectively calculating a first average value of the descending slopes corresponding to all descending curve segments and a second average value of the ascending slopes corresponding to all ascending curve segments in each remaining fuel curve; Determine a first average value of the descending slopes corresponding to all the descending curve segments as a first data feature, and determine a second average value of the ascending slopes corresponding to all the ascending curve segments as a second data feature; The remaining fuel data feature of each region is constituted by the first data feature and the second data feature.

5. An intelligent multi-regional fuel coordinated transportation method according to claim condition 4, characterized in that: The inventory status of each region is analyzed and evaluated based on the remaining fuel data characteristics to obtain an inventory status evaluation value of each region, including: Obtaining a first data feature and a second data feature corresponding to each region, and respectively determining a first average value corresponding to the first data feature and a second average value corresponding to the second data feature; Subtracting a first average value corresponding to the first data feature from a preset first threshold value to obtain a first difference value, and subtracting a second average value corresponding to the second data feature from a preset second threshold value to obtain a second difference value; The first data feature is evaluated and valued according to the size of the first difference to obtain a first evaluation value, and the second data feature is evaluated and valued according to the size of the second difference to obtain a second evaluation value; The inventory status evaluation value of each region is obtained by performing calculation based on the first evaluation value and the second evaluation value of each region and the preset weight corresponding to each evaluation value.

6. An intelligent multi-regional fuel coordinated transportation method according to claim condition 5, characterized in that: The calculation based on the first evaluation value and the second evaluation value of each region and the preset weight corresponding to each evaluation value to obtain the inventory status evaluation value of each region includes: An evaluation calculation is performed based on the first evaluation value and the second evaluation value of each region and the preset weight corresponding to each evaluation value to obtain the inventory status evaluation value of each region, wherein the calculation formula of the inventory status evaluation value of each region is: ； Among them, S is the inventory status evaluation value of each region, represents a natural exponential function, A is a first evaluation value, α is a preset weight corresponding to the first evaluation value, B is a second evaluation value, and β is a preset weight corresponding to the second evaluation value.

7. An intelligent multi-regional fuel coordinated transportation method according to claim condition 6, characterized in that: Determining the fuel transportation priority of each region according to the inventory status evaluation value includes: A fuel transfer priority-inventory status assessment value interval correspondence relationship is preset, and the fuel transfer priority-inventory status assessment value interval correspondence relationship is associated with a corresponding fuel transfer priority for each inventory status assessment value interval; The inventory status assessment value is obtained, and based on the mapping relationship between the inventory status assessment value interval to which the inventory status assessment value belongs and the fuel transfer priority-inventory status assessment value interval correspondence relationship, the fuel transfer priority corresponding to the inventory status assessment value interval is selected and determined as the fuel transfer priority for each area.

8. An intelligent multi-regional fuel coordinated transportation system, characterized in that: include: An acquisition module, used for acquiring the fuel consumption data of each area and preprocessing the fuel consumption data of each area; A prediction module is used to construct a fuel consumption prediction model for each area based on the preprocessed fuel consumption data of each area, and to perform prediction according to the fuel consumption prediction model of each area to obtain fuel consumption prediction data for each area; A determination module, used to obtain fuel inventory data of each area, and determine the remaining fuel data characteristics of each area based on the fuel consumption forecast data and the fuel inventory data of each area; An evaluation module, used to analyze and evaluate the inventory status of each area based on the remaining fuel data characteristics, and obtain an inventory status evaluation value of each area; The transportation module is used to determine the fuel transportation priority of each area according to the inventory status assessment value, and to coordinate the transportation of fuel to each area according to the fuel transportation priority.

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