CN112488799A

CN112488799A - Oil data processing method and device based on refueling station end and storage medium

Info

Publication number: CN112488799A
Application number: CN202011471560.7A
Authority: CN
Inventors: 佟力; 鲁冰; 裴修尧; 杜彪; 朱云鹏; 钟子豪
Original assignee: Beijing Yixingyuan Petrochemical Technology Co ltd
Current assignee: Beijing Yixingyuan Petrochemical Technology Co ltd
Priority date: 2020-12-14
Filing date: 2020-12-14
Publication date: 2021-03-12
Anticipated expiration: 2040-12-14
Also published as: CN112488799B

Abstract

The embodiment of the invention discloses a method, a device and a storage medium for processing petroleum data based on a refueling station end, wherein a pre-constructed information recognition neural network model is utilized to determine a first to-be-pushed petroleum type to be recommended according to owner information and vehicle information of a resident user of a refueling station, a first target petroleum commodity meeting the requirements of the user can be screened from petroleum commodities of the refueling station according to the first to-be-pushed petroleum type, and relevant information of the first target petroleum commodity is sent to a user end through a server to be displayed. Like this, can according to the intelligent selection of the car owner information of each user that lives in of filling station and vehicle information accord with the oil commodity of user's demand to recommend for the user that lives in that corresponds, make the oil recommendation that the user received more accurate, need not the staff manual recommendation of filling station, simple and convenient.

Description

Oil data processing method and device based on refueling station end and storage medium

Technical Field

The invention belongs to the technical field of data processing, and particularly relates to a method and a device for processing petroleum data based on a refueling station terminal and a storage medium.

Background

Traditional filling station refuels service, the procedure is complicated, and the process is redundant slowly, needs the user to drive the car into filling station back usually, selects again to add the oil number, refuels amount of money etc. waits to refuel and finishes, still needs to settle accounts through modes such as cash payment, cash change, has caused a large amount of time wastes. If the passenger flow of the gas station is overlarge, the congestion of the vehicles behind can be caused, the refueling efficiency is reduced, and the negative influence is brought to the operation of the gas station.

Based on the situation, a plurality of APP client services corresponding to the gas stations are available at present, and are used for providing the service of ordering and refueling for the gas stations.

However, the existing online refueling service is relatively limited, and the refueling station end recommends refueling for vehicle users according to sales volume and geographic position, but the recommendations are not consistent with the refueling requirements of vehicle owners or vehicles, so that the popularization of petroleum commodities of the refueling station is influenced.

Disclosure of Invention

In view of the above problems, the present invention provides a method, an apparatus and a storage medium for processing petroleum data based on a fueling station terminal, so as to overcome the limitation of the existing online fueling service, in which the fueling station terminal recommends fueling for a vehicle user according to sales volume and geographic position, but these recommendations are often not in line with the fueling requirements of vehicle owners or vehicles, thereby affecting the technical problem of petroleum commodity popularization of the fueling station.

According to the first aspect of the invention, the invention provides a method for processing oil data based on a refueling station terminal, which comprises the following steps:

logging in a pre-established gas station account, wherein the gas station account is correspondingly provided with a resident user database, and the resident user database stores owner information and/or vehicle information corresponding to each resident user;

acquiring owner information and/or vehicle information of the resident user from a resident user database corresponding to the gas station account, and determining a first to-be-pushed petroleum type according to the owner information and/or the vehicle information by utilizing a pre-constructed information recognition neural network model;

searching a first target petroleum commodity matched with the first to-be-pushed petroleum type from the petroleum commodity information corresponding to the gas station account;

and adding an identifier corresponding to the resident user in the related information of the first target petroleum commodity, and sending the related information of the first target petroleum commodity to a server so that the server can send the related information of the first target petroleum commodity to a terminal corresponding to the identifier for displaying according to the identifier.

Further, the relevant oil order data of the oil products purchased by the resident user in the corresponding gas station account will be stored in the corresponding resident user database, and then the method further comprises:

searching petroleum order data corresponding to the resident user from the resident user database, and determining a second petroleum type to be pushed according to the petroleum order data by utilizing a pre-established order recognition neural network model;

combining the first to-be-pushed petroleum type with the second to-be-pushed petroleum type, and searching a second target petroleum commodity matched with the first to-be-pushed petroleum type and/or the second to-be-pushed petroleum type from the petroleum commodity information corresponding to the gas station account;

and adding an identifier corresponding to the resident user in the related information of the second target petroleum commodity, and sending the related information of the second target petroleum commodity to a server so that the server can send the related information of the second target petroleum commodity to a terminal corresponding to the identifier for displaying according to the identifier.

Further, the logging in a pre-established gas station account specifically includes:

receiving account information and an account password, and packaging the account information and the account password;

sending the packaged data to a server for authentication, wherein the server authenticates the packaged data with pre-established gas station account information, acquires a Token signature after the authentication is successful, combines the Token signature with the packaged data to generate Token data, and generates JWT data according to the Token data;

after receiving the JWT data returned by the server, storing the Token data in the JWT data in a cookie repository;

generating a resource access request through GET or POST according to JWT data;

searching corresponding Token data in a cookie repository according to the resource access request, calling signature information and an encryption key in a configuration file if the Token data is found, decoding the resource access request by using the encryption key, and performing signature verification on the Token data by using the signature information;

after decoding is successful and signature verification is successful, acquiring corresponding role authority information, and sending the role authority information to the server so that the server can acquire corresponding display information according to the role authority information;

and receiving the display information fed back by the server and displaying the display information on a display screen.

Further, before the owner information and/or the vehicle information of the resident user is acquired from the resident user database corresponding to the gas station account, and the first petroleum type to be pushed is determined according to the owner information and/or the vehicle information by using the pre-constructed information recognition neural network model, the method further comprises the following steps:

acquiring a preset amount of information sample data, and adding a corresponding petroleum type label to each information sample data, wherein the information sample data comprises: the number of the petroleum type labels is one or more;

pre-constructing an information recognition initial neural network, wherein the information recognition initial neural network comprises: the system comprises an information identification input layer, N information identification hidden layers and an information identification output layer;

inputting the information sample data from an information identification input layer, and processing the information sample data through the N information identification hidden layers, wherein the first information identification hidden layer receives data content output from the information identification input layer, and the rest information identification hidden layers receive data content output after the last information identification hidden layer is processed;

the last information identification hidden layer outputs the processing result data to an information identification output layer so that the information identification output layer can determine the corresponding petroleum type according to the processing result data;

judging whether the output petroleum type is the same as the corresponding petroleum type label or not, if so, training next information sample data, and if not, adjusting the parameters of each information identification hidden layer to enable the output petroleum type to be the same as the corresponding petroleum type label;

and (4) taking the information identification initial neural network after all information sample data are trained as an information identification neural network model.

Further, before searching for oil order data corresponding to the resident user from the resident user database and determining a second petroleum type to be pushed according to the oil order data by using a pre-constructed order recognition neural network model, the method further comprises:

acquiring preset quantity of petroleum order sample data, and adding a corresponding petroleum type label for each petroleum order sample data, wherein the quantity of the petroleum type labels is one or more;

pre-constructing an order identification initial neural network, wherein the order identification initial neural network comprises: the system comprises an order identification input layer, M order identification hidden layers and an order identification output layer;

inputting the petroleum order sample data from an order identification input layer, and processing the petroleum order sample data through the M order identification hidden layers, wherein the first order identification hidden layer receives data content output from the order identification input layer, and the rest order identification hidden layers receive data content output after the last order identification hidden layer is processed;

the last order identification hidden layer outputs the processing result data to an order identification output layer, so that the order identification output layer determines the corresponding petroleum type according to the processing result data;

judging whether the output petroleum type is the same as the corresponding petroleum type label, if so, training the sample data of the next petroleum order, if not, calculating an order loss function according to the output petroleum type and the corresponding petroleum type label, and adjusting the parameters of the hidden layer for order recognition according to the order loss function to ensure that the output petroleum type is the same as the corresponding petroleum type label;

and taking the order recognition initial neural network after the petroleum order sample data is completely trained as an order recognition neural network model.

Further, the method further comprises:

receiving the information of ordering the user to refuel sent by the server, wherein the information of ordering the user to refuel comprises a target petroleum commodity, position information of a user side and a refueling time period;

extracting the position information of the user side in the refueling order information of the user, and calculating the time consumed by the vehicle journey according to the position information of the refueling station side;

if the current time plus the journey consumed time is not more than the latest time point of the refueling time period, acquiring whether the target petroleum commodity of the gas station account in the refueling time period has a residual oil outlet, if so, confirming and receiving the refueling order information of the user, generating a confirmation receiving instruction and sending the confirmation receiving instruction to the user side through the server, otherwise, refusing to receive the refueling order information of the user, generating a refusing and receiving instruction and sending the refusing and receiving instruction to the user side through the server;

and if the current time plus the vehicle journey consumed time is larger than the latest time point of the refueling time period, refusing to receive the refueling and ordering information of the user, generating a re-confirmation refueling time period instruction, and sending the re-confirmation refueling time period instruction to the user side through the server.

Further, the method further comprises:

receiving an invoice request instruction sent by the server;

acquiring corresponding petroleum order information and an invoicing record of the corresponding petroleum order information according to the invoice request instruction;

if the billing record is empty, acquiring the amount data of the petroleum order information, extracting the billing account information in the invoice request instruction, correspondingly generating electronic invoice information, sending the electronic invoice information to the user side, and if the billing record has the corresponding electronic invoice information, generating a billing rejection instruction and sending the billing rejection instruction to the user side through the server.

According to a second aspect of the present invention, a fueling station-side-based oil data processing apparatus is provided, comprising:

the system comprises a login module, a service station account and a service station management module, wherein the login module is used for logging in a pre-established service station account, a resident user database is correspondingly arranged on the service station account, and vehicle owner information and/or vehicle information corresponding to each resident user are stored in the resident user database;

the information identification module is used for acquiring owner information and/or vehicle information of the resident user from a resident user database corresponding to the gas station account, and determining a first to-be-pushed petroleum type according to the owner information and/or the vehicle information by utilizing a pre-established information identification neural network model;

the matching module is used for searching a first target petroleum commodity matched with the first to-be-pushed petroleum type from the petroleum commodity information corresponding to the gas station account;

and the sending module is used for adding an identifier corresponding to the resident user in the related information of the first target petroleum commodity, and sending the related information of the first target petroleum commodity to a server so that the server can send the related information of the first target petroleum commodity to a terminal corresponding to the identifier for displaying according to the identifier.

According to a third aspect of the present invention, an electronic device is provided, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the fueling station-side-based oil data processing method according to the first aspect when executing the computer program.

According to a fourth aspect of the present invention, a storage medium is proposed, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the fueling station-side based oil data processing method according to the first aspect.

The petroleum data processing method, the device and the storage medium based on the refueling station terminal provided by the embodiment of the invention have the following beneficial effects:

according to the technical scheme, the pre-constructed information recognition neural network model can be used for determining the first to-be-pushed petroleum type to be recommended according to the owner information and the vehicle information of the resident user of the gas station, screening out the first target petroleum commodity meeting the user requirement from the petroleum commodities of the gas station according to the first to-be-pushed petroleum type, and sending the related information of the first target petroleum commodity to the user side through the server for displaying. Like this, can according to the intelligent selection of the car owner information of each user that lives in of filling station and vehicle information accord with the oil commodity of user's demand to recommend for the user that lives in that corresponds, make the oil recommendation that the user received more accurate, need not the staff manual recommendation of filling station, simple and convenient.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.

The invention will be more clearly understood from the following detailed description, taken with reference to the accompanying drawings, in which:

FIG. 1 is a flow chart of a fueling station-based oil data processing method according to an embodiment of the present invention;

FIG. 2 is a block diagram of a fueling station-based oil data processing apparatus according to an embodiment of the present invention;

fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

As shown in fig. 1, this embodiment provides a method for processing petroleum data based on a fueling station terminal, which is suitable for the fueling station terminal, where the fueling station terminal may be an intelligent terminal such as a mobile phone, a tablet, a laptop, or a desktop, a corresponding APP and an applet embedded in an instant messaging are installed on the fueling station terminal, and the following processes are executed through the APP or the applet.

The method comprises the following steps:

step 101, logging in a pre-established gas station account, wherein a resident user database is correspondingly arranged on the gas station account, and owner information and/or vehicle information corresponding to each resident user is stored in the resident user database.

Wherein, the gas station wants to establish corresponding gas station account number on the platform of this application, need download the APP that the installation corresponds and/or the applet that imbeds on the instant messaging on the gas station end in advance, when opening the APP that the gas station was held to correspond or the applet for the first time, establish the gas station account number earlier. Firstly, a newly-built account key is triggered, then a role selection window pops up, two roles of 'personal user' and 'gas station' are displayed in the window, and after the 'gas station' role is selected, an account registration interface pops up. The method comprises the steps of inputting a gas station account name and a password, packaging and sending the gas station account name, the password and a selected gas station role to a server, matching a corresponding gas station interface by the server according to the gas station role, and establishing a corresponding storage database for the corresponding gas station account for storing data information of the gas station account.

After the creation is completed, various fueling services of the gas station and corresponding petroleum commodity information can be added to the gas station account.

And then, receiving the parking request information sent by the user side through the server, verifying the parking request information, and taking the user side as the parking user side after the verification is passed. And acquiring owner information and/or vehicle information in the parking request information, associating the owner information and/or the vehicle information with the parking user side, and adding the owner information and/or the vehicle information to the parking user database.

Specifically, the parking process of the parking client is as follows:

step S1, the user terminal sends the order of filling gas station to the server, and the server obtains the position information of the user terminal according to the order of filling gas station.

And step S2, the resident user side sends the position information to the server, and the server determines the information of the gas station of which the distance between the resident user side and the position information is less than or equal to the distance threshold value according to the position information.

In step S3, the server sends the information of the determined gas station to the resident user terminal and displays it on the display screen for the resident user to select.

Step S4, the parking user end receives the selected target parking gas station information and sends the target parking gas station information to the server;

in step S5, the server retrieves the data related to the target fueling station, forms a fueling instruction and sends the fueling instruction to the fueling station, and sends the data related to the target fueling station to the fueling user.

And step S6, the user terminal receives the data of the target station and displays it on the display screen, and stores the data into the database.

And step S7, the gas station end receives the parking instruction, establishes connection with the parking user end, and adds the relevant information of the parking user to the storage database of the corresponding gas station account in the gas station end.

In the scheme, the user can select one gas station as the resident gas station from the vicinity of the place of residence according to the actual requirement and the geographical position of the user, so that the resident gas station can be stored in the resident cache library of the user. The various gas stations can also adopt discount or discount for each resident user, and specific discount or discount measures are selected according to the actual situation of each gas station. The user can enter the homepage of the gas station through the APP or the applet and check the corresponding preferential information in the homepage. The user can select to stay in the gas station according to the preferential information correspondingly published by each gas station and the actual position of the user.

One or more filling stations selected by the user can be selected, so that if the corresponding filling station publishes new petroleum information, the new petroleum information can be displayed in the information recommendation column of the corresponding display screen.

The fueling station end can display in the corresponding parking fueling station column, the display sequence can be arranged in sequence according to the distance from the current position of the user, the selected parking time, the access amount, the order amount and the like, and the user can select the corresponding arrangement mode according to the actual needs of the user.

Through the scheme, the function of selecting to park the gas station can be provided for the user, so that the user can check some petroleum information and some preferential information issued by the corresponding parking gas station in time, and convenience is provided for the user.

102, acquiring owner information and/or vehicle information of a resident user from a resident user database corresponding to a gas station account, and determining a first to-be-pushed petroleum type according to the owner information and/or the vehicle information by utilizing a pre-constructed information recognition neural network model.

In the step, the information recognition neural network model is obtained by utilizing a large amount of owner information and/or vehicle information with the specifically required petroleum type through learning and training of the neural network, and the owner information and/or the vehicle information can be processed to obtain the first to-be-pushed petroleum type required by the corresponding user. The first petroleum type to be pushed is the petroleum type meeting the requirements of the resident users. The first to-be-pushed petroleum type may include one or more petroleum types, and is not particularly limited herein.

The vehicle owner information includes: name, age, gender, personal preferences, weight, etc., and the vehicle information includes: vehicle type, brand, displacement, vehicle model, color, etc.

The information identification input layer of the corresponding information identification neural network model comprises two types of input ports: the system comprises an owner information input port and a vehicle information input port, wherein received owner information and/or vehicle information is input from the corresponding input ports, after data processing is carried out on the owner information and/or the vehicle information through an information identification input layer, text data are converted into code data so that an information identification hidden layer of an information identification neural network model can carry out further information processing according to the data, and finally the corresponding first to-be-pushed petroleum type is obtained.

And 103, searching a first target petroleum commodity matched with the first to-be-pushed petroleum type from the petroleum commodity information corresponding to the gas station account.

In this step, when the corresponding petroleum commodity information is entered into the gas station account, the corresponding petroleum type is labeled for each petroleum commodity. Therefore, after the first petroleum type to be pushed is obtained, the first target petroleum product matched with the first petroleum type to be pushed can be searched from the petroleum product information. Wherein, the first target petroleum commodity which is correspondingly searched comprises one or more petroleum commodities.

And if the petroleum commodity matched with the first petroleum type to be pushed cannot be found from the petroleum commodity information of the gas station account, generating corresponding prompt information. The gas station is prompted to add the first petroleum commodity to be pushed to the resident user for selection. Meanwhile, the petroleum commodity similar to the first petroleum to be pushed is found out from the petroleum commodity information of the gas station and serves as a first target petroleum commodity.

And 104, adding an identifier corresponding to the resident user in the related information of the first target petroleum commodity, and sending the related information of the first target petroleum commodity to a server so that the server can send the related information of the first target petroleum commodity to a terminal corresponding to the identifier for displaying according to the identifier.

In this step, if there are a plurality of first target petroleum products obtained, the plurality of first target petroleum products may be sorted according to the fueling amount, or the goodness of appreciation. Therefore, the information of the first target petroleum commodity can be recommended to the terminal of the resident user according to the corresponding sequence for displaying, so that the resident user can quickly select the recommended petroleum commodity.

Through the scheme, the pre-constructed information recognition neural network model can be used for determining the first to-be-pushed petroleum type to be recommended according to the owner information and the vehicle information of the resident user of the gas station, the first target petroleum commodity meeting the user requirement can be screened out from the petroleum commodities of the gas station according to the first to-be-pushed petroleum type, and the related information of the first target petroleum commodity is sent to the user side through the server to be displayed. Like this, can according to the intelligent selection of the car owner information of each user that lives in of filling station and vehicle information accord with the oil commodity of user's demand to recommend for the user that lives in that corresponds, make the oil recommendation that the user received more accurate, need not the staff manual recommendation of filling station, simple and convenient.

In a specific embodiment, the relevant oil order data of the oil product purchased by the resident user in the corresponding gas station account is stored in the corresponding resident user database, and the method further includes:

and 105, searching petroleum order data corresponding to the resident user from the resident user database, and determining a second petroleum type to be pushed according to the petroleum order data by utilizing a pre-constructed order recognition neural network model.

In this step, the oil order data is historical order data corresponding to the resident user, and the order data includes: the position of the gas station, the type of refueling, the refueling amount, the refueling time and other information.

The order recognition neural network model is obtained by performing learning training on the neural network according to multiple groups of petroleum order data serving as samples, wherein each group of petroleum orders are multiple order data of the same user.

The order identification input layer of the order identification neural network model comprises a plurality of input ports, and each input port can input order data. The number of input ports is greater than or equal to the maximum order data number. To ensure that multiple order data can be input simultaneously. And the order identification input layer performs data conversion processing on the order data to convert the order data into code data, so that the order identification hidden layer of the order identification neural network model can be processed according to the code data, and finally the corresponding second petroleum type to be pushed is obtained.

And 106, combining the first to-be-pushed petroleum type with the second to-be-pushed petroleum type, and searching a second target petroleum commodity matched with the first to-be-pushed petroleum type and/or the second to-be-pushed petroleum type from the petroleum commodity information corresponding to the account number of the gas station.

In this step, a plurality of petroleum types may be previously labeled for each petroleum commodity, for example, a low impurity petroleum type and a high heavy petroleum type may be labeled for petroleum commodity a.

In this way, it is preferable that the petroleum commodity conforming to both the first to-be-pushed petroleum type and the second to-be-pushed petroleum type be the second target petroleum commodity. And secondly, selecting the petroleum commodity which accords with the first petroleum type to be pushed as a second target petroleum commodity, and selecting the petroleum commodity which accords with the second petroleum type to be pushed as a second target petroleum commodity.

And if the first to-be-pushed petroleum type or the second to-be-pushed petroleum type does not have a petroleum commodity which is in accordance with the first to-be-pushed petroleum type or the second to-be-pushed petroleum type, generating corresponding prompt information. And prompting the gas station to add petroleum commodities which accord with the first to-be-pushed petroleum type and the second to-be-pushed petroleum type to be supplied to the resident user for selection.

And 107, adding an identifier corresponding to the resident user in the related information of the second target petroleum commodity, and sending the related information of the second target petroleum commodity to the server so that the server can send the related information of the second target petroleum commodity to the terminal corresponding to the identifier for displaying according to the identifier.

In this step, if there are a plurality of second target petroleum products obtained, the plurality of second target petroleum products may be sorted according to the fueling amount, or the goodness of appreciation. Therefore, the information of the second target petroleum commodity can be recommended to the terminal of the resident user according to the corresponding sequence for displaying, so that the resident user can quickly select the recommended petroleum commodity.

Through the scheme, the filling station can identify the first petroleum type to be pushed obtained by the information identification neural network model, the second petroleum type to be pushed which needs to be recommended is determined to be combined with the petroleum type to be pushed by the pre-established order identification neural network model according to the petroleum order data issued by the filling user, the second target petroleum commodity determined in this way meets the requirement of the filling user better, intelligent recommendation can be completed without any operation of the filling station, the use of the filling station is facilitated, the popularization of the petroleum commodity of the filling station is facilitated, and meanwhile convenience can be provided for the filling user.

In a specific embodiment, step 101 specifically includes:

step 1011, receiving the account information and the account password, and packaging the account information and the account password.

In this step, the staff or the responsible person of the gas station wants to log in his account, and needs to input the corresponding account information and the account password in the APP or the embedded applet of the gas station terminal, and the gas station terminal maps and associates the account information and the account password to realize the packaging process.

Step 1012, sending the packaged data to a server for authentication, wherein the server authenticates the packaged data with pre-established gas station account information, acquires a Token signature after the authentication is successful, combines the Token signature with the packaged data to generate Token data, and generates JWT data according to the Token data.

Among them, Token (Token), JWT (Json web Token, network data tagging specification).

In the step, after the server receives the packaged data, account information in the packaged data is extracted, a corresponding storage database is searched according to the account information, then an account password is extracted, the account password is compared with passwords stored in the storage database, the comparison is successful, namely the authentication is successful, the storage database of the server has unique Token signature data belonging to the account, and the Token signature and the packaged data are integrated to generate Token data.

Generating JWT data according to the Token data, specifically: the JWT data includes a header, a payload, and a signature, wherein the header is a corresponding file type, the payload is a data object (e.g., oil order data information of a user), and the like, and the signature is Token data, which is arranged as JWT data. The JWT data is then sent to the corresponding fueling station side.

Step 1013, after receiving the JWT data returned by the server, storing the Token data in the JWT data in the cookie repository.

In the above steps, after receiving the JWT data, the APP or applet in the fueling station side extracts Token data therein and stores the Token data in the cookie repository in the fueling station side. Wherein the cookie repository is a database stored on the gas station local terminal.

Step 1014, generating a resource access request by GET or POST based on the JWT data.

In this step, the GET or POST is two basic request commands of the http request, and any one of the two basic request commands may be selected to generate the resource access request. The resource access request comprises: the method comprises the steps of personal information access, personal position information access, historical order information access and other information access requests corresponding to information which can be presented in an interface corresponding to an APP or an applet, and all the information access requests are concentrated in resource access requests.

Step 1015, according to the resource access request, searching corresponding Token data in the cookie repository, if Token data is found, calling signature information and encryption key in the configuration file, decoding the resource access request by using the encryption key, and performing signature verification on the Token data by using the signature information.

In this step, the resource access request includes corresponding Token data. After the resource access request is generated, in order to ensure the confidentiality of the resource access request, the resource access request needs to be encrypted in advance, wherein the Token data is not encrypted during encryption so as to be called and searched in a later period.

Extracting the Token data in the resource access request to find whether matched Token data exists in a cookie storage bank, if so, proving that the matching is successful, directly calling a configuration file stored in the local, acquiring signature information and an encryption key corresponding to the Token data from the configuration file, decoding the resource access request by using the encryption key, and simultaneously performing signature verification on the signature information and the Token data.

And step 1016, after the decoding is successful and the signature verification is successful, acquiring corresponding role authority information, and sending the role authority information to the server so that the server can acquire corresponding display information according to the role authority information.

In this step, only after the two are successful, the corresponding role authority information can be obtained and sent to the server, and the server can obtain the corresponding display information according to the role authority information. For example, some display contents required by the fueling station targeted by the fueling station terminal and contents stored in the database corresponding to the fueling station account are merged into the display information and sent to the fueling station terminal.

And step 1017, receiving the display information fed back by the server and displaying the display information on the display screen.

In this step, after receiving the display information, the APP or the applet at the fueling station end will be displayed in the display interface of the APP or the applet.

Through the technical scheme, the confidentiality of the information of the gas station can be guaranteed, the whole data transmission process can be accelerated, and the efficiency is guaranteed.

In a particular embodiment, prior to step 102, the method further comprises:

step A1, obtaining a predetermined amount of information sample data, and adding a corresponding petroleum type label to each information sample data, wherein the information sample data comprises: the number of the oil type labels is one or more than one.

Step A2, pre-constructing an information identification initial neural network, wherein the information identification initial neural network comprises: the system comprises an information identification input layer, N information identification hidden layers and an information identification output layer.

Step A3, inputting information sample data from the information identification input layer, processing the information sample data through N information identification hidden layers, wherein the first information identification hidden layer receives the data content output from the information identification input layer, and the rest information identification hidden layer data is the data content output after the last information identification hidden layer is processed.

And step A4, the last information identification hidden layer outputs the processing result data to the information identification output layer, so that the information identification output layer determines the corresponding petroleum type according to the processing result data.

Step A5, judging whether the output petroleum type is the same as the corresponding petroleum type label, if so, training the next information sample data, and if not, adjusting the parameters of each information identification hidden layer to ensure that the output petroleum type is the same as the corresponding petroleum type label.

And step A6, using the information recognition initial neural network after all information sample data are trained as an information recognition neural network model.

In the above scheme, the information identification input layer includes two types of input ports, specifically: the system comprises an owner information input port and a vehicle information input port, wherein owner information and/or vehicle information in information sample data are input from the corresponding input ports, and after data processing is carried out through an information identification input layer, character data are converted into code data so that an information identification hidden layer can further carry out information processing according to the data.

The number of the information identification hidden layers can be set according to the number of information types in the owner sample information or the vehicle sample information, one information identification hidden layer correspondingly processes one type of sample information, then the processing result is transmitted to the next information identification hidden layer to be processed, the next information identification hidden layer combines the processing result of the previous information identification hidden layer with the processing result of the sample information of the type corresponding to the information identification hidden layer and then transmits the combined result to the next information identification hidden layer, and the like is repeated until the last information identification hidden layer transmits the final processing result to the information identification output layer, and the information identification output layer converts the processing result into the character information of the corresponding petroleum type and outputs the character information.

If the output petroleum type is the same as the corresponding petroleum type label, the next information sample data is directly processed without processing, if the output result is different, the output result of the information identification initial neural network is proved to be incorrect, the information identification initial neural network needs to be adjusted, and the parameters of each information identification hidden layer can be manually adjusted according to experience until the output result is the same as the petroleum type label. Or calculating a corresponding loss function according to the output petroleum type and petroleum type labels, automatically adjusting each information identification hidden layer according to the loss function, and then reprocessing the information sample data by using the adjusted information identification initial neural network until the output result is consistent with the petroleum type labels.

After all information sample data are trained, detecting the trained information recognition initial neural network by using a preset amount of detection sample data, judging the accuracy rate of a recognition result, if the accuracy rate exceeds a corresponding threshold value, proving that the trained information recognition initial neural network meets the standard and can be used as an information recognition neural network model, if the accuracy rate is less than the corresponding threshold value, reselecting the information sample data, and training the information recognition initial neural network again until the obtained accuracy rate of the information recognition initial neural network exceeds the corresponding threshold value.

Through the scheme, the information recognition neural network model can be used for recognizing and judging the vehicle owner information and/or the vehicle information, and the corresponding first to-be-pushed petroleum type is determined so as to be provided for the refueling station end to recommend petroleum commodities according to the first to-be-pushed petroleum type.

In a particular embodiment, prior to step 105, the method further comprises:

and step B1, acquiring a preset amount of petroleum order sample data, and adding a corresponding petroleum type label for each petroleum order sample data, wherein the number of the petroleum type labels is one or more.

Step B2, an order identification initial neural network is pre-constructed, wherein the order identification initial neural network comprises: the order recognition system comprises an order recognition input layer, M order recognition hidden layers and an order recognition output layer.

And step B3, inputting petroleum order sample data from the order identification input layer, and processing the petroleum order sample data through M order identification hidden layers, wherein the first order identification hidden layer receives the data content output by the order identification input layer, and the rest order identification hidden layers receive the data content output by the last order identification hidden layer after processing.

And step B4, the last order identification hidden layer outputs the processing result data to the order identification output layer, so that the order identification output layer determines the corresponding petroleum type according to the processing result data.

And step B5, judging whether the output petroleum type is the same as the corresponding petroleum type label, if so, training the sample data of the next petroleum order, if not, calculating an order loss function according to the output petroleum type and the corresponding petroleum type label, and adjusting the parameters of the order identification hidden layer according to the order loss function so that the output petroleum type is the same as the corresponding petroleum type label.

And step B6, taking the order recognition initial neural network after the petroleum order sample data is completely trained as an order recognition neural network model.

In the above scheme, a plurality of input ports are included, and each input port can input order data. The number of input ports is greater than or equal to the maximum order data number. To ensure that multiple order data can be input simultaneously. The order identification input layer carries out data conversion processing on the order sample data and converts the order sample data into code data, so that the order identification hidden layer can carry out processing according to the code data.

The order sample data comprises a plurality of order data groups, each order data group is order data of the same user, each order data group can contain one or more order data, and the quantity of the input port takes the maximum order quantity in the order data groups as the set quantity of the input port. Or a predetermined value (e.g., 5) higher than the maximum order number as a set number of the input ports so as to ensure that the number of the input ports is in accordance with the demand in a practical situation.

The number of the order identification hidden layers is consistent with that of the input ports, each order is processed by one order data correspondingly by the hidden layers, then the processing result is transmitted to the next order identification hidden layer to be processed, the next order identification hidden layer combines the processing result of the previous order identification hidden layer with the processing result of the order data corresponding to the order identification hidden layer and then transmits the combined result to the next order identification hidden layer, and the like are repeated until the last order identification hidden layer transmits the final processing result to the order identification output layer, and the order identification output layer converts the processing result into the character information of the corresponding petroleum type and outputs the character information.

If the output petroleum type is the same as the corresponding petroleum type label, the next information sample data is directly processed without processing, if the output result of the order recognition initial neural network is different, the order recognition initial neural network is proved to be incorrect, the order recognition initial neural network needs to be adjusted, and the parameters of each order recognition hidden layer can be manually adjusted according to experience until the output result is the same as the petroleum type label. Or calculating a corresponding loss function according to the output petroleum type and petroleum type labels, automatically adjusting each order identification hidden layer according to the loss function, and then reprocessing the order sample data by using the adjusted order identification initial neural network until the output result is consistent with the petroleum type labels.

After all the order sample data are trained, detecting the trained order identification initial neural network by using a predetermined amount of detection sample data, judging the accuracy rate of the identification result, if the accuracy rate exceeds a corresponding threshold value, proving that the trained order identification initial neural network meets the standard and can be used as an order identification neural network model, if the accuracy rate is less than the corresponding threshold value, reselecting the information sample data, and training the order identification initial neural network again until the obtained accuracy rate of the order identification initial neural network exceeds the corresponding threshold value. And taking the trained order recognition initial neural network as an order recognition neural network model.

By the scheme, the oil order data of the user can be identified and judged by using the order identification neural network model, and the corresponding second petroleum type to be pushed is determined, so that the refueling station end can recommend the petroleum commodity meeting the requirement to the resident user end according to the combination of the first petroleum type to be pushed and the second petroleum type to be pushed.

In addition, the information recognition neural network model and the order recognition neural network model obtained by the invention have a relearning function, and can be continuously adjusted according to actual use and actual needs and the training process, so that the recognition accuracy is continuously improved.

In a particular embodiment, the method further comprises:

step 1081, receiving the information of ordering the user to refuel sent by the server, wherein the information of ordering the user to refuel comprises the target petroleum commodity, the position information of the user terminal and the refueling time period.

Wherein, if user's vehicle need refuel, can trigger the corresponding oiling service of refueling button or pronunciation trigger in APP or the applet on the user terminal, and then form the user and refuel the information of placing an order.

And step 1082, extracting the position information of the user side in the refueling order information of the user, and calculating the time consumed by the vehicle journey according to the position information of the refueling station side.

The method comprises the steps of marking in a map according to position information of a user side and position information of a refueling station end, calculating a route from the position of the user side to the position of the refueling station end, and further calculating the time consumed by a vehicle journey according to the route and the average time of each vehicle passing through the route.

And 1083, if the current time plus the journey consumption time is less than or equal to the latest time point of the refueling time period, acquiring whether a target petroleum commodity of the account of the gas station in the refueling time period has a residual oil outlet, if so, confirming and receiving refueling order information of the user, generating a confirmation receiving instruction and sending the confirmation receiving instruction to the user side through the server, and if not, refusing to receive the refueling order information of the user, generating a refusing and receiving instruction and sending the refusing and receiving instruction to the user side through the server.

And if the calculated time consumption of the vehicle journey and the latest time point when the current time is less than or equal to the refueling time period are added, the fact that the user can arrive at a refueling station within the refueling time period is proved to refuel. In this case, if the order quantity of the corresponding target petroleum commodity of the gas station has reached the maximum order quantity within the refueling time period, it is proved that the user cannot refuel even if the user arrives at the gas station in the refueling time period. And correspondingly generating a receiving refusing instruction, wherein the receiving refusing instruction comprises other available order time periods of the target petroleum commodity. Therefore, after the user side receives the final receiving instruction, the user can reselect other time periods to refuel according to the actual situation of the gas station. Or the user can select other gas stations to refuel, and the selection is determined according to the actual needs of the user.

And 1084, if the current time plus the vehicle journey consumed time is larger than the latest time point of the refueling time period, refusing to receive refueling and ordering information of the user, generating a re-confirmation refueling time period instruction, and sending the re-confirmation refueling time period instruction to the user side through the server.

If the current time plus the vehicle journey consumed time is larger than the latest time point of the refueling time period, the fact that the user cannot arrive at the refueling station in the refueling time period is proved, so that the refueling station end automatically refuses to receive the refueling order placing information, other available ordering time periods of the target petroleum commodity of the refueling station are added to the re-confirmation refueling time period instruction and sent to the user end, and the user can reselect other time periods to refuel according to the actual situation of the refueling station. Or the user can select other gas stations to refuel, and the selection is determined according to the actual needs of the user.

Through the scheme, the intelligent order receiving of the gas station can be facilitated, the inconvenience caused to the user due to the fact that the user cannot arrive is reduced, and the user experience is improved.

In a particular embodiment, the method further comprises:

step 1091, the received invoice request command sent by the server is received.

Step 1092, acquiring corresponding oil order information and an invoicing record of the corresponding oil order information according to the invoice request instruction.

Step 1093, if the billing record is empty, acquiring the amount data of the petroleum order information, extracting the billing account information in the invoice request instruction, correspondingly generating electronic invoice information, and sending the electronic invoice information to the user side, and if the billing record has the corresponding electronic invoice information, generating a billing rejection instruction and sending the billing rejection instruction to the user side through the server.

In the above scheme, if the user wants to invoice, the user can select to send an invoice request instruction to the refueling station end through the APP or applet of the user end, and issue a corresponding electronic invoice. The method specifically comprises the following steps:

a user triggers an APP (application) or an invoice issuing key in a petroleum commodity after successful transaction in an applet through a user side to form an invoice request instruction, the invoice request instruction is sent to a server, the invoice request instruction comprises information of a corresponding gas station, the server searches the corresponding gas station according to the information of the gas station, and the invoice request instruction is sent to a gas station side;

and after the refueling station end confirms that the corresponding transaction exists and succeeds, checking whether the transaction has invoicing information or not, if not, issuing corresponding invoice information according to the transaction amount and the invoicing information carried in the corresponding invoice request instruction, and forwarding the invoice information to the user end through the server for display.

Through the technical scheme, the invoice issuing function and the function of counting various data information are provided for the user, so that the user can use the invoice issuing function and the invoice counting function more conveniently.

Based on the embodiment corresponding to fig. 1, the present embodiment provides an oil data processing apparatus based on a fueling station side, as shown in fig. 2, including:

the logging module 21 is configured to log in a pre-established gas station account, where the gas station account is correspondingly provided with a resident user database, and the resident user database stores owner information and/or vehicle information corresponding to each resident user.

And the information identification module 22 is configured to acquire owner information and/or vehicle information of the resident user from a database of the resident user corresponding to the gas station account, and determine the first to-be-pushed petroleum type according to the owner information and/or the vehicle information by using a pre-established information identification neural network model.

The matching module 23 is configured to search a first target petroleum commodity matched with the first to-be-pushed petroleum type from the petroleum commodity information corresponding to the gas station account.

The sending module 24 is configured to add an identifier corresponding to the resident user in the related information of the first target petroleum product, and send the related information of the first target petroleum product to the server, so that the server sends the related information of the first target petroleum product to the terminal corresponding to the identifier according to the identifier for displaying.

In a specific embodiment, the relevant oil order data of the oil product purchased by the resident user in the corresponding gas station account is stored in the corresponding resident user database, and the apparatus further comprises:

the order identification module is used for searching petroleum order data corresponding to the resident user from the resident user database, and determining a second petroleum type to be pushed according to the petroleum order data by utilizing a pre-established order identification neural network model;

the combination matching module 23 is configured to combine the first to-be-pushed petroleum type with the second to-be-pushed petroleum type, and search a second target petroleum commodity matched with the first to-be-pushed petroleum type and/or the second to-be-pushed petroleum type from the petroleum commodity information corresponding to the gas station account;

the sending module 24 is further configured to add an identifier corresponding to the resident user in the related information of the second target petroleum product, and send the related information of the second target petroleum product to the server, so that the server sends the related information of the second target petroleum product to the terminal corresponding to the identifier according to the identifier for displaying.

In a specific embodiment, the login module 21 specifically includes:

the packaging unit is used for receiving the account information and the account password and packaging the account information and the account password;

the authentication unit is used for sending the packaged data to a server for authentication, wherein the server authenticates the packaged data and pre-established gas station account information, acquires a Token signature after the authentication is successful, combines the Token signature with the packaged data to generate Token data, and generates JWT data according to the Token data;

the storage unit is used for storing Token data in the JWT data in a cookie repository after the JWT data returned by the server is received;

a generating unit, configured to generate a resource access request through GET or POST according to JWT data;

the searching unit is used for searching corresponding Token data in the cookie repository according to the resource access request, calling signature information and an encryption key in the configuration file if the Token data is found, decoding the resource access request by using the encryption key, and performing signature verification on the Token data by using the signature information;

the acquisition unit is used for acquiring corresponding role authority information after the decoding is successful and the signature verification is successful, and sending the role authority information to the server so that the server can acquire corresponding display information according to the role authority information;

and the display unit is used for receiving the display information fed back by the server and displaying the display information on the display screen.

In a specific embodiment, the apparatus further comprises:

the system comprises a sample acquisition module, a data processing module and a data processing module, wherein the sample acquisition module is used for acquiring information sample data of a preset quantity and adding a corresponding petroleum type label for each information sample data, and the information sample data comprises: the number of the petroleum type labels is one or more;

the network construction module is used for constructing an information identification initial neural network in advance, wherein the information identification initial neural network comprises the following components: the system comprises an information identification input layer, N information identification hidden layers and an information identification output layer;

the processing module is used for inputting information sample data from the information identification input layer and processing the information sample data through the N information identification hidden layers, wherein the first information identification hidden layer receives data content output by the information identification input layer, and the rest information identification hidden layers receive data content output by the last information identification hidden layer after being processed;

the output module is used for outputting the processing result data to the information identification output layer by the last information identification hidden layer so that the information identification output layer can determine the corresponding petroleum type according to the processing result data;

the judging module is used for judging whether the output petroleum type is the same as the corresponding petroleum type label or not, if so, training next information sample data, and if not, adjusting the parameters of each information identification hidden layer to enable the output petroleum type to be the same as the corresponding petroleum type label;

and the model determining module is used for taking the information identification initial neural network after all information sample data are trained as the information identification neural network model.

In a specific embodiment:

the sample acquisition module is also used for acquiring petroleum order sample data with a preset quantity and adding a corresponding petroleum type label for each petroleum order sample data, wherein the quantity of the petroleum type labels is one or more;

the network construction module is further used for constructing an order identification initial neural network in advance, wherein the order identification initial neural network comprises the following steps: the system comprises an order identification input layer, M order identification hidden layers and an order identification output layer;

the processing module is further used for inputting petroleum order sample data from the order identification input layer and processing the petroleum order sample data through the M order identification hidden layers, wherein the first order identification hidden layer receives data content output by the order identification input layer, and the rest of the order identification hidden layers receive data content output by the previous order identification hidden layer after being processed;

the output module is also used for outputting the processing result data to the order identification output layer by the last order identification hidden layer so that the order identification output layer can determine the corresponding petroleum type according to the processing result data;

the judging module is also used for judging whether the output petroleum type is the same as the corresponding petroleum type label or not, if so, training the sample data of the next petroleum order, if not, calculating an order loss function according to the output petroleum type and the corresponding petroleum type label, and adjusting the parameter of the order identification hidden layer according to the order loss function so that the output petroleum type is the same as the corresponding petroleum type label;

and the model determining module is also used for taking the order recognition initial neural network after the petroleum order sample data is completely trained as an order recognition neural network model.

In a specific embodiment, the apparatus further comprises:

the ordering receiving module is used for receiving the information of ordering the user by filling oil sent by the server, wherein the information of ordering the user by filling oil comprises a target petroleum commodity, position information of a user side and an oil filling time period; extracting the position information of a user side in the refueling order information of the user, and calculating the time consumed by the vehicle journey according to the position information of the refueling station side; if the current time plus the bus trip time consumption is less than or equal to the latest time point of the refueling time period, acquiring whether a target petroleum commodity of a gas station account in the refueling time period has a residual oil outlet, if so, confirming and receiving refueling ordering information of a user, generating a confirming and receiving instruction and sending the confirming and receiving instruction to a user side through a server, otherwise, refusing to receive the refueling ordering information of the user, generating a refusing and receiving instruction and sending the refusing and receiving instruction to the user side through the server; and if the current time plus the vehicle journey consumed time is larger than the latest time point of the refueling time period, refusing to receive refueling order information of the user, generating a re-confirmation refueling time period instruction, and sending the re-confirmation refueling time period instruction to the user side through the server.

In a specific embodiment, the apparatus further comprises: the electronic billing module is used for requesting the received invoice sent by the server; acquiring corresponding petroleum order information and a billing record of the corresponding petroleum order information according to the invoice request instruction; if the billing record is empty, acquiring the amount data of the petroleum order information, extracting billing account information in the invoice request instruction, correspondingly generating electronic invoice information, sending the electronic invoice information to the user side, and if the billing record has the corresponding electronic invoice information, generating a billing rejection instruction and sending the billing rejection instruction to the user side through the server.

Based on the above embodiments of the method shown in fig. 1 and the apparatus shown in fig. 2, in order to achieve the above object, an electronic device is further provided in the embodiments of the present application, as shown in fig. 3, and includes a memory 32 and a processor 31, where the memory 32 and the processor 31 are both disposed on a bus 33, the memory 32 stores a computer program, and the processor 31 implements the method for processing oil data based on the fueling station side shown in fig. 1 when executing the computer program.

Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a non-volatile memory (which may be a CD-ROM, a usb disk, a removable hard disk, or the like), and includes several instructions for enabling an electronic device (which may be a personal computer, a server, or a network device, or the like) to execute the method according to the implementation scenarios of the present application.

Optionally, the device may also be connected to a user interface, a network interface, a camera, Radio Frequency (RF) circuitry, sensors, audio circuitry, a WI-FI module, and so forth. The user interface may include a Display screen (Display), an input unit such as a keypad (Keyboard), etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (e.g., a bluetooth interface, WI-FI interface), etc.

It will be understood by those skilled in the art that the structure of an electronic device provided in the present embodiment does not constitute a limitation of the physical device, and may include more or less components, or some components in combination, or a different arrangement of components.

Based on the above-mentioned embodiments of the method shown in fig. 1 and the apparatus shown in fig. 2, correspondingly, the present application further provides a storage medium, on which a computer program is stored, and the program, when executed by a processor, implements the above-mentioned oil data processing method based on the fueling station side shown in fig. 1.

The storage medium may further include an operating system and a network communication module. An operating system is a program that manages the hardware and software resources of an electronic device, supporting the operation of information handling programs, as well as other software and/or programs. The network communication module is used for realizing communication among components in the storage medium and communication with other hardware and software in the electronic equipment.

Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus a necessary general hardware platform, and can also be implemented by hardware.

By applying the technical scheme, the pre-constructed information recognition neural network model can be used for determining the first to-be-pushed petroleum type to be recommended according to the owner information and the vehicle information of the resident user of the gas station, the first target petroleum commodity meeting the user requirement can be screened out from the petroleum commodities of the gas station according to the first to-be-pushed petroleum type, and the related information of the first target petroleum commodity is sent to the user side through the server to be displayed. Like this, can according to the intelligent selection of the car owner information of each user that lives in of filling station and vehicle information accord with the oil commodity of user's demand to recommend for the user that lives in that corresponds, make the oil recommendation that the user received more accurate, need not the staff manual recommendation of filling station, simple and convenient.

Those skilled in the art will appreciate that the figures are merely schematic representations of one preferred implementation scenario and that the blocks or flow diagrams in the figures are not necessarily required to practice the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario may be distributed in the devices in the implementation scenario according to the description of the implementation scenario, or may be located in one or more devices different from the present implementation scenario with corresponding changes. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.

The above application serial numbers are for description purposes only and do not represent the superiority or inferiority of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of the present application, but the present application is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present application.

Claims

1. A petroleum data processing method based on a refueling station end is characterized by comprising the following steps:

2. The station-based petroleum data processing method according to claim 1, wherein the relevant petroleum order data of the petroleum products purchased by the resident user in the corresponding gas station account is stored in the corresponding resident user database, and the method further comprises:

3. The oil data processing method based on the refueling station terminal as claimed in claim 1, wherein the logging in of the pre-established refueling station account specifically comprises:

generating a resource access request through GET or POST according to JWT data;

4. The fueling station-side-based oil data processing method according to claim 1, wherein before the owner information and/or the vehicle information of the resident user is obtained from the resident user database corresponding to the fueling station account, and the first to-be-pushed oil type is determined according to the owner information and/or the vehicle information by using a pre-established information recognition neural network model, the method further comprises:

5. The station-side based oil data processing method according to claim 2, wherein before the step of searching the database of the resident users for corresponding oil order data of the resident users and determining a second to-be-pushed oil type according to the oil order data by using the pre-constructed order recognition neural network model, the method further comprises:

6. The fueling station-side based oil data processing method of claim 1, further comprising:

7. The fueling station-side based oil data processing method of claim 1, further comprising:

receiving an invoice request instruction sent by the server;

8. A kind of petroleum data processing unit based on refueling station end, characterized by comprising:

9. An electronic device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program implements the steps of the fueling station-side based oil data processing method according to any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the fueling station-side based oil data processing method of any one of claims 1 to 7.