A digital intelligent management method and system for gas stations

Abstract

The application discloses a gas station digital intelligent management method and system, comprising: obtaining target position information and historical operation data of a target gas station, generating operation correlation based on the target position information and the historical operation data; obtaining target oil quantity information of the target gas station, predicting the predicted operation data of the target gas station in a preset future time period according to the target oil quantity information and the operation correlation; generating economic benefit data according to the predicted operation data and performing data fitting operation on the historical operation data and the economic benefit data to obtain a data fitting result; determining operation optimization parameters based on the data fitting result, and generating target management parameters according to the operation optimization parameters. It can be seen that the application can combine various information of the gas station to generate the management parameters of the gas station, which is beneficial to improving the intelligence of the management of the gas station, thereby improving the efficiency and accuracy of the management of the gas station.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology, and in particular to a digital intelligent management method and system for gas stations. Background Technology

[0002] With the continuous development of science and technology and the rapid growth of the economy, people's demand for vehicles and petroleum is increasing, leading to a year-on-year increase in the number of gas stations and a growing operational burden. Currently, most gas stations rely on staff to refuel vehicles, and operations are managed manually, with data processing and staff overseeing the process. This management method suffers from low intelligence and efficiency, and is prone to errors. Therefore, providing a new digital management method for gas stations to achieve intelligent management and improve efficiency and accuracy is crucial. Summary of the Invention

[0003] This invention provides a digital intelligent management method for gas stations, which can combine various information from the gas station to generate management parameters, thereby improving the intelligence of gas station management and thus improving the efficiency and accuracy of gas station management.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a digital intelligent management method for gas stations, the method comprising:

[0005] Obtain the target location information of the target gas station and the historical operation data of the target gas station. Based on the target location information and the historical operation data, generate an operational relationship between the location of the target gas station and the historical operation data.

[0006] Obtain the target fuel quantity information of the target gas station, and predict the predicted operating data of the target gas station within a preset future time period based on the target fuel quantity information and the operational correlation. The target fuel quantity information includes the remaining fuel quantity information of the target gas station, and the predicted operating data includes one or more of the following: predicted operating duration data, predicted operating fuel quantity data, and predicted operating revenue amount data of the target gas station.

[0007] Based on the predicted operational data, economic benefit data for the target gas station is generated, and a data fitting operation is performed on the historical operational data and the economic benefit data to obtain the data fitting result.

[0008] Based on the data fitting results, the operational optimization parameters corresponding to the target gas station are determined;

[0009] Based on the operational optimization parameters, target management parameters for the target gas station are generated.

[0010] As an optional implementation, in the first aspect of the present invention, before generating the target management parameters for the target gas station based on the operational optimization parameters, the method further includes:

[0011] Obtain real-time information about the target area, and determine the target vehicle based on the real-time information about the target area;

[0012] Collect real-time vehicle information of all target vehicles, and determine whether there are target demand vehicles among all target vehicles based on the real-time vehicle information, wherein the target demand vehicles include vehicles that have a demand for the target gas station;

[0013] When it is determined that the target demand vehicle exists among all the target vehicles, the vehicle demand information corresponding to the target demand vehicle is determined.

[0014] Obtain the real-time operation information of the target gas station, and generate demand recommendation information based on the vehicle demand information corresponding to the target demand vehicle and the real-time operation information;

[0015] The step of generating target management parameters for the target gas station based on the operational optimization parameters includes:

[0016] Based on the demand recommendation information and the operation optimization parameters, the target management parameters for the target gas station are generated.

[0017] As an optional implementation, in the first aspect of the present invention, performing a data fitting operation on the historical operating data and the economic benefit data to obtain a data fitting result includes:

[0018] Based on the economic benefit data, a predicted data curve for the target gas station within a preset future time period is generated, and based on the historical operating data, a historical data curve for the target gas station within a preset historical time period is generated.

[0019] A data fitting operation is performed on the predicted data curve and the historical data curve to obtain a target data curve. Based on the target data curve, a data correlation relationship is generated between the economic benefit data and the historical data curve.

[0020] Based on the data correlation, a data fitting result is generated.

[0021] As an optional implementation, in the first aspect of the present invention, determining the operational optimization parameters corresponding to the target gas station based on the data fitting results includes:

[0022] Based on the data fitting results, the operational difference parameters corresponding to the target gas station are determined. Based on the operational difference parameters, target optimization parameters that match the operational difference parameters are determined from a pre-determined set of optimization parameters.

[0023] Based on all the target optimization parameters, determine the operational optimization parameters corresponding to the target gas station;

[0024] And, generating the target management parameters for the target gas station based on the operational optimization parameters includes:

[0025] Obtain the real-time management parameters of the target gas station, and determine the management difference information between the real-time management parameters and the operation optimization parameters based on the real-time management parameters and the operation optimization parameters;

[0026] Based on the management difference information, target management parameters for the target gas station are generated.

[0027] As an optional implementation, in the first aspect of the present invention, generating demand recommendation information based on the vehicle demand information corresponding to the target demand vehicle and the real-time operation information includes:

[0028] Based on the real-time vehicle information corresponding to the target demand vehicle, the key vehicle information corresponding to the target demand vehicle is determined, wherein the key vehicle information includes the real-time vehicle location information, target vehicle model information, refueling amount information, refueling location information, and historical refueling frequency information corresponding to the target demand vehicle.

[0029] Based on the key vehicle information, vehicle demand parameters between the target vehicle and the target gas station are determined. Based on the vehicle demand parameters and the real-time operation information of the target gas station, demand recommendation information for the target vehicle is generated. The demand recommendation information is used to guide the target vehicle to the target gas station to perform an operation that matches the vehicle demand information.

[0030] As an optional implementation, in the first aspect of the present invention, before generating demand recommendation information based on the vehicle demand information corresponding to the target demand vehicle and the real-time operation information, the method further includes:

[0031] Based on the real-time vehicle information of the target demand vehicle, determine the real-time vehicle status corresponding to the target demand vehicle, and based on the real-time vehicle status, determine whether the target demand vehicle meets the preset vehicle guidance conditions.

[0032] When it is determined that the target vehicle meets the preset vehicle guidance conditions, the operation of generating demand recommendation information based on the vehicle demand information corresponding to the target vehicle and the real-time operation information is triggered.

[0033] When it is determined that the target vehicle does not meet the preset vehicle guidance conditions, the state adjustment parameters corresponding to the target vehicle are determined according to the real-time vehicle status and the preset vehicle guidance conditions. A state adjustment operation matching the state adjustment parameters is then performed on the target vehicle to update its real-time vehicle status. The operation of determining whether the target vehicle meets the preset vehicle guidance conditions based on its real-time vehicle status is then triggered again.

[0034] As an optional implementation, in the first aspect of the present invention, generating the target management parameters for the target gas station based on the demand recommendation information and the operation optimization parameters includes:

[0035] Based on the demand recommendation information, operational demand information between all the target demand vehicles and the target gas stations is generated. The operational demand information includes one or more of the following: operational demand duration information, operational demand time information, operational demand fuel quantity information, and operational demand item information.

[0036] Based on the operational optimization parameters, the target gas station is simulated to perform operational operations that match the operational optimization parameters, and the simulated operational results are obtained. It is then determined whether the simulated operational results match the operational demand information.

[0037] When it is determined that the simulated operation results match the operation demand information, the target management parameters for the target gas station are generated based on the operation optimization parameters and the operation demand information.

[0038] When it is determined that the simulated operation results do not match the operational demand information, the influencing factors of the mismatch between the simulated operation results and the operational demand information are identified. Based on all the influencing factors, optimization adjustment parameters corresponding to the operational optimization parameters are generated to update the operational optimization parameters. Based on the updated operational optimization parameters and the operational demand information, the target management parameters of the target gas station are generated.

[0039] A second aspect of this invention discloses a digital intelligent management system for gas stations, the system comprising:

[0040] The acquisition module is used to acquire the target location information of the target gas station and the historical operating data of the target gas station;

[0041] The generation module is used to generate an operational relationship between the location of the target gas station and the historical operational data based on the target location information and the historical operational data.

[0042] The acquisition module is also used to acquire the target fuel quantity information of the target gas station;

[0043] The prediction module is used to predict the predicted operating data of the target gas station within a preset future time period based on the target fuel quantity information and the operational correlation. The target fuel quantity information includes the remaining fuel quantity information corresponding to the target gas station, and the predicted operating data includes one or more of the following: predicted operating duration data, predicted operating fuel quantity data, and predicted operating revenue amount data corresponding to the target gas station.

[0044] The generation module is also used to generate economic benefit data for the target gas station based on the predicted operational data;

[0045] The fitting module is used to perform data fitting operations on the historical operating data and the economic benefit data to obtain data fitting results.

[0046] The determination module is used to determine the operational optimization parameters corresponding to the target gas station based on the data fitting results;

[0047] The generation module is also used to generate target management parameters for the target gas station based on the operation optimization parameters.

[0048] As an optional implementation, in a second aspect of the present invention, the acquisition module is further configured to acquire real-time regional information corresponding to the target area before the generation module generates the target management parameters of the target gas station based on the operation optimization parameters;

[0049] The determining module is further configured to determine the target vehicle based on the real-time information of the area;

[0050] The acquisition module is also used to collect real-time vehicle information of all the target vehicles;

[0051] The system also includes:

[0052] The judgment module is used to determine, based on the real-time vehicle information, whether there is a target demand vehicle among all the target vehicles, wherein the target demand vehicle includes vehicles that have a demand for the target gas station;

[0053] The determining module is further configured to determine the vehicle demand information corresponding to the target demand vehicle when the judging module determines that the target demand vehicle exists among all the target vehicles.

[0054] The acquisition module is also used to acquire the real-time operating information of the target gas station;

[0055] The generation module is also used to generate demand recommendation information based on the vehicle demand information corresponding to the target demand vehicle and the real-time operation information;

[0056] The specific method by which the generation module generates the target management parameters for the target gas station based on the operation optimization parameters includes:

[0057] Based on the demand recommendation information and the operation optimization parameters, the target management parameters for the target gas station are generated.

[0058] As an optional implementation, in the second aspect of the present invention, the fitting module performs a data fitting operation on the historical operating data and the economic benefit data to obtain the data fitting result in the following specific ways:

[0059] Based on the economic benefit data, a predicted data curve for the target gas station within a preset future time period is generated, and based on the historical operating data, a historical data curve for the target gas station within a preset historical time period is generated.

[0060] A data fitting operation is performed on the predicted data curve and the historical data curve to obtain a target data curve. Based on the target data curve, a data correlation relationship is generated between the economic benefit data and the historical data curve.

[0061] Based on the data correlation, a data fitting result is generated.

[0062] As an optional implementation, in the second aspect of the present invention, the specific method by which the determining module determines the operational optimization parameters corresponding to the target gas station based on the data fitting results includes:

[0063] Based on the data fitting results, the operational difference parameters corresponding to the target gas station are determined. Based on the operational difference parameters, target optimization parameters that match the operational difference parameters are determined from a pre-determined set of optimization parameters.

[0064] Based on all the target optimization parameters, determine the operational optimization parameters corresponding to the target gas station;

[0065] Furthermore, the specific method by which the generation module generates the target management parameters for the target gas station based on the operational optimization parameters includes:

[0066] Obtain the real-time management parameters of the target gas station, and determine the management difference information between the real-time management parameters and the operation optimization parameters based on the real-time management parameters and the operation optimization parameters;

[0067] Based on the management difference information, target management parameters for the target gas station are generated.

[0068] As an optional implementation, in the second aspect of the present invention, the specific method by which the generation module generates demand recommendation information based on the vehicle demand information corresponding to the target demand vehicle and the real-time operation information includes:

[0069] Based on the real-time vehicle information corresponding to the target demand vehicle, the key vehicle information corresponding to the target demand vehicle is determined, wherein the key vehicle information includes the real-time vehicle location information, target vehicle model information, refueling amount information, refueling location information, and historical refueling frequency information corresponding to the target demand vehicle.

[0070] Based on the key vehicle information, vehicle demand parameters between the target vehicle and the target gas station are determined. Based on the vehicle demand parameters and the real-time operation information of the target gas station, demand recommendation information for the target vehicle is generated. The demand recommendation information is used to guide the target vehicle to the target gas station to perform an operation that matches the vehicle demand information.

[0071] As an optional implementation, in a second aspect of the present invention, the determining module is further configured to determine the real-time status of the vehicle corresponding to the target demand vehicle based on the real-time vehicle information of the target demand vehicle before the generating module generates demand recommendation information based on the vehicle demand information corresponding to the target demand vehicle and the real-time operation information.

[0072] The judgment module is further configured to determine whether the target demand vehicle meets the preset vehicle guidance conditions based on the real-time status of the vehicle; when it is determined that the target demand vehicle meets the preset vehicle guidance conditions, the generation module is triggered to perform the operation of generating demand recommendation information based on the vehicle demand information corresponding to the target demand vehicle and the real-time operation information.

[0073] The determining module is further configured to determine the status adjustment parameters corresponding to the target demand vehicle based on the real-time status of the vehicle and the preset vehicle guidance conditions when the judging module determines that the target demand vehicle does not meet the preset vehicle guidance conditions.

[0074] The system also includes:

[0075] The adjustment module is used to perform a state adjustment operation on the target demand vehicle that matches the state adjustment parameters, so as to update the real-time vehicle state of the target demand vehicle, and re-trigger the judgment module to perform the operation of judging whether the target demand vehicle meets the preset vehicle guidance conditions based on the real-time vehicle state.

[0076] As an optional implementation, in the second aspect of the present invention, the specific method by which the generation module generates the target management parameters of the target gas station based on the demand recommendation information and the operation optimization parameters includes:

[0077] Based on the demand recommendation information, operational demand information between all the target demand vehicles and the target gas stations is generated. The operational demand information includes one or more of the following: operational demand duration information, operational demand time information, operational demand fuel quantity information, and operational demand item information.

[0078] Based on the operational optimization parameters, the target gas station is simulated to perform operational operations that match the operational optimization parameters, and the simulated operational results are obtained. It is then determined whether the simulated operational results match the operational demand information.

[0079] When it is determined that the simulated operation results match the operation demand information, the target management parameters for the target gas station are generated based on the operation optimization parameters and the operation demand information.

[0080] When it is determined that the simulated operation results do not match the operational demand information, the influencing factors of the mismatch between the simulated operation results and the operational demand information are identified. Based on all the influencing factors, optimization adjustment parameters corresponding to the operational optimization parameters are generated to update the operational optimization parameters. Based on the updated operational optimization parameters and the operational demand information, the target management parameters of the target gas station are generated.

[0081] A third aspect of this invention discloses another digital intelligent management system for gas stations, the system comprising:

[0082] Memory containing executable program code;

[0083] A processor coupled to the memory;

[0084] The processor calls the executable program code stored in the memory to execute the digital intelligent management method for gas stations disclosed in the first aspect of the present invention.

[0085] The fourth aspect of the present invention discloses a computer-storable medium storing computer instructions, which, when invoked, are used to execute the digital intelligent management method for gas stations disclosed in the first aspect of the present invention.

[0086] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0087] In this embodiment of the invention, target location information and historical operational data of the target gas station are acquired; based on the target location information and historical operational data, operational relationships are generated; target fuel quantity information of the target gas station is acquired; based on the target fuel quantity information and operational relationships, predicted operational data of the target gas station within a preset future time period is predicted; based on the predicted operational data, economic benefit data is generated, and a data fitting operation is performed on the historical operational data and economic benefit data to obtain data fitting results; based on the data fitting results, operational optimization parameters are determined, and target management parameters are generated based on the operational optimization parameters. Therefore, implementing this invention can combine multiple aspects of gas station information to generate gas station management parameters, which is beneficial to improving the intelligence of gas station management, thereby improving the efficiency and accuracy of gas station management. Attached Figure Description

[0088] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0089] Figure 1 This is a flowchart illustrating a digital intelligent management method for gas stations disclosed in an embodiment of the present invention;

[0090] Figure 2 This is a flowchart illustrating another digital intelligent management method for gas stations disclosed in an embodiment of the present invention;

[0091] Figure 3 This is a schematic diagram of the structure of a digital intelligent management system for gas stations disclosed in an embodiment of the present invention;

[0092] Figure 4 This is a schematic diagram of another digital intelligent management system for gas stations disclosed in an embodiment of the present invention;

[0093] Figure 5 This is a schematic diagram of the structure of another digital intelligent management system for gas stations disclosed in an embodiment of the present invention. Detailed Implementation

[0094] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0095] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0096] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0097] This invention discloses a digital intelligent management method and system for gas stations. It can combine various information from the gas station to generate management parameters, which improves the intelligence of gas station management, thereby increasing its efficiency and accuracy. Detailed descriptions follow.

[0098] Example 1

[0099] Please see Figure 1 , Figure 1 This is a flowchart illustrating a digital intelligent management method for gas stations disclosed in an embodiment of the present invention. Wherein, Figure 1 The described digital intelligent management method for gas stations can be applied to a digital intelligent management system for gas stations, which can be integrated on a local server or a cloud server; this embodiment of the invention does not impose any limitations. Figure 1 As shown, the digital intelligent management method for gas stations can include the following operations:

[0100] 101. Obtain the target location information of the target gas station and the historical operation data of the target gas station. Based on the target location information and the historical operation data, generate the operation relationship between the location of the target gas station and the historical operation data.

[0101] In this embodiment of the invention, optionally, the target location information of the target gas station can be obtained through methods such as GPS positioning, satellite positioning, Wi-Fi positioning, base station positioning, and IP positioning. Optionally, the target location information of the target gas station may include one or more of the following: the location information of the target gas station and the administrative region information of the target gas station. Further, the target location information of the target gas station may also include traffic flow information and surrounding environmental information.

[0102] In this embodiment of the invention, optionally, the historical operating data of the target gas station may include one or more of the following: historical refueling volume data, historical operating amount data, historical operating duration data, and historical revenue amount data. Further, the historical operating data of the target gas station may also include historical oil sales volume data, historical revenue amount data, and historical customer traffic data.

[0103] In this embodiment of the invention, optionally, the above-mentioned generation of the operational correlation between the location of the target gas station and the historical operational data based on the target location information and historical operational data may include:

[0104] Based on the target location information, the location of the target gas station is determined, and based on the location of the target gas station and historical operating data, location operation association information between the location of the target gas station and historical operating data is generated. The location operation association information includes one of the following: operating amount information, operating revenue information, and operating duration information between the location of the target gas station and historical operating data.

[0105] Based on location-based operational information, generate operational relationships between the target gas station's location and historical operational data.

[0106] 102. Obtain the target fuel volume information of the target gas station, and based on the target fuel volume information and operational relationships, predict the predicted operational data of the target gas station within a preset future time period.

[0107] In this embodiment of the invention, the target fuel quantity information includes the remaining fuel quantity information corresponding to the target gas station, and the predicted operating data includes one or more of the following: predicted operating duration data, predicted operating fuel quantity data, and predicted operating revenue amount data corresponding to the target gas station.

[0108] In this embodiment of the invention, optionally, the remaining fuel information corresponding to the target gas station may include the remaining fuel information corresponding to each gasoline grade; for example, the remaining fuel information corresponding to the target gas station may include the remaining fuel information corresponding to 92-octane gasoline and the remaining fuel information corresponding to 95-octane gasoline.

[0109] In this embodiment of the invention, optionally, the predicted operating data of the target gas station within a preset future time period includes one or more of the following: predicted operating amount data, predicted operating revenue data, predicted operating duration data, and predicted operating fuel volume data.

[0110] In this embodiment of the invention, optionally, the above-mentioned prediction of the target gas station's predicted operational data within a preset future time period based on target oil volume information and operational correlation may include:

[0111] The target oil volume information and operational relationships are input into a pre-determined operational data prediction model to obtain operational prediction data output results. Based on the operational prediction data output results, the predicted operational data of the target gas station for a preset future time period is generated.

[0112] 103. Based on the predicted operating data, generate the economic benefit data of the target gas station, and perform data fitting operation on the historical operating data and economic benefit data to obtain the data fitting results.

[0113] In this embodiment of the invention, optionally, the economic benefit data of the target gas station may include at least one or more of the predicted revenue data and predicted profit data corresponding to the target gas station. Further optionally, the economic benefit data of the target gas station may be determined based on predicted operating data.

[0114] 104. Based on the data fitting results, determine the corresponding operational optimization parameters for the target gas station.

[0115] In this embodiment of the invention, optionally, the operational optimization parameters corresponding to the target gas station may include one or more of the following: operational refueling volume optimization parameters, operational duration optimization parameters, operational time period optimization parameters, operational staff optimization parameters, and operational cost optimization parameters.

[0116] 105. Generate target management parameters for the target gas station based on the operation optimization parameters.

[0117] In this embodiment of the invention, optionally, the target management parameters for the target gas station may include one or more of the following: operating duration management parameters, operating time period management parameters, operating fuel quantity management parameters, operating fuel type management parameters, operating area management parameters, operating personnel management parameters, operating quantity management parameters, and operating cost management parameters. For example, the operating fuel type management parameters may include management parameters for the fuel types operated by the target gas station; the operating area management parameters may include parking space management parameters corresponding to the target gas station; and the operating quantity management parameters may include one or more of the following: operating material management parameters and operating commodity management parameters.

[0118] It is evident that implementation Figure 1 The described digital intelligent management method for gas stations can acquire target location information and historical operational data of the target gas station, and generate operational correlations based on the target location information and historical operational data; acquire target fuel volume information of the target gas station, and predict the target gas station's operational data within a preset future time period based on the target fuel volume information and operational correlations; generate economic benefit data based on the predicted operational data, and perform data fitting operations on the historical operational data and economic benefit data to obtain data fitting results; determine operational optimization parameters based on the data fitting results, and generate target management parameters based on the operational optimization parameters. This method can intelligently analyze the operational status of the target gas station, which is conducive to improving operational efficiency and operational intelligence. Furthermore, it can increase sales and revenue through more accurate prediction and optimization of pricing strategies, thereby improving operational effectiveness and profitability. Through more accurate inventory management and service time prediction, it can improve customer experience, thereby improving the user experience and convenience of using the target gas station. Through prediction and optimization, it can reduce the risks brought by market fluctuations and uncertainties, thereby improving the operational accuracy and intelligence of the target gas station, improving the intelligence of gas station management, and further improving the efficiency and accuracy of gas station management.

[0119] Example 2

[0120] Please see Figure 2 , Figure 2 This is a flowchart illustrating another digital intelligent management method for gas stations disclosed in an embodiment of the present invention. Figure 2 The described digital intelligent management method for gas stations can be applied to a digital intelligent management system for gas stations, which can be integrated on a local server or a cloud server; this embodiment of the invention does not impose any limitations. Figure 2 As shown, the digital intelligent management method for gas stations can include the following operations:

[0121] 201. Obtain the target location information of the target gas station and the historical operation data of the target gas station. Based on the target location information and the historical operation data, generate the operation relationship between the location of the target gas station and the historical operation data.

[0122] 202. Obtain the target fuel volume information of the target gas station, and based on the target fuel volume information and operational relationships, predict the predicted operational data of the target gas station within a preset future time period.

[0123] 203. Based on the predicted operating data, generate the economic benefit data of the target gas station, and perform data fitting operation on the historical operating data and economic benefit data to obtain the data fitting results.

[0124] 204. Based on the data fitting results, determine the corresponding operational optimization parameters for the target gas station.

[0125] In this embodiment of the invention, for a detailed description of steps 201-204, please refer to the other descriptions of steps 101-104 in Embodiment 1. This embodiment of the invention will not repeat them.

[0126] 205. Obtain real-time information about the target area and determine the target vehicle based on the real-time information about the target area.

[0127] In this embodiment of the invention, optionally, the target area may include the management area corresponding to the target gas station. Further, the target area may also include an area with a radius of ten kilometers centered on the target gas station.

[0128] In this embodiment of the invention, optionally, the real-time information of the target area may include one or more of the following: real-time traffic conditions, real-time weather conditions, and real-time unexpected events.

[0129] In this embodiment of the invention, optionally, the target vehicle may include vehicles traveling in the target area. Furthermore, the number of target vehicles may be one or more; this embodiment of the invention does not impose a specific limitation.

[0130] 206. Collect real-time vehicle information for all target vehicles, and determine whether the target vehicle exists among all target vehicles based on the real-time vehicle information.

[0131] In this embodiment of the invention, the target demand vehicle includes vehicles that have a demand for the target gas station.

[0132] In this embodiment of the invention, optionally, the real-time vehicle information of the target vehicle may include one or more of the following: vehicle speed information, vehicle location information, vehicle real-time fuel level information, and vehicle real-time destination information.

[0133] In this embodiment of the invention, optionally, the above-mentioned determination of whether the target demand vehicle exists among all target vehicles based on real-time vehicle information may include:

[0134] Based on real-time vehicle information, determine the real-time fuel quantity for each target vehicle, and based on the real-time fuel quantity for each target vehicle, determine whether there is a target fuel quantity that is less than or equal to a preset fuel quantity threshold among all real-time fuel quantities.

[0135] If it is determined that there is a target fuel quantity less than or equal to the preset fuel quantity threshold among all real-time fuel quantities, it is determined that there is a target demand vehicle among all target vehicles; if it is determined that there is no target fuel quantity less than or equal to the preset fuel quantity threshold among all real-time fuel quantities, it is determined that there is no target demand vehicle among all target vehicles.

[0136] 207. When it is determined that there is a vehicle with target demand among all target vehicles, determine the vehicle demand information corresponding to the vehicle with target demand.

[0137] Optionally, in this embodiment of the invention, when it is determined that there is no target vehicle among all target vehicles, the process can be terminated.

[0138] In this embodiment of the invention, optionally, the vehicle demand information corresponding to the target demand vehicle may include one or more of the following: fuel quantity demand information, fuel type demand information, and fuel type demand information.

[0139] 208. Obtain real-time operation information of the target gas station, and generate demand recommendation information based on the vehicle demand information corresponding to the target demand vehicle and the real-time operation information.

[0140] In this embodiment of the invention, optionally, the real-time operational information of the target gas station includes one or more of the following: oil inventory information, gas station service status information, and refueling queue information.

[0141] In this embodiment of the invention, optionally, the demand recommendation information may include one or more of the following: suggested refueling time, recommended fuel type, recommended refueling space, and recommended refueling location information corresponding to the target demand vehicle.

[0142] In this embodiment of the invention, optionally, the generation of demand recommendation information based on vehicle demand information corresponding to the target demand vehicle and real-time operation information may include:

[0143] Extract the first target information from the vehicle demand information corresponding to the target demand vehicle, and extract the second target information from the real-time operation information. Based on the first target information and the second target information, generate demand recommendation information.

[0144] 209. Based on the recommended information and operational optimization parameters, generate the target management parameters for the target gas station.

[0145] In this embodiment of the invention, optionally, management strategies for gas stations can be formulated based on demand recommendation information and operational optimization parameters, such as adjusting fuel supply and optimizing service processes.

[0146] It is evident that implementation Figure 2 The described digital intelligent management method for gas stations can acquire real-time regional information corresponding to the target area and identify target vehicles. It collects real-time vehicle information of target vehicles to determine if a vehicle with the target demand exists among all target vehicles. If so, it determines the vehicle demand information corresponding to the target demand vehicle. It acquires real-time operational information of the target gas station and generates demand recommendation information based on the vehicle demand information. Based on the demand recommendation information and operational optimization parameters, it generates target management parameters for the target gas station. This method can quickly identify demand and provide services, reducing customer waiting time and improving the convenience and comfort of users accessing the services at the target gas station. It adjusts resource allocation according to real-time demand, improving operational efficiency and enhancing the intelligence and efficiency of target gas station management. Furthermore, it can improve customer satisfaction through personalized recommendations and timely services, and reduce excess inventory and waste through more accurate demand forecasting. Through efficient operational management, it can improve market competitiveness, enhance the operational efficiency and effectiveness of the target gas station, and ultimately improve the operational accuracy and intelligence of the target gas station, thus improving the intelligence of gas station management and further enhancing the efficiency and accuracy of gas station management.

[0147] In an optional embodiment, a data fitting operation is performed on historical operating data and economic benefit data to obtain data fitting results, including:

[0148] Based on economic benefit data, generate the predicted data curve of the target gas station in the preset future time period, and based on historical operating data, generate the historical data curve of the target gas station in the preset historical time period.

[0149] Perform data fitting operations on the predicted data curve and historical data curve to obtain the target data curve. Based on the target data curve, generate the data correlation between the economic benefit data and the historical data curve.

[0150] Based on the data correlation, generate data fitting results.

[0151] In this optional embodiment, the above-mentioned generation of a predicted data curve for the target gas station within a preset future time period based on economic benefit data, and the generation of a historical data curve for the target gas station within a preset historical time period based on historical operating data, may include:

[0152] Based on economic benefit data, operational trend information of the target gas station within a preset future time period is generated, and a predicted data curve of the target gas station within the preset future time period is generated based on the operational trend information. The operational trend information includes the operational mode information of the target gas station within the future time period, and the predicted data curve includes data development information of the target gas station within the preset future time period.

[0153] Based on historical operational data, historical operational trend information of the target gas station within a preset historical time period is generated, and historical data curves of the target gas station within the preset historical time period are generated based on the historical operational trend information. The historical data curves include historical data trend information of the target gas station within the preset historical time period.

[0154] In this optional embodiment, the above-mentioned data fitting operation on the predicted data curve and historical data curve to obtain the target data curve, and the generation of the data correlation between the economic benefit data and the historical data curve based on the target data curve, may include:

[0155] A fitting and comparison operation is performed on the predicted data curve and the historical data curve to obtain the target data curve. Based on the target data curve and the pre-determined target algorithm, a data correlation relationship between the economic benefit data and the historical data curve is generated. The pre-determined target algorithm may include one or more of the following: linear regression algorithm and multinomial regression algorithm.

[0156] In this optional embodiment, the above-mentioned generation of data fitting results based on data correlation may include: determining the correlation parameters between economic benefit data and historical data based on data correlation, and generating data fitting results based on the correlation parameters; wherein, the correlation parameters include data used to identify the influence between economic benefit data and historical operating data.

[0157] As can be seen, implementing this optional embodiment can generate a predicted data curve for the target gas station within a preset future time period based on economic benefit data, and generate a historical data curve for the target gas station within a preset historical time period based on historical operating data. A data fitting operation is performed on the predicted data curve and the historical data curve to obtain the target data curve, thereby generating data correlations. Based on these correlations, a data fitting result is generated. Through data fitting, future operational and economic benefits can be predicted more accurately, providing a more reliable basis for decision-making. Furthermore, by combining the relationship between historical and predicted data, it helps to allocate resources more effectively, such as inventory management and human resources. In addition to oil resources, data-driven decision-making can optimize operational processes, reduce waste, and improve efficiency. This is beneficial for improving the intelligence and efficiency of target gas station management. By optimizing service and product supply, customer satisfaction and loyalty can be improved. And through more accurate demand forecasting, excess inventory and waste can be reduced. Thus, through efficient operation management, market competitiveness can be improved, which is conducive to improving the operational efficiency and effectiveness of target gas stations. This, in turn, is conducive to improving the operational accuracy and intelligence of target gas stations, and to improving the intelligence of gas station management, which in turn is conducive to improving the efficiency and accuracy of gas station management.

[0158] In another optional embodiment, based on the data fitting results, the operational optimization parameters corresponding to the target gas station are determined, including:

[0159] Based on the data fitting results, the operational difference parameters corresponding to the target gas station are determined. Based on the operational difference parameters, the target optimization parameters that match the operational difference parameters are determined from the pre-determined set of optimization parameters.

[0160] Based on all the target optimization parameters, determine the corresponding operational optimization parameters for the target gas station;

[0161] In addition, based on operational optimization parameters, target management parameters for the target gas station are generated, including:

[0162] Obtain the real-time management parameters of the target gas station, and determine the management differences between the real-time management parameters and the operation optimization parameters based on the real-time management parameters and the operation optimization parameters;

[0163] Based on the management difference information, generate target management parameters for the target gas station.

[0164] In this optional embodiment, the operational difference parameter corresponding to the target gas station may optionally include the operational state difference parameter between the current operational state of the target gas station and the ideal operational state of the target gas station. Further optionally, the number of operational difference parameters may be one or more; this embodiment of the invention does not impose a specific limitation.

[0165] In this optional embodiment, the process of determining the target optimization parameter that matches the operational difference parameter from a pre-determined set of optimization parameters based on the operational difference parameter may include:

[0166] Based on the operational difference parameters, determine the operational difference keywords, calculate the keyword matching degree between each optimization parameter included in the pre-determined set of optimization parameters and the operational difference keywords, and select target key matching degrees that are greater than or equal to the preset key matching degree threshold from all keyword matching degrees. Then, determine the optimization parameters corresponding to all target key matching degrees as target optimization parameters that match the operational difference parameters.

[0167] In this optional embodiment, the operational optimization parameters corresponding to the target gas station may optionally include at least all target optimization parameters.

[0168] In this optional embodiment, the real-time management parameters of the target gas station may optionally include one or more of the following: real-time operating fuel quantity parameters, real-time fuel inventory parameters, real-time personnel configuration parameters, real-time service process parameters, real-time operating duration parameters, and real-time operating operation parameters.

[0169] In this optional embodiment, the determination of the management difference information between the real-time management parameters and the operation optimization parameters based on the real-time management parameters and the operation optimization parameters may include:

[0170] Extract the first target parameter corresponding to the real-time management parameters and the second target parameter corresponding to the operation optimization parameters. Perform a parameter comparison operation on the first target parameter and the second target parameter to obtain the parameter comparison result. Based on the parameter comparison result, determine the management difference information between the real-time management parameters and the operation optimization parameters.

[0171] In this optional embodiment, the above-mentioned generation of target management parameters for the target gas station based on management difference information may include: determining management optimization information corresponding to the management difference information based on the management difference information, and generating target management parameters for the target gas station based on the management optimization information.

[0172] As can be seen, implementing this optional embodiment can determine the operational difference parameters corresponding to the target gas station based on the data fitting results, thereby determining the matching target optimization parameters. It then determines the operational optimization parameters corresponding to the target gas station based on the target optimization parameters, obtains the real-time management parameters of the target gas station, determines management difference information based on the real-time management parameters and operational optimization parameters, and generates target management parameters for the target gas station based on the management difference information. This data-driven decision-making is more objective and accurate, reducing subjective bias. Furthermore, it allows for rapid response to changes in the market and operating environment, timely adjustment of operational strategies, and quick identification of demand and provision of services, reducing customer waiting time and improving user experience of the services provided by the target gas station. Convenience and comfort, along with effective cost-benefit analysis to control operating costs, and adjustments to service strategies based on real-time data to improve customer satisfaction, all contribute to enhancing the intelligence and efficiency of target gas station management. Optimizing service and product supply further improves customer satisfaction and loyalty, and continuous operational optimization enhances the gas station's market competitiveness. This efficient operational management leads to improved market competitiveness, increased operational efficiency and effectiveness, and ultimately, improved operational accuracy and intelligence. Ultimately, it enhances the intelligence of gas station management and improves the efficiency and accuracy of gas station management.

[0173] In another optional embodiment, demand recommendation information is generated based on vehicle demand information corresponding to the target demand vehicle and real-time operation information, including:

[0174] Based on the real-time vehicle information corresponding to the target demand vehicle, determine the key vehicle information corresponding to the target demand vehicle. The key vehicle information includes the real-time vehicle location information, target vehicle model information, refueling amount information, refueling location information, and historical refueling frequency information corresponding to the target demand vehicle.

[0175] Based on key vehicle information, determine the vehicle demand parameters between the target vehicle and the target gas station, and generate demand recommendation information for the target vehicle based on the vehicle demand parameters and the real-time operation information of the target gas station. The demand recommendation information is used to guide the target vehicle to the target gas station to perform operations that match the vehicle demand information.

[0176] In this optional embodiment, key data, including location, vehicle model, refueling amount, refueling location, and historical refueling frequency, may be extracted from the real-time information of the target demand vehicle.

[0177] In this optional embodiment, vehicle demand parameters related to the target gas station may be analyzed and determined based on key vehicle information.

[0178] In this optional embodiment, optionally, the process of determining the vehicle demand parameters between the target demand vehicle and the target gas station based on key vehicle information, and generating demand recommendation information for the target demand vehicle based on the vehicle demand parameters and the real-time operating information of the target gas station, may include:

[0179] The system performs information analysis on key vehicle information to obtain information analysis results. Based on the information analysis results, it determines the refueling demand information of the target vehicle and generates vehicle demand parameters between the target vehicle and the target gas station. The refueling demand information includes one or more of the following: fuel tank capacity information, refueling frequency information, refueling quantity demand information, refueling rate demand information, and refueling duration demand information corresponding to the target vehicle.

[0180] The vehicle demand parameters and the real-time operation information of the target gas station are input into the pre-determined demand recommendation model to obtain the recommendation output results. Based on the recommendation output results, the demand recommendation information of the target demand vehicle is generated.

[0181] The demand recommendation information includes at least one or more of the following: vehicle refueling time, refueling location, refueling method, refueling duration, and refueling rate. Furthermore, the demand recommendation information may also include one or more of the following: recommended optimal route, estimated arrival time, and passenger pick-up / drop-off point information.

[0182] In this optional embodiment, the demand recommendation information may further include personalized recommendation information corresponding to each target demand vehicle. The personalized recommendation information may include one or more of the following: the location of the recommended gas station, the estimated arrival time, the estimated amount of fuel, and the estimated cost.

[0183] In this optional embodiment, for example, the vehicle demand parameters may also include: if the vehicle is about to run out of fuel, prioritizing the nearest gas station; if the vehicle has specific requirements for fuel, considering whether the gas station provides the corresponding fuel.

[0184] In this optional embodiment, the recommended information may be presented to the driver via a user interface, such as through an in-vehicle navigation system, mobile application, or other communication methods; or via voice. Furthermore, the effectiveness of the recommendation system is monitored in real time and adjusted according to actual conditions to address factors such as changes in traffic conditions and gas station operating status.

[0185] As can be seen, implementing this optional embodiment can determine key vehicle information based on real-time vehicle information corresponding to the target demand vehicle, determine vehicle demand parameters between the target demand vehicle and the target gas station based on the key vehicle information, and generate demand recommendation information for the target demand vehicle based on the vehicle demand parameters and the real-time operation information of the target gas station. It can recommend the nearest gas station to the target demand vehicle through real-time location information and demand parameters, reducing vehicle refueling travel time. Furthermore, based on the vehicle's refueling volume information and the gas station's inventory, it ensures that the gas station can meet the vehicle's refueling needs, avoiding resource waste. In addition, by combining vehicle model and historical refueling data, it provides customized refueling services, such as recommending suitable fuel types for the vehicle model, and recommending vehicles to refuel during off-peak hours by analyzing the gas station's busyness. By reducing waiting times and providing accurate refueling recommendations and a convenient refueling experience, gas station service satisfaction is improved. Guiding vehicles to refuel at appropriate times and locations reduces traffic congestion and accident risks. Continuous collection and analysis of vehicle and gas station data enables gas stations to make more data-driven operational decisions. The demand recommendation system can quickly adjust recommendation strategies based on real-time data to address market factors such as oil price fluctuations and traffic changes. This efficient operational management enhances market competitiveness, improves the operational efficiency and effectiveness of target gas stations, and consequently improves the accuracy and intelligence of gas station operations, leading to more intelligent and efficient gas station management.

[0186] In yet another optional embodiment, before generating demand recommendation information based on vehicle demand information corresponding to the target demand vehicle and real-time operation information, the method further includes:

[0187] Based on the real-time vehicle information of the target vehicle, determine the real-time status of the vehicle corresponding to the target vehicle, and based on the real-time vehicle status, determine whether the target vehicle meets the preset vehicle guidance conditions.

[0188] When it is determined that the target vehicle meets the preset vehicle guidance conditions, the operation of generating demand recommendation information based on the vehicle demand information corresponding to the target vehicle and real-time operation information is triggered.

[0189] When it is determined that the target vehicle does not meet the preset vehicle guidance conditions, the status adjustment parameters corresponding to the target vehicle are determined based on the real-time vehicle status and the preset vehicle guidance conditions. The status adjustment operation matching the status adjustment parameters is then performed on the target vehicle to update its real-time vehicle status. The operation of determining whether the target vehicle meets the preset vehicle guidance conditions based on its real-time vehicle status is then triggered again.

[0190] In this optional embodiment, the real-time vehicle status may include the vehicle operating status corresponding to the target demand vehicle, wherein the vehicle operating status may include one or more of the following: vehicle location status, speed status, fuel level status, number of passengers status, passenger seating position status in the target demand vehicle, and passenger behavior status.

[0191] In this optional embodiment, the above-mentioned determination of whether the target vehicle meets the preset vehicle guidance conditions based on the real-time vehicle status may include:

[0192] Determine whether the real-time status of the vehicle matches the status of the target vehicle corresponding to the preset vehicle guidance conditions.

[0193] When it is determined that the real-time vehicle status matches the target vehicle status corresponding to the preset vehicle guidance conditions, the target vehicle is determined to meet the preset vehicle guidance conditions; when it is determined that the real-time vehicle status does not match the target vehicle status corresponding to the preset vehicle guidance conditions, the target vehicle is determined not to meet the preset vehicle guidance conditions.

[0194] In this optional embodiment, the state adjustment parameters corresponding to the target demand vehicle may optionally include one or more of the following: vehicle speed adjustment parameters, vehicle route adjustment parameters, vehicle service adjustment parameters, and vehicle passenger behavior state adjustment parameters.

[0195] As can be seen, implementing this optional embodiment can determine the real-time status of the target vehicle based on its real-time information, thereby judging whether the target vehicle meets the preset vehicle guidance conditions. If it does, the operation of generating demand recommendation information based on the vehicle demand information and real-time operation information corresponding to the target vehicle is executed. If it does not meet the conditions, the corresponding status adjustment parameters are determined based on the vehicle's real-time status and the preset vehicle guidance conditions, and a status adjustment operation matching the status adjustment parameters is performed on the target vehicle to update its real-time status and re-trigger the operation of judging whether the target vehicle meets the preset vehicle guidance conditions based on its real-time status. Through real-time monitoring and intelligent recommendation, the operating efficiency of the vehicle can be improved. This system improves efficiency, reduces waiting and empty-running time, and allows for adjustments to vehicle status based on real-time demand. It enables better resource allocation to meet passenger needs and avoids unsafe factors such as overloading and speeding through pre-defined condition judgments and status adjustments. Furthermore, intelligent recommendations provide passengers with more convenient and punctual service. Optimizing routes and vehicle status reduces fuel consumption and maintenance costs. Through efficient operation and management, it enhances market competitiveness, improves the operational efficiency and effectiveness of target gas stations, and ultimately improves the accuracy and intelligence of gas station operations, enhancing the intelligence of gas station management and improving the efficiency and accuracy of gas station management.

[0196] In another optional embodiment, target management parameters for the target gas station are generated based on demand recommendation information and operational optimization parameters, including:

[0197] Based on the demand recommendation information, generate operational demand information between all target demand vehicles and target gas stations. The operational demand information includes one or more of the following: operational demand duration information, operational demand time information, operational demand fuel quantity information, and operational demand item information.

[0198] Based on the operational optimization parameters, the target gas station is simulated to perform operational operations that match the operational optimization parameters, and the simulated operational results are obtained to determine whether the simulated operational results match the operational demand information.

[0199] When it is determined that the simulated operation results match the operation requirements information, target management parameters for the target gas station are generated based on the operation optimization parameters and the operation requirements information.

[0200] When it is determined that the simulated operation results do not match the operational demand information, the influencing factors of the mismatch are identified, and optimization adjustment parameters corresponding to the operational optimization parameters are generated based on all influencing factors to update the operational optimization parameters. Based on the updated operational optimization parameters and operational demand information, the target management parameters for the target gas station are generated.

[0201] In this optional embodiment, the operational demand information may further include one or more of the following: demand duration information, time information, fuel quantity information, and item information.

[0202] In this optional embodiment, the above-mentioned simulation of the target gas station performing operational operations matching the operational optimization parameters to obtain simulated operational results may include:

[0203] The operation optimization parameters are input into a pre-determined simulation operation model to simulate the target gas station performing operations that match the operation optimization parameters, obtain the simulation operation output results, and generate simulation operation results based on the simulation operation output results.

[0204] In this optional embodiment, the determination of whether the simulated operation results match the operational demand information may include:

[0205] Determine the result matching degree between the simulated operation results and the corresponding operational demand results, and determine whether the result matching degree is greater than or equal to the preset result matching degree threshold.

[0206] When the result matching degree is determined to be greater than or equal to the preset result matching degree threshold, it is determined that the simulated operation result matches the operation requirement information; when the result matching degree is determined to be less than the preset result matching degree threshold, it is determined that the simulated operation result does not match the operation requirement information.

[0207] In this optional embodiment, the number of influencing factors may be one or more, and the embodiments of the present invention do not specifically limit the number of influencing factors.

[0208] In this optional embodiment, the above-mentioned generation of optimization adjustment parameters corresponding to the operational optimization parameters based on all influencing factors to update the operational optimization parameters may include:

[0209] Based on all influencing factors, a target influencing parameter is generated. Based on the target influencing parameter, at least one optimization parameter matching the target influencing parameter is determined from a pre-determined adjustment parameter library. Based on all optimization parameters, optimization adjustment parameters corresponding to the operational optimization parameters are generated to update the operational optimization parameters.

[0210] As can be seen, implementing this optional embodiment can generate operational demand information between all target demand vehicles and target gas stations based on demand recommendation information. It simulates operational operations at the target gas station that match the operational optimization parameters to obtain simulated operational results. It then determines whether the simulated operational results match the operational demand information. If they match, it generates target management parameters for the target gas station based on the operational optimization parameters and operational demand information. If they do not match, it identifies the influencing factors of the mismatch between the simulated operational results and operational demand information, and generates optimization adjustment parameters to update the operational optimization parameters. Finally, it generates target management parameters for the target gas station based on the updated operational optimization parameters and operational demand information. This approach can reduce gas station operating costs and time waste by accurately matching demand and supply, and ensure that gas station resources (such as fuel and supplies) can meet actual demand. By avoiding oversupply or shortage and adjusting operational strategies in real time, gas stations can better adapt to changes in market and customer demand, ensuring a smoother customer experience, reducing waiting times, and mitigating risks from operational errors through simulation and optimization. This allows gas station operational decisions to be more data-driven and analytically based, rather than solely based on experience. Optimizing fuel and product supply reduces unnecessary transportation and storage, lowering environmental impact. Through efficient operational management, gas stations can gain a stronger competitive edge in the market, improving operational efficiency and effectiveness. This, in turn, enhances the accuracy and intelligence of gas station operations, leading to more intelligent and efficient management.

[0211] Example 3

[0212] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a digital intelligent management system for gas stations disclosed in an embodiment of the present invention. Figure 3 As shown, the digital intelligent management system for gas stations may include:

[0213] The acquisition module 301 is used to acquire the target location information of the target gas station and the historical operating data of the target gas station;

[0214] The generation module 302 is used to generate an operational relationship between the location of the target gas station and the historical operational data based on the target location information and historical operational data.

[0215] The acquisition module 301 is also used to acquire the target fuel quantity information of the target gas station;

[0216] The prediction module 303 is used to predict the predicted operating data of the target gas station within a preset future time period based on the target oil quantity information and the operational relationship. The target oil quantity information includes the remaining oil quantity information of the target gas station, and the predicted operating data includes one or more of the following: predicted operating duration data, predicted operating oil quantity data, and predicted operating revenue amount data of the target gas station.

[0217] The generation module 302 is also used to generate economic benefit data for the target gas station based on the predicted operational data;

[0218] The fitting module 304 is used to perform data fitting operations on historical operating data and economic benefit data to obtain data fitting results.

[0219] Module 305 is used to determine the operational optimization parameters corresponding to the target gas station based on the data fitting results;

[0220] The generation module 302 is also used to generate target management parameters for the target gas station based on the operation optimization parameters.

[0221] It is evident that implementation Figure 3 The described device can acquire target location information and historical operational data of a target gas station, and generate operational correlations based on the target location information and historical operational data; acquire target fuel quantity information of the target gas station, and predict the target gas station's predicted operational data within a preset future time period based on the target fuel quantity information and operational correlations; generate economic benefit data based on the predicted operational data, and perform data fitting operations on the historical operational data and economic benefit data to obtain data fitting results; determine operational optimization parameters based on the data fitting results, and generate target management parameters based on the operational optimization parameters. This device can intelligently analyze the operational status of the target gas station, which is beneficial for improving operational efficiency and intelligence. Furthermore, it can increase sales and revenue through more accurate prediction and optimization of pricing strategies, thereby improving operational effectiveness and profitability. More accurate inventory management and service time prediction improve customer experience, thereby enhancing the user experience and convenience of using the target gas station. Through prediction and optimization, it reduces the risks brought by market fluctuations and uncertainties, thus improving the operational accuracy and intelligence of the target gas station, enhancing the intelligence of gas station management, and further improving the efficiency and accuracy of gas station management.

[0222] In an optional embodiment, such as Figure 4 As shown, the acquisition module 301 is also used to acquire real-time information of the target area before the generation module 302 generates the target management parameters of the target gas station based on the operation optimization parameters;

[0223] The determination module 305 is also used to determine the target vehicle based on real-time regional information;

[0224] The acquisition module 301 is also used to collect real-time vehicle information of all target vehicles;

[0225] The system also includes:

[0226] The judgment module 306 is used to determine, based on real-time vehicle information, whether there are any vehicles with target demand among all target vehicles, including vehicles that have a demand for the target gas station.

[0227] The determining module 305 is also used to determine the vehicle demand information corresponding to the target demand vehicle when the judging module 306 determines that there is a target demand vehicle among all target vehicles.

[0228] The acquisition module 301 is also used to acquire real-time operating information of the target gas station;

[0229] The generation module 302 is also used to generate demand recommendation information based on the vehicle demand information corresponding to the target demand vehicle and the real-time operation information.

[0230] The specific methods by which the generation module 302 generates the target management parameters for the target gas station based on the operational optimization parameters include:

[0231] Based on demand recommendations and operational optimization parameters, target management parameters for the target gas station are generated.

[0232] It is evident that implementation Figure 4 The described device can acquire real-time information about the target area and identify target vehicles. It collects real-time vehicle information to determine if a vehicle with the target demand exists among all target vehicles. If so, it determines the vehicle demand information corresponding to the target demand vehicle. It acquires real-time operational information of the target gas station and generates demand recommendation information based on the vehicle demand information. Based on the demand recommendation information and operational optimization parameters, it generates target management parameters for the target gas station. This allows for rapid demand identification and service provision, reducing customer waiting time and improving the convenience and comfort of users accessing the services at the target gas station. It adjusts resource allocation according to real-time demand, improving operational efficiency and enhancing the intelligence and efficiency of target gas station management. Furthermore, it improves customer satisfaction through personalized recommendations and timely service, and reduces excess inventory and waste through more accurate demand forecasting. This efficient operational management enhances market competitiveness, improves the operational efficiency and effectiveness of the target gas station, and ultimately improves the operational accuracy and intelligence of the target gas station, enhancing the intelligence of gas station management and further improving the efficiency and accuracy of gas station management.

[0233] In another alternative embodiment, such as Figure 4 As shown, the fitting module 304 performs data fitting operations on historical operating data and economic benefit data, and the specific methods for obtaining the data fitting results include:

[0234] Based on economic benefit data, generate the predicted data curve of the target gas station in the preset future time period, and based on historical operating data, generate the historical data curve of the target gas station in the preset historical time period.

[0235] Perform data fitting operations on the predicted data curve and historical data curve to obtain the target data curve. Based on the target data curve, generate the data correlation between the economic benefit data and the historical data curve.

[0236] Based on the data correlation, generate data fitting results.

[0237] It is evident that implementation Figure 4 The described device can generate a predicted data curve for a target gas station within a preset future time period based on economic benefit data, and a historical data curve for the target gas station within a preset historical time period based on historical operating data. It performs a data fitting operation on the predicted and historical data curves to obtain the target data curve, thereby generating data correlations and generating data fitting results based on these correlations. Through data fitting, future operations and economic benefits can be predicted more accurately, providing a more reliable basis for decision-making. Furthermore, by combining the relationship between historical and predicted data, it helps to allocate resources more effectively, such as inventory management, human resources, and fuel resources. Data-driven decision-making can optimize operational processes, reduce waste, and improve efficiency, thus enhancing the intelligence and efficiency of target gas station management. Optimizing service and product supply improves customer satisfaction and loyalty, and more accurate demand forecasting reduces excess inventory and waste. Therefore, efficient operational management enhances market competitiveness, improves the operational efficiency and effectiveness of the target gas station, and ultimately improves the operational accuracy and intelligence of the target gas station, enhancing the intelligence of gas station management and further improving the efficiency and accuracy of gas station management.

[0238] In yet another alternative embodiment, such as Figure 4 As shown, the specific methods by which module 305 determines the operational optimization parameters corresponding to the target gas station based on the data fitting results include:

[0239] Based on the data fitting results, the operational difference parameters corresponding to the target gas station are determined. Based on the operational difference parameters, the target optimization parameters that match the operational difference parameters are determined from the pre-determined set of optimization parameters.

[0240] Based on all the target optimization parameters, determine the corresponding operational optimization parameters for the target gas station;

[0241] Furthermore, the specific methods by which the generation module generates target management parameters for the target gas station based on operational optimization parameters include:

[0242] Obtain the real-time management parameters of the target gas station, and determine the management differences between the real-time management parameters and the operation optimization parameters based on the real-time management parameters and the operation optimization parameters;

[0243] Based on the management difference information, generate target management parameters for the target gas station.

[0244] It is evident that implementation Figure 4 The described device can determine the operational difference parameters corresponding to a target gas station based on data fitting results, and then determine the matching target optimization parameters. It also determines the corresponding operational optimization parameters for the target gas station based on the target optimization parameters, acquires the real-time management parameters of the target gas station, determines management difference information based on the real-time management parameters and operational optimization parameters, and generates target management parameters for the target gas station based on the management difference information. This data-driven decision-making is more objective and accurate, reducing subjective bias. Furthermore, it can quickly respond to changes in the market and operating environment, adjust operational strategies in a timely manner, quickly identify needs and provide services, reduce customer waiting time, and improve the convenience for users of the services corresponding to the target gas station. This approach enhances comfort and effectively controls operating costs through precise cost-benefit analysis. Adjusting service strategies based on real-time data improves customer satisfaction and enhances the intelligence and efficiency of target gas station management. Optimizing service and product supply increases customer satisfaction and loyalty, and continuous operational optimization improves the gas station's market competitiveness. Ultimately, efficient operational management improves market competitiveness, leading to increased operational efficiency and effectiveness, which in turn enhances the accuracy and intelligence of gas station operations, improves the intelligence of gas station management, and further improves the efficiency and accuracy of gas station management.

[0245] In yet another alternative embodiment, such as Figure 4 As shown, the specific methods by which the generation module 302 generates demand recommendation information based on the vehicle demand information corresponding to the target demand vehicle and the real-time operation information include:

[0246] Based on the real-time vehicle information corresponding to the target demand vehicle, determine the key vehicle information corresponding to the target demand vehicle. The key vehicle information includes the real-time vehicle location information, target vehicle model information, refueling amount information, refueling location information, and historical refueling frequency information corresponding to the target demand vehicle.

[0247] Based on key vehicle information, determine the vehicle demand parameters between the target vehicle and the target gas station, and generate demand recommendation information for the target vehicle based on the vehicle demand parameters and the real-time operation information of the target gas station. The demand recommendation information is used to guide the target vehicle to the target gas station to perform operations that match the vehicle demand information.

[0248] It is evident that implementation Figure 4 The described device can determine key vehicle information based on real-time vehicle information corresponding to the target vehicle, determine vehicle demand parameters between the target vehicle and the target gas station based on the key vehicle information, and generate demand recommendation information for the target vehicle based on the vehicle demand parameters and the real-time operation information of the target gas station. It can recommend the nearest gas station to the target vehicle using real-time location information and demand parameters, reducing refueling travel time. Furthermore, based on the vehicle's refueling quantity information and the gas station's inventory, it ensures that the gas station can meet the vehicle's refueling needs, avoiding resource waste. In addition, by combining vehicle model and historical refueling data, it provides customized refueling services, such as recommending suitable fuel types for the vehicle model and recommending refueling during off-peak hours by analyzing the gas station's busyness, reducing queuing. By providing accurate refueling recommendations and a convenient refueling experience, waiting time can be reduced, thus improving customer satisfaction with gas station services. Guiding vehicles to refuel at appropriate times and locations reduces traffic congestion and accident risks within gas stations. Continuous collection and analysis of vehicle and gas station data enables gas stations to make more data-driven operational decisions. The demand recommendation system can quickly adjust recommendation strategies based on real-time data to address market factors such as oil price fluctuations and changes in traffic conditions. This efficient operational management enhances market competitiveness, improves the operational efficiency and effectiveness of target gas stations, and consequently improves the accuracy and intelligence of gas station operations, leading to more intelligent gas station management and ultimately, increased efficiency and accuracy in gas station management.

[0249] In yet another alternative embodiment, such as Figure 4 As shown, the determining module 305 is also used to determine the real-time status of the vehicle corresponding to the target demand vehicle based on the real-time vehicle information of the target demand vehicle before the generating module 302 generates demand recommendation information based on the vehicle demand information and real-time operation information corresponding to the target demand vehicle.

[0250] The judgment module 306 is also used to determine whether the target demand vehicle meets the preset vehicle guidance conditions based on the real-time status of the vehicle; when it is determined that the target demand vehicle meets the preset vehicle guidance conditions, the generation module 302 is triggered to generate demand recommendation information based on the vehicle demand information corresponding to the target demand vehicle and the real-time operation information.

[0251] The determining module 305 is also used to determine the status adjustment parameters corresponding to the target vehicle based on the real-time vehicle status and the preset vehicle guidance conditions when the judging module 306 determines that the target vehicle does not meet the preset vehicle guidance conditions.

[0252] The system also includes:

[0253] The adjustment module 307 is used to perform a state adjustment operation that matches the state adjustment parameters on the target demand vehicle, so as to update the real-time state of the target demand vehicle and re-determine whether the target demand vehicle meets the preset vehicle guidance conditions based on the real-time state of the vehicle triggered by the judgment module 306.

[0254] It is evident that implementation Figure 4 The described device can determine the real-time status of a target vehicle based on its real-time information, and then judge whether the target vehicle meets preset vehicle guidance conditions. If it does, it generates demand recommendation information based on the target vehicle's demand information and real-time operating information. If it does not meet the conditions, it determines the corresponding status adjustment parameters based on the vehicle's real-time status and the preset vehicle guidance conditions, and performs a status adjustment operation matching the parameters to update the target vehicle's real-time status. It then re-triggers the operation of judging whether the target vehicle meets the preset vehicle guidance conditions based on its real-time status. Through real-time monitoring and intelligent recommendations, it can improve vehicle operating efficiency and reduce... Minimizing waiting and empty-running time, and the ability to adjust vehicle status based on real-time demand, allows for better resource allocation to meet passenger needs. Furthermore, by judging and adjusting pre-defined conditions, unsafe factors such as vehicle overloading and speeding can be avoided. Intelligent recommendations provide passengers with more convenient and punctual service. Optimizing routes and vehicle status reduces fuel consumption and maintenance costs. Through efficient operation and management, market competitiveness is enhanced, improving the operational efficiency and effectiveness of target gas stations. This, in turn, improves the operational accuracy and intelligence of gas station management, ultimately leading to improved efficiency and accuracy in gas station management.

[0255] In yet another alternative embodiment, such as Figure 4 As shown, the specific methods by which the generation module 302 generates target management parameters for the target gas station based on demand recommendation information and operational optimization parameters include:

[0256] Based on the demand recommendation information, generate operational demand information between all target demand vehicles and target gas stations. The operational demand information includes one or more of the following: operational demand duration information, operational demand time information, operational demand fuel quantity information, and operational demand item information.

[0257] Based on the operational optimization parameters, the target gas station is simulated to perform operational operations that match the operational optimization parameters, and the simulated operational results are obtained to determine whether the simulated operational results match the operational demand information.

[0258] When it is determined that the simulated operation results match the operation requirements information, target management parameters for the target gas station are generated based on the operation optimization parameters and the operation requirements information.

[0259] When it is determined that the simulated operation results do not match the operational demand information, the influencing factors of the mismatch are identified, and optimization adjustment parameters corresponding to the operational optimization parameters are generated based on all influencing factors to update the operational optimization parameters. Based on the updated operational optimization parameters and operational demand information, the target management parameters for the target gas station are generated.

[0260] It is evident that implementation Figure 4The described device can generate operational demand information between all target demand vehicles and target gas stations based on demand recommendation information. It simulates operational operations at the target gas station that match the operational optimization parameters to obtain simulated operational results. It then determines whether the simulated operational results match the operational demand information. If they match, it generates target management parameters for the target gas station based on the operational optimization parameters and operational demand information. If they do not match, it identifies the influencing factors of the mismatch between the simulated operational results and operational demand information, and generates optimization adjustment parameters to update the operational optimization parameters. Finally, it generates target management parameters for the target gas station based on the updated operational optimization parameters and operational demand information. This device can reduce gas station operating costs and time waste by accurately matching demand and supply, ensuring that gas station resources (such as fuel and supplies) meet actual demand and avoiding over-suppliing. By monitoring surpluses and shortages and adjusting operational strategies in real time, gas stations can better adapt to changes in market and customer demand, ensuring a smoother customer experience, reducing waiting times, and mitigating risks from operational errors through simulation and optimization. This allows gas station operational decisions to be more data-driven and analytically based, rather than solely based on experience. Optimizing fuel and product supply reduces unnecessary transportation and storage, lowering environmental impact. Through efficient operational management, gas stations can gain a stronger competitive edge in the market, improving operational efficiency and effectiveness. This, in turn, enhances the accuracy and intelligence of gas station operations, leading to more intelligent and efficient management.

[0261] Example 4

[0262] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of another digital intelligent management system for gas stations disclosed in an embodiment of the present invention. (See diagram below.) Figure 5 As shown, the digital intelligent management system for gas stations may include:

[0263] Memory 401 storing executable program code;

[0264] Processor 402 coupled to memory 401;

[0265] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the digital intelligent management method for gas stations described in Embodiment 1 or Embodiment 2 of the present invention.

[0266] Example 5

[0267] This invention discloses a computer-storable medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the digital intelligent management method for gas stations described in Embodiment 1 or Embodiment 2 of this invention.

[0268] Example 6

[0269] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the digital intelligent management method for gas stations described in Embodiment 1 or Embodiment 2.

[0270] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0271] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0272] Finally, it should be noted that the digital intelligent management method and system for gas stations disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for digital intelligent management of a gas station, characterized in that, The method comprises: obtaining target position information of a target gas station, and obtaining historical operation data of the target gas station, and generating an operation correlation between the target position information and the historical operation data of the target gas station based on the target position information and the historical operation data; obtaining target oil quantity information of the target gas station, and predicting predicted operation data of the target gas station in a preset future time period according to the target oil quantity information and the operation correlation, wherein the target oil quantity information comprises residual oil quantity information corresponding to the target gas station, and the predicted operation data comprises one or more of predicted operation time data, predicted operation oil quantity data, and predicted operation income amount data corresponding to the target gas station; generating economic benefit data of the target gas station according to the predicted operation data, and performing data fitting on the historical operation data and the economic benefit data to obtain a data fitting result; determining operation optimization parameters corresponding to the target gas station based on the data fitting result, wherein the operation optimization parameters corresponding to the target gas station are determined based on the data fitting result, comprising: determining operation difference parameters corresponding to the target gas station based on the data fitting result, and determining target optimization parameters matched with the operation difference parameters from a set of pre-determined optimization parameters according to the operation difference parameters; determining operation optimization parameters corresponding to the target gas station according to all the target optimization parameters; generating target management parameters of the target gas station according to the operation optimization parameters, wherein the target management parameters of the target gas station are generated according to the operation optimization parameters, comprising: obtaining real-time management parameters of the target gas station, and determining management difference information between the real-time management parameters and the operation optimization parameters according to the real-time management parameters and the operation optimization parameters; generating target management parameters of the target gas station according to the management difference information; before the target management parameters of the target gas station are generated according to the operation optimization parameters, the method further comprises: obtaining regional real-time information corresponding to a target region, and determining target vehicles according to the regional real-time information; collecting vehicle real-time information of all the target vehicles, and determining whether there is a target demand vehicle among all the target vehicles according to the vehicle real-time information, wherein the target demand vehicle comprises a vehicle having a demand for the target gas station; when it is determined that there is the target demand vehicle among all the target vehicles, determining vehicle demand information corresponding to the target demand vehicle; obtaining real-time running information of the target gas station, and generating demand recommendation information according to the vehicle demand information corresponding to the target demand vehicle and the real-time running information; wherein the target management parameters of the target gas station are generated according to the operation optimization parameters, comprising: generating the target management parameters of the target gas station according to the demand recommendation information and the operation optimization parameters; Before the generating of the demand recommendation information according to the vehicle demand information corresponding to the target demand vehicle and the real-time operation information, the method further comprises: determining a real-time state of the target demand vehicle according to the real-time information of the target demand vehicle, and determining whether the target demand vehicle meets a preset vehicle guide condition according to the real-time state of the target demand vehicle; when it is determined that the target demand vehicle meets the preset vehicle guide condition, triggering the operation of generating the demand recommendation information according to the vehicle demand information corresponding to the target demand vehicle and the real-time operation information; when it is determined that the target demand vehicle does not meet the preset vehicle guide condition, determining a state adjustment parameter corresponding to the target demand vehicle according to the real-time state of the target demand vehicle and the preset vehicle guide condition, and performing a state adjustment operation matched with the state adjustment parameter on the target demand vehicle to update the real-time state of the target demand vehicle, and triggering the operation of determining whether the target demand vehicle meets the preset vehicle guide condition according to the real-time state of the target demand vehicle again; wherein the state adjustment parameter corresponding to the target demand vehicle comprises one or more of a vehicle speed adjustment parameter, a vehicle route adjustment parameter, a vehicle service adjustment parameter, and a vehicle passenger behavior state adjustment parameter; the generating of the target management parameter of the target gas station according to the demand recommendation information and the operation optimization parameter comprises: generating operation demand information between all the target demand vehicles and the target gas station according to the demand recommendation information, wherein the operation demand information comprises one or more of operation demand time length information, operation demand time point information, operation demand oil quantity information, and operation demand article information of the target demand vehicle to the target gas station; simulating an operation of the target gas station matched with the operation optimization parameter according to the operation optimization parameter to obtain a simulation operation result, and determining whether the simulation operation result matches the operation demand information; when it is determined that the simulation operation result matches the operation demand information, generating the target management parameter of the target gas station according to the operation optimization parameter and the operation demand information; when it is determined that the simulation operation result does not match the operation demand information, determining an influence factor of the simulation operation result not matching the operation demand information, and generating an optimization adjustment parameter corresponding to the operation optimization parameter according to all the influence factors to update the operation optimization parameter, and generating the target management parameter of the target gas station according to the updated operation optimization parameter and the operation demand information; the data fitting operation on the historical operation data and the economic benefit data to obtain a data fitting result comprises: generating a prediction data curve of the target gas station in a preset future time period according to the economic benefit data, and generating a historical data curve of the target gas station in a preset historical time period according to the historical operation data; performing a data fitting operation on the predicted data curve and the historical data curve to obtain a target data curve, and generating a data correlation between the economic benefit data and the historical data curve according to the target data curve; generating a data fitting result according to the data correlation; According to the economic benefit data, the target gas station in the preset future time period is generated. The future time period is generated. The historical data curve of the target gas station in the preset historical time period is generated according to the historical operation data, including: According to the economic benefit data, the operation trend information of the target gas station in the preset future time period is generated, and the predicted data curve of the target gas station in the preset future time period is generated according to the operation trend information, wherein the operation trend information includes operation mode information of the target gas station in the future time period, and the predicted data curve includes data development information of the target gas station in the preset future time period; According to the historical operation data, the historical operation trend information of the target gas station in the preset historical time period is generated, and the historical data curve of the target gas station in the preset historical time period is generated according to the historical operation trend information, wherein the historical data curve includes data historical trend information of the target gas station in the preset historical time period; The data fitting operation is performed on the predicted data curve and the historical data curve to obtain a target data curve, and a data correlation between the economic benefit data and the historical data curve is generated according to the target data curve, including: Performing a fitting comparison operation on the predicted data curve and the historical data curve to obtain a target data curve, and generating a data correlation between the economic benefit data and the historical data curve according to the target data curve and a predetermined target algorithm; wherein the predetermined target algorithm includes one or more of linear regression algorithm, polynomial regression algorithm; And, the data fitting result is generated according to the data correlation, including: According to the data correlation, determine the correlation parameter between the economic benefit data and the historical data, and generate a data fitting result based on the correlation parameter; wherein the correlation parameter includes influence data for identifying the economic benefit data and the historical operation data.

2. The digital intelligent management method for gas stations according to claim 1, characterized in that, According to the vehicle demand information corresponding to the target demand vehicle and the real-time running information, the demand recommendation information is generated, including: According to the vehicle real-time information corresponding to the target demand vehicle, determine the key vehicle information corresponding to the target demand vehicle, wherein the key vehicle information includes real-time vehicle location information, target vehicle model information, refueling amount information, refueling location information, and historical refueling frequency information corresponding to the target demand vehicle; According to the key vehicle information, a vehicle demand parameter between the target demand vehicle and the target gas station is determined, and demand recommendation information of the target demand vehicle is generated according to the vehicle demand parameter and real-time operation information of the target gas station; wherein the demand recommendation information is used to guide the target demand vehicle to reach the target gas station to perform an operation matched with the vehicle demand information.

3. A digital intelligent management system for gas stations, characterized in that, The system comprises: An acquisition module is configured to acquire target position information of a target gas station and historical operation data of the target gas station; A generation module is configured to generate an operation association relationship between a position corresponding to the target gas station and the historical operation data based on the target position information and the historical operation data; The acquisition module is further configured to acquire target oil amount information of the target gas station; A prediction module is configured to predict predicted operation data of the target gas station in a preset future time period according to the target oil amount information and the operation association relationship, wherein the target oil amount information comprises residual oil amount information corresponding to the target gas station, and the predicted operation data comprises one or more of predicted operation duration data, predicted operation oil amount data, and predicted operation income amount data corresponding to the target gas station; The generation module is further configured to generate economic benefit data of the target gas station according to the predicted operation data; A fitting module is configured to perform a data fitting operation on the historical operation data and the economic benefit data to obtain a data fitting result; A determination module is configured to determine an operation optimization parameter corresponding to the target gas station based on the data fitting result; the determination of the operation optimization parameter corresponding to the target gas station based on the data fitting result comprises: determining an operation difference parameter corresponding to the target gas station based on the data fitting result, and determining a target optimization parameter matched with the operation difference parameter from a set of optimization parameters determined in advance according to the operation difference parameter; determining the operation optimization parameter corresponding to the target gas station according to all the target optimization parameters; The generation module is further configured to generate a target management parameter of the target gas station according to the operation optimization parameter; the generation of the target management parameter of the target gas station according to the operation optimization parameter comprises: acquiring real-time management parameters of the target gas station, determining management difference information between the real-time management parameters and the operation optimization parameter according to the real-time management parameters and the operation optimization parameter, and generating the target management parameter of the target gas station according to the management difference information; The acquisition module is further configured to acquire regional real-time information corresponding to a target region before the generation module generates the target management parameter of the target gas station according to the operation optimization parameter; The determination module is further configured to determine a target vehicle according to the regional real-time information; The acquisition module is further configured to collect vehicle real-time information of all the target vehicles; The system further comprises: ​ determining whether there is a target demand vehicle in all the target vehicles according to the vehicle real-time information, wherein the target demand vehicle includes a vehicle that has a demand for the target gas station; the determining module is further configured to determine vehicle demand information corresponding to the target demand vehicle when the target demand vehicle is determined to exist in all the target vehicles by the judging module; the obtaining module is further configured to obtain real-time operation information of the target gas station; the generating module is further configured to generate demand recommendation information according to the vehicle demand information corresponding to the target demand vehicle and the real-time operation information; the generating module generates target management parameters of the target gas station according to the demand recommendation information and the operation optimization parameters; the determining module is further configured to determine a vehicle real-time state corresponding to the target demand vehicle according to the vehicle real-time information of the target demand vehicle before the generating module generates demand recommendation information according to the vehicle demand information corresponding to the target demand vehicle and the real-time operation information; the judging module is further configured to determine whether the target demand vehicle meets a preset vehicle guide condition according to the vehicle real-time state, and trigger the generating module to perform the operation of generating demand recommendation information according to the vehicle demand information corresponding to the target demand vehicle and the real-time operation information when it is determined that the target demand vehicle meets the preset vehicle guide condition; the determining module is further configured to determine a state adjustment parameter corresponding to the target demand vehicle according to the vehicle real-time state and the preset vehicle guide condition when it is determined by the judging module that the target demand vehicle does not meet the preset vehicle guide condition; the system further comprises: an adjustment module configured to perform a state adjustment operation matched with the state adjustment parameter on the target demand vehicle to update the vehicle real-time state of the target demand vehicle, and trigger the judging module to perform the operation of determining whether the target demand vehicle meets the preset vehicle guide condition according to the vehicle real-time state again; wherein the state adjustment parameter corresponding to the target demand vehicle includes one or more of a vehicle speed adjustment parameter, a vehicle route adjustment parameter, a vehicle service adjustment parameter, and a vehicle passenger behavior state adjustment parameter; the generating module generates target management parameters of the target gas station according to the demand recommendation information and the operation optimization parameters in the following specific manner: generate operation demand information between all the target demand vehicles and the target gas station according to the demand recommendation information, wherein the operation demand information includes one or more of operation demand time length information, operation demand time information, operation demand oil amount information, and operation demand article information of the target demand vehicle for the target gas station; ​ According to the operation optimization parameter, simulation is performed on the target gas station to execute an operation operation matching the operation optimization parameter, to obtain a simulation operation result, and it is judged whether the simulation operation result matches the operation demand information; When it is judged that the simulation operation result matches the operation demand information, a target management parameter of the target gas station is generated according to the operation optimization parameter and the operation demand information; When it is judged that the simulation operation result does not match the operation demand information, an influence factor of the simulation operation result not matching the operation demand information is determined, an optimization adjustment parameter corresponding to the operation optimization parameter is generated according to all the influence factors, to update the operation optimization parameter, and a target management parameter of the target gas station is generated according to the updated operation optimization parameter and the operation demand information; The specific manner in which the fitting module performs data fitting operation on the historical operation data and the economic benefit data to obtain a data fitting result comprises: According to the economic benefit data, a predicted data curve of the target gas station in a preset future time period is generated, and according to the historical operation data, a historical data curve of the target gas station in a preset historical time period is generated; Data fitting operation is performed on the predicted data curve and the historical data curve to obtain a target data curve, and a data correlation between the economic benefit data and the historical data curve is generated according to the target data curve; According to the data correlation, a data fitting result is generated; The specific manner in which the fitting module generates, according to the economic benefit data, a predicted data curve of the target gas station in a preset future time period, and generates, according to the historical operation data, a historical data curve of the target gas station in a preset historical time period comprises: According to the economic benefit data, operation trend information of the target gas station in a preset future time period is generated, and a predicted data curve of the target gas station in a preset future time period is generated according to the operation trend information, wherein the operation trend information comprises operation mode information of the target gas station in the future time period, and the predicted data curve comprises data development information of the target gas station in a preset future time period; According to the historical operation data, historical operation trend information of the target gas station in a preset historical time period is generated, and a historical data curve of the target gas station in a preset historical time period is generated according to the historical operation trend information, wherein the historical data curve comprises data historical trend information of the target gas station in a preset historical time period; The specific manner in which the fitting module performs data fitting operation on the predicted data curve and the historical data curve to obtain a target data curve, and generates a data correlation between the economic benefit data and the historical data curve according to the target data curve comprises: The fitting comparison operation is performed on the predicted data curve and the historical data curve to obtain a target data curve, and a data correlation between the economic benefit data and the historical data curve is generated according to the target data curve and a target algorithm determined in advance; wherein the target algorithm determined in advance includes one or more of a linear regression algorithm, a polynomial regression algorithm; The specific manner in which the fitting module generates the data fitting result according to the data correlation includes: According to the data correlation, an associated parameter between the economic benefit data and the historical data is determined, and a data fitting result is generated based on the associated parameter; wherein the associated parameter includes an influence data used to identify the economic benefit data and the historical operation data.

4. A digital intelligent management system for gas stations, characterized in that, The system includes: a memory storing executable program codes; a processor coupled with the memory; The processor invokes the executable program codes stored in the memory to execute the method for digital intelligent management of gas stations according to any one of claims 1-2.

5. A computer storage medium, characterized in that The computer storage medium stores computer instructions, which when invoked, are used to execute the method for digital intelligent management of gas stations according to any one of claims 1-2.

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