Digitalized intelligent management method and system for gas station

Abstract

The invention discloses a gas station digital intelligent management method and system, and the method comprises the steps: obtaining the target position information and historical operation data of a target gas station, and generating an operation association relationship based on the target position information and historical operation data; acquiring target oil mass 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 mass information and the operation association relationship; according to the predicted operation data, economic benefit data are generated, and data fitting operation is performed on the historical operation data and the economic benefit data to obtain a data fitting result; and based on the data fitting result, determining an operation optimization parameter, and generating a target management parameter according to the operation optimization parameter. Visibly, the management parameters of the gas station can be generated in combination with multi-aspect information of the gas station, the intelligence of management of the gas station is improved, and the efficiency and accuracy of management of the gas station are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology, and in particular to a digital intelligent management method and system for a gas station. Background Art

[0002] With the continuous development of science and technology, the economic level is also developing rapidly. People's demand for vehicles and oil is getting higher and higher. The number of gas stations is also increasing year by year, and the operating load of gas stations is also gradually increasing. At present, most gas stations refuel cars through gas station staff, and the operation of gas stations is through manual data collation and then the gas stations are managed by staff. This management method not only has technical problems of low intelligence and efficiency, but also is prone to errors in the management of gas stations. It can be seen that it is particularly important to provide a new digital management method for gas stations to realize the intelligent management of gas stations and improve the efficiency and accuracy of gas station management. Summary of the invention

[0003] The present invention provides a digital intelligent management method for a gas station, which can combine various information of the gas station to jointly generate management parameters of the gas station, which is beneficial to improving the intelligence of the management of the gas station, thereby facilitating improving the efficiency and accuracy of the management of the gas station.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a digital intelligent management method for a gas station, the method comprising:

[0005] Obtaining target location information of a target gas station and obtaining historical operation data of the target gas station, and generating an operation association relationship between a location corresponding to the target gas station and the historical operation data based on the target location information and the historical operation data;

[0006] Obtaining target fuel 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 fuel quantity information and the operation association relationship, wherein the target fuel quantity information includes remaining fuel quantity information corresponding to the target gas station, and the predicted operation data includes one or more of predicted operation duration data, predicted operation fuel quantity data, and predicted operation income amount data corresponding to the target gas station;

[0007] Generate economic benefit data of the target gas station based on the predicted operation data, and perform a data fitting operation on the historical operation data and the economic benefit data to obtain a data fitting result;

[0008] Based on the data fitting result, determining the operation optimization parameters corresponding to the target gas station;

[0009] According to the operation optimization parameters, target management parameters of 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 of the target gas station according to the operation optimization parameters, the method further includes:

[0011] Acquire the real-time regional information corresponding to the target area, and determine the target vehicle according to the real-time regional information;

[0012] Collecting real-time vehicle information of all the target vehicles, and judging whether there is a target demand vehicle among all the target vehicles according to the real-time vehicle information, wherein the target demand vehicle includes a vehicle that has a demand for the target gas station;

[0013] When it is determined that the target required vehicle exists among all the target vehicles, determining the vehicle required information corresponding to the target required vehicle;

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

[0015] Wherein, generating the target management parameters of the target gas station according to the operation optimization parameters includes:

[0016] The target management parameters of the target gas station are generated according to the demand recommendation information and the operation optimization parameters.

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

[0018] Generate a forecast data curve of the target gas station in the preset future time period based on the economic benefit data, and generate a historical data curve of the target gas station in the preset historical time period based on the historical operation data;

[0019] 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 association relationship between the economic benefit data and the historical data curve according to the target data curve;

[0020] According to the data association relationship, a data fitting result is generated.

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

[0022] Based on the data fitting result, determining the operation difference parameter corresponding to the target gas station, and according to the operation difference parameter, determining the target optimization parameter matching the operation difference parameter from a predetermined optimization parameter set;

[0023] Determine the operation optimization parameters corresponding to the target gas station according to all the target optimization parameters;

[0024] And, generating target management parameters of the target gas station according to the operation optimization parameters includes:

[0025] Acquire 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 according to the real-time management parameters and the operation optimization parameters;

[0026] The target management parameters of the target gas station are generated according to the management difference information.

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

[0028] Determine the key vehicle information corresponding to the target vehicle according to the real-time vehicle information corresponding to the target vehicle, wherein the key vehicle information includes the real-time vehicle position information, target vehicle model information, refueling amount information, refueling location information, and historical refueling times information corresponding to the target vehicle;

[0029] Based on the key vehicle information, the vehicle demand parameters between the target vehicle and the target gas station are determined, and 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; wherein the demand recommendation information is used to guide the target vehicle to arrive at the target gas station to perform operations matching the vehicle demand information.

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

[0031] Determine the real-time status of the vehicle corresponding to the target vehicle according to the real-time vehicle information of the target vehicle, and determine whether the target vehicle meets the preset vehicle guidance condition according to the real-time status of the vehicle;

[0032] When it is determined that the target demand vehicle meets the preset vehicle guidance condition, 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 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 based on the real-time state of the vehicle and the preset vehicle guidance conditions, and a state adjustment operation matching the state adjustment parameters is performed on the target vehicle to update the real-time state of the target vehicle, and re-trigger the operation of determining whether the target vehicle meets the preset vehicle guidance conditions based on the real-time state of the vehicle.

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

[0035] 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 the operation demand duration information, operation demand time information, operation demand oil quantity information, and operation demand item information of the target demand vehicle for the target gas station;

[0036] According to the operation optimization parameters, simulating the target gas station to perform operation operations matching the operation optimization parameters, obtaining simulated operation results, and determining whether the simulated operation results match the operation demand information;

[0037] When it is determined that the simulated operation result matches the operation demand information, generating target management parameters of the target gas station according to the operation optimization parameters and the operation demand information;

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

[0039] The second aspect of the present invention discloses a digital intelligent management system for a gas station, the system comprising:

[0040] An acquisition module, used to acquire target location information of a target gas station and acquire historical operation data of the target gas station;

[0041] A generating module, configured to generate an operational association relationship between a location corresponding to the target gas station and the historical operating data based on the target location information and the historical operating data;

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

[0043] A prediction module, used to predict 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 association relationship, wherein the target oil quantity information includes the remaining oil quantity information corresponding to the target gas station, and the predicted operation data includes one or more of the predicted operation duration data, predicted operation oil quantity data, and predicted operation income amount data corresponding to the target gas station;

[0044] The generating module is further used to generate economic benefit data of the target gas station according to the predicted operation data;

[0045] A fitting module, used to perform a data fitting operation on the historical operation data and the economic benefit data to obtain a data fitting result;

[0046] A determination module, used to determine the operation optimization parameters corresponding to the target gas station based on the data fitting result;

[0047] The generating module is further used to generate target management parameters of the target gas station according to the operation optimization parameters.

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

[0049] The determination module is further used to determine the target vehicle according to the real-time information of the area;

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

[0051] The system further comprises:

[0052] A judgment module, used for judging whether there is a target demand vehicle among all the target vehicles according to the real-time vehicle information, wherein the target demand vehicle includes a vehicle that has a demand for the target gas station;

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

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

[0055] 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;

[0056] The specific manner in which the generating module generates the target management parameters of the target gas station according to the operation optimization parameters includes:

[0057] The target management parameters of the target gas station are generated according to the demand recommendation information and the operation optimization parameters.

[0058] As an optional implementation, in the second aspect of the present invention, the fitting module performs a data fitting operation on the historical operation data and the economic benefit data, and a specific method of obtaining a data fitting result includes:

[0059] Generate a forecast data curve of the target gas station in the preset future time period based on the economic benefit data, and generate a historical data curve of the target gas station in the preset historical time period based on the historical operation data;

[0060] 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 association relationship between the economic benefit data and the historical data curve according to the target data curve;

[0061] According to the data association relationship, a data fitting result is generated.

[0062] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the operation optimization parameters corresponding to the target gas station based on the data fitting result includes:

[0063] Based on the data fitting result, determining the operation difference parameter corresponding to the target gas station, and according to the operation difference parameter, determining the target optimization parameter matching the operation difference parameter from a predetermined optimization parameter set;

[0064] Determine the operation optimization parameters corresponding to the target gas station according to all the target optimization parameters;

[0065] And, the specific manner in which the generating module generates the target management parameters of the target gas station according to the operation optimization parameters includes:

[0066] Acquire 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 according to the real-time management parameters and the operation optimization parameters;

[0067] The target management parameters of the target gas station are generated according to the management difference information.

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

[0069] Determine the key vehicle information corresponding to the target vehicle according to the real-time vehicle information corresponding to the target vehicle, wherein the key vehicle information includes the real-time vehicle position information, target vehicle model information, refueling amount information, refueling location information, and historical refueling times information corresponding to the target vehicle;

[0070] Based on the key vehicle information, the vehicle demand parameters between the target vehicle and the target gas station are determined, and 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; wherein the demand recommendation information is used to guide the target vehicle to arrive at the target gas station to perform operations matching the vehicle demand information.

[0071] As an optional implementation, in the second aspect of the present invention, the determination module is further used to determine the real-time vehicle status corresponding to the target demand vehicle according to the real-time vehicle information of the target demand vehicle before the generation module generates the demand recommendation information according to the vehicle demand information corresponding to the target demand vehicle and the real-time operation information;

[0072] The judging module is further configured to judge whether the target demand vehicle meets the preset vehicle guidance conditions according to the real-time status of the vehicle; when it is judged that the target demand vehicle meets the preset vehicle guidance conditions, the generating module is triggered to execute 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;

[0073] The determination module is further configured to determine a state adjustment parameter corresponding to the target vehicle according to the real-time state of the vehicle and the preset vehicle guidance condition when the determination module determines that the target vehicle does not meet the preset vehicle guidance condition;

[0074] The system further comprises:

[0075] The adjustment module is used to perform a state adjustment operation on the target vehicle that matches the state adjustment parameters to update the real-time vehicle state of the target vehicle, and re-trigger the judgment module to execute the operation of judging whether the target vehicle satisfies 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 manner in which the generating module generates the target management parameters of the target gas station according to the demand recommendation information and the operation optimization parameters includes:

[0077] 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 the operation demand duration information, operation demand time information, operation demand oil quantity information, and operation demand item information of the target demand vehicle for the target gas station;

[0078] According to the operation optimization parameters, simulating the target gas station to perform operation operations matching the operation optimization parameters, obtaining simulated operation results, and determining whether the simulated operation results match the operation demand information;

[0079] When it is determined that the simulated operation result matches the operation demand information, generating target management parameters of the target gas station according to the operation optimization parameters and the operation demand information;

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

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

[0082] A memory storing 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, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, they 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 an embodiment of the present invention, the target location information and historical operation data of the target gas station are obtained, and an operation association relationship is generated based on the target location information and the historical operation data; the target oil volume information of the target gas station is obtained, and the predicted operation data of the target gas station in a preset future time period is predicted based on the target oil volume information and the operation association relationship; based on the predicted operation data, economic benefit data is generated and a data fitting operation is performed on the historical operation data and the economic benefit data to obtain a data fitting result; based on the data fitting result, the operation optimization parameters are determined, and the target management parameters are generated based on the operation optimization parameters. It can be seen that the implementation of the present invention can combine various aspects of information of the gas station to jointly generate the management parameters of the gas station, which is conducive to improving the intelligence of the management of the gas station, thereby facilitating the improvement of the efficiency and accuracy of the management of the gas station. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0089] Figure 1 It is a flow chart of a digital intelligent management method for a gas station disclosed in an embodiment of the present invention;

[0090] Figure 2 It is a flow chart of another gas station digital intelligent management method disclosed in an embodiment of the present invention;

[0091] Figure 3 It is a structural diagram of a digital intelligent management system for a gas station disclosed in an embodiment of the present invention;

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

[0093] Figure 5 It is a structural diagram of another digital intelligent management system for gas stations disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0094] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0095] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or terminal including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or terminals.

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

[0097] The present invention discloses a digital intelligent management method and system for a gas station, which can combine various information of the gas station to jointly generate management parameters of the gas station, which is conducive to improving the intelligence of the management of the gas station, thereby improving the efficiency and accuracy of the management of the gas station. The following are detailed descriptions.

[0098] Embodiment 1

[0099] See also Figure 1 , Figure 1 This is a flow chart of a digital intelligent management method for a gas station disclosed in an embodiment of the present invention. Figure 1 The described gas station digital intelligent management method can be applied to a gas station digital intelligent management system, wherein the gas station digital intelligent management system can be integrated in a local server or a cloud server, which is not limited in the embodiments of the present invention. Figure 1 As shown, the gas station digital intelligent management method may include the following operations:

[0100] 101. Obtain target location information of a target gas station and historical operation data of the target gas station, and generate an operation association relationship between a location corresponding to the target gas station and the historical operation data based on the target location information and the historical operation data.

[0101] In the embodiment of the present invention, optionally, the target location information of the target gas station may be obtained by GPS positioning, satellite positioning, wifi positioning, base station positioning, IP positioning, etc. Optionally, the target location information of the target gas station may include one or more of the location information of the target gas station and the administrative area information of the target gas station. Furthermore, the target location information of the target gas station may also include the traffic flow information and the surrounding environment information of the target gas station.

[0102] In the embodiment of the present invention, optionally, the historical operation data of the target gas station may include one or more of the historical refueling volume data, historical operation amount data, historical operation time data, and historical revenue amount data of the target gas station. Further, the historical operation data of the target gas station may also include historical oil product sales volume data, historical revenue amount data, and historical customer flow data.

[0103] In the embodiment of the present invention, optionally, the generating of the operation association relationship between the location corresponding to the target gas station and the historical operation data based on the target location information and the historical operation data may include:

[0104] Determine the location corresponding to the target gas station based on the target location information, and generate location operation association information between the location corresponding to the target gas station and the historical operation data according to the location corresponding to the target gas station and the historical operation data, wherein the location operation association information includes one of the operation amount information, operation income information, and operation time information between the location corresponding to the target gas station and the historical operation data;

[0105] Based on the location-operation association information, an operation association relationship between the location corresponding to the target gas station and the historical operation data is generated.

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

[0107] In an embodiment of the present invention, the target fuel quantity information includes the remaining fuel quantity information corresponding to the target gas station, and the predicted operation data includes one or more of the predicted operation time data, predicted operation fuel quantity data, and predicted operation income amount data corresponding to the target gas station.

[0108] In an embodiment of the present invention, optionally, the remaining fuel information corresponding to the target gas station may include the remaining fuel information corresponding to each current gasoline grade; for example, the remaining fuel information corresponding to the target gas station may include the remaining fuel information corresponding to No. 92 gasoline and the remaining fuel information corresponding to No. 95 gasoline.

[0109] In an embodiment of the present invention, optionally, the predicted operating data of the target gas station in a preset future time period includes one or more of predicted operating amount data, predicted operating revenue data, predicted operating duration data, and predicted operating oil volume data.

[0110] In the embodiment of the present invention, optionally, predicting the predicted operation data of the target gas station in a preset future time period based on the target fuel volume information and the operation association relationship may include:

[0111] The target fuel volume information and the operation association relationship are input into a predetermined operation data prediction model to obtain an operation prediction data output result, and based on the operation prediction data output result, the predicted operation data of the target gas station in a preset future time period is generated.

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

[0113] In the embodiment of the present 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 the predicted operation data.

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

[0115] In an embodiment of the present invention, optionally, the operation optimization parameters corresponding to the target gas station may include one or more of the operation fueling volume optimization parameters, operation duration optimization parameters, operation time period optimization parameters, operation staff optimization parameters, and operation cost optimization parameters corresponding to the target gas station.

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

[0117] In an embodiment of the present invention, optionally, the target management parameters of the target gas station may include one or more of the target gas station's operating time management parameters, operating time period management parameters, operating oil volume management parameters, operating oil number management parameters, operating area management parameters, operating personnel management parameters, operating quantity management parameters, and operating cost management parameters. For example, the operating oil number management parameters may include the management parameters of the oil model operated by the target gas station; the operating area management parameters may include the parking space management parameters corresponding to the target gas station; and the operating quantity management parameters may include one or more of the operating material management parameters and the operating commodity management parameters.

[0118] It can be seen that the implementation Figure 1 The described gas station digital intelligent management method can obtain the target location information and historical operation data of the target gas station, and generate an operation association relationship based on the target location information and the historical operation data; obtain the target oil volume information of the target gas station, and predict the predicted operation data of the target gas station in a preset future time period according to the target oil volume information and the operation association relationship; generate economic benefit data according to the predicted operation data and perform data fitting operations on the historical operation data and the economic benefit data to obtain data fitting results; determine the operation optimization parameters based on the data fitting results, and generate target management parameters according to the operation optimization parameters, and can intelligently analyze the operation status of the target gas station, which is conducive to improving the operation efficiency and operation intelligence, and can increase sales and revenue through more accurate prediction and optimization of price strategies, thereby helping to improve the operation effect and operation income, improve customer experience through more accurate inventory management and service time prediction, thereby improving the user experience and convenience of using the target gas station, and reduce the risks brought by market fluctuations and uncertainties through prediction and optimization, thereby helping to improve the operation accuracy and intelligence of the target gas station, improve the intelligence of gas station management, and further help to improve the efficiency and accuracy of gas station management.

[0119] Embodiment 2

[0120] See also Figure 2 , Figure 2 It is a flow chart of another gas station digital intelligent management method disclosed in an embodiment of the present invention. Figure 2 The described gas station digital intelligent management method can be applied to a gas station digital intelligent management system, wherein the gas station digital intelligent management system can be integrated in a local server or a cloud server, which is not limited in the embodiments of the present invention. Figure 2 As shown, the gas station digital intelligent management method may include the following operations:

[0121] 201. Obtain target location information of a target gas station, and obtain historical operation data of the target gas station, and generate an operation association relationship between the location corresponding to the target gas station and the historical operation data based on the target location information and the historical operation data.

[0122] 202. Obtain target fuel volume information of the target gas station, and predict the predicted operation data of the target gas station in a preset future time period according to the target fuel volume information and the operation association relationship.

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

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

[0125] In the embodiment of the present invention, for the detailed description of steps 201 to 204, please refer to the other descriptions of steps 101 to 104 in the first embodiment, and the embodiment of the present invention will not be repeated.

[0126] 205. Acquire regional real-time information corresponding to the target area, and determine the target vehicle according to the regional real-time information.

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

[0128] In the embodiment of the present invention, optionally, the real-time regional information corresponding to the target area may include one or more of real-time traffic condition information, real-time weather condition information, and real-time accident information of the target area.

[0129] In the embodiment of the present invention, the target vehicle may optionally include vehicles traveling in the target area. Further, the number of the target vehicles may be one or more, which is not specifically limited in the embodiment of the present invention.

[0130] 206. Collect the real-time vehicle information of all target vehicles, and determine whether there is a target required vehicle among all target vehicles based on the real-time vehicle information.

[0131] In the embodiment of the present invention, the target demand vehicle includes a vehicle that has a demand for the target gas station.

[0132] In an embodiment of the present invention, optionally, the real-time vehicle information of the target vehicle may include one or more of the vehicle speed information, vehicle location information, vehicle real-time fuel quantity information, and vehicle real-time destination information of the target vehicle.

[0133] In the embodiment of the present invention, optionally, judging whether there is a target required vehicle among all target vehicles according to the real-time vehicle information may include:

[0134] Determine the real-time fuel volume corresponding to each target vehicle according to the real-time vehicle information, and determine whether there is a target fuel volume less than or equal to a preset fuel volume threshold value among all the real-time fuel volumes according to the real-time fuel volume corresponding to each target vehicle;

[0135] When it is determined that there is a target fuel amount less than or equal to a preset fuel amount threshold among all real-time fuel amounts, it is determined that there is a target demand vehicle among all target vehicles; when it is determined that there is no target fuel amount less than or equal to the preset fuel amount threshold among all real-time fuel amounts, it is determined that there is no target demand vehicle among all target vehicles.

[0136] 207. When it is determined that a target required vehicle exists among all target vehicles, vehicle requirement information corresponding to the target required vehicle is determined.

[0137] In the embodiment of the present invention, optionally, when it is determined that there is no target required vehicle among all target vehicles, the process can be terminated.

[0138] In an embodiment of the present invention, optionally, the vehicle demand information corresponding to the target demand vehicle may include one or more of refueling quantity demand information, refueling oil number demand information, and refueling oil type demand information.

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

[0140] In the embodiment of the present invention, optionally, the real-time operation information of the target gas station includes one or more of the target gas station's oil inventory information, gas station service status information, and gas station queuing information.

[0141] In an embodiment of the present invention, optionally, the demand recommendation information may include one or more of the recommended refueling time, recommended fuel product information, recommended refueling parking space information, and recommended refueling location information corresponding to the target demand vehicle.

[0142] In the embodiment of the present invention, optionally, the generating of demand recommendation information according to the vehicle demand information and real-time operation information corresponding to the target demand vehicle may include:

[0143] The first target information in the vehicle demand information corresponding to the target demand vehicle is extracted, and the second target information in the real-time operation information is extracted, and the demand recommendation information is generated based on the first target information and the second target information.

[0144] 209. Generate target management parameters for the target gas station based on the demand recommendation information and operation optimization parameters.

[0145] In the embodiment of the present invention, optionally, a management strategy for a gas station is formulated based on the demand recommendation information and the operation optimization parameters, such as adjusting the oil supply, optimizing the service process, etc.

[0146] It can be seen that the implementation Figure 2 The described gas station digital intelligent management method can obtain the real-time information of the area corresponding to the target area and determine the target vehicle, collect the real-time information of the target vehicle to determine whether there is a target demand vehicle among all the target vehicles, and if so, determine the vehicle demand information corresponding to the target demand vehicle, obtain the real-time operation information of the target gas station and generate demand recommendation information in combination with the vehicle demand information, generate the target management parameters of the target gas station according to the demand recommendation information and the operation optimization parameters, can quickly identify the demand and provide services, reduce the customer waiting time, and is conducive to improving the convenience and comfort of users using the services corresponding to the target gas station, adjust resource allocation according to real-time demand, improve operation efficiency, and is conducive to improving the management of the target gas station and the intelligence and efficiency of the target gas station management, and can also improve customer satisfaction through personalized recommendations and timely services, and reduce excess inventory and waste through more accurate demand forecasting, so that it can improve market competitiveness through efficient operation management, which is conducive to improving the operation efficiency and operation effect of the target gas station, and then it is conducive to improving the operation accuracy and intelligence of the target gas station, which is conducive to improving the intelligence of the management of the gas station, and further conducive to improving the efficiency and accuracy of the management of the gas station.

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

[0148] Generate a forecast data curve of the target gas station in a preset future time period based on the economic benefit data, and generate a historical data curve of the target gas station in a preset historical time period based on the historical operation data;

[0149] Performing data fitting operations on the forecast data curve and the historical data curve to obtain a target data curve, and generating a data correlation relationship between the economic benefit data and the historical data curve based on the target data curve;

[0150] Generate data fitting results based on data association relationships.

[0151] In this optional embodiment, optionally, the above-mentioned generating a forecast data curve of the target gas station in a preset future time period based on the economic benefit data, and generating a historical data curve of the target gas station in a preset historical time period based on the historical operation data, may include:

[0152] Generate the operation trend information of the target gas station in a preset future time period according to the economic benefit data, and generate the prediction data curve of the target gas station in the preset future time period according to the operation trend information, wherein the operation trend information includes the operation mode information of the target gas station in the future time period, and the prediction data curve includes the data development information for representing the target gas station in the preset future time period;

[0153] Based on the historical operation data, historical operation trend information of the target gas station within a preset historical time period is generated, and based on the historical operation trend information, a historical data curve of the target gas station within the preset historical time period is generated, wherein the historical data curve includes historical trend information of data used to represent the target gas station within the preset historical time period.

[0154] In this optional embodiment, optionally, performing a data fitting operation on the forecast data curve and the historical data curve to obtain a target data curve, and generating a data association relationship between the economic benefit data and the historical data curve according to 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 a target data curve, and a data correlation relationship between the economic benefit data and the historical data curve is generated based on the target data curve and a predetermined target algorithm; wherein the predetermined target algorithm may include one or more of a linear regression algorithm and a polynomial regression algorithm.

[0156] In this optional embodiment, optionally, generating data fitting results based on data association relationships may include: determining association parameters between economic benefit data and historical data based on the data association relationships, and generating data fitting results based on the association parameters; wherein the association parameters include data for identifying impact data between economic benefit data and historical operating data.

[0157] It can be seen that the implementation of this optional embodiment can generate a predicted data curve of the target gas station in a preset future time period based on the economic benefit data, and generate a historical data curve of the target gas station in a preset historical time period based on the historical operation data, perform data fitting operations on the predicted data curve and the historical data curve to obtain the target data curve and then generate a data association relationship, and generate a data fitting result based on the data association relationship. Through data fitting, future operations and economic benefits can be more accurately predicted, providing a more reliable basis for decision-making, and by combining the relationship between historical data and predicted data, it is helpful to more effectively allocate resources, such as inventory management and human resources. And oil resources, etc., and through data-driven decision-making, it can optimize operational processes, reduce waste, and improve efficiency, which is conducive to improving the management of target gas stations and the intelligence and efficiency of target gas station management, and by optimizing service and product supply, improving customer satisfaction and loyalty, and through more accurate demand forecasting, reducing excess inventory and waste, so that through efficient operation management, it can improve market competitiveness, which is conducive to improving the operating benefits and operating results of the target gas station, and then It is conducive to improving the operational accuracy and intelligence of the target gas station, which is conducive to improving the intelligence of gas station management, and further It is conducive to improving the efficiency and accuracy of gas station management.

[0158] In another optional embodiment, based on the data fitting result, determining the operation optimization parameters corresponding to the target gas station includes:

[0159] Based on the data fitting results, the operation difference parameters corresponding to the target gas station are determined, and according to the operation difference parameters, the target optimization parameters matching the operation difference parameters are determined from the predetermined optimization parameter set;

[0160] Determine the operation optimization parameters corresponding to the target gas station based on all target optimization parameters;

[0161] And, based on the operation optimization parameters, generate the target management parameters of the target gas station, including:

[0162] Acquire 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 according to the real-time management parameters and the operation optimization parameters;

[0163] Based on the management difference information, the target management parameters of the target gas station are generated.

[0164] In this optional embodiment, the operation difference parameter corresponding to the target gas station may optionally include an operation state difference parameter between the current operation state corresponding to the target gas station and the ideal operation state corresponding to the target gas station. Further optionally, the number of the operation difference parameters may be one or more, which is not specifically limited in the embodiment of the present invention.

[0165] In this optional embodiment, optionally, the above-mentioned determining, according to the operation difference parameter, a target optimization parameter matching the operation difference parameter from a predetermined optimization parameter set may include:

[0166] Based on the operation difference parameters, the operation difference keywords are determined, the keyword matching degree between each optimization parameter included in the predetermined optimization parameter set and the operation difference keyword is calculated, and the target key matching degree greater than or equal to the preset key matching degree threshold is screened out from all keyword matching degrees, and the optimization parameters corresponding to all target key matching degrees are determined as the target optimization parameters that match the operation difference parameters.

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

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

[0169] In this optional embodiment, optionally, the determining of the management difference information between the real-time management parameter and the operation optimization parameter according to the real-time management parameter and the operation optimization parameter may include:

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

[0171] In this optional embodiment, optionally, generating target management parameters of 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 of the target gas station based on the management optimization information.

[0172] It can be seen that the implementation of this optional embodiment can determine the operation difference parameters corresponding to the target gas station based on the data fitting results and then determine the matching target optimization parameters, determine the operation optimization parameters corresponding to the target gas station according to the target optimization parameters, and obtain the real-time management parameters of the target gas station, determine the management difference information according to the real-time management parameters and the operation optimization parameters, and generate the target management parameters of the target gas station according to the management difference information. This can make data-based decisions more objective and accurate, reduce subjective bias, and quickly respond to changes in the market and operating environment, adjust operating strategies in a timely manner, quickly identify needs and provide services, reduce customer waiting time, and help improve users' use of the services corresponding to the target gas station. Convenience and comfort, as well as effective control of operating costs through precise cost-benefit analysis, adjustment of service strategies based on real-time data, and improvement of customer satisfaction are conducive to improving the management of target gas stations and the intelligence and efficiency of target gas station management, and by optimizing service and product supply, improving customer satisfaction and loyalty, and through continuous operational optimization, improving the market competitiveness of gas stations, thereby being able to improve market competitiveness through efficient operational management, which is conducive to improving the operating benefits and operating results of the target gas stations, and thus helping to improve the operational accuracy and intelligence of the target gas stations, helping to improve the intelligence of gas station management, and further helping to improve the efficiency and accuracy of gas station management.

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

[0174] Determine the key vehicle information corresponding to the target vehicle according to the real-time vehicle information corresponding to the target vehicle, wherein the key vehicle information includes the real-time vehicle location information corresponding to the target vehicle, the target vehicle model information, the refueling amount information, the refueling location information, and the historical refueling times information;

[0175] Based on key vehicle information, the vehicle demand parameters between the target vehicle and the target gas station are determined, and 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; wherein the demand recommendation information is used to guide the target vehicle to arrive at the target gas station to perform operations that match the vehicle demand information.

[0176] In this optional embodiment, key data is optionally extracted from the real-time information of the target vehicle, including location, vehicle model, refueling amount, refueling location, historical refueling times, etc.

[0177] In this optional embodiment, optionally, vehicle demand parameters associated with the target gas station are analyzed and determined based on key vehicle information.

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

[0179] Performing information analysis operations on key vehicle information to obtain information analysis results, and based on the information analysis results, determining refueling demand information of the target demand vehicle, and generating vehicle demand parameters between the target demand vehicle and the target gas station according to the refueling demand information, wherein the refueling demand information includes one or more of the following: fuel tank capacity information, refueling frequency information, refueling fuel quantity demand information, refueling rate demand information, and refueling time demand information corresponding to the target demand vehicle;

[0180] Inputting vehicle demand parameters and real-time operation information of a target gas station into a predetermined demand recommendation model to obtain a recommendation output result, and generating demand recommendation information of a target demand vehicle based on the recommendation output result;

[0181] The demand recommendation information includes at least one or more of the vehicle refueling time, vehicle refueling location, vehicle refueling method, vehicle refueling duration, and vehicle refueling rate corresponding to the target demand vehicle. Furthermore, the demand recommendation information may also include one or more of the recommended optimal route, estimated arrival time, and passenger boarding and alighting point information.

[0182] In this optional embodiment, further optionally, the demand recommendation information may include personalized recommendation information corresponding to each target demand vehicle, and the personalized recommendation information may include one or more of 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, give priority to the nearest gas station; if the vehicle has specific requirements for fuel products, consider whether the gas station provides the corresponding fuel products.

[0184] In this optional embodiment, the recommendation information is further optionally presented to the vehicle driver through a user interface, such as through a vehicle navigation system, a mobile application or other communication methods; or presented to the vehicle driver through voice. Furthermore, based on real-time monitoring of the effect of the recommendation system, adjustments are made according to actual conditions to cope with factors such as changes in traffic conditions and changes in the operating status of gas stations.

[0185] It can be seen that the implementation of this optional embodiment can determine the key vehicle information according to the real-time vehicle information corresponding to the target demand vehicle, determine the vehicle demand parameters between the target demand vehicle and the target gas station according to the key vehicle information, and generate the demand recommendation information of the target demand vehicle according to 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 the real-time location information and demand parameters to reduce the travel time for refueling of the vehicle, and ensure that the gas station can meet the refueling needs of the vehicle according to the vehicle's refueling amount information and the inventory status of the gas station to avoid waste of resources, and provide customized refueling services in combination with the vehicle model and historical refueling data, such as recommending the type of oil suitable for the vehicle model, and recommending vehicles to refuel during non-peak hours by analyzing the busyness of the gas station, thereby reducing Reduce waiting time in queues, improve customer satisfaction with gas station services by providing accurate refueling recommendations and convenient refueling experience, reduce traffic congestion in gas stations and reduce accident risks by guiding vehicles to refuel at appropriate times and places. By continuously collecting and analyzing vehicle and gas station data, gas stations can make more data-driven operational decisions. The demand recommendation system can quickly adjust recommendation strategies based on real-time data to respond to market factors such as oil price fluctuations and changes in traffic conditions, thereby improving market competitiveness through efficient operational management, which is conducive to improving the operating benefits and operating results of the target gas station, and then helping to improve the operational accuracy and intelligence of the target gas station, and helping to improve the intelligence of gas station management, and further helping to improve the efficiency and accuracy of gas station management.

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

[0187] Determine the real-time status of the target vehicle according to the real-time information of the target vehicle, and judge whether the target vehicle meets the preset vehicle guidance conditions according to the real-time status of the vehicle;

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

[0189] 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 based on the real-time state of the vehicle and the preset vehicle guidance conditions, and a state adjustment operation matching the state adjustment parameters is performed on the target vehicle to update the real-time state of the target vehicle and re-trigger the operation of determining whether the target vehicle meets the preset vehicle guidance conditions based on the real-time state of the vehicle.

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

[0191] In this optional embodiment, optionally, judging whether the target vehicle meets the preset vehicle guidance condition according to the real-time status of the vehicle may include:

[0192] Determine whether the real-time state of the vehicle matches the state of the target vehicle corresponding to the target demand vehicle and the preset vehicle guidance condition;

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

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

[0195] It can be seen that the implementation of this optional embodiment can determine the real-time status of the corresponding vehicle based on the real-time vehicle information of the target vehicle and then judge whether the target vehicle meets the preset vehicle guidance conditions. If so, 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 not, the corresponding state adjustment parameters are determined based on the real-time state of the vehicle and the preset vehicle guidance conditions, and the state adjustment operation matching the state adjustment parameters is performed on the target vehicle to update the real-time state of the target vehicle and re-trigger the operation of judging whether the target vehicle meets the preset vehicle guidance conditions based on the real-time state of the vehicle. The operation can improve the vehicle's operating efficiency through real-time monitoring and intelligent recommendation. rate, reduce waiting and idle time, and be able to adjust the vehicle status according to real-time demand, so as to better allocate resources and meet the needs of passengers. It can also avoid unsafe factors such as vehicle overloading and speeding through the judgment and status adjustment of preset conditions, and can also provide passengers with more convenient and punctual services through intelligent recommendations, and can also reduce fuel consumption and maintenance costs by optimizing routes and vehicle status, so as to improve market competitiveness through efficient operation management, which is conducive to improving the operating efficiency and operation effect of the target gas station, and then it is conducive to improving the operation accuracy and intelligence of the target gas station, which is conducive to improving the intelligence of gas station management, and further conducive to improving the efficiency and accuracy of gas station management.

[0196] In yet another optional embodiment, target management parameters of the target gas station are generated according to the demand recommendation information and the operation optimization parameters, including:

[0197] Generate operation demand information between all target demand vehicles and target gas stations based on the demand recommendation information, wherein the operation demand information includes one or more of the target demand vehicle's operation demand duration information, operation demand time information, operation demand fuel quantity information, and operation demand item information for the target gas station;

[0198] According to the operation optimization parameters, simulate the target gas station to perform operation matching the operation optimization parameters, obtain the simulated operation results, and judge whether the simulated operation results match the operation demand information;

[0199] When it is determined that the simulation operation result matches the operation demand information, the target management parameters of the target gas station are generated according to the operation optimization parameters and the operation demand information;

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

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

[0202] In this optional embodiment, optionally, the above-mentioned simulating the target gas station to perform an operation matching the operation optimization parameters according to the operation optimization parameters to obtain the simulated operation result may include:

[0203] The operation optimization parameters are input into a predetermined simulation operation model, so as to simulate the target gas station to perform operation operations matching the operation optimization parameters through the simulation operation model, obtain simulation operation output results, and generate simulation operation results based on the simulation operation output results.

[0204] In this optional embodiment, optionally, the above-mentioned determination of whether the simulation operation result matches the operation demand information may include:

[0205] Determine the result matching degree between the simulated operation result and the operation demand result corresponding to the operation demand information, and judge whether the result matching degree is greater than or equal to a preset result matching degree threshold;

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

[0207] In this optional embodiment, the number of influencing factors may be one or more, which is not specifically limited in the embodiment of the present invention.

[0208] In this optional embodiment, optionally, generating the optimization adjustment parameters corresponding to the operation optimization parameters according to all the influencing factors to update the operation optimization parameters may include:

[0209] According to all the influencing factors, the target influencing parameters are generated, and based on the target influencing parameters, at least one optimization parameter matching the target influencing parameters is determined in a predetermined adjustment parameter library, and based on all the optimization parameters, the optimization adjustment parameters corresponding to the operational optimization parameters are generated to update the operational optimization parameters.

[0210] It can be seen that the implementation of this optional embodiment can generate the operation demand information between all target demand vehicles and the target gas station according to the demand recommendation information, simulate the operation of the target gas station that matches the operation optimization parameters according to the operation optimization parameters to obtain the simulated operation results, and judge whether the simulated operation results match the operation demand information. If they match, the target management parameters of the target gas station are generated according to the operation optimization parameters and the operation demand information. If they do not match, the influencing factors of the mismatch between the simulated operation results and the operation demand information are determined, and then the optimization adjustment parameters are generated to update the operation optimization parameters, and then the target management parameters of the target gas station are generated according to the updated operation optimization parameters and the operation demand information. By accurately matching demand and supply, the operating cost and time waste of the gas station can be reduced, and it can ensure that the resources of the gas station (such as oil volume, goods, etc.) can meet the actual demand. By avoiding surplus or shortage and adjusting operational strategies in real time, gas stations can better adapt to changes in the market and customer demand, and ensure that customers have a smoother experience at gas stations and reduce waiting time. Through simulation and optimization, the risks caused by operational errors can be reduced, and the operational decisions of gas stations can be based more on data and analysis rather than experience alone. By optimizing the supply of oil and goods, reducing unnecessary transportation and storage, and reducing the impact on the environment, gas stations can gain stronger competitiveness in the market through efficient operational management, thereby improving market competitiveness through efficient operational management, which is conducive to improving the operating benefits and operating results of the target gas station, and thus helping to improve the operational accuracy and intelligence of the target gas station, and helping to improve the intelligence of gas station management, and further helping to improve the efficiency and accuracy of gas station management.

[0211] Embodiment 3

[0212] See also Figure 3 , Figure 3 Schematic diagram of a digital intelligent management system for gas stations disclosed in an embodiment of the present invention. Figure 3 As shown, the gas station digital intelligent management system may include:

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

[0214] A generating module 302, for generating an operational association relationship between a location corresponding to a target gas station and the historical operational data based on the target location information and the historical operational data;

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

[0216] The prediction module 303 is used to predict the predicted operation data of the target gas station in a preset future time period according to the target fuel quantity information and the operation association relationship, wherein the target fuel quantity information includes the remaining fuel quantity information corresponding to the target gas station, and the predicted operation data includes one or more of the predicted operation time data, predicted operation fuel quantity data, and predicted operation income amount data corresponding to the target gas station;

[0217] The generating module 302 is further used to generate economic benefit data of the target gas station according to the predicted operation data;

[0218] A fitting module 304 is used to perform a data fitting operation on the historical operation data and the economic benefit data to obtain a data fitting result;

[0219] A determination module 305 is used to determine the operation optimization parameters corresponding to the target gas station based on the data fitting result;

[0220] The generation module 302 is further used to generate target management parameters of the target gas station according to the operation optimization parameters.

[0221] It can be seen that the implementation Figure 3 The described device can obtain the target location information and historical operation data of the target gas station, and generate an operation association relationship based on the target location information and the historical operation data; obtain the target oil volume information of the target gas station, and predict the predicted operation data of the target gas station in a preset future time period according to the target oil volume information and the operation association relationship; generate economic benefit data according to the predicted operation data and perform data fitting operations on the historical operation data and the economic benefit data to obtain data fitting results; determine the operation optimization parameters based on the data fitting results, and generate target management parameters according to the operation optimization parameters, and can intelligently analyze the operation status of the target gas station, which is conducive to improving the operation efficiency and operation intelligence, and can increase sales and revenue through more accurate prediction and optimization of price strategies, thereby helping to improve the operation effect and operation income, improve customer experience through more accurate inventory management and service time prediction, thereby improving the user experience and convenience of using the target gas station, and reduce the risks brought by market fluctuations and uncertainties through prediction and optimization, thereby helping to improve the operation accuracy and intelligence of the target gas station, improve the intelligence of gas station management, and further help to improve the efficiency and accuracy of gas station management.

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

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

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

[0225] The system also includes:

[0226] The judgment module 306 is used to judge whether there is a target demand vehicle among all target vehicles according to the real-time vehicle information, wherein the target demand vehicle includes a vehicle that has a demand for the target gas station;

[0227] The determination module 305 is further configured to determine the vehicle demand information corresponding to the target demand vehicle when the determination module 306 determines that there is a target demand vehicle among all the target vehicles;

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

[0229] The generating module 302 is further used to generate demand recommendation information according to the vehicle demand information and real-time operation information corresponding to the target demand vehicle;

[0230] The specific manner in which the generating module 302 generates the target management parameters of the target gas station according to the operation optimization parameters includes:

[0231] Generate target management parameters for target gas stations based on demand recommendation information and operation optimization parameters.

[0232] It can be seen that the implementation Figure 4 The described device can obtain the real-time information of the area corresponding to the target area and determine the target vehicle, collect the real-time information of the target vehicle to determine whether there is a target demand vehicle among all the target vehicles, and if so, determine the vehicle demand information corresponding to the target demand vehicle, obtain the real-time operation information of the target gas station and generate demand recommendation information in combination with the vehicle demand information, generate the target management parameters of the target gas station according to the demand recommendation information and the operation optimization parameters, can quickly identify the demand and provide services, reduce the customer waiting time, and is conducive to improving the convenience and comfort of users using the services corresponding to the target gas station, adjust resource allocation according to real-time demand, improve operational efficiency, and is conducive to improving the management of the target gas station and the intelligence and efficiency of the target gas station management, and can also improve customer satisfaction through personalized recommendations and timely services, and reduce excess inventory and waste through more accurate demand forecasting, so that it can improve market competitiveness through efficient operation management, which is conducive to improving the operational benefits and operation effects of the target gas station, and then it is conducive to improving the operational accuracy and intelligence of the target gas station, which is conducive to improving the intelligence of the management of the gas station, and further conducive to improving the efficiency and accuracy of the management of the gas station.

[0233] In another optional embodiment, Figure 4 As shown, the fitting module 304 performs data fitting operations on the historical operation data and the economic benefit data, and the specific method of obtaining the data fitting results includes:

[0234] Generate a forecast data curve of the target gas station in a preset future time period based on the economic benefit data, and generate a historical data curve of the target gas station in a preset historical time period based on the historical operation data;

[0235] Performing data fitting operations on the forecast data curve and the historical data curve to obtain a target data curve, and generating a data correlation relationship between the economic benefit data and the historical data curve based on the target data curve;

[0236] Generate data fitting results based on data association relationships.

[0237] It can be seen that the implementation Figure 4 The described device can generate a predicted data curve of a target gas station in a preset future time period according to economic benefit data, and generate a historical data curve of the target gas station in a preset historical time period according to historical operation data, perform data fitting operations on the predicted data curve and the historical data curve to obtain a target data curve and then generate a data association relationship, and generate a data fitting result according to the data association relationship. Through data fitting, future operations and economic benefits can be more accurately predicted, providing a more reliable basis for decision-making, and by combining the relationship between historical data and predicted data, it is helpful to more effectively allocate resources, such as inventory management and human resources and oil resources, and through data-driven decision-making, it can optimize the operation process, reduce waste, and improve efficiency, which is conducive to improving the management of the target gas station and the intelligence and efficiency of the target gas station management, by optimizing service and product supply, improving customer satisfaction and loyalty, and through more accurate demand forecasting, reducing excess inventory and waste, so that through efficient operation management, market competitiveness can be improved, which is conducive to improving the operation efficiency and operation effect of the target gas station, and then conducive to improving the operation accuracy and intelligence of the target gas station, and conducive to improving the intelligence of the management of the gas station, and further conducive to improving the efficiency and accuracy of the management of the gas station.

[0238] In yet another optional embodiment, Figure 4 As shown, the specific method of determining the operation optimization parameters corresponding to the target gas station by the determination module 305 based on the data fitting result includes:

[0239] Based on the data fitting results, the operation difference parameters corresponding to the target gas station are determined, and according to the operation difference parameters, the target optimization parameters matching the operation difference parameters are determined from the predetermined optimization parameter set;

[0240] Determine the operation optimization parameters corresponding to the target gas station based on all target optimization parameters;

[0241] And, the specific method of generating the target management parameters of the target gas station according to the operation optimization parameters by the generation module includes:

[0242] Acquire 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 according to the real-time management parameters and the operation optimization parameters;

[0243] Based on the management difference information, the target management parameters of the target gas station are generated.

[0244] It can be seen that the implementation Figure 4 The described device can determine the operation difference parameters corresponding to the target gas station based on the data fitting results and then determine the matching target optimization parameters, determine the operation optimization parameters corresponding to the target gas station according to the target optimization parameters, and obtain the real-time management parameters of the target gas station, determine the management difference information according to the real-time management parameters and the operation optimization parameters, and generate the target management parameters of the target gas station according to the management difference information. The decision-making based on data can be more objective and accurate, and subjective bias can be reduced. It can also quickly respond to changes in the market and operation environment, adjust the operation strategy in time, quickly identify needs and provide services, reduce customer waiting time, and help improve the convenience of users using the services corresponding to the target gas station. and comfort, and through precise cost-benefit analysis, effectively control operating costs, adjust service strategies according to real-time data, improve customer satisfaction, which is conducive to improving the management of target gas stations and the intelligence and efficiency of target gas station management, and through optimizing service and product supply, improve customer satisfaction and loyalty, and through continuous operation optimization, improve the market competitiveness of gas stations, so that through efficient operation management, improve market competitiveness, which is conducive to improving the operating benefits and operating results of the target gas stations, and then help improve the operational accuracy and intelligence of the target gas stations, help improve the intelligence of gas station management, and further help improve the efficiency and accuracy of gas station management.

[0245] In yet another optional embodiment, Figure 4 As shown, the specific manner in which the generating module 302 generates the demand recommendation information according to the vehicle demand information and the real-time operation information corresponding to the target demand vehicle includes:

[0246] Determine the key vehicle information corresponding to the target vehicle according to the real-time vehicle information corresponding to the target vehicle, wherein the key vehicle information includes the real-time vehicle location information corresponding to the target vehicle, the target vehicle model information, the refueling amount information, the refueling location information, and the historical refueling times information;

[0247] Based on key vehicle information, the vehicle demand parameters between the target vehicle and the target gas station are determined, and 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; wherein the demand recommendation information is used to guide the target vehicle to arrive at the target gas station to perform operations that match the vehicle demand information.

[0248] It can be seen that the implementation Figure 4 The described device can determine key vehicle information based on the real-time vehicle information corresponding to the target demand vehicle, determine the 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 to reduce the vehicle's refueling travel time, and ensure that the gas station can meet the vehicle's refueling needs based on the vehicle's refueling volume information and the gas station's inventory status to avoid waste of resources, and provide customized refueling services based on the vehicle's model and historical refueling data, such as recommending the type of oil suitable for the model, and recommending vehicles to refuel during non-peak hours by analyzing the busyness of the gas station to reduce queuing. Waiting time, by providing accurate refueling recommendations and convenient refueling experience, improve customer satisfaction with gas station services, and by guiding vehicles to refuel at appropriate times and places, reduce traffic congestion in gas stations and reduce accident risks. By continuously collecting and analyzing vehicle and gas station data, gas stations can make more data-driven operational decisions. The demand recommendation system can quickly adjust the recommendation strategy based on real-time data to respond to market factors such as oil price fluctuations and changes in traffic conditions. It can improve market competitiveness through efficient operation management, which is conducive to improving the operating benefits and operating results of the target gas station, and then help improve the accuracy and intelligence of the operation of the target gas station, which is conducive to improving the intelligence of gas station management, and further conducive to improving the efficiency and accuracy of gas station management.

[0249] In yet another optional embodiment, Figure 4 As shown, the determination module 305 is further used to determine the real-time vehicle status corresponding to the target demand vehicle according to the real-time vehicle information of the target demand vehicle before the generation module 302 generates the demand recommendation information according to the vehicle demand information and the real-time operation information corresponding to the target demand vehicle;

[0250] The judgment module 306 is further used to judge whether the target demand vehicle meets the preset vehicle guidance conditions according to the real-time status of the vehicle; when it is judged that the target demand vehicle meets the preset vehicle guidance conditions, the generation module 302 is triggered to generate the demand recommendation information according to the vehicle demand information and real-time operation information corresponding to the target demand vehicle;

[0251] The determination module 305 is further configured to determine a state adjustment parameter corresponding to the target demand vehicle according to the real-time state of the vehicle and the preset vehicle guidance condition when the determination module 306 determines that the target demand vehicle does not meet the preset vehicle guidance condition;

[0252] The system also includes:

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

[0254] It can be seen that the implementation Figure 4 The described device can determine the real-time status of the target vehicle according to the real-time vehicle information of the target vehicle, and then judge whether the target vehicle meets the preset vehicle guidance conditions. If so, it will execute the operation of generating demand recommendation information according to the vehicle demand information and real-time operation information corresponding to the target vehicle. If not, it will determine the corresponding state adjustment parameters according to the real-time state of the vehicle and the preset vehicle guidance conditions, and perform the state adjustment operation matching the state adjustment parameters on the target vehicle to update the real-time vehicle state of the target vehicle and re-trigger the operation of judging whether the target vehicle meets the preset vehicle guidance conditions according to the real-time state of the vehicle. Through real-time monitoring and intelligent recommendation, it can improve the operation efficiency of the vehicle and reduce Reducing waiting and idle time, and being able to adjust the vehicle status according to real-time demand, can better allocate resources to meet passenger needs, and can avoid unsafe factors such as vehicle overloading and speeding through the judgment and status adjustment of preset conditions, and can also provide passengers with more convenient and punctual services through intelligent recommendations, and can also reduce fuel consumption and maintenance costs by optimizing routes and vehicle status, thereby improving market competitiveness through efficient operation management, which is conducive to improving the operating efficiency and operating results of the target gas station, and then helping to improve the operational accuracy and intelligence of the target gas station, which is conducive to improving the intelligence of gas station management, and further conducive to improving the efficiency and accuracy of gas station management.

[0255] In yet another optional embodiment, Figure 4 As shown, the specific manner in which the generating module 302 generates the target management parameters of the target gas station according to the demand recommendation information and the operation optimization parameters includes:

[0256] Generate operation demand information between all target demand vehicles and target gas stations based on the demand recommendation information, wherein the operation demand information includes one or more of the target demand vehicle's operation demand duration information, operation demand time information, operation demand fuel quantity information, and operation demand item information for the target gas station;

[0257] According to the operation optimization parameters, simulate the target gas station to perform operation matching the operation optimization parameters, obtain the simulated operation results, and judge whether the simulated operation results match the operation demand information;

[0258] When it is determined that the simulation operation result matches the operation demand information, the target management parameters of the target gas station are generated according to the operation optimization parameters and the operation demand information;

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

[0260] It can be seen that the implementation Figure 4The described device can generate operation demand information between all target demand vehicles and target gas stations according to demand recommendation information, simulate the operation of the target gas station that matches the operation optimization parameters according to the operation optimization parameters to obtain simulated operation results, and judge whether the simulated operation results match the operation demand information. If they match, the target management parameters of the target gas station are generated according to the operation optimization parameters and the operation demand information. If they do not match, the influencing factors of the mismatch between the simulated operation results and the operation demand information are determined, and then the optimization adjustment parameters are generated to update the operation optimization parameters, and then the target management parameters of the target gas station are generated according to the updated operation optimization parameters and the operation demand information. By accurately matching demand and supply, the operating cost and time waste of the gas station can be reduced, and it can ensure that the resources of the gas station (such as oil volume, goods, etc.) can meet the actual demand and avoid over-supply. By predicting surplus or shortage and adjusting operation strategies in real time, gas stations can better adapt to changes in the market and customer demand, ensure that customers have a smoother experience at gas stations, reduce waiting time, and then reduce the risks caused by operational errors through simulation and optimization, so that gas station operation decisions are more based on data and analysis rather than experience alone. By optimizing the supply of oil and goods, reducing unnecessary transportation and storage, and reducing the impact on the environment, gas stations can gain stronger competitiveness in the market through efficient operation management, thereby improving market competitiveness through efficient operation management, which is conducive to improving the operating benefits and operating results of the target gas station, and then helping to improve the operational accuracy and intelligence of the target gas station, which is conducive to improving the intelligence of gas station management, and further conducive to improving the efficiency and accuracy of gas station management.

[0261] Embodiment 4

[0262] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of another gas station digital intelligent management system disclosed in an embodiment of the present invention. Figure 5 As shown, the gas station digital intelligent management system may include:

[0263] A memory 401 storing executable program codes;

[0264] a processor 402 coupled to the 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 the first embodiment of the present invention or the second embodiment of the present invention.

[0266] Embodiment 5

[0267] An embodiment of the present invention discloses a computer storable medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the digital intelligent management method for gas stations described in Embodiment 1 or Embodiment 2 of the present invention.

[0268] Embodiment 6

[0269] An embodiment of the present 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 enable a computer to execute the steps in the digital intelligent management method for gas stations described in Example 1 or Example 2.

[0270] The system embodiment described above is only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without paying creative labor.

[0271] Through the specific 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 a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic 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 only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital intelligent management method for a gas station, characterized in that: The method comprises: Obtaining target location information of a target gas station and obtaining historical operation data of the target gas station, and generating an operation association relationship between a location corresponding to the target gas station and the historical operation data based on the target location information and the historical operation data; Obtaining target fuel 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 fuel quantity information and the operation association relationship, wherein the target fuel quantity information includes remaining fuel quantity information corresponding to the target gas station, and the predicted operation data includes one or more of predicted operation duration data, predicted operation fuel quantity data, and predicted operation income amount data corresponding to the target gas station; Generate economic benefit data of the target gas station based on the predicted operation data, and perform a data fitting operation on the historical operation data and the economic benefit data to obtain a data fitting result; Based on the data fitting result, determining the operation optimization parameters corresponding to the target gas station; According to the operation optimization parameters, target management parameters of the target gas station are generated.

2. The digital intelligent management method for gas stations according to claim 1 is characterized in that: Before generating the target management parameters of the target gas station according to the operation optimization parameters, the method further includes: Acquire the real-time regional information corresponding to the target area, and determine the target vehicle according to the real-time regional information; Collecting real-time vehicle information of all the target vehicles, and judging whether there is a target demand vehicle among all the target vehicles according to the real-time vehicle information, wherein the target demand vehicle includes a vehicle that has a demand for the target gas station; When it is determined that the target required vehicle exists among all the target vehicles, determining the vehicle required information corresponding to the target required vehicle; Acquire the real-time operation information of the target gas station, and generate demand recommendation information according to the vehicle demand information corresponding to the target demand vehicle and the real-time operation information; Wherein, generating the target management parameters of the target gas station according to the operation optimization parameters includes: The target management parameters of the target gas station are generated according to the demand recommendation information and the operation optimization parameters.

3. The digital intelligent management method for gas stations according to claim 1 is characterized in that: The performing of a data fitting operation on the historical operation data and the economic benefit data to obtain a data fitting result includes: Generate a forecast data curve of the target gas station in the preset future time period based on the economic benefit data, and generate a historical data curve of the target gas station in the preset historical time period based on 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 association relationship between the economic benefit data and the historical data curve according to the target data curve; According to the data association relationship, a data fitting result is generated.

4. The digital intelligent management method for gas stations according to claim 1 is characterized in that: The determining, based on the data fitting result, the operation optimization parameters corresponding to the target gas station includes: Based on the data fitting result, determining the operation difference parameter corresponding to the target gas station, and according to the operation difference parameter, determining the target optimization parameter matching the operation difference parameter from a predetermined optimization parameter set; Determine the operation optimization parameters corresponding to the target gas station according to all the target optimization parameters; And, generating target management parameters of the target gas station according to the operation optimization parameters includes: Acquire 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 according to the real-time management parameters and the operation optimization parameters; The target management parameters of the target gas station are generated according to the management difference information.

5. The digital intelligent management method for gas stations according to claim 1 is characterized in that: The generating demand recommendation information according to the vehicle demand information corresponding to the target demand vehicle and the real-time operation information includes: Determine the key vehicle information corresponding to the target vehicle according to the real-time vehicle information corresponding to the target vehicle, wherein the key vehicle information includes the real-time vehicle position information, target vehicle model information, refueling amount information, refueling location information, and historical refueling times information corresponding to the target vehicle; Based on the key vehicle information, the vehicle demand parameters between the target vehicle and the target gas station are determined, and 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; wherein the demand recommendation information is used to guide the target vehicle to arrive at the target gas station to perform operations matching the vehicle demand information.

6. The digital intelligent management method for gas stations according to claim 2 is characterized in that: Before generating demand recommendation information according to the vehicle demand information corresponding to the target demand vehicle and the real-time operation information, the method further includes: Determine the real-time status of the vehicle corresponding to the target vehicle according to the real-time vehicle information of the target vehicle, and determine whether the target vehicle meets the preset vehicle guidance condition according to the real-time status of the vehicle; When it is determined that the target demand vehicle meets the preset vehicle guidance condition, 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 is triggered; 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 based on the real-time state of the vehicle and the preset vehicle guidance conditions, and a state adjustment operation matching the state adjustment parameters is performed on the target vehicle to update the real-time state of the target vehicle, and re-trigger the operation of determining whether the target vehicle meets the preset vehicle guidance conditions based on the real-time state of the vehicle.

7. The digital intelligent management method for gas stations according to claim 2 is characterized in that: Generating the target management parameters of the target gas station according to the demand recommendation information and the operation optimization parameters includes: 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 the operation demand duration information, operation demand time information, operation demand oil quantity information, and operation demand item information of the target demand vehicle for the target gas station; According to the operation optimization parameters, simulating the target gas station to perform operation operations matching the operation optimization parameters, obtaining simulated operation results, and determining whether the simulated operation results match the operation demand information; When it is determined that the simulated operation result matches the operation demand information, generating target management parameters of the target gas station according to the operation optimization parameters and the operation demand information; When it is determined that the simulated operation result does not match the operation demand information, the influencing factors of the mismatch between the simulated operation result and the operation demand information are determined, and based on all the influencing factors, the optimization adjustment parameters corresponding to the operation optimization parameters are generated to update the operation optimization parameters, and based on the updated operation optimization parameters and the operation demand information, the target management parameters of the target gas station are generated.

8. A digital intelligent management system for gas stations, characterized in that: The system comprises: An acquisition module, used to acquire target location information of a target gas station and acquire historical operation data of the target gas station; A generating module, configured to generate an operational association relationship between a location corresponding to the target gas station and the historical operating data based on the target location information and the historical operating data; The acquisition module is further used to acquire target fuel quantity information of the target gas station; A prediction module, used to predict 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 association relationship, wherein the target oil quantity information includes the remaining oil quantity information corresponding to the target gas station, and the predicted operation data includes one or more of the predicted operation duration data, predicted operation oil quantity data, and predicted operation income amount data corresponding to the target gas station; The generating module is further used to generate economic benefit data of the target gas station according to the predicted operation data; A fitting module, used 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, used to determine the operation optimization parameters corresponding to the target gas station based on the data fitting result; The generating module is further used to generate target management parameters of the target gas station according to the operation optimization parameters.

9. A digital intelligent management system for gas stations, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the digital intelligent management method for gas stations as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, which, when called, are used to execute the digital intelligent management method for gas stations as described in any one of claims 1-7.

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