A method, device and terminal device for real-time display of gas station information

Abstract

The embodiments of the present invention provide a method, apparatus, and terminal device for real-time display of gas station information, belonging to the field of data processing technology. The method includes: clustering historical evaluation information and target information to obtain clustering results, and determining a first recommendation parameter based on the clustering results; obtaining relevant information of surrounding objects, and determining a second recommendation parameter based on the relevant information and target information; obtaining historical vehicle flow of associated road sections between the target object and surrounding objects based on sensors; obtaining the association relationship between associated road sections based on the historical vehicle flow; determining a flow prediction model based on the historical vehicle flow, historical user flow, and the association relationship between associated road sections in combination with the first recommendation parameter and the second recommendation parameter; performing user flow prediction on the target object based on the flow prediction model to obtain a target prediction result; determining a target state of the target object based on the target prediction result, and performing real-time display based on the target state to obtain a target display result.

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 real-time display of gas station information. Background Art

[0002] In gas station operations, real-time large-screen displays have become a common and important means of presenting information. However, most gas stations currently use these displays for limited functionality, simply listing and displaying existing or directly accessible data. This limitation to direct data display prevents the real-time large-screen displays from fully realizing their potential. Existing real-time display methods for gas stations fail to provide managers with more in-depth and forward-looking decision-making, hindering improvements in operational efficiency and service quality. Summary of the Invention

[0003] The main purpose of the embodiments of the present invention is to provide a method, device and terminal device for real-time display of gas station information, aiming to solve the problem that the real-time display methods in related technologies cannot provide gas station managers with more in-depth and forward-looking decision-making basis, which is not conducive to improving the operational efficiency and service quality of gas stations.

[0004] In a first aspect, an embodiment of the present invention provides a method for displaying gas station information in real time, comprising:

[0005] Obtain the historical user traffic and historical evaluation information of the target object, and obtain the target information corresponding to the target object on social media;

[0006] Clustering the historical evaluation information and the target information to obtain a clustering result, and determining a first recommendation parameter based on the clustering result;

[0007] Obtaining relevant information corresponding to surrounding objects of the target object, and determining a second recommendation parameter based on the relevant information and the target information;

[0008] Determining an associated road section according to the target object and the surrounding objects, and obtaining a historical vehicle flow of the associated road section according to a sensor;

[0009] Obtaining corresponding association relationships between the associated road sections according to the historical vehicle flow;

[0010] Determine a traffic prediction model based on the historical vehicle traffic, the historical user traffic, and the corresponding association relationship between the associated road sections in combination with the first recommendation parameter and the second recommendation parameter;

[0011] Performing user traffic prediction on the target object according to the traffic prediction model to obtain a target prediction result;

[0012] The target state of the target object is determined according to the target prediction result, and a target display result is obtained by performing real-time display according to the target state.

[0013] In a second aspect, an embodiment of the present invention provides a device for displaying gas station information in real time, comprising:

[0014] The data acquisition module is used to obtain the historical user traffic and historical evaluation information of the target object, and obtain the target information corresponding to the target object on social media;

[0015] a data clustering module, configured to cluster the historical evaluation information and the target information to obtain a clustering result, and determine a first recommendation parameter based on the clustering result;

[0016] a parameter determination module, configured to obtain relevant information corresponding to surrounding objects of the target object, and determine a second recommended parameter based on the relevant information and the target information;

[0017] a traffic flow determination module, configured to determine an associated road section according to the target object and the surrounding objects, and obtain a historical vehicle flow of the associated road section according to a sensor;

[0018] a relationship determination module, configured to obtain corresponding association relationships between the associated road sections based on the historical vehicle flow;

[0019] a model determination module, configured to determine a traffic prediction model based on the historical vehicle traffic, the historical user traffic, and the corresponding association relationship between the associated road sections in combination with the first recommended parameter and the second recommended parameter;

[0020] A data prediction module, configured to perform user traffic prediction on the target object according to the traffic prediction model to obtain a target prediction result;

[0021] The data display module is used to determine the target state of the target object according to the target prediction result, and to display the target state in real time to obtain a target display result.

[0022] In a third aspect, an embodiment of the present invention further provides a terminal device, comprising a processor, a memory, a computer program stored on 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 real-time display of gas station information provided in the specification of the present invention are implemented.

[0023] An embodiment of the present invention provides a method, apparatus, and terminal device for real-time display of gas station information, the method comprising: obtaining historical user traffic and historical evaluation information of a target object, and obtaining target information corresponding to the target object under social media, thereby being able to understand the internal operating conditions of the target object and its external environment, thereby providing support for subsequent provision of more accurate prediction results, clustering the historical evaluation information and target information to obtain a clustering result, and determining a first recommendation parameter based on the clustering result; thereby obtaining relevant information corresponding to surrounding objects of the target object, and determining a second recommendation parameter based on the relevant information and the target information, thereby identifying the potential impact of external factors on the traffic of the target object; thereby determining associated road sections based on the target object and the surrounding objects, and obtaining the historical vehicle traffic of the associated road sections based on sensors, and obtaining the corresponding correlation relationship between the associated road sections based on the historical vehicle traffic; thereby determining a traffic prediction model based on the historical vehicle traffic, historical user traffic, and the corresponding correlation relationship between the associated road sections in combination with the first recommendation parameter and the second recommendation parameter, so that the model fully considers multiple factors and can more accurately simulate the user traffic changes of the target object. By learning and training from a large amount of historical data, the model can capture the complex relationships between various factors, thereby providing users with accurate prediction results. The model then predicts user traffic for the target object based on the traffic prediction model, obtaining a target prediction result. Finally, the target state of the target object is determined based on the target prediction result, and a real-time display based on the target state is generated to obtain a target display result. This allows gas station managers to proactively allocate resources based on the target display result, improving the station's operational efficiency. At the same time, this can better meet user needs, enhance service quality, and increase user satisfaction. This also addresses the problem of related real-time display methods, which fail to provide gas station managers with a more in-depth and forward-looking decision-making basis, hindering the improvement of gas station operational efficiency and service quality. This approach improves the operational efficiency and service quality of target gas stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. 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 any creative work.

[0025] Figure 1 A flowchart of a method for real-time display of gas station information provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of the module structure of a real-time display device for gas station information provided by an embodiment of the present invention;

[0027] Figure 3A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0029] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0030] It should be understood that the terms used in this specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0031] Embodiments of the present invention provide a method, apparatus, and terminal device for displaying gas station information in real time. The method can be applied to a terminal device, which can be an electronic device such as a tablet computer, laptop computer, desktop computer, personal digital assistant, or wearable device. The terminal device can also be a server or a server cluster.

[0032] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0033] Please refer to Figure 1 , Figure 1 A flowchart of a method for real-time display of gas station information provided by an embodiment of the present invention.

[0034] like Figure 1 As shown, the method for real-time display of gas station information includes steps S101 to S108.

[0035] Step S101: Obtain historical user traffic and historical evaluation information of a target object, and obtain target information corresponding to the target object on social media.

[0036] For example, the target object is a gas station to be analyzed. First, the user flow data corresponding to the target object is collected from the management system corresponding to the target object and stored in the corresponding database. Then, the historical user flow of the target object is obtained according to the database query statement, where the historical user flow can refer to the number of vehicles entering the target object per day in the historical time period, and the historical evaluation information of users on the target object is extracted from the customer feedback platform.

[0037] For example, keywords related to gas stations are used to search on public social media such as Tik Tok, Weibo, or local life service platforms or community forums, such as the name, address, brand, etc. of the gas station, to obtain target information corresponding to the evaluation of the target object by users on social media.

[0038] In some embodiments, obtaining target information corresponding to a target object on social media includes: obtaining a first position corresponding to the target object, and collecting first information corresponding to the target object on social media based on the first position, the first information being used to characterize user-published information corresponding to the social media and the first position, the first information including first text information and first image information; performing target association from the first text information to obtain relevant text information associated with the target object, and performing target classification on the first image information to obtain an image description object corresponding to the first image information; obtaining relevant image information associated with the target object from the first image information based on the image description object; performing position prediction on the first information based on the relevant text information and the relevant image information to obtain relevant position information corresponding to the first information; and filtering the first information based on the relevant position information and the first position to obtain the target information corresponding to the target object.

[0039] Exemplarily, the longitude and latitude information corresponding to the target object is directly obtained as the first position through a geographic positioning system or map data.

[0040] Exemplarily, data collection is performed on social media platforms based on the first location and the target object using open interfaces or data scraping tools on social media (under the premise of legality and compliance). For example, for social media with location tagging capabilities, the search range is set to a certain radius centered on the first location, and the search is conducted for first information related to the target object, where the first information represents information posted by a user corresponding to the first location on the social media platform, and the first information includes first text information and first image information.

[0041] For example, methods such as keyword matching and semantic understanding are used to extract content related to the target object from the first text information to obtain relevant text information associated with the target object. Computer vision technology is then used to classify the first image information. Using a trained image classification model, elements such as the subject, scene, and object in the image are identified to determine the object depicted in the image.

[0042] Exemplarily, the first image information is further filtered according to the image description object. If the image description object is associated with the target object, the image in the image description object that is associated with the target object is used as the relevant image information associated with the target object.

[0043] For example, location prediction is performed by combining relevant text information and relevant image information. For relevant text information, the location name, direction description, distance information, etc. mentioned therein are analyzed. For relevant image information, the location can be inferred based on geographic features in the image (such as landmarks, road signs, etc.) or image shooting parameters (such as GPS tags). Using geographic information system technology, this information is combined to perform location prediction on the first information, thereby obtaining relevant location information corresponding to the first information.

[0044] For example, the first information is filtered based on the relevant location information and the first location. If the relevant location information and the first location are within a certain error range (the error range can be set according to actual needs), the information is considered to be closely related to the target object and is used as the target information corresponding to the target object.

[0045] Specifically, by processing and filtering the first information, it is possible to accurately obtain information related to the target object from massive amounts of social media data. This avoids interference from irrelevant information and improves the quality and relevance of the information. Location prediction and filtering ensures that the obtained target information is relevant to the target object's location.

[0046] In some embodiments, the performing position prediction on the first information based on the relevant text information and the relevant image information to obtain the relevant position information corresponding to the first information includes: using the image coding layer of the position prediction model to perform image feature extraction on the relevant image information to obtain a first feature vector; using the text coding layer of the position prediction model to perform text feature extraction on the relevant text information to obtain a second feature vector; using the image-text matching layer of the position prediction model to determine the information matching value between the relevant image information and the relevant text information based on the first feature vector and the second feature vector; using the image position determination layer of the position prediction model to perform position sign extraction on the relevant image information to obtain the image position information corresponding to the relevant image information; using the text position determination layer of the position prediction model to perform position sign extraction on the relevant text information to obtain the text position information corresponding to the relevant text information; using the feature fusion layer of the position prediction model to fuse the image position information and the text position information according to the information matching value to obtain a target fusion feature; using the position classification layer of the position prediction model to perform position prediction on the first information using the target fusion feature to obtain the relevant position information corresponding to the first information.

[0047] For example, a location prediction model is constructed, comprising an image encoding layer, a text encoding layer, an image-text matching layer, an image location determination layer, a text location determination layer, a feature fusion layer, and a location classification layer. The image encoding layer can utilize a pre-trained convolutional neural network (CNN), such as ResNet or VGG. Relevant image information is input into the image encoding layer, where these pre-trained CNNs perform multi-scale feature extraction on the image to obtain a first feature vector, which represents a feature representation of the relevant image information.

[0048] For example, the text encoding layer can use pre-trained language models such as BERT and GPT. When the relevant text information is input into the text encoding layer, the language model performs semantic understanding and feature extraction on the text. These models consider information such as vocabulary, grammar, and context, converting the text into a vector representation, the second feature vector. This second feature vector can reflect the semantic characteristics of the relevant text information.

[0049] For example, the image-text matching layer receives the first and second feature vectors and uses metrics such as cosine similarity and Euclidean distance to calculate the similarity between the two vectors. The similarity value serves as the information matching value between the relevant image information and the relevant text information. For example, a cosine similarity closer to 1 indicates a higher correlation between the image and text; a cosine similarity closer to 0 indicates a lower correlation.

[0050] For example, the image location determination layer further analyzes the relevant image information, extracting location features from the image using geographic features such as landmarks and road signs, as well as image capture parameters (e.g., GPS tags). By analyzing and processing these location features, the image location information corresponding to the relevant image information is determined, such as latitude and longitude coordinates or a specific address description.

[0051] For example, the text location determination layer performs natural language processing on the relevant text information. By identifying location-related words and phrases in the text, such as place names, directions, and distance information, the text location information corresponding to the relevant text information can be extracted. For example, if the text mentions "next to the library in the city center," this location information can be extracted.

[0052] For example, the feature fusion layer fuses the image position information and the text position information based on the information matching value calculated previously. If the information matching value is high, it indicates a strong correlation between the image and text, and a higher weight will be given to both during fusion. If the information matching value is low, it indicates a weak correlation, and the weight may be adjusted based on the specific situation to obtain the target fusion feature.

[0053] Exemplarily, the location classification layer receives the target fusion features and uses them to predict the location of the first information. Classification algorithms such as decision trees and support vector machines, or deep learning models such as fully connected neural networks, can be used. Based on the patterns and regularities learned from the target fusion features, the location of the first information is predicted, ultimately outputting relevant location information corresponding to the first information, such as specific geographic coordinates or an address. The relevant location information can also be a determination of whether the information is located near a target gas station. Therefore, if the relevant location information describes a location near a target gas station, the published information corresponding to the relevant location information can be determined as a review of the target gas station.

[0054] Specifically, by combining image and text information, we can analyze and judge locations from different perspectives. Images can provide intuitive geographic features, while text can supplement detailed location descriptions. The combination of the two can provide a more comprehensive understanding of the location characteristics of the information, thereby improving the accuracy of location prediction.

[0055] Step S102: cluster the historical evaluation information and the target information to obtain a clustering result, and determine a first recommendation parameter according to the clustering result.

[0056] For example, the historical evaluation information is the evaluation content collected by users on the customer feedback platform corresponding to the target object, and the target information is the content of the user's evaluation of the target object on social media, so that the historical evaluation information and the target information are clustered according to clustering algorithms such as DBSCAN and K-means to obtain clustering results.

[0057] For example, before performing data analysis on each cluster in the clustering results, we must first ensure that the data within the cluster is in a state suitable for analysis, and then perform preprocessing operations on the text data in the cluster, such as removing stop words, performing stem extraction and part-of-speech tagging, and then use the word frequency inverse document frequency method to find the words that appear frequently in the cluster and the words with important weights, and then determine the theme of the cluster based on the words that appear frequently and the words with important weights. For example, if words such as "oil price discounts" and "gas card activities" appear frequently in a certain cluster, then it can be determined that the theme of the cluster is related to the promotional activities of the gas station; if words such as "good service attitude" and "clean environment" appear frequently, then the theme of the cluster may be related to the service and environment of the gas station.

[0058] For example, after clarifying the themes of each cluster, it is necessary to analyze the relationship between each theme and the user's choice of gas station. Different themes may have different degrees of influence on the user's choice of gas station. For example, for price-sensitive users, the cluster with the theme of "discounted oil prices" may have a greater impact on their choice; while for users who focus on service experience, the cluster with the theme of "good service attitude" is more important. In addition to the theme of the cluster, the amount of data in the cluster is also an important consideration. The more data in the cluster, the more common the characteristics represented by the cluster are in the overall data, and the greater the impact on the user's choice. Each cluster can be assigned a corresponding weight based on the amount of data it has. Clusters with more data have higher weights, and clusters with less data have lower weights.

[0059] For example, the first recommendation parameter corresponding to the target object is determined by comprehensively considering the themes and corresponding weights of each cluster. A mathematical model or rule can be established to quantify the cluster's theme information and weights, resulting in a comprehensive numerical value that serves as the first recommendation parameter. For example, a weighted summation method can be used to multiply the degree of influence of each cluster's theme on the user's choice by the cluster's weight, and then sum the results for all clusters to obtain the final first recommendation parameter. The first recommendation parameter can be used to characterize the likelihood that a user will select the target object among a number of gas stations. The higher the parameter value, the more closely the target object's performance in various aspects meets the user's needs and preferences, and the greater the likelihood that the user will choose that gas station. Furthermore, the first recommendation parameter can also be used to characterize the target object's appeal to users. It integrates the information reflected by each cluster and can comprehensively reflect the target object's strengths and characteristics. A gas station with a higher first recommendation parameter is more attractive in the market and can attract more users to refuel.

[0060] In some embodiments, clustering the historical evaluation information and the target information to obtain a clustering result includes: text merging the historical evaluation information and the target information to obtain a text set, and performing word segmentation processing on each sub-text information in the text set to obtain the associated words corresponding to the sub-text information; determining the first text similarity corresponding to any two of the sub-text information based on the associated words; determining the target dimension, and obtaining the word representation vector corresponding to the associated words, and determining the word dimension association value corresponding to the associated words under the target dimension based on the word representation vector; determining the weight information corresponding to the sub-text information under the target dimension based on the word dimension association value; determining the text dimension association value corresponding to the sub-text information when it belongs to the target dimension based on the word dimension association value and the weight information; determining the second text similarity corresponding to any two of the sub-text information based on the text dimension association value; clustering the text set based on the first text similarity and the second text similarity to obtain the clustering result.

[0061] For example, the historical evaluation information and target information are integrated into a unified text set. This step can centralize the scattered information and more comprehensively reflect various information related to the gas station.

[0062] For example, a word segmentation operation is performed on each sub-text information in the text collection. Word segmentation is to split the continuous text into meaningful words, so that the text information can be converted into basic units that are easier to analyze and thus obtain the associated words corresponding to each sub-text information.

[0063] Exemplarily, the associated words corresponding to any two sub-text information in the text set are obtained, and then the proportion of the number of identical associated words in the two sub-texts to the total number of associated words is counted based on the associated words corresponding to the two sub-text information. The higher the proportion, the higher the similarity, thereby obtaining the first text similarity corresponding to the sub-text information.

[0064] For example, the target dimension, i.e., the entire data semantic space in which all sub-text information in the text collection is distributed, is determined. The target dimension can be a single semantic dimension or multiple semantic dimensions. A pre-trained word vector model, such as Word2Vec or GloVe, is then used to convert the associated words into vector form, i.e., word representation vectors. Vector decomposition is then performed based on the word representation vectors to map the associated words to the target dimension, thereby obtaining the dimensional representation vectors corresponding to the associated words in the target dimension. Furthermore, the word dimension association values ​​corresponding to the associated words in the target dimension are determined based on the dimensional representation vectors.

[0065] For example, the dimensional representation vector corresponding to each associated word under each target dimension is obtained, and then the dimensional semantic center calculation is performed on all dimensional representation vectors under the target dimension to obtain the corresponding dimensional center vector under the target dimension, and then the distance information between the dimensional representation vector and the dimensional center vector is calculated, and then the word dimension association value corresponding to the associated word under the target dimension is determined based on the distance information.

[0066] For example, a target threshold is determined, and then the word dimension association values ​​corresponding to the associated words in the target dimension are compared with the target threshold one by one. The word dimension association value reflects the closeness of the association between the associated words and the target dimension. When the word dimension association value of a associated word is greater than the target threshold, it indicates that the associated word has a strong association with the target dimension, and thus the associated word can be determined to be a related word of the sub-text information in the target dimension.

[0067] For example, after determining the relevant words for the subtext information in the target dimension, the weight information corresponding to the subtext information in the target dimension is further determined based on the number of these relevant words. The number of relevant words reflects, to a certain extent, the strength of the association between the subtext information and the target dimension. Generally speaking, the more relevant words there are, the more obvious the characteristics of the subtext information in the target dimension, and the higher the corresponding weight.

[0068] For example, the text dimension relevance value of the sub-text information when it belongs to the target dimension is calculated by weighted summation based on the weight information and the word dimension relevance value. Specifically, the word dimension relevance value of each related word is multiplied by its corresponding weight, and then all the results are added together. The resulting sum is the text dimension relevance value of the sub-text information in the target dimension. This value comprehensively considers the degree of relevance of the related words and the importance of the sub-text information in the target dimension, and can more comprehensively and accurately reflect the relevance of the sub-text information to the target dimension.

[0069] For example, once the text dimension association value corresponding to each sub-text information under each target dimension is obtained, the text dimension association values ​​under all target dimensions can be further summed. By adding the text dimension association values ​​of each sub-text information under each target dimension, the text information representation value corresponding to the sub-text information can be obtained. This representation value is a comprehensive reflection of the sub-text information under multiple target dimensions, and it can more comprehensively reflect the characteristics and connotations of the sub-text information.

[0070] For example, on this basis, the difference in the text information representation values ​​between any two sub-text information is calculated. This difference is the second text similarity between any two sub-text information. Unlike traditional text similarity calculation methods, this similarity fully considers the degree of association between the texts under the target dimension. It not only focuses on the superficial word overlap of the text, but also deeply explores the inherent connections between the texts in different target dimensions. Therefore, the second text similarity can more deeply and accurately reflect the similarity between texts, providing a more reliable basis for subsequent tasks such as text clustering, information retrieval, and data analysis.

[0071] Exemplarily, a method such as weighted averaging is used to combine the first text similarity and the second text similarity to obtain a comprehensive similarity index, and then a clustering algorithm such as hierarchical clustering is used based on the comprehensive similarity index to divide the text collection into different clusters. The texts in each cluster have a higher similarity, thereby obtaining a clustering result.

[0072] Specifically, the clustering process calculates both primary and secondary text similarities, combining them to consider multiple aspects of the text. Primary text similarity measures text similarity based on word overlap, while secondary text similarity incorporates information from the target dimension, further exploring the connections between texts along that specific dimension. This multi-dimensional approach makes clustering results more accurate and comprehensive, better reflecting the true relationships between texts.

[0073] In some embodiments, determining the first text similarity between any two of the subtext information based on the associated words includes: obtaining any two of the subtext information as a first subtext and a second subtext, and obtaining a first word corresponding to the first subtext and a second word corresponding to the second subtext; determining a first part of speech and a first word position corresponding to the first word in the first subtext, and determining a first weight corresponding to the first word based on the first part of speech and the first word position; determining a second part of speech and a second word position corresponding to the second word in the second subtext, and determining a second weight corresponding to the second word based on the second part of speech and the second word position; determining a first key degree corresponding to the first word based on a first frequency corresponding to the first word in the text set; determining a second key degree corresponding to the second word based on a second frequency corresponding to the second word in the text set; performing text similarity calculation based on the first word and the second word to obtain word similarity, and fusing the word similarity based on the first weight, the second weight, the first key degree, and the second key degree to obtain a first similarity between the first subtext and the second subtext; performing data adjustment on the first similarity based on the word similarity to obtain the first text similarity between the first subtext and the second subtext; wherein the first similarity is obtained according to the following formula:

[0074]

[0075] in, represents the first similarity between the i-th first subtext and the j-th second subtext, represents the number of the first words corresponding to the i-th first subtext; represents the number of the second words corresponding to the j-th second subtext; represents the word similarity between the tth first word in the i-th first subtext and the hth second word in the j-th second subtext, represents the first key degree corresponding to the t-th first word in the i-th first subtext, represents the first weight corresponding to the t-th first word in the i-th first subtext, represents the second key degree corresponding to the hth second word in the jth second subtext, represents the second weight corresponding to the hth second word in the jth second subtext, represents the similarity between the mth first word and the nth first word in the i-th first subtext, represents the first key degree corresponding to the mth first word in the i-th first subtext, represents the first weight corresponding to the mth first word in the i-th first subtext, represents the first key degree corresponding to the nth first word in the i-th first subtext, represents the first weight corresponding to the nth first word in the i-th first subtext, represents the similarity between the kth second word and the qth second word in the jth second subtext, represents the second key degree corresponding to the kth second word in the jth second subtext, represents the second weight corresponding to the kth second word of the jth second subtext, The second key degree corresponding to the qth second word in the jth second subtext, The second weight corresponding to the qth second word in the jth second subtext is represented.

[0076] For example, any two subtexts are selected from the text set as the first subtext and the second subtext, respectively. Then, word segmentation is performed on the two subtexts to obtain a first word corresponding to the first subtext and a second word corresponding to the second subtext.

[0077] For example, for a first word, its part of speech (e.g., noun, verb, adjective, etc.) and position in the first subtext are determined. Different parts of speech have varying importance in textual expression. For example, nouns are typically the core objects of textual description and are relatively more important, while some function words may be less important. The position of a word also affects its weight. For example, words at the beginning or end of a sentence may receive more attention and receive a correspondingly higher weight. Taking into account the first part of speech and the first word position, a corresponding first weight is determined for the first word. The same method is applied to the second word in the second subtext. The second part of speech and second word position of the second word are determined, and then, based on this information, a corresponding second weight is determined for the second word.

[0078] For example, the frequency of occurrence of a first word in the entire text collection is counted, i.e., the first frequency. Words with a high frequency of occurrence may be general vocabulary with a relatively low criticality; words with a low frequency of occurrence but that play a key role in a specific subtext have a higher criticality. Based on the first frequency, the first criticality corresponding to the first word is determined. Similarly, the second frequency of the second word in the text collection is counted, and the second criticality corresponding to the second word is determined accordingly.

[0079] Exemplarily, a pre-trained word vector model is used to perform word vector representation on the first word and the second word to obtain a first vector corresponding to the first word and a second vector corresponding to the second word, and then the semantic similarity between the first word and the second word is determined using the cosine formula based on the first vector and the second vector.

[0080] Exemplarily, the first similarity between the first subtext and the second subtext is obtained by fusing the first weight, the second weight, the first key degree, the second key degree, and the word similarity according to the following formula:

[0081]

[0082] in, represents the first similarity between the i-th first subtext and the j-th second subtext, represents the number of first words corresponding to the i-th first subtext; represents the number of second words corresponding to the j-th second subtext; represents the word similarity between the tth first word of the i-th first subtext and the hth second word of the j-th second subtext, Indicates the first key degree corresponding to the t-th first word of the i-th first subtext, represents the first weight corresponding to the tth first word of the i-th first subtext, represents the second key degree corresponding to the hth second word of the jth second subtext, represents the second weight corresponding to the hth second word of the jth second subtext, represents the similarity between the mth first word and the nth first word of the i-th first subtext, Indicates the first key degree corresponding to the mth first word of the i-th first subtext, represents the first weight corresponding to the mth first word of the i-th first subtext, Indicates the first key degree corresponding to the nth first word of the i-th first subtext, represents the first weight corresponding to the nth first word of the i-th first subtext, represents the similarity between the kth second word and the qth second word of the jth second subtext, represents the second key degree corresponding to the kth second word of the jth second subtext, represents the second weight corresponding to the kth second word of the jth second subtext, The second key degree corresponding to the qth second word of the jth second subtext, Indicates the second weight corresponding to the qth second word of the jth second subtext.

[0083] Exemplarily, according to the above formula, the first weight, the second weight, the first key degree and the second key degree are integrated with the word similarity to obtain the first similarity between the first sub-text and the second sub-text. This introduces the key degree and weight information of the words, which can more accurately reflect the importance of different words in the text, and thus can more accurately determine the first similarity between the first sub-text and the second sub-text.

[0084] For example, the first similarity is data-adjusted based on the word similarity. The adjustment method can be determined based on specific needs and data characteristics, such as through linear transformation, nonlinear mapping, etc., so that the final first text similarity can more accurately reflect the similarity between the two subtexts.

[0085] Specifically, while traditional text similarity calculations may only consider superficial word matching or simple semantic similarity, this method comprehensively considers multiple factors, including a word's part of speech, position, and criticality. These factors more comprehensively reflect the importance and role of a word in a text, allowing the calculated similarity to more accurately reflect the true similarity between two subtexts. By considering a word's part of speech and position, the structural and semantic characteristics of the text can be better captured. Words of different parts of speech perform different functions in a text, and a word's position also affects its focus. Furthermore, a word's criticality takes into account its rarity and importance within the entire text collection. By combining these factors, a deeper understanding of the text's connotations can be achieved, improving the ability to capture textual features.

[0086] In some embodiments, the data adjustment of the first similarity according to the word similarity to obtain the first text similarity corresponding to the first subtext and the second subtext includes: determining a first associated word corresponding to the first word in the second word according to the word similarity, and obtaining a third associated word corresponding to the first associated word from the first word; determining a second associated word corresponding to the second word in the first word according to the word similarity, and obtaining a fourth associated word corresponding to the second associated word from the second word; determining a word overlap degree corresponding to the first subtext and the second subtext according to the word similarity; obtaining a first distribution position corresponding to the first associated word from the second subtext, and obtaining the third associated word from the first subtext. a corresponding second distribution position; determining the first word order information corresponding to the first subtext and the second subtext based on the first distribution position and the second distribution position; obtaining the third distribution position corresponding to the second associated word from the first subtext, and obtaining the fourth distribution position corresponding to the fourth associated word from the second subtext; determining the second word order information between the first subtext and the second subtext based on the third distribution position and the fourth distribution position; determining the similarity adjustment parameter between the first subtext and the second subtext based on the first word order information and the second word order information; adjusting the first similarity value based on the similarity adjustment parameter to obtain the first text similarity corresponding to the first subtext and the second subtext; wherein the similarity adjustment parameter is obtained according to the following formula:

[0087]

[0088]

[0089] in, represents the similarity adjustment parameter corresponding to the i-th first subtext and the j-th second subtext, represents the number of similar related words between the i-th first subtext and the j-th second subtext, represents the number of the first words corresponding to the i-th first subtext, represents a constant, represents the number of the second words corresponding to the j-th second subtext, represents the first word order information corresponding to the i-th first subtext and the j-th second subtext, Indicates the second word order information corresponding to the j-th second subtext and the i-th first subtext.

[0090] For example, using the previously calculated word similarity, words similar to the first word are found in the second word. These similar second words are defined as the first associated words corresponding to the first word, and the words in the first word corresponding to the first associated words are determined as third associated words. For example, the first words include "good service" and "average quality", and the second words include "good service" and "not recommended". Based on the word similarity, it is determined that "good service" is similar to "good service". In this case, "good service" is the first associated word, and "good service" is the corresponding third associated word.

[0091] For example, similarly, based on word similarity, a word similar to the second word is found in the first word as the second associated word; then a word corresponding to the second associated word is found in the second word as the fourth associated word.

[0092] For example, the number of identical or similar words in the first subtext and the second subtext is counted based on the word similarity, so as to determine the word overlap between the two subtexts.

[0093] For example, the occurrence position of the first associated word in the second subtext is determined as the first distribution position; the occurrence position of the third associated word in the first subtext is determined as the second distribution position. Similarly, the occurrence position of the second associated word in the first subtext is determined as the third distribution position; the occurrence position of the fourth associated word in the second subtext is determined as the fourth distribution position.

[0094] For example, the order relationship between the first and third associated words in their respective subtexts is analyzed based on the first and second distribution positions. To quantify this word order difference, it is necessary to determine the number of moves required for the first associated word in the second subtext to convert to the order of the third associated word in the first subtext. This can be achieved through a simulated move method. Specifically, starting from the position of the first associated word in the second subtext, we attempt to move it to the position of the third associated word in the first subtext. During the move, the number of moves is recorded for each word passed. The total number of moves required to convert the first associated word to the third associated word can be determined as the first word order information corresponding to the first and second subtexts. This first word order information is a quantitative indicator that intuitively reflects the degree of word order difference between the two subtexts in the portion related to the first and third associated words. A greater number of moves indicates a greater word order difference between the two subtexts in this portion; a fewer number of moves indicates a more similar word order. The first word order information determined in this way is of great significance in text similarity analysis. It provides us with a new perspective, allowing us to not only focus on the semantic similarity of words, but also to deeply explore the word order characteristics of the text. Considering word order information can more accurately judge the similarity between texts, thereby improving accuracy.

[0095] For example, the order relationship between the second and fourth associated terms in their respective subtexts is analyzed based on the third and fourth distribution positions. To quantify this difference in word order, it is necessary to determine the number of shifts required to convert the second associated term in the first subtext to the order of the fourth associated term in the second subtext. The total number of shifts required to convert the second associated term to the fourth associated term can then be determined as the corresponding second word order information between the second and first subtexts. This second word order information is a quantitative indicator that intuitively reflects the degree of word order difference between the two subtexts in the relevant parts of the second and fourth associated terms.

[0096] For example, the first word order information and the second word order information are combined, and factors such as the consistency and degree of difference of the word order are considered to determine the similarity adjustment parameter between the first subtext and the second subtext using the following formula:

[0097]

[0098]

[0099] in, represents the similarity adjustment parameter corresponding to the i-th first subtext and the j-th second subtext, Indicates the number of similar related words between the i-th first subtext and the j-th second subtext, represents the number of first words corresponding to the i-th first subtext, represents a constant, represents the number of second words corresponding to the j-th second subtext, represents the first word order information corresponding to the i-th first subtext and the j-th second subtext, Indicates the second word order information corresponding to the j-th second subtext and the i-th first subtext.

[0100] For example, the first word order information and the second word order information reflect the word order relationship of the associated words between the two sub-texts from different perspectives. By combining these two types of word order information, the word order characteristics of the text can be captured more comprehensively, and then combined with the word order information, the subtle differences in their expressions can be discovered. The similarity adjustment parameter takes word order information into consideration, making the assessment of text similarity more in line with actual conditions. Factors such as the number of similar associated words, the number of first words, and the number of second words are introduced into the formula. The number of similar associated words reflects the degree of semantic association between the two sub-texts, while the length of the text plays a regulatory role in this degree of association. For example, in a longer text, even if there are a certain number of similar associated words, it cannot be simply assumed that they are very similar, because it may be that only part of the content is similar. By combining these factors, the semantic similarity between the two sub-texts can be measured more accurately, avoiding misjudgments caused by the influence of text length or the number of associated words.

[0101] For example, the calculation of the similarity adjustment parameter takes into account the consistency and difference of word order. When the word order of two subtexts is relatively consistent, the similarity adjustment parameter will increase accordingly, indicating that their similarity in word order has a positive contribution to the overall similarity. Conversely, when the word order differs significantly, the similarity adjustment parameter will decrease, thereby reducing the impact of this word order difference on the overall similarity.

[0102] Exemplarily, the calculated similarity adjustment parameter is multiplied by the previously calculated first similarity to obtain the first text similarity between the first subtext and the second subtext.

[0103] Specifically, the method takes into account the correspondence between word order information and associated words, can adapt to such diverse text expressions, and improve compatibility with different text styles, thereby being able to more accurately obtain the corresponding first text similarity between the first subtext and the second subtext.

[0104] Step S103: Obtain relevant information corresponding to surrounding objects of the target object, and determine a second recommendation parameter based on the relevant information and the target information.

[0105] For example, the surrounding objects are gas stations around the target object, and then keywords related to the surrounding gas stations are searched from public social media such as Tik Tok, Weibo, or local life service platforms or community forums, such as the name, address, brand, etc. of the gas station, to obtain relevant information corresponding to the evaluation of the surrounding objects by users on social media.

[0106] For example, a clustering algorithm such as k-means clustering is used to perform text clustering on the relevant information and target information to obtain a target clustering result. Then, sentiment classification is performed on each subcluster within the target clustering result to obtain the sentiment type corresponding to each subcluster. Furthermore, each subtext within each subcluster is distinguished as being derived from relevant information or target information, thereby obtaining the amount of relevant information and the amount of target information in each subcluster, and further obtaining the proportion of target information within the subcluster. Based on the proportion of target information within each subcluster and the sentiment type corresponding to the subcluster, a target score calculation rule is defined. For example, if the sentiment type is positive and the proportion of target information is high, a higher score is assigned; if the sentiment type is negative and the proportion of target information is low, a higher score is assigned. The above scores are summed to obtain the target score corresponding to the target object. A mapping relationship is established between the target score and the second recommendation parameter. Based on the target score of the target object, the corresponding second recommendation parameter value is searched, thereby determining the second recommendation parameter corresponding to the target object when competing with surrounding objects. The second recommendation parameter is used to represent the user's preference parameter when selecting services from the target object and surrounding objects.

[0107] Step S104: determining an associated road section according to the target object and the surrounding objects, and obtaining a historical vehicle flow of the associated road section according to a sensor.

[0108] Exemplarily, accurate geographic location information of the target object and surrounding objects is obtained through a geographic information system (GIS), a global positioning system (GPS) or related map data, and then related road network data is obtained based on the geographic location information, and associated road sections are determined based on the road network data. The associated road sections are used to represent the corresponding roads when passing through the target object or surrounding objects.

[0109] For example, cameras installed on the road are used to count the number of vehicles using image recognition technology, or microwave sensors are used to detect vehicles by emitting microwave signals and receiving reflected signals to obtain historical vehicle flow on the associated road section.

[0110] Step S105: Obtain corresponding association relationships between the associated road sections according to the historical vehicle flow.

[0111] Exemplarily, the first road where the target object is located is obtained from the associated road section, and then the first data corresponding to the first road is obtained from the historical vehicle flow, and the second road other directly or indirectly connected to the first road is obtained from the associated road section, and the second data corresponding to the second road is obtained from the historical vehicle flow.

[0112] Exemplarily, a first change trend corresponding to the first data and a second change trend corresponding to the second data are calculated, and data alignment processing is performed according to the first change trend and the second change trend, so as to determine the correlation value between the first road and the second road according to the data alignment result, and then when the correlation value is greater than a preset value, it is determined that the correlation relationship between the first road and the second road is a strong correlation, and when the correlation value is less than or equal to the preset value, it is determined that the correlation relationship between the first road and the second road is a weak correlation.

[0113] In some embodiments, obtaining the corresponding association relationship between the associated road sections based on the historical vehicle flow includes: obtaining the direct road section corresponding to the target object from the associated road section, and obtaining other road sections except the direct road section from the associated road section; obtaining first flow data corresponding to the direct road section and second flow data corresponding to the other road sections from the historical vehicle flow; determining first time information corresponding to the first flow data and determining second time information corresponding to the second flow data; determining a data alignment result between the first flow data and the second flow data based on the first flow data, the second flow data combined with the first time information and the second time information; and determining the corresponding association relationship between the direct road section and the other road sections based on the data alignment result.

[0114] For example, the location of the target gas station is determined by finding the road section directly connected to the target gas station from the associated road sections based on the actual connection of the road, that is, the direct-connected road section. The direct-connected road section is used to represent the road where the target object is located. Among the associated road sections, the directly-connected road sections that have been determined are removed, and the remaining road sections are other road sections.

[0115] For example, the flow data corresponding to the directly connected road section is selected from the collected historical vehicle flow data and defined as the first flow data. Similarly, the flow data corresponding to other road sections is found from the historical vehicle flow data, i.e., the second flow data.

[0116] For example, the first traffic data is matched with corresponding time information, which can reflect the vehicle flow situation of the directly connected road section at different times. The second traffic data is matched with corresponding time information to understand the changes in vehicle flow of other road sections at different times.

[0117] Exemplarily, the first time information and the second time information are converted to a unified time scale to ensure that the two are comparable in the time dimension. According to the unified time scale, the first traffic data and the second traffic data are matched according to the dynamic time warping algorithm, and the traffic data at the same time point are correspondingly associated to obtain the data alignment result between the first traffic data and the second traffic data.

[0118] For example, statistical methods such as correlation analysis and regression analysis are used to calculate the correlation coefficient between the first and second traffic flow data based on the data alignment results, thereby determining the association between the directly connected road segment and the other road segment based on the correlation coefficient. A high correlation coefficient indicates a strong association between the two, potentially indicating that vehicles are moving more frequently between the directly connected road segment and the other road segment; conversely, a weak association indicates a low correlation.

[0119] For example, if the calculated correlation coefficient is high, close to 1 or -1, it indicates a strong correlation between the first and second traffic data. When the correlation coefficient is close to 1, it indicates a positive correlation between the directly connected road segment and the other road segment. That is, when vehicle flow on the other road segment increases, vehicle flow on the directly connected road segment is likely to increase as well. This may indicate that vehicles flow between the directly connected road segment and the other road segment is more frequent. Conversely, if the correlation coefficient is close to 0, the correlation between the two is weak. This may indicate that vehicle travel patterns on the directly connected road segment and the other road segment are relatively independent, with less vehicle flow between them.

[0120] In some embodiments, determining the corresponding association relationship between the directly connected section and the other sections based on the data alignment result includes: determining the data alignment quantity between the first flow data and the second flow data based on the data alignment result; when the data alignment quantity is greater than a preset value, determining the corresponding flow gap between the directly connected section and the other sections based on the data alignment quantity in combination with the first flow data and the second flow data; determining the first association coefficient corresponding to the first flow data under the association relationship based on the data alignment quantity; determining the second association coefficient corresponding to the second flow data under the association relationship based on the data alignment quantity; determining the corresponding association characterization value between the directly connected section and the other sections based on the flow gap in combination with the first association coefficient and the second association coefficient; and determining the corresponding association relationship between the directly connected section and the other sections based on the association characterization value.

[0121] For example, after completing the data alignment operation for the first and second traffic data, the number of data points that can be successfully aligned in the time dimension is counted. This number is the data alignment number. For example, after time scale alignment and data matching, if there are 50 time points at which both the first and second traffic data have valid records, then the data alignment number is 50.

[0122] For example, a numerical value is pre-set as a judgment criterion, namely a preset value. The calculated number of data alignments is compared with the preset value. The setting of the preset value needs to be determined based on the actual situation and data characteristics. For example, it can be based on a comprehensive consideration of factors such as the overall size of the data and the reliability of the data. If the number of data alignments is greater than the preset value, it means that there are sufficient data points for further analysis and the next step is to proceed. If it is less than or equal to the preset value, it may be that due to insufficient data, accurate correlation analysis cannot be performed, or the correlation may be directly determined to be weak.

[0123] For example, when the number of data alignments exceeds a preset value, the first and second flow data are combined to calculate the flow difference between the directly connected road segment and other road segments. Specifically, for each aligned data point, the difference between the first and second flow data at that time point is calculated. These differences can then be processed using statistical methods such as averaging and median to obtain a value that represents the overall flow difference. For example, for 50 aligned data points, the flow difference at each time point is calculated, and then the average of these differences is calculated. The resulting average is the flow difference.

[0124] Exemplarily, a first quantity corresponding to the first flow data is obtained, and a first correlation coefficient corresponding to the first flow data under the correlation relationship is determined based on a ratio between the data alignment quantity and the first quantity. Similarly, a second quantity corresponding to the second flow data is obtained, and a second correlation coefficient corresponding to the second flow data under the correlation relationship is determined based on a ratio between the data alignment quantity and the second quantity.

[0125] Exemplarily, the first quantity and the second quantity are summed to obtain a third quantity, the second quantity and the third quantity are then ratio-calculated to obtain a first ratio, and the first quantity and the third quantity are ratio-calculated to obtain a second ratio.

[0126] For example, the first ratio and the first correlation coefficient are multiplied to obtain a first result, and the second ratio and the second correlation coefficient are multiplied to obtain a second result. The first result and the second result are summed to obtain a target correlation coefficient. The flow rate difference and the target correlation coefficient are then multiplied to obtain a corresponding correlation representation value between the directly connected road segment and the other road segment. The correlation representation value comprehensively considers the flow rate difference and the influence of the first flow data and the second flow data in the correlation relationship.

[0127] For example, the association relationship between the directly connected road segment and the other road segment is determined based on the calculated association characterization value. For example, different association characterization value ranges can be pre-set, corresponding to different association relationship types. For example, when the association characterization value is greater than a certain higher threshold, it indicates that there is a strong association between the directly connected road segment and the other road segment, and vehicles flow between the two road segments more frequently; when the association characterization value is in the middle range, it indicates a moderate association; and when the association characterization value is less than a certain lower threshold, it indicates a weak association.

[0128] Step S106: Determine a traffic prediction model according to the historical vehicle traffic, the historical user traffic, and the corresponding association relationship between the associated road sections in combination with the first recommendation parameter and the second recommendation parameter.

[0129] Exemplarily, historical vehicle traffic, the corresponding correlation between associated road sections, the first recommended parameter and the second recommended parameter are determined as the input data of the traffic prediction model, and historical user traffic is determined as the output data of the traffic prediction model, and then the model type of the traffic prediction model is determined, such as machine learning model (such as decision tree, random forest) and deep learning model (such as convolutional neural network), etc., and model evaluation indicators such as mean square error, mean absolute error, etc. are determined.

[0130] For example, the entire dataset of historical vehicle traffic, the corresponding relationships between associated road segments, and the first and second recommended parameters is divided into a training set, a validation set, and a test set. Generally speaking, the training set accounts for the majority of the total data (e.g., 70%-80%) and is used for model parameter learning; the validation set is used to adjust the model's hyperparameters and select the optimal model structure; and the test set is used to evaluate the model's final performance. The traffic prediction model is then trained using the training set, and by continuously adjusting the model's parameters, the error between the model's predictions and historical user traffic is minimized. During the training process, cross-validation can be used to improve the model's generalization capabilities.

[0131] For example, based on the evaluation results of the validation set, grid search, random search, and other methods are used to find the optimal hyperparameter combination to optimize the model. The optimized model is then evaluated using the test set, and the previously determined evaluation metrics are calculated to determine whether the model's performance meets the requirements. Based on the results of model evaluation and validation, the model with the best performance is selected as the final traffic prediction model.

[0132] Exemplarily, the final traffic prediction model is deployed in actual applications to predict future user traffic of the target object.

[0133] Step S107: Perform user traffic prediction on the target object according to the traffic prediction model to obtain a target prediction result.

[0134] Exemplarily, the current vehicle flow corresponding to each road in the associated roads is collected, and then the current vehicle flow, the corresponding association relationship between the associated road sections, the first recommended parameter and the second recommended parameter are input into the traffic prediction model to achieve the purpose of user flow prediction for the target object, thereby obtaining the target prediction result.

[0135] Step S108: determining the target state of the target object according to the target prediction result, and performing real-time display according to the target state to obtain a target display result.

[0136] For example, the target object's state type is determined, such as idle, busy, or busy, and the flow interval corresponding to each state type is set. The target prediction result is then compared with the previously determined flow intervals corresponding to each state type. Based on the comparison results, the target object's current state is determined.

[0137] Exemplarily, the display method of the target state on the target screen is designed, and the display method may include text description, icon, color identification, etc., so that the target state is displayed on the target screen corresponding to the target object according to the display method to obtain the target display result.

[0138] See also Figure 2 , Figure 2A gas station information real-time display device 200 provided in an embodiment of the present application includes a data acquisition module 201, a data clustering module 202, a parameter determination module 203, a flow determination module 204, a relationship determination module 205, a model determination module 206, a data prediction module 207, and a data display module 208, wherein the data acquisition module 201 is used to obtain the historical user flow and historical evaluation information of the target object, and obtain the target information corresponding to the target object under social media; the data clustering module 202 is used to cluster the historical evaluation information and the target information to obtain a clustering result, and determine the first recommended parameter according to the clustering result; the parameter determination module 203 is used to obtain relevant information corresponding to the surrounding objects of the target object, and according to the relevant information and the target information Determine the second recommended parameter; a traffic determination module 204 is used to determine the associated road sections according to the target object and the surrounding objects, and obtain the historical vehicle traffic of the associated road sections according to the sensor; a relationship determination module 205 is used to obtain the corresponding association relationship between the associated road sections according to the historical vehicle traffic; a model determination module 206 is used to determine the traffic prediction model according to the historical vehicle traffic, the historical user traffic and the corresponding association relationship between the associated road sections in combination with the first recommended parameter and the second recommended parameter; a data prediction module 207 is used to perform user traffic prediction on the target object according to the traffic prediction model to obtain a target prediction result; a data display module 208 is used to determine the target state of the target object according to the target prediction result, and perform real-time display according to the target state to obtain a target display result.

[0139] In some embodiments, a real-time display device 200 for gas station information can be applied to a terminal device.

[0140] It should be noted that, those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described gas station information real-time display device 200 can refer to the corresponding process in the aforementioned gas station information real-time display method embodiment, and will not be repeated here.

[0141] 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.

[0142] 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 I 2 C (Inter-Integrated Circuit) bus.

[0143] Specifically, processor 301 is used to provide computing and control capabilities to support the operation of the entire terminal device. Processor 301 can be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0144] 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.

[0145] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the 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 shown in the figure, or combine certain components, or have a different component arrangement.

[0146] The processor is configured to run a computer program stored in a memory, and implement any one of the methods for real-time display of gas station information provided by an embodiment of the present invention when executing the computer program.

[0147] In one embodiment, the processor is configured to run a computer program stored in the memory, and implement the following steps when executing the computer program:

[0148] Obtain the historical user traffic and historical evaluation information of the target object, and obtain the target information corresponding to the target object on social media;

[0149] Clustering the historical evaluation information and the target information to obtain a clustering result, and determining a first recommendation parameter based on the clustering result;

[0150] Obtaining relevant information corresponding to surrounding objects of the target object, and determining a second recommendation parameter based on the relevant information and the target information;

[0151] Determining an associated road section according to the target object and the surrounding objects, and obtaining a historical vehicle flow of the associated road section according to a sensor;

[0152] Obtaining corresponding association relationships between the associated road sections according to the historical vehicle flow;

[0153] Determine a traffic prediction model based on the historical vehicle traffic, the historical user traffic, and the corresponding association relationship between the associated road sections in combination with the first recommendation parameter and the second recommendation parameter;

[0154] Performing user traffic prediction on the target object according to the traffic prediction model to obtain a target prediction result;

[0155] The target state of the target object is determined according to the target prediction result, and a target display result is obtained by performing real-time display according to the target state.

[0156] It should be noted that those skilled in the art can clearly understand that, for the sake of convenience and brevity of description, the specific working process of the terminal device described above can refer to the corresponding process in the aforementioned embodiment of the method for real-time display of gas station information, and will not be repeated here.

[0157] 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 real-time display of gas station information provided in the description of the embodiment of the present invention.

[0158] The storage medium may be an internal storage unit of the terminal device described in the aforementioned 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 media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the terminal device.

[0159] Those skilled in the art will appreciate that all or some of the steps, systems, and functional modules / units in the methods, systems, and devices disclosed above may be implemented as software, firmware, hardware, or any combination thereof. In hardware embodiments, the division between functional modules / units described above does not necessarily correspond to the division between physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all of the 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 as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media encompasses both 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 includes, but is 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 tape, 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, as is well known to those skilled in the art, communication media typically embodies 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.

[0160] 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 variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or system. In the absence of further limitations, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system that includes the element.

[0161] The serial numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art 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 scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be based on the scope of protection of the claims.

Claims

1. A method for real-time display of gas station information, characterized in that: The method comprises: Obtain the historical user traffic and historical evaluation information of the target object, and obtain the target information corresponding to the target object on social media; Clustering the historical evaluation information and the target information to obtain a clustering result, and determining a first recommendation parameter based on the clustering result; wherein the first recommendation parameter is used to represent the likelihood of a user selecting the target object from among a plurality of gas stations; determining the first recommendation parameter based on the clustering result includes: establishing a mathematical model or rule to quantify the subject information and weight of each cluster in the clustering result to obtain a comprehensive value as the first recommendation parameter; Obtaining relevant information corresponding to surrounding objects of the target object, and determining a second recommendation parameter based on the relevant information and the target information; wherein the relevant information is extracted from user evaluations of the surrounding objects on social media; and the second recommendation parameter is used to represent a user's preference parameter when selecting a service from the target object and the surrounding objects; Determining an associated road section according to the target object and the surrounding objects, and obtaining a historical vehicle flow of the associated road section according to a sensor; Obtaining corresponding association relationships between the associated road sections according to the historical vehicle flow; Determine a traffic prediction model based on the historical vehicle traffic, the historical user traffic, and the corresponding association relationship between the associated road sections in combination with the first recommendation parameter and the second recommendation parameter; Performing user traffic prediction on the target object according to the traffic prediction model to obtain a target prediction result; Determining a target state of the target object according to the target prediction result, and performing real-time display according to the target state to obtain a target display result; Wherein, obtaining the corresponding association relationship between the associated road sections according to the historical vehicle flow includes: Obtaining a directly connected road segment corresponding to the target object from the associated road segments, and obtaining other road segments except the directly connected road segment from the associated road segments; Obtaining first traffic flow data corresponding to the directly connected road section and second traffic flow data corresponding to the other road sections from the historical vehicle traffic flow; Determining first time information corresponding to the first flow data and determining second time information corresponding to the second flow data; Determine a data alignment result between the first flow data and the second flow data according to the first flow data, the second flow data, the first time information, and the second time information; The corresponding association relationship between the directly connected road segment and the other road segments is determined according to the data alignment result.

2. The method according to claim 1, characterized in that The step of obtaining target information corresponding to a target object on social media includes: Obtaining a first location corresponding to the target object, and collecting first information corresponding to the target object on social media based on the first location, wherein the first information is used to represent user-posted information corresponding to the first location on social media, and the first information includes first text information and first image information; Performing target association on the first text information to obtain relevant text information associated with the target object, and performing target classification on the first image information to obtain an image description object corresponding to the first image information; obtaining relevant image information associated with the target object from the first image information according to the image description object; Performing position prediction on the first information according to the relevant text information and the relevant image information to obtain relevant position information corresponding to the first information; The first information is filtered according to the relevant location information and the first location to obtain the target information corresponding to the target object.

3. The method according to claim 2, characterized in that The performing position prediction on the first information according to the relevant text information and the relevant image information to obtain relevant position information corresponding to the first information includes: Performing image feature extraction on the relevant image information using an image coding layer of a position prediction model to obtain a first feature vector; Using the text encoding layer of the position prediction model to perform text feature extraction on the relevant text information to obtain a second feature vector; Determining an information matching value between the relevant image information and the relevant text information according to the first feature vector and the second feature vector using the image-text matching layer of the position prediction model; Performing position feature extraction on the relevant image information using the image position determination layer of the position prediction model to obtain image position information corresponding to the relevant image information; Performing position feature extraction on the relevant text information using the text position determination layer of the position prediction model to obtain text position information corresponding to the relevant text information; Using the feature fusion layer of the position prediction model to fuse the image position information and the text position information according to the information matching value to obtain a target fusion feature; The position classification layer of the position prediction model uses the target fusion feature to perform position prediction on the first information to obtain the relevant position information corresponding to the first information.

4. The method according to claim 1, wherein The clustering of the historical evaluation information and the target information to obtain a clustering result includes: Merging the historical evaluation information and the target information to obtain a text set, and performing word segmentation processing on each sub-text information in the text set to obtain associated words corresponding to the sub-text information; Determining a first text similarity between any two of the sub-text information based on the associated words; Determine a target dimension, obtain a word representation vector corresponding to the associated word, and determine a word dimension association value corresponding to the associated word under the target dimension based on the word representation vector; Determining the weight information corresponding to the sub-text information under the target dimension according to the word dimension association value; Determining, based on the word dimension association value and the weight information, a text dimension association value corresponding to when the sub-text information belongs to the target dimension; Determining the second text similarity between any two of the sub-text information according to the text dimension association value; The text set is clustered according to the first text similarity and the second text similarity to obtain the clustering result.

5. The method according to claim 4, characterized in that The determining of the first text similarity between any two sub-text information according to the associated words includes: Obtaining any two subtext information, namely a first subtext and a second subtext, and obtaining a first word corresponding to the first subtext and a second word corresponding to the second subtext; Determining a first part of speech and a first word position corresponding to the first word in the first subtext, and determining a first weight corresponding to the first word based on the first part of speech and the first word position; Determining a second part of speech and a second word position corresponding to the second word in the second subtext, and determining a second weight corresponding to the second word according to the second part of speech and the second word position; Determining a first criticality corresponding to the first word according to a first frequency corresponding to the first word in the text collection; Determining a second criticality corresponding to the second word according to a second frequency corresponding to the second word in the text collection; Calculating text similarity based on the first word and the second word to obtain word similarity, and fusing the word similarity based on the first weight, the second weight, the first key degree, and the second key degree to obtain a first similarity between the first subtext and the second subtext; The first similarity is data-adjusted according to the word similarity to obtain the first text similarity corresponding to the first subtext and the second subtext.

6. The method according to claim 5, characterized in that The step of performing data adjustment on the first similarity according to the word similarity to obtain the first text similarity corresponding to the first subtext and the second subtext includes: Determining, based on the word similarity, a first associated word corresponding to the first word in the second word, and obtaining a third associated word corresponding to the first associated word from the first word; Determining, based on the word similarity, a second associated word in the first word that is close to the second word, and obtaining a fourth associated word corresponding to the second associated word from the second word; determining a degree of word overlap between the first subtext and the second subtext according to the word similarity; Obtaining a first distribution position corresponding to the first associated word from the second subtext, and obtaining a second distribution position corresponding to the third associated word from the first subtext; determining first word order information corresponding to the first subtext and the second subtext according to the first distribution position and the second distribution position; Obtaining a third distribution position corresponding to the second associated word from the first subtext, and obtaining a fourth distribution position corresponding to the fourth associated word from the second subtext; determining second word order information between the first subtext and the second subtext according to the third distribution position and the fourth distribution position; determining a similarity adjustment parameter between the first subtext and the second subtext according to the first word order information and the second word order information; Adjusting the first similarity value according to the similarity adjustment parameter to obtain the first text similarity corresponding to the first subtext and the second subtext; The similarity adjustment parameter is obtained according to the following formula: in, represents the similarity adjustment parameter corresponding to the i-th first subtext and the j-th second subtext, represents the number of similar related words between the i-th first subtext and the j-th second subtext, represents the number of the first words corresponding to the i-th first subtext, represents a constant, represents the number of the second words corresponding to the j-th second subtext, represents the first word order information corresponding to the i-th first subtext and the j-th second subtext, Indicates the second word order information corresponding to the j-th second subtext and the i-th first subtext.

7. The method according to claim 1, characterized in that The determining the corresponding association relationship between the directly connected road segment and the other road segments according to the data alignment result includes: determining a data alignment quantity between the first flow data and the second flow data according to the data alignment result; When the data alignment amount is greater than a preset value, determining a corresponding flow difference between the directly connected section and the other sections according to the data alignment amount in combination with the first flow data and the second flow data; Determining a first correlation coefficient corresponding to the first traffic data under the correlation relationship according to the data alignment quantity; Determining a second correlation coefficient corresponding to the second traffic data under the correlation relationship according to the data alignment quantity; Determine the corresponding correlation representation value between the directly connected road segment and the other road segments according to the traffic difference in combination with the first correlation coefficient and the second correlation coefficient; The corresponding association relationship between the directly connected road segment and the other road segments is determined according to the association representation value.

8. A real-time display device for gas station information, characterized in that: include: The data acquisition module is used to obtain the historical user traffic and historical evaluation information of the target object, and obtain the target information corresponding to the target object on social media; a data clustering module, configured to cluster the historical evaluation information and the target information to obtain a clustering result, and determine a first recommendation parameter based on the clustering result; wherein the first recommendation parameter is used to represent the likelihood of a user selecting the target object from among a plurality of gas stations; determining the first recommendation parameter based on the clustering result includes: establishing a mathematical model or rule to quantify the subject information and weight of each cluster in the clustering result to obtain a comprehensive value as the first recommendation parameter; a parameter determination module, configured to obtain relevant information corresponding to surrounding objects of the target object, and determine a second recommendation parameter based on the relevant information and the target information; wherein the relevant information is extracted from user evaluations of the surrounding objects on social media; and the second recommendation parameter is used to represent a user's preference parameter when selecting a service from the target object and the surrounding objects; a traffic flow determination module, configured to determine an associated road section according to the target object and the surrounding objects, and obtain a historical vehicle flow of the associated road section according to a sensor; a relationship determination module, configured to obtain corresponding association relationships between the associated road sections based on the historical vehicle flow; a model determination module, configured to determine a traffic prediction model based on the historical vehicle traffic, the historical user traffic, and the corresponding association relationship between the associated road sections in combination with the first recommended parameter and the second recommended parameter; A data prediction module, configured to perform user traffic prediction on the target object according to the traffic prediction model to obtain a target prediction result; A data display module is used to determine the target state of the target object according to the target prediction result, and to display the target state in real time to obtain a target display result; The relationship determination module, when obtaining the corresponding association relationship between the associated road sections based on the historical vehicle flow, is specifically used to: obtain the directly connected road section corresponding to the target object from the associated road section, and obtain other road sections from the associated road sections except the directly connected road section; obtain the first flow data corresponding to the directly connected road section and the second flow data corresponding to the other road sections from the historical vehicle flow; determine the first time information corresponding to the first flow data and determine the second time information corresponding to the second flow data; determine the data alignment result between the first flow data and the second flow data based on the first flow data, the second flow data combined with the first time information and the second time information; and determine the corresponding association relationship between the directly connected road section and the other road sections based on the data alignment result.

9. 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 configured to execute the computer program and implement the method for real-time display of gas station information according to any one of claims 1 to 7 when executing the computer program.

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