Gas station user information and local service linkage method and device and terminal equipment

Abstract

The embodiment of the invention provides a gas station user information and local service linkage method and device and terminal equipment, and belongs to the technical field of data processing. The method comprises the steps of obtaining first consumption information corresponding to a historical user in a target gas station in a target area and second consumption information corresponding to the historical user in a target shop in the target area, and obtaining initial evaluation information corresponding to the target shop; performing false detection on the initial evaluation information to obtain target evaluation information corresponding to the target shop; determining a target consumption model corresponding to a historical user in the target area according to the first consumption information, the second consumption information and the target evaluation information; obtaining third consumption information corresponding to the target user in the target gas station and fourth consumption information corresponding to the related shop in the target area; and determining a target consumption route corresponding to the target user by using a target consumption model according to the third consumption information and the fourth consumption information.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device and terminal equipment for linking gas station user information with local services. Background Art

[0002] Traditional gas station and local merchant service data are usually stored and managed independently, lacking effective integration and sharing, leading to serious data silo problems. This data silo not only hinders the comprehensive analysis of user consumption behavior, but also makes it impossible for merchants to accurately grasp the consumption routes and preferences of target users in a specific area. As a result, merchants are unable to prepare in advance and provide personalized service experiences, which ultimately leads to a significant reduction in consumer user satisfaction and consumption experience. For example, after refueling at a gas station, users may need to go to a nearby restaurant or supermarket, but due to the lack of a unified data platform, merchants cannot understand user needs in advance, thus missing the opportunity to provide customized services, making users feel inconvenienced during the consumption process. Summary of the invention

[0003] The main purpose of the embodiments of the present invention is to provide a method, device and terminal equipment for linking gas station user information with local services, aiming to solve the problem in the related technology that merchants are unable to accurately grasp the consumption routes and preferences of target users in a specific area, so merchants are unable to prepare in advance and provide personalized service experience, which ultimately leads to a significant reduction in consumer user satisfaction and consumption experience.

[0004] In a first aspect, an embodiment of the present invention provides a method for linking gas station user information with local services, including:

[0005] Obtaining first consumption information corresponding to a historical user at a target gas station in a target area and second consumption information corresponding to the historical user at a target store in the target area, and obtaining initial evaluation information corresponding to the target store;

[0006] Performing false detection on the initial evaluation information to obtain target evaluation information corresponding to the target store;

[0007] Determine a target consumption model corresponding to the historical user in the target area according to the first consumption information, the second consumption information and the target evaluation information;

[0008] Obtain third consumption information corresponding to the target user at the target gas station and fourth consumption information corresponding to relevant stores in the target area;

[0009] The target consumption route corresponding to the target user is determined using the target consumption model according to the third consumption information and the fourth consumption information.

[0010] In a second aspect, an embodiment of the present invention provides a gas station user information and local service linkage device, including:

[0011] A data acquisition module, used to obtain first consumption information corresponding to a historical user at a target gas station in a target area and second consumption information corresponding to the historical user at a target store in the target area, and to obtain initial evaluation information corresponding to the target store;

[0012] A false detection module, used to perform false detection on the initial evaluation information to obtain target evaluation information corresponding to the target store;

[0013] A model determination module, configured to determine a target consumption model corresponding to the historical user in the target area according to the first consumption information, the second consumption information and the target evaluation information;

[0014] A data collection module, used to obtain third consumption information corresponding to the target user at the target gas station and fourth consumption information corresponding to the relevant stores in the target area;

[0015] A route determination module is used to determine a target consumption route corresponding to the target user using the target consumption model according to the third consumption information and the fourth consumption information.

[0016] In a third aspect, an embodiment of the present invention further provides a terminal device, comprising a processor, a memory, a computer program stored in the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the computer program is executed by the processor, the steps of any one of the methods for linking gas station user information with local services provided in the specification of the present invention are implemented.

[0017] The embodiment of the present invention provides a method, device and terminal device for linking user information of a gas station with local services. The method comprises: obtaining first consumption information corresponding to a historical user at a target gas station in a target area and second consumption information corresponding to a historical user at a target store in the target area, and obtaining initial evaluation information corresponding to the target store; performing false detection on the initial evaluation information to obtain target evaluation information corresponding to the target store; determining a target consumption model corresponding to the historical user in the target area according to the first consumption information, the second consumption information and the target evaluation information; obtaining third consumption information corresponding to the target user at a target gas station and fourth consumption information corresponding to a relevant store in the target area; and determining a target consumption route corresponding to the target user using the target consumption model according to the third consumption information and the fourth consumption information. The method realizes the integration of multi-source data by obtaining consumption information of historical users at a target gas station and a target store in the target area, as well as evaluation information of the target store. In this way, the user's behavior patterns and preferences can be more comprehensively understood, laying a foundation for establishing an accurate consumption model. Performing false detection on the initial evaluation information ensures the authenticity and reliability of the evaluation of the target store. This helps to improve the accuracy of the consumption model and avoid misleading due to false evaluation. Based on the consumption information of historical users and the evaluation information of target stores, a target consumption model corresponding to historical users in the target area is constructed. This model can capture the consumption habits and potential needs of users in different scenarios. Therefore, by obtaining the consumption information of target users at target gas stations and related stores, and using the previously established target consumption model, a personalized target consumption route can be determined for the target user. This not only improves the user experience, but also may promote the sales of merchants, achieving a win-win situation, and then realizes the accurate prediction of user consumption behavior and route optimization, thereby improving the overall service quality and user experience. It also solves the problem in related technologies that merchants cannot accurately grasp the consumption routes and preferences of target users in a specific area, so merchants cannot prepare in advance and provide personalized service experiences, which ultimately leads to a significant reduction in consumer user satisfaction and consumption experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A flow chart of a method for linking gas station user information with local services provided by an embodiment of the present invention;

[0020] Figure 2A schematic diagram of the module structure of a gas station user information and local service linkage device provided by an embodiment of the present invention;

[0021] Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0023] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0024] It should be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0025] The embodiment of the present invention provides a method, device and terminal device for linking user information of a gas station with local services. The method for linking user information of a gas station with local services can be applied to a terminal device, which can be an electronic device such as a tablet computer, a laptop computer, a desktop computer, a personal digital assistant and a wearable device. The terminal device can be a server or a server cluster.

[0026] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0027] Please refer to Figure 1 , Figure 1 A flow chart of a method for linking gas station user information with local services provided in an embodiment of the present invention.

[0028] like Figure 1 As shown, the method for linking gas station user information with local services includes steps S101 to S105.

[0029] Step S101, obtaining first consumption information corresponding to a historical user at a target gas station in a target area and second consumption information corresponding to the historical user at a target store in the target area, and obtaining initial evaluation information corresponding to the target store.

[0030] Exemplarily, the first consumption information of historical users at the target gas station is obtained from the data management system of the gas station, including but not limited to user ID, consumption time, consumption amount, consumption items (such as refueling, car washing, etc.). The second consumption information of historical users is obtained from the data management system of the target store, including but not limited to user ID, consumption time, consumption amount, consumption items (such as purchased goods or services), and the initial evaluation information of historical users is obtained from the online evaluation platform (such as Dianping) or the internal evaluation system of the target store, including but not limited to user ID, evaluation time, evaluation content, score, etc. Among them, the target store and the target gas station are distributed in the target area.

[0031] Step S102: Perform false detection on the initial evaluation information to obtain target evaluation information corresponding to the target store.

[0032] Exemplarily, false evaluation criteria are determined, such as evaluation content that is too exaggerated, contains a large amount of repetitive or templated language, a large number of similar evaluations appear in a short period of time, etc., and then historical evaluation information is annotated according to the false evaluation criteria to obtain annotated training data, and then a machine learning model, such as a classification model (such as logistic regression, support vector machine, random forest, etc.) or a deep learning model is used to train the model on the annotated training data to obtain a corresponding false classification model.

[0033] Exemplarily, false detection is performed on the initial evaluation information according to the false classification model to obtain target evaluation information corresponding to the target store.

[0034] In some embodiments, the false detection of the initial evaluation information to obtain the target evaluation information corresponding to the target store includes: obtaining the first evaluation text and evaluation image corresponding to the initial evaluation information, and obtaining the first evaluation user corresponding to the first evaluation text from the initial evaluation information; obtaining the second evaluation text corresponding to the evaluation of the first evaluation text, and the second evaluation user corresponding to the second evaluation text; establishing a user network connection graph according to the first evaluation text, the first evaluation user, the second evaluation text and the second evaluation user, wherein the first evaluation text, the first evaluation user, the second evaluation text and the second evaluation user are respectively determined as nodes corresponding to the user network connection graph, and the connections between the first evaluation text and the first evaluation user, the second evaluation text and the second evaluation user, and the first evaluation text and the second evaluation text are determined as graph relationships corresponding to the user network connection graph; using the graph feature extraction layer of the false recognition model to obtain the user social feature vector corresponding to the first evaluation text under the graph relationship involved from the user network connection graph; using the text extraction layer of the false recognition model to extract the user social feature vector corresponding to the first evaluation text under the graph relationship involved The feature extraction layer performs text representation on the first evaluation text to obtain a first text vector; the image feature extraction layer of the false recognition model is used to extract image information from the evaluation image to obtain an image feature vector; the text generation layer of the false recognition model is used to perform text description on the evaluation image to obtain a description image text corresponding to the evaluation image; the text feature extraction layer of the false recognition model is used to perform text representation on the description image text to obtain a second text vector; the feature fusion layer of the false recognition model is used to perform feature fusion on the first text vector, the second text vector, the image feature vector and the user social feature vector to obtain multiple initial fused feature vectors; the feature splicing layer of the false recognition model is used to perform feature splicing on the user social feature vector, the first text vector, the image feature vector, the second text vector and multiple initial fused feature vectors to obtain a target fused feature vector; the information classification layer of the false recognition model is used to perform data classification on the target fused feature vector to obtain a classification category corresponding to the first evaluation text; the initial evaluation information is subjected to false detection according to the classification category to obtain the target evaluation information corresponding to the target store.

[0035] Exemplarily, a first evaluation text and an evaluation image are obtained from the initial evaluation information, and a first evaluation user corresponding to the first evaluation text is obtained, and a second evaluation text corresponding to the evaluation of the first evaluation text and a second evaluation user corresponding to the second evaluation text are obtained from the evaluation system.

[0036] Exemplarily, the first evaluation text, the first evaluation user, the second evaluation text, and the second evaluation user are respectively used as nodes of the user network connection map. The following connections are established in the user network connection map: the connection between the first evaluation text and the first evaluation user, the connection between the second evaluation text and the second evaluation user, and the connection between the first evaluation text and the second evaluation text.

[0037] Exemplarily, the graph feature extraction layer of the false identification model extracts user social feature vectors related to the graph relationship with the first evaluation user from the user network connection graph.

[0038] Exemplarily, the first evaluation text is processed using the text feature extraction layer of the false recognition model, such as BERT, to obtain a first text vector. The evaluation image is processed using the image feature extraction layer of the false recognition model to obtain an image feature vector, and the text generation layer of the false recognition model performs a text description on the evaluation image to obtain the description image text corresponding to the evaluation image, and then the description image text is processed using the text feature extraction layer to obtain a second text vector.

[0039] Exemplarily, the feature fusion layer of the false recognition model, such as the attention network, is used to fuse the first text vector, the second text vector, the image feature vector, and the user social feature vector to generate multiple initial fused feature vectors. For example, the first text vector, the second text vector, and the image feature vector are used to obtain an initial fused feature vector according to the attention network, and the second text vector, the image feature vector, and the user social feature vector are used to obtain another initial fused feature vector according to the attention network.

[0040] Exemplarily, the feature concatenation layer of the false recognition model is used to concatenate the user social feature vector, the first text vector, the image feature vector, the second text vector, and multiple initial fused feature vectors to generate a target fused feature vector, and then the information classification layer of the false recognition model is used to classify the target fused feature vector to determine the classification category (such as true evaluation or false evaluation) corresponding to the first evaluation text. Finally, the initial evaluation information is subjected to false detection according to the classification category, false evaluations are eliminated, and true evaluation information is retained to form the target evaluation information of the target store.

[0041] In some embodiments, the graph feature extraction layer using the false recognition model obtains the user social feature vector corresponding to the first evaluation text under the graph relationship involved from the user network connection graph, including: using the text processing network of the graph feature extraction layer to perform text representation on the first evaluation text to obtain a first node representation corresponding to the first evaluation text; using the text processing network of the graph feature extraction layer to perform text representation on the second evaluation text to obtain a second node representation corresponding to the second evaluation text; using the node initialization network of the graph feature extraction layer to perform vector mean processing on the first evaluation text corresponding to the first evaluation user and the third evaluation text when the first evaluation user evaluates the evaluation text of other users, to obtain a third node representation corresponding to the first evaluation user; using the The node initialization network of the graph feature extraction layer performs vector mean processing on the second evaluation text corresponding to the second evaluation user and the fourth evaluation text published by the second evaluation user on other stores to obtain the fourth node representation corresponding to the second evaluation user; the node relationship network of the graph feature extraction layer is used to perform adjacent node calculation based on the graph relationship and the first node representation in combination with the second node representation, the third node representation and the fourth node representation to obtain the node similarity between any adjacent nodes in the graph relationship; the path determination network of the graph feature extraction layer is used to determine the node path corresponding to the first evaluation text according to the node similarity and a preset threshold; the feature splicing network of the graph feature extraction layer is used to determine the user social feature vector corresponding to the first evaluation text according to the node path.

[0042] Exemplarily, the first evaluation text is processed using the text processing network of the graph feature extraction layer to generate a first node representation, and then the second evaluation text is processed using the text processing network of the graph feature extraction layer to generate a second node representation.

[0043] Exemplarily, the node initialization network of the graph feature extraction layer is used to perform vector mean processing on the first evaluation text corresponding to the first evaluation user and the text vector corresponding to the third evaluation text of the first evaluation user on other users, so as to generate the third node representation corresponding to the first evaluation user. Through mean processing, the overall characteristics of the first evaluation user in the evaluation behavior are captured.

[0044] Exemplarily, the node initialization network of the graph feature extraction layer is used to perform vector mean processing on the second evaluation text corresponding to the second evaluation user and the text vectors corresponding to the fourth evaluation text of the second evaluation user on other stores to generate a fourth node representation corresponding to the second evaluation user.

[0045] Exemplarily, according to the graph relationship in the user network connection graph (such as the connection between the first evaluation text and the second evaluation text, and the first evaluation user and the second evaluation user), the first node representation, the second node representation, the third node representation and the fourth node representation are combined to calculate the adjacent nodes, and obtain the node similarity between any adjacent nodes in the graph relationship. Thus, the relevance and interaction strength between nodes are measured through similarity calculation.

[0046] Exemplarily, when the node similarity is greater than or equal to a preset threshold, the node associated with the first evaluation text is determined as the node path corresponding to the first evaluation text. The node path represents the propagation path or association path of the first evaluation text in the user network graph. Through path determination, the propagation mode and key path of the first evaluation text in the user network are captured.

[0047] Exemplarily, according to the determined node path, a user social feature vector related to the first evaluation text is extracted. The user social feature vector integrates the node representation and node relationship information on the node path. Thus, a comprehensive user social feature vector is formed through the node representation and node relationship information on the path for subsequent false detection.

[0048] Step S103: determining a target consumption model corresponding to the historical user in the target area according to the first consumption information, the second consumption information and the target evaluation information.

[0049] Exemplarily, feature extraction is performed on the first consumption information and the second consumption information to obtain the consumption amount, consumption frequency, consumption time and consumption location.

[0050] For example, sentiment analysis tools are used to classify the target evaluation text into sentiments such as positive, neutral, and negative, thereby combining consumption features and evaluation features to form a comprehensive feature vector. For example, features such as consumption amount, consumption frequency, consumption time, consumption location, evaluation sentiment, evaluation time, and evaluation object can be combined to form a high-dimensional feature vector.

[0051] For example, a suitable machine learning model is selected, such as linear regression, decision tree, random forest, gradient boosting tree, neural network, etc. The selected model is trained using a high-dimensional feature vector, and the model parameters are adjusted to achieve optimal performance, thereby obtaining a target consumption model. The target consumption model can be used to predict user consumption behavior, identify high-value users, optimize marketing strategies, etc.

[0052] In some embodiments, determining the target consumption model corresponding to the historical user in the target area based on the first consumption information, the second consumption information and the target evaluation information includes: determining the consumption category corresponding to the historical user and the consumption frequency corresponding to the consumption category based on the first consumption information and the second consumption information; determining a first association matrix between the historical user and the consumption category based on the consumption category and the consumption frequency; determining a second association matrix between the historical user and the consumption category based on the consumption category, the consumption frequency and the target evaluation information; determining the corresponding similarities between the historical users based on the first association matrix and the second association matrix; determining the consumption score of the historical user for the consumption category based on the similarity, and determining the target consumption model corresponding to the historical user based on the similarity and the consumption score.

[0053] Exemplarily, based on the first consumption information and the second consumption information, the consumption categories of the historical users are extracted. For example, according to the commodity categories, service types, etc. in the consumption records, the number of consumptions of each historical user in each consumption category is counted. For example, user A consumed 10 times in the catering category and 5 times in the shopping category.

[0054] Exemplarily, the first association matrix represents the association between historical users and consumption categories based on consumption frequency. The rows of the first association matrix represent historical users, the columns represent consumption categories, and the matrix elements are the consumption frequencies of users under the categories. The second association matrix combines consumption categories, consumption frequencies, and target evaluation information to construct a more comprehensive association matrix between users and consumption categories, combining target evaluation information (such as evaluation scores, positive / negative evaluations, etc.) with consumption frequencies to form a weighted association degree.

[0055] Exemplarily, the cosine similarity and the Pearson correlation coefficient are used to combine the first correlation matrix and the second correlation matrix to obtain the similarity between any two historical users.

[0056] For example, based on similarity, historical user ratings for each consumption category are predicted. For example, if two users have similar consumption habits and one user's rating for a certain category is known, the other user's rating for that category can be predicted. Thus, similarity and consumption ratings are comprehensively considered to construct a target consumption model. This model can be used to predict users' consumption tendencies and ratings for different consumption categories in the future, thereby guiding personalized recommendations and marketing strategies.

[0057] In some embodiments, the historical users include at least a first user and a second user, and the determining of the corresponding similarity between the historical users according to the first association matrix and the second association matrix includes: obtaining a first relevant consumption category corresponding to the first user and a first relevant consumption frequency corresponding to the first relevant consumption category from the first association matrix; obtaining a second relevant consumption category corresponding to the second user and a second relevant consumption frequency corresponding to the second relevant consumption category from the first association matrix; performing intersection processing on the first relevant consumption category and the second relevant consumption category to obtain a target relevant consumption category corresponding to the first user and the second user; determining a first consumption average value corresponding to the first user according to the first relevant consumption frequency and a second consumption average value corresponding to the second user according to the second relevant consumption frequency; obtaining a third relevant consumption frequency corresponding to the first user under the target relevant consumption category from the first relevant consumption frequency and obtaining a fourth relevant consumption frequency corresponding to the second user under the target relevant consumption category from the second relevant consumption frequency; fusing the first consumption average value, the second consumption average value, the third relevant consumption frequency and the fourth relevant consumption frequency to determine the first correlation value corresponding to the first user and the second user; obtaining a first consumption preference corresponding to the first user and a first consumption preference corresponding to the first consumption preference from the second association matrix. a second consumption access value; obtain the second consumption preference corresponding to the second user and the second consumption access value corresponding to the second consumption preference from the second association matrix; perform intersection processing on the first consumption preference and the second consumption preference to obtain the target consumption preference corresponding to the first user and the second user; determine the first evaluation score corresponding to the target consumption preference of the first user according to the first valid evaluation corresponding to the first user, and determine the first attenuation factor corresponding to the target consumption preference of the first user according to the first evaluation score; determine the second evaluation score corresponding to the target consumption preference of the second user according to the second valid evaluation corresponding to the second user, and determine the second attenuation factor corresponding to the target consumption preference of the second user according to the second evaluation score; determine the first access average value corresponding to the first user according to the first consumption access value and determine the second access average value corresponding to the second user according to the second consumption access value; obtain the third consumption access value corresponding to the first user under the target consumption preference from the first consumption access value and obtain the fourth consumption access value corresponding to the second user under the target consumption preference from the second consumption access value; use the first attenuation factor and the second attenuation factor to fuse the first access average value, the second access average value, the third consumption access value and the fourth consumption access value to determine the second correlation value corresponding to the first user and the second user;The first correlation value and the second correlation value are combined to determine the corresponding similarity between the first user and the second user. ;

[0058] Exemplarily, the consumption frequency of the first user under each consumption category is extracted from the first association matrix to form a first related consumption category and a corresponding first related consumption frequency. The consumption frequency of the second user under each consumption category is extracted from the first association matrix to form a second related consumption category and a corresponding second related consumption frequency, and then the first related consumption category and the second related consumption category are subjected to intersection processing to obtain a target related consumption category common to the first user and the second user.

[0059] Exemplarily, according to the first relevant consumption frequency, the first consumption average value of the first user in all relevant consumption categories is calculated. According to the second relevant consumption frequency, the second consumption average value of the second user in all relevant consumption categories is calculated.

[0060] Exemplarily, the third relevant consumption frequency of the first user under the target relevant consumption category is obtained from the first relevant consumption frequency, and the fourth relevant consumption frequency of the second user under the target relevant consumption category is obtained from the second relevant consumption frequency.

[0061] Exemplarily, the first correlation value corresponding to the first user and the second user is calculated by fusing the first consumption average value, the second consumption average value, the third relevant consumption frequency, and the fourth relevant consumption frequency according to the Pierre correlation coefficient.

[0062] Exemplarily, the consumption access values ​​of the first user under each consumption preference are extracted from the second association matrix to form a first consumption preference and a corresponding first consumption access value. The consumption access values ​​of the second user under each consumption preference are extracted from the second association matrix to form a second consumption preference and a corresponding second consumption access value. The first consumption preference and the second consumption preference are subjected to intersection processing to obtain a common target consumption preference of the first user and the second user.

[0063] Exemplarily, a first valid evaluation corresponding to the first user is obtained from the initial evaluation information, and then each first valid evaluation is converted into a score to obtain a first evaluation score of the first user's target consumption preference, and then the first evaluation score is compared with a preset score to obtain a first quantity corresponding to when the first evaluation score is greater than the preset score, and then the first quantity is ratio-calculated with the total number of users to obtain a first ratio, and then the first ratio is added by 1 and the logarithm with base 10 is taken, and then the first attenuation factor can be obtained by adding 1 to the logarithm and taking the inverse.

[0064] Exemplarily, a second valid evaluation corresponding to the second user is obtained from the initial evaluation information, and then each second valid evaluation is converted into a score to obtain the second evaluation score of the second user's target consumption preference, and then the second evaluation score is compared with the preset score to obtain the second quantity corresponding to when the second evaluation score is greater than the preset score, and then the second quantity is ratio-calculated with the total number of users to obtain a second ratio, and then the second ratio is added by 1 and the logarithm with base 10 is taken, and then the second attenuation factor can be obtained by adding 1 to the logarithm and taking the inverse.

[0065] Exemplarily, according to the first consumption visit value, the first visit average value of the first user under all target consumption preferences is calculated, and according to the second consumption visit value, the second visit average value of the second user under all target consumption preferences is calculated.

[0066] Exemplarily, a third consumption access value of the first user under the target consumption preference is obtained from the first consumption access value. A fourth consumption access value of the second user under the target consumption preference is obtained from the second consumption access value.

[0067] Exemplarily, a first attenuation factor is used to fuse the first difference between the first access average and the third consumption access value, and a second attenuation factor is used to fuse the second difference between the second access average and the fourth consumption access value, thereby combining the Pierre correlation coefficient to calculate the second correlation value corresponding to the first user and the second user using the first difference and the second difference.

[0068] Exemplarily, a first weight corresponding to the first correlation value and a second weight corresponding to the second correlation value are determined, so that the first correlation value and the second correlation value are fused according to the first weight and the second weight to determine the similarity between the first user and the second user.

[0069] In some embodiments, determining the consumption score corresponding to the consumption category by the historical user according to the similarity includes: determining the related user corresponding to the historical user according to the similarity, and obtaining the first relevant information corresponding to the historical user and the second relevant information corresponding to the related user from the target evaluation information; using the scoring prediction model to score the first related information to obtain a first score value corresponding to the first related information and using the scoring prediction model to score the second related information to obtain a second score value corresponding to the second related information; performing mean calculation based on the first score value to obtain a first mean value corresponding to the historical user and performing mean calculation based on the second score value to obtain a second mean value corresponding to the related user; determining the consumption category and obtaining the third score value corresponding to the consumption category by the related user from the second score value; determining the consumption score corresponding to the consumption category by the historical user according to the similarity, the third score value, the first mean value and the second mean value; wherein the consumption score is obtained according to the following formula:

[0070]

[0071] in, represents the consumption score corresponding to the i-th consumption category by the u-th historical user, represents the first mean value corresponding to the u-th historical user, n represents the number of users corresponding to the relevant user, represents the similarity between the u-th historical user and the j-th related user, represents the third rating value corresponding to the i-th consumption category by the j-th related user, represents the second mean corresponding to the j-th related user.

[0072] Exemplarily, based on the previously calculated similarity, related users similar to the historical user are determined. Several users with the highest similarity can be selected as related users. First related information corresponding to the historical user is extracted from the target evaluation information, such as the consumption record, evaluation, etc. of the historical user. And second related information corresponding to the related user is extracted from the target evaluation information, which also includes consumption record, evaluation, etc.

[0073] Exemplarily, the score prediction model is used to predict the score of the first relevant information to obtain a first score value corresponding to the first relevant information. The score prediction model is used to predict the score of the second relevant information to obtain a second score value corresponding to the second relevant information.

[0074] Exemplarily, the first average value corresponding to the historical user is obtained by performing mean calculation according to the first score value, and the second average value corresponding to the relevant user is obtained by performing mean calculation according to the second score value.

[0075] Exemplarily, the consumption category to be evaluated is determined. The third score value corresponding to the specified consumption category of the relevant user is extracted from the second score value. Thus, the consumption score corresponding to the specified consumption category of the historical user is calculated by the following formula using the similarity, the third score value, the first mean and the second mean:

[0076]

[0077] in, represents the consumption score of the u-th historical user for the i-th consumption category, represents the first mean value corresponding to the u-th historical user, n represents the number of users corresponding to the relevant user, represents the similarity between the u-th historical user and the j-th related user, represents the third rating value of the jth related user for the ith consumption category, represents the second mean corresponding to the j-th related user.

[0078] Step S104: Obtain third consumption information corresponding to the target user at the target gas station and fourth consumption information corresponding to relevant stores in the target area.

[0079] Exemplarily, the third consumption information of the target user at the target gas station is obtained from the data management system of the gas station, including but not limited to user ID, consumption time, consumption amount, consumption items (such as refueling, car washing, etc.), etc. And the fourth consumption information of the target user is obtained from the data management system of the relevant store, including but not limited to user ID, consumption time, consumption amount, consumption items (such as purchased goods or services), etc.

[0080] Step S105: Determine the target consumption route corresponding to the target user using the target consumption model according to the third consumption information and the fourth consumption information.

[0081] Exemplarily, the third consumption information and the fourth consumption information obtain the similarity between the target user and the historical users, thereby screening out the relevant users corresponding to the target user based on the similarity, thereby determining the scores of the relevant users for each consumption category based on the target consumption model, and then obtaining the predicted consumption shop corresponding to the target user based on the score, thereby obtaining the store location corresponding to the predicted consumption shop and the current location of the target user based on Beidou positioning, and then using the consumption score corresponding to the predicted consumption shop to perform route planning based on the current location and the shop location, thereby obtaining the target consumption route corresponding to the target user.

[0082] In some embodiments, determining the target consumption route corresponding to the target user using the target consumption model based on the third consumption information and the fourth consumption information includes: obtaining associated users corresponding to the target user from the historical users based on the third consumption information and the fourth consumption information in combination with the first consumption information and the second consumption information; determining the initial consumption category corresponding to the target user and the initial consumption score corresponding to the initial consumption category from the target consumption model based on the associated users; obtaining the current time, and adjusting the initial consumption score corresponding to the initial consumption category based on the current time to obtain the target consumption score corresponding to the initial consumption category; adjusting the initial consumption category based on the target consumption score to obtain the target consumption category; and determining the target consumption route corresponding to the target user based on the target consumption category.

[0083] Exemplarily, according to the third consumption information and the fourth consumption information, combined with the first consumption information and the second consumption information, the associated users most similar to the target user are screened out from historical users by calculating similarity (such as cosine similarity, Pearson correlation coefficient, etc.).

[0084] Exemplarily, the initial consumption category corresponding to the target user is determined from the target consumption model according to the information of the associated user, and the consumption score of the associated user under the initial consumption category is obtained from the target consumption model as the initial consumption score corresponding to the initial consumption category of the target user.

[0085] Exemplarily, the current time such as hour and minute is obtained, and then the initial consumption score corresponding to the initial consumption category is adjusted according to the current time to obtain the target consumption score corresponding to the target consumption category. For example, the initial consumption score can be adjusted according to a time period (such as weekdays, weekends, holidays, etc.) or a specific time period (such as morning peak, evening peak, etc.) to obtain the target consumption score.

[0086] Exemplarily, the initial consumption category is adjusted according to the adjusted target consumption score to obtain the final target consumption category. For example, if the score of a certain consumption category at the current time is significantly improved, it can be used as the target consumption category.

[0087] For example, according to the final determined target consumption category, a corresponding consumption route is recommended for the target user. For example, if the target consumption category is dining, a nearby high-rated restaurant may be recommended; if the target consumption category is shopping, a popular shopping mall may be recommended.

[0088] In some embodiments, the initial consumption score corresponding to the initial consumption category is adjusted according to the current time to obtain a target consumption score corresponding to the initial consumption category, including: obtaining a first time corresponding to the third consumption information and a second time corresponding to the fourth consumption information; calculating a consumption similarity value between the target user and the associated user at the first time and the second time; obtaining a current consumption category corresponding to the associated user at the current time from the initial consumption category; obtaining a related consumption score corresponding to the current consumption category from the initial consumption score, and adjusting the related consumption score according to the consumption similarity value to obtain the target consumption score corresponding to the related consumption score.

[0089] Exemplarily, the specific time point at which the third consumption information occurs is determined to be recorded as the first time, and the specific time point at which the fourth consumption information occurs is determined to be recorded as the second time. Based on the consumption behaviors of the target user and the associated user at the first time and the second time, the consumption similarity value between the two is calculated. For example, if the target user and the associated user perform similar types of consumption in the same time period, the similarity value will be higher.

[0090] Exemplarily, the current consumption category corresponding to the associated user at the current time is extracted from the initial consumption category, and then the relevant consumption score corresponding to the current consumption category is obtained from the initial consumption score, and then the relevant consumption score is adjusted according to the calculated consumption similarity value to obtain the target consumption score. For example, if the consumption similarity value is high, the relevant consumption score can be appropriately increased; if the consumption similarity value is low, the relevant consumption score can be appropriately reduced. By calculating the consumption similarity value at a specific time, the consumption behavior of the user in a specific time period can be more accurately reflected, the accuracy of the recommendation can be improved, and then the consumption score can be dynamically adjusted according to the consumption similarity value, so that the recommendation results can be more in line with the actual needs and behavior habits of the user.

[0091] See also Figure 2 , Figure 2A gas station user information and local service linkage device 200 provided in an embodiment of the present application includes a data acquisition module 201, a false detection module 202, a model determination module 203, a data collection module 204, and a route determination module 205, wherein the data acquisition module 201 is used to obtain first consumption information corresponding to a historical user at a target gas station in a target area and second consumption information corresponding to the historical user at a target store in the target area, and obtain initial evaluation information corresponding to the target store; the false detection module 202 is used to perform false detection on the initial evaluation information and obtain target evaluation information corresponding to the target store; the model determination module 203 is used to determine a target consumption model corresponding to the historical user in the target area according to the first consumption information, the second consumption information and the target evaluation information; the data collection module 204 is used to obtain third consumption information corresponding to the target user at the target gas station and fourth consumption information corresponding to the relevant store in the target area; the route determination module 205 is used to determine the target consumption route corresponding to the target user using the target consumption model according to the third consumption information and the fourth consumption information.

[0092] In some implementations, the gas station user information and local service linkage device 200 may be applied to a terminal device.

[0093] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the gas station user information and local service linkage device 200 described above can refer to the corresponding process in the aforementioned gas station user information and local service linkage method embodiment, and will not be repeated here.

[0094] See also Figure 3 , Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention.

[0095] like Figure 3 As shown, the terminal device 300 includes a processor 301 and a memory 302 , and the processor 301 and the memory 302 are connected via a bus 303 , such as an I2C (Inter-integrated Circuit) bus.

[0096] Specifically, the processor 301 is used to provide computing and control capabilities to support the operation of the entire terminal device. The processor 301 can be a central processing unit (CPU), and the processor 301 can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0097] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0098] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the embodiment of the present invention, and does not constitute a limitation on the terminal device to which the embodiment of the present invention is applied. The specific server may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0099] The processor is used to run a computer program stored in the memory, and implement any one of the gas station user information and local service linkage methods provided in the embodiments of the present invention when executing the computer program.

[0100] In one embodiment, the processor is used to run a computer program stored in the memory, and implements the following steps when executing the computer program:

[0101] Obtaining first consumption information corresponding to a historical user at a target gas station in a target area and second consumption information corresponding to the historical user at a target store in the target area, and obtaining initial evaluation information corresponding to the target store;

[0102] Performing false detection on the initial evaluation information to obtain target evaluation information corresponding to the target store;

[0103] Determine a target consumption model corresponding to the historical user in the target area according to the first consumption information, the second consumption information and the target evaluation information;

[0104] Obtain third consumption information corresponding to the target user at the target gas station and fourth consumption information corresponding to relevant stores in the target area;

[0105] The target consumption route corresponding to the target user is determined using the target consumption model according to the third consumption information and the fourth consumption information.

[0106] It should be noted that technical personnel in the relevant field can clearly understand that, for the convenience and brevity of description, the specific working process of the terminal device described above can refer to the corresponding process in the aforementioned gas station user information and local service linkage method embodiment, and will not be repeated here.

[0107] An embodiment of the present invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any method for linking gas station user information with local services as provided in the description of the embodiment of the present invention.

[0108] The storage medium may be an internal storage unit of the terminal device described in the foregoing embodiment, such as a hard disk or memory of the terminal device. The storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc., equipped on the terminal device.

[0109] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0110] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0111] The serial numbers of the embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. The above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for linking gas station user information with local services, characterized in that: The method comprises: Obtaining first consumption information corresponding to a historical user at a target gas station in a target area and second consumption information corresponding to the historical user at a target store in the target area, and obtaining initial evaluation information corresponding to the target store; Performing false detection on the initial evaluation information to obtain target evaluation information corresponding to the target store; Determine a target consumption model corresponding to the historical user in the target area according to the first consumption information, the second consumption information and the target evaluation information; Obtain third consumption information corresponding to the target user at the target gas station and fourth consumption information corresponding to relevant stores in the target area; The target consumption route corresponding to the target user is determined using the target consumption model according to the third consumption information and the fourth consumption information.

2. The method according to claim 1, characterized in that: The performing false detection on the initial evaluation information to obtain target evaluation information corresponding to the target store includes: Obtaining a first evaluation text and an evaluation image corresponding to the initial evaluation information, and obtaining a first evaluation user corresponding to the first evaluation text from the initial evaluation information; Obtaining a second evaluation text corresponding to the evaluation of the first evaluation text, and a second evaluation user corresponding to the second evaluation text; Establish a user network connection graph according to the first evaluation text, the first evaluation user, the second evaluation text and the second evaluation user, wherein the first evaluation text, the first evaluation user, the second evaluation text and the second evaluation user are respectively determined as nodes corresponding to the user network connection graph, and the connections between the first evaluation text and the first evaluation user, the second evaluation text and the second evaluation user, and the first evaluation text and the second evaluation text are determined as graph relationships corresponding to the user network connection graph; Obtaining a user social feature vector corresponding to the first evaluation text under the involved graph relationship from the user network connection graph using the graph feature extraction layer of the false recognition model; Using the text feature extraction layer of the false recognition model to perform text representation on the first evaluation text to obtain a first text vector; Using the image feature extraction layer of the false recognition model to extract image information from the evaluation image to obtain an image feature vector; Using the text generation layer of the false recognition model to perform text description on the evaluation image to obtain a description image text corresponding to the evaluation image; Using the text feature extraction layer of the false recognition model to perform text representation on the description image text to obtain a second text vector; Using the feature fusion layer of the false recognition model, the first text vector, the second text vector, the image feature vector and the user social feature vector are subjected to feature fusion to obtain a plurality of initial fused feature vectors; Using the feature concatenation layer of the false recognition model, concatenating the user social feature vector, the first text vector, the image feature vector, the second text vector, and a plurality of the initial fused feature vectors to obtain a target fused feature vector; Using the information classification layer of the false recognition model to perform data classification on the target fusion feature vector to obtain the classification category corresponding to the first evaluation text; The initial evaluation information is subjected to false detection according to the classification category to obtain the target evaluation information corresponding to the target store.

3. The method according to claim 2, characterized in that The graph feature extraction layer using the false recognition model obtains the user social feature vector corresponding to the first evaluation text under the graph relationship involved from the user network connection graph, including: Using the text processing network of the graph feature extraction layer to perform text representation on the first evaluation text to obtain a first node representation corresponding to the first evaluation text; Using the text processing network of the graph feature extraction layer to perform text representation on the second evaluation text to obtain a second node representation corresponding to the second evaluation text; Using the node initialization network of the graph feature extraction layer, vector mean processing is performed on the first evaluation text corresponding to the first evaluation user and the third evaluation text when the first evaluation user evaluates the evaluation text of other users to obtain a third node representation corresponding to the first evaluation user; Using the node initialization network of the graph feature extraction layer, vector mean processing is performed on the second evaluation text corresponding to the second evaluation user and the fourth evaluation text published by the second evaluation user on other stores to obtain a fourth node representation corresponding to the second evaluation user; Utilizing the node relationship network of the graph feature extraction layer to perform adjacent node calculation according to the graph relationship and the first node representation in combination with the second node representation, the third node representation and the fourth node representation, to obtain node similarity between any adjacent nodes in the graph relationship; Determine the node path corresponding to the first evaluation text according to the node similarity and a preset threshold value using the path determination network of the graph feature extraction layer; The user social feature vector corresponding to the first evaluation text is determined according to the node path using the feature concatenation network of the graph feature extraction layer.

4. The method according to claim 1, characterized in that: The determining, according to the first consumption information, the second consumption information and the target evaluation information, a target consumption model corresponding to the historical user in the target area includes: Determine the consumption category corresponding to the historical user and the consumption frequency corresponding to the consumption category according to the first consumption information and the second consumption information; Determine a first association matrix between the historical users and the consumption categories according to the consumption categories and the consumption frequencies; Determine a second association matrix between the historical users and the consumption categories according to the consumption categories, the consumption frequencies and the target evaluation information; Determine the corresponding similarities between the historical users according to the first association matrix and the second association matrix; The consumption score of the historical user corresponding to the consumption category is determined according to the similarity, and the target consumption model corresponding to the historical user is determined according to the similarity and the consumption score.

5. The method according to claim 4, characterized in that The historical users include at least a first user and a second user, and determining the corresponding similarities between the historical users according to the first association matrix and the second association matrix includes: Obtaining a first relevant consumption category corresponding to the first user and a first relevant consumption frequency corresponding to the first relevant consumption category from the first association matrix; Obtaining from the first association matrix a second relevant consumption category corresponding to the second user and a second relevant consumption frequency corresponding to the second relevant consumption category; Performing intersection processing on the first related consumption category and the second related consumption category to obtain target related consumption categories corresponding to the first user and the second user; Determine a first consumption average value corresponding to the first user according to the first relevant consumption frequency and determine a second consumption average value corresponding to the second user according to the second relevant consumption frequency; Obtaining a third relevant consumption frequency corresponding to the first user under the target relevant consumption category from the first relevant consumption frequency and obtaining a fourth relevant consumption frequency corresponding to the second user under the target relevant consumption category from the second relevant consumption frequency; Determine first correlation values ​​corresponding to the first user and the second user by fusing the first consumption average value, the second consumption average value, the third relevant consumption frequency, and the fourth relevant consumption frequency; Obtaining a first consumption preference corresponding to the first user and a first consumption access value corresponding to the first consumption preference from the second association matrix; Obtaining, from the second association matrix, a second consumption preference corresponding to the second user and a second consumption access value corresponding to the second consumption preference; Performing intersection processing on the first consumption preference and the second consumption preference to obtain target consumption preferences corresponding to the first user and the second user; Determine a first evaluation score corresponding to the target consumption preference of the first user according to a first valid evaluation corresponding to the first user, and determine a first attenuation factor corresponding to the target consumption preference of the first user according to the first evaluation score; Determining a second evaluation score corresponding to the target consumption preference of the second user according to the second valid evaluation corresponding to the second user, and determining a second attenuation factor corresponding to the target consumption preference of the second user according to the second evaluation score; Determine a first access average value corresponding to the first user according to the first consumption access value and determine a second access average value corresponding to the second user according to the second consumption access value; Obtaining a third consumption access value corresponding to the first user under the target consumption preference from the first consumption access value and obtaining a fourth consumption access value corresponding to the second user under the target consumption preference from the second consumption access value; Determine second correlation values ​​corresponding to the first user and the second user by fusing the first access average value, the second access average value, the third consumption access value, and the fourth consumption access value using the first attenuation factor and the second attenuation factor; The first correlation value and the second correlation value are combined to determine the corresponding similarity between the first user and the second user.

6. The method according to claim 4, characterized in that Determining the consumption score corresponding to the consumption category by the historical user according to the similarity includes: Determine the related user corresponding to the historical user according to the similarity, and obtain the first related information corresponding to the historical user and the second related information corresponding to the related user from the target evaluation information; Using the scoring prediction model to score the first relevant information to obtain a first scoring value corresponding to the first relevant information, and using the scoring prediction model to score the second relevant information to obtain a second scoring value corresponding to the second relevant information; Performing mean calculation according to the first scoring value to obtain a first mean value corresponding to the historical user and performing mean calculation according to the second scoring value to obtain a second mean value corresponding to the related user; Determine the consumption category, and obtain a third scoring value corresponding to the consumption category by the relevant user from the second scoring value; Determine the consumption score corresponding to the consumption category by the historical user according to the similarity, the third score value, the first mean value, and the second mean value; The consumption score is obtained according to the following formula: ; in, represents the consumption score corresponding to the i-th consumption category by the u-th historical user, represents the first mean value corresponding to the u-th historical user, n represents the number of users corresponding to the relevant user, represents the similarity between the u-th historical user and the j-th related user, represents the third rating value corresponding to the i-th consumption category by the j-th related user, represents the second mean corresponding to the j-th related user.

7. The method according to claim 1, characterized in that The determining the target consumption route corresponding to the target user by using the target consumption model according to the third consumption information and the fourth consumption information includes: Obtaining associated users corresponding to the target user from the historical users according to the third consumption information and the fourth consumption information combined with the first consumption information and the second consumption information; Determining, according to the associated user, from the target consumption model, an initial consumption category corresponding to the target user and an initial consumption score corresponding to the initial consumption category; Obtaining the current time, and adjusting the initial consumption score corresponding to the initial consumption category according to the current time to obtain a target consumption score corresponding to the initial consumption category; Adjusting the initial consumption category according to the target consumption score to obtain a target consumption category; The target consumption route corresponding to the target user is determined according to the target consumption category.

8. The method according to claim 7, characterized in that The adjusting the initial consumption score corresponding to the initial consumption category according to the current time to obtain the target consumption score corresponding to the initial consumption category includes: Obtaining a first time corresponding to the third consumption information and a second time corresponding to the fourth consumption information; Calculate the consumption similarity value between the target user and the associated user at the first time and the second time; Obtaining a current consumption category corresponding to the associated user at the current time from the initial consumption category; The relevant consumption score corresponding to the current consumption category is obtained from the initial consumption score, and the relevant consumption score is adjusted according to the consumption similarity value to obtain the target consumption score corresponding to the relevant consumption score.

9. A gas station user information and local service linkage device, characterized in that: include: A data acquisition module, used to obtain first consumption information corresponding to a historical user at a target gas station in a target area and second consumption information corresponding to the historical user at a target store in the target area, and to obtain initial evaluation information corresponding to the target store; A false detection module, used to perform false detection on the initial evaluation information to obtain target evaluation information corresponding to the target store; A model determination module, configured to determine a target consumption model corresponding to the historical user in the target area according to the first consumption information, the second consumption information and the target evaluation information; A data collection module, used to obtain third consumption information corresponding to the target user at the target gas station and fourth consumption information corresponding to the relevant stores in the target area; A route determination module is used to determine a target consumption route corresponding to the target user using the target consumption model according to the third consumption information and the fourth consumption information.

10. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer program and implement the gas station user information and local service linkage method according to any one of claims 1 to 8 when executing the computer program.

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