Advertisement updating method and system for electronic stop board

Abstract

The invention relates to the technical field of advertisement updating, and discloses an advertisement updating method and system for an electronic station board, and the method comprises the steps: obtaining user habit data of a station and peripheral business information data of the station, carrying out the first calculation operation according to the user habit data and the peripheral business information data, obtaining user tendency data, and carrying out the second calculation operation according to the user tendency data. Performing a second calculation operation according to the user tendency data to obtain advertisement tendency data, performing a third calculation operation according to the advertisement tendency data to obtain the tendency degree of the user to the advertisement, and performing a fourth calculation operation according to the user habit data to obtain a user access proportion, and performing product calculation according to the advertisement tendency degree of the user and the user access proportion to obtain a to-be-updated advertisement. The method has the effects of realizing personalized and accurate advertisement updating and improving the advertisement updating accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of advertisement updating, and in particular to an advertisement updating method and system for an electronic bus stop board. Background Art

[0002] At present, bus stops, as an important part of urban infrastructure, play a vital role in daily life. With the development of technology, electronic bus stops have gradually replaced traditional bus stops, providing richer information interaction and advertising functions. The core advantage of electronic bus stops is that they can display dynamic content, including real-time bus information and commercial advertisements, which brings convenience to urban transportation and commercial activities.

[0003] In one prior art, when advertisements are placed, electronic bus stops mainly use a centralized management platform to update the advertisement content. This update method usually cannot meet the needs of some users and displays the same advertisement content to all users. The selection of advertisements is generally screened by advertisers according to their needs and updated by the control terminal. For example, an electronic bus stop in a commercial district will display advertisements for high-end products, but these advertisements are not suitable for most commuters in the area because they are more concerned about daily consumer goods, and the advertisement content cannot be adjusted based on user behavioral feedback such as viewing time and access rate of the advertisements. This makes it difficult to measure the accuracy of advertisement placement, and advertisers cannot know which advertisements are more popular and which ones need improvement.

[0004] Existing technologies lack personalized adjustments based on user behaviors and preferences, and are unable to deliver precise delivery based on user travel habits. Summary of the invention

[0005] The present invention provides an advertisement updating method and system for an electronic bus stop board, so as to realize personalized and accurate advertisement updating and improve the accuracy of advertisement updating.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides an advertisement updating method for an electronic station sign, comprising:

[0007] Obtain user habit data of the site and surrounding commercial information data of the site;

[0008] Performing a first calculation operation according to the user habit data and the surrounding commercial information data to obtain user tendency data;

[0009] Performing a second calculation operation according to the user tendency data to obtain advertisement tendency data;

[0010] performing a third calculation operation according to the advertisement preference data to obtain the user's preference degree for the advertisement;

[0011] performing a fourth calculation operation according to the user habit data to obtain a user access ratio;

[0012] The advertisement to be updated is obtained by performing a product calculation based on the user's preference for the advertisement and the user's access ratio.

[0013] In an optional implementation, the user habit data includes data on users arriving at the station by various travel modes, demographic data on the city where the station is located, data on the expectations of station users for advertisements, daily visits by station users, the number of users within ten kilometers of the station, and visit data on advertisements by station users;

[0014] The surrounding commercial information data includes the population ratio of each commercial area around the site, the expected data of commercial advertisements in the area around the site, the viewing data of commercial advertisements in the area around the site, and the annual passenger flow data of the commercial area around the site.

[0015] In an optional implementation, performing a first calculation operation based on the user habit data and the surrounding commercial information data to obtain user tendency data includes:

[0016] The user tendency data is calculated by the following formula:

[0017] T(U1,U2,U3,U4,U5)=U1·C1+U2·C2+U3·C3+U4·C4+U5·C5

[0018] Among them, T(U1, U2, U3, U4, U5) is the user tendency data, U1 is the proportion of people who take a taxi to the station, U2 is the proportion of people who take the subway to the station, U3 is the proportion of people who walk to the station, U4 is the proportion of people in nearby parks and attractions, and U5 is the proportion of people in nearby office places. C1 is the viewing time of people who take a taxi to the station, C2 is the viewing time of people who take the subway to the station, C3 is the viewing time of people who walk to the station, C4 is the viewing time of people who arrive at the station from parks and attractions, and C5 is the viewing time of people who arrive at the station from offices.

[0019] In an optional implementation, performing a second calculation operation according to the user tendency data to obtain advertisement tendency data includes:

[0020] The advertising tendency data is calculated by the following formula:

[0021] F(A1,A2,A3,A4,A5(=T(U1,U2,U3,U4,U5)·G(A1,A2,A3,A4,A5)

[0022] Among them, F(A1, A2, A3, A4, A5) is the advertising tendency data, and G(A1, A2, A3, A4, A5) is the number of people who watch the advertisement.

[0023] G(A1,A2,A3,A4,A5)=A1·C1+A2·C2+A3·C3+A4·C4+A5·C5

[0024] Among them, A1 is the proportion of people who watch the advertisement when they arrive at the station by taxi, A2 is the proportion of people who watch the advertisement when they arrive at the station by subway, A3 is the proportion of people who watch the advertisement when they arrive at the station on foot, A4 is the proportion of people who watch the advertisement when they arrive at the station from nearby parks and attractions, and A5 is the proportion of people who watch the advertisement when they arrive at the station from nearby office places.

[0025] In an optional implementation, performing a third calculation operation according to the advertisement preference data to obtain the user's preference for the advertisement includes:

[0026] The user's preference for advertisements is calculated using the following formula:

[0027] W=F(A1,A2,A3,A4,A5)·T1·T2·T3·T4

[0028] Among them, W is the user's preference for advertising, F(A1, A2, A3, A4, A5) is the advertising preference data, T1 is the expectation that users of the site like the same type of advertisements, T2 is the expectation whether the advertisement caters to the characteristics of the crowd, T3 is the expectation that the advertisement meets the preferences of the people in the site area, and T4 is the expectation that the advertisement location is reasonable.

[0029] In an optional implementation, performing a fourth formula calculation according to the user habit data to obtain the user access ratio includes:

[0030] The user access ratio is calculated by the following formula:

[0031]

[0032] Among them, P is the user access ratio, Q is the daily visits of site users, M is the number of users within ten kilometers of the site, and t is the preset time weight factor, which can be adjusted according to the importance of the time period.

[0033] In an optional implementation manner, performing a product calculation based on the advertisement tendency and the user access ratio to obtain the advertisement to be updated includes:

[0034] According to the tendency degree and the user access ratio, a product calculation is performed to obtain an advertisement update score;

[0035] The advertisements are sorted in descending order according to the values ​​of the advertisement update scores, and the advertisement with the highest ranking is determined as the advertisement to be updated.

[0036] In a second aspect, the present invention provides an electronic station sign advertising update system, comprising:

[0037] A data acquisition module is used to acquire user habit data of the site and surrounding commercial information data of the site;

[0038] A user tendency data module, configured to perform a first calculation operation according to the user habit data and the surrounding commercial information data to obtain user tendency data;

[0039] An advertisement tendency data module, configured to perform a second calculation operation according to the user tendency data to obtain advertisement tendency data;

[0040] A tendency degree module, used to perform a third calculation operation according to the advertisement tendency data to obtain the user's tendency degree for the advertisement;

[0041] A user access ratio module, configured to perform a fourth calculation operation according to the user habit data to obtain a user access ratio;

[0042] The advertisement update result module is used to perform product calculation according to the user's advertisement preference degree and the user access ratio to obtain the advertisement update result.

[0043] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements an advertisement updating method for an electronic bus stop sign as described in any one of the above.

[0044] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for updating advertisements of electronic bus stops.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention proposes an advertisement updating method and system for electronic bus stops, which realizes personalized and precise advertisement content updating by comprehensively analyzing the user's travel habits and surrounding commercial information data. Specifically, the technical solution includes the following key steps: first, obtaining the user habit data and surrounding commercial information data of the site; second, performing a series of calculation operations based on these data to obtain user tendency data and advertisement tendency data; then, calculating the user's tendency degree for advertisements and the user's access ratio; finally, performing a product calculation based on the user's tendency degree for advertisements and the access ratio to obtain the advertisement to be updated. This process involves the collaborative work of multiple modules such as data acquisition, tendency data calculation, tendency degree evaluation and access ratio analysis. Through the interaction of these modules, dynamic updating and optimization of advertisement content is realized.

[0047] In the derivation process, the present invention first collects data such as the user's travel mode, population structure, advertising expectations, and the proportion of people in the surrounding commercial areas, commercial advertising expectations, and other information through the data acquisition module. Then, the first calculation operation is performed using these data to obtain user tendency data. This step involves analyzing the user's travel habits and evaluating the user's expectations and preferences for advertising content. Subsequently, a second calculation operation is performed based on the user tendency data to obtain advertising tendency data. This step is mainly to quantitatively analyze the matching degree between advertising content and user preferences. Next, through the third calculation operation, the user's preference for advertising is obtained. This step is to further quantify the attractiveness of advertising and user interest. Finally, the fourth calculation operation is performed in combination with the user access ratio to obtain the final advertising update result. The entire process ensures the timeliness and accuracy of advertising updates through precise calculation and analysis.

[0048] Compared with the prior art, the beneficial effect of the present invention is that, by comprehensively analyzing the user habit data and surrounding commercial information data, the personalized and accurate update of the electronic station sign advertisement is realized. This method can not only make dynamic adjustments according to the user's travel habits, but also make the advertisement content closer to the actual needs of the user by analyzing the user's preference for advertisements and the access ratio, thereby enhancing the effectiveness of the advertisement and the user's satisfaction. In addition, by dynamically adjusting the advertising delivery strategy, it is ensured that the advertisement content always maintains a high degree of match with the user's needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of an advertisement updating method for an electronic station sign provided by the first embodiment of the present invention;

[0050] Figure 2 It is a schematic diagram of the structure of an advertisement updating system for an electronic bus stop provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] At present, bus stops, as an important part of urban infrastructure, play a vital role in daily life. With the development of technology, electronic bus stops have gradually replaced traditional bus stops, providing richer information interaction and advertising functions. The core advantage of electronic bus stops is that they can display dynamic content, including real-time bus information and commercial advertisements, which brings convenience to urban transportation and commercial activities.

[0053] In one prior art, when advertising is placed, electronic bus stops mainly use a centralized management platform to update the advertising content. This update method usually cannot meet the needs of some users and displays the same advertising content to all users. The selection of advertisements is generally screened by advertisers according to their needs and updated by the control terminal.

[0054] The existing technology lacks personalized adjustments based on user behavior and preferences, and cannot accurately deliver ads based on user travel habits. Specifically, an electronic bus stop in a commercial district will display ads for high-end products, but these ads are not suitable for most commuters in the area because they are more concerned about daily consumer goods, and the ad content cannot be adjusted based on user behavioral feedback such as viewing time and visit rate of ads. This makes it difficult to measure the accuracy of ad delivery, and advertisers cannot know which ads are more popular and which need improvement.

[0055] In order to solve the above technical problems, refer to Figure 1 The first embodiment of the present invention provides an advertisement updating method for an electronic station sign, comprising the following steps:

[0056] S11, obtaining user habit data of the site and surrounding commercial information data of the site;

[0057] S12, performing a first calculation operation according to the user habit data and the surrounding commercial information data to obtain user tendency data;

[0058] S13, performing a second calculation operation according to the user tendency data to obtain advertisement tendency data;

[0059] S14, performing a third calculation operation according to the advertisement preference data to obtain the user's preference degree for the advertisement;

[0060] S15, performing a fourth calculation operation according to the user habit data to obtain a user access ratio;

[0061] S16, performing a product calculation based on the user's preference for advertisements and the user's access ratio to obtain the advertisement to be updated.

[0062] In step S11, user habit data of the site and surrounding commercial information data of the site are obtained.

[0063] Specifically, user habit data covers data on users arriving at the station by various modes of travel, demographic data on the city where the station is located, data on users’ expectations of advertisements at the station, daily visits to the station, the number of users within 10 kilometers of the station, and data on users’ visits to advertisements at the station, while surrounding commercial information data includes the percentage of people in each commercial area around the station, data on expectations for commercial advertisements in the area around the station, data on viewing commercial advertisements in the area around the station, and annual passenger flow data for commercials in the area around the station. The collection of these data provides the basis for subsequent calculation operations and is the prerequisite for achieving personalized and precise advertising updates.

[0064] It should be noted that in order to obtain user habit data at the site, the information on passengers' travel mode, time, frequency, etc. is collected by using technical means such as smart cameras, sensors and mobile applications installed at the site. At the same time, users' expectations and preferences for advertisements are collected through market research, online questionnaires, etc. For surrounding commercial information data, geographic information systems (GIS) and business intelligence tools are used to collect and analyze the proportion of people in commercial areas around the site, viewing data of commercial advertisements, etc. The collected raw data needs to be converted into useful information through steps such as data preprocessing, cleaning and analysis. For example, by analyzing the user's travel data, the changes in passenger flow during peak and off-peak periods, as well as the differences in user preferences for advertising content in different time periods can be identified. Finally, the user habit data and surrounding commercial information data are integrated to form a comprehensive data set, which will be used for subsequent calculation operations. In the process of data integration, it is necessary to ensure the consistency and accuracy of the data in order to provide reliable input for calculation.

[0065] In general, a comprehensive user behavior and business environment database can be constructed based on the user habit data of the site and the surrounding business information data of the site, which is crucial for the subsequent calculation of user tendency data, the acquisition of advertising tendency data, and the evaluation of the degree of user tendency to advertising. The effect of this step is reflected in providing accurate data support for the advertising update system, so that the subsequent advertising delivery strategy can be more in line with the actual needs of users and the actual conditions of the business environment, and improve the accuracy and effect of advertising updates.

[0066] Among them, Geographic Information System (GIS) is a framework for capturing, storing, analyzing and managing geographic spatial data. GIS is mainly used to obtain business information in the following ways:

[0067] Spatial data collection:

[0068] Remote sensing: Using satellite or aerial photography to collect images of the Earth's surface and using image processing techniques to identify businesses, buildings, and other geographic features.

[0069] Ground survey: Collect detailed information about a specific business area, including store types, sizes, locations, etc., through field visits.

[0070] Geo-tagged data integration:

[0071] Business Databases: Databases of integrated business information providers, these databases typically contain information such as store locations, business hours, types of services, etc.

[0072] Open data sources: Use open data provided by the government or public institutions, such as business license information, urban planning maps, etc.

[0073] User Generated Content:

[0074] Social Media and Online Reviews: Analyze social media posts and online reviews to extract information about business activities and user feedback.

[0075] Location-sharing services: Sharing data through users’ location, such as Foursquare or Facebook’s check-in features, to collect information about popularity and activity in commercial areas.

[0076] GIS Analysis Tools:

[0077] Buffer analysis: Determine the population distribution, traffic flow, etc. within a certain distance (such as 1 km, 5 km) around a specific commercial area.

[0078] Hot spot analysis: Use GIS hot spot analysis tools (such as the Getis-Ord Gi* statistic) to identify concentrated areas of commercial activity.

[0079] Network Analysis: Analyze transportation networks and pedestrian movement patterns to determine accessibility and flow in business areas.

[0080] Partners and Data Sharing:

[0081] Collaborate with business organizations: Collaborate with chambers of commerce, shopping center management agencies, etc. to obtain business activity and customer flow data.

[0082] Data Exchange Platform: Participate in the Data Exchange Platform to share and access business-relevant geospatial data with other organizations.

[0083] In step S12, a first calculation operation is performed based on the user habit data and the surrounding commercial information data to obtain user tendency data.

[0084] Specifically, step S12 uses statistical analysis and pattern recognition technology to conduct in-depth analysis of user travel methods, time choices, advertising preferences and other behavioral data, and combines the characteristics of the surrounding commercial environment, such as the proportion of people in the commercial area and the expected data of commercial advertisements. Through these analyses, the user's potential interest and reaction to different advertising content can be quantified, thereby obtaining user tendency data.

[0085] It should be noted that step S12 needs to first integrate the user habit data and surrounding commercial information data collected in step S11 to form a unified data set. In this process, the data needs to be cleaned, invalid or erroneous data records need to be removed, and missing values ​​need to be processed to ensure the quality and consistency of the data. Then, the integrated data is analyzed using statistical methods and machine learning algorithms, such as logistic regression, decision trees, or neural networks. For example, a model can be constructed that uses the user's travel mode (such as walking, subway, taxi, etc.) and surrounding commercial information (such as the proportion of people in commercial areas, advertising viewing data) as input features, and the relationship between these features and the user's advertising tendency is learned through the model, thereby calculating the tendency score of each user or user group for different advertising content. Finally, the user tendency data and the tendency scores of different user groups for different advertising content are calculated as the output result. The score reflects the user's interest in specific advertising content and provides a basis for subsequent advertising selection and delivery.

[0086] It should be noted that the effect of step S12 is that it provides a scientific basis for subsequent advertising decisions. Through the obtained user preference data, the advertising update system can predict the user's acceptance and response to specific advertising content, which is crucial for achieving personalized recommendations and precise delivery of advertisements. In addition, this step also helps to optimize the configuration of advertising resources. By predicting the advertising responses of different user groups, advertising resources can be allocated more specifically, improving the efficiency and effectiveness of advertising delivery.

[0087] The calculation formula of the user tendency data is:

[0088] T(U1,U2,U3,U4,U5)=U1·C1+U2·C2+U3·C3+U4·C4+U5·C5

[0089] Among them, T(U1, U2, U3, U4, U5) is the user tendency data, U1 is the proportion of people who take a taxi to the station, U2 is the proportion of people who take the subway to the station, U3 is the proportion of people who walk to the station, U4 is the proportion of people in nearby parks and attractions, and U5 is the proportion of people in nearby office places. C1 is the viewing time of people who take a taxi to the station, C2 is the viewing time of people who take the subway to the station, C3 is the viewing time of people who walk to the station, C4 is the viewing time of people who arrive at the station from parks and attractions, and C5 is the viewing time of people who arrive at the station from offices.

[0090] In step S13, a second calculation operation is performed based on the user tendency data to obtain advertisement tendency data.

[0091] Specifically, step S13 calculates the attractiveness and relevance of each advertising content to different user groups by analyzing the behavior patterns and preferences in the user tendency data, combining the characteristics of the advertising content, and applying weighted summation and other algorithm formulas.

[0092] It should be noted that in step S13, the user tendency data and advertising features need to be associated and analyzed first. This includes analyzing the user's response to different advertising content (such as product type, brand, promotional information, etc.), and combining the user's historical behavior data such as viewing time to evaluate the user's potential interest in a specific advertisement. Then, the algorithm model is used to match the user tendency data and advertising features to predict the user's tendency data for a specific advertisement. These models are capable of processing large-scale data sets and identifying the association between complex user behavior patterns and advertising content. Based on the above analysis, the tendency score of each advertisement for different user groups is calculated. This score reflects the degree of match between the advertisement and the user's preferences. The higher the score, the more attractive the advertisement is to a specific user group.

[0093] It should be noted that the effect of step S13 is that it provides a more accurate quantitative basis for advertising. Through the obtained advertising tendency data, the popularity of different advertising content in a specific user group can be predicted, thereby providing a scientific basis for the selection and optimization of advertisements. This not only improves the accuracy of advertising delivery, but also helps to improve advertising effects and user satisfaction.

[0094] The calculation formula of the advertising tendency data is:

[0095] F(A1,A2,A3,A4,A5)=T(U1,U2,U3,U4,U5)·G(A1,A2,A3,A4,A5)

[0096] Among them, F(A1, A2, A3, A4, A5) is the advertising tendency data, and G(A1, A2, A3, A4, A5) is the number of people who watch the advertisement.

[0097] G(A1,A2,A3,A4,A5)=A1·C1+A2·C2+A3·C3+A4·C4+A5·C5

[0098] Among them, A1 is the proportion of people who watch the advertisement when they arrive at the station by taxi, A2 is the proportion of people who watch the advertisement when they arrive at the station by subway, A3 is the proportion of people who watch the advertisement when they arrive at the station on foot, A4 is the proportion of people who watch the advertisement when they arrive at the station from nearby parks and attractions, and A5 is the proportion of people who watch the advertisement when they arrive at the station from nearby office places.

[0099] In step S14, a third calculation operation is performed based on the advertisement preference data to obtain the user's preference degree for advertisements.

[0100] Specifically, in step S14, the advertising preference data is processed by applying a mathematical model to calculate the user preference level for each advertisement. This process involves quantitative analysis of the matching degree between the advertising content and the user characteristics, as well as the evaluation of the user's potential interest and response to the advertisement. Specifically, factors such as the content, form, and display location of the advertisement, as well as the user's viewing habits and preferences, are considered and converted into quantifiable indicators.

[0101] It should be noted that step S14 requires further calculation and analysis of the advertising tendency data obtained in step S13. This step involves building a comprehensive evaluation model that combines advertising tendency data with multiple factors such as user expectations for advertising, whether the advertisement caters to the characteristics of the crowd, and whether the advertisement conforms to the preferences of the people in the site area. Through this model, the attractiveness of each advertisement to a specific user group can be quantified, thereby deriving the degree of user inclination towards the advertisement. In order to achieve this quantitative analysis, it is necessary to apply statistical analysis and machine learning techniques, such as logistic regression, decision trees, random forests, or neural networks. These techniques can help analyze the relationship between advertising tendency data and user responses, and predict user acceptance and preference for specific advertising content.

[0102] It should be noted that by quantifying the user's preference for advertisements, the advertisement updating system can identify which advertisements are more likely to attract the attention of a specific user group, thereby achieving more accurate advertisement delivery. This accurate delivery can improve the advertising effect. By displaying advertisements that users are more likely to be interested in, step S14 helps to improve user experience and satisfaction. Users will be exposed to more relevant and personalized advertising content, which can not only reduce the interference of advertisements, but also increase the user's participation and interactivity in the advertising content, thereby improving the overall advertising delivery effect.

[0103] The calculation formula for the user's preference for advertisements is:

[0104] W=F(A1,A2,A3,A4,A5)·T1·T2·T3·T4

[0105] Among them, W is the user's preference for advertising, F(A1, A2, A3, A4, A5) is the advertising preference data, T1 is the expectation that users of the site like the same type of advertisements, T2 is the expectation whether the advertisement caters to the characteristics of the crowd, T3 is the expectation that the advertisement meets the preferences of the people in the site area, and T4 is the expectation that the advertisement location is reasonable.

[0106] In step S15, the fourth formula is calculated based on the user habit data to obtain the user access ratio.

[0107] Specifically, step S15 involves collecting and analyzing data such as the daily visits of site users and the number of users within ten kilometers of the site. Through these data, combined with a preset time weight factor, the user visit ratio can be calculated. This ratio reflects the frequency of a specific advertisement being visited by users relative to the total site visits within a certain period of time. The calculation process includes data normalization, time series analysis, and application of a specific mathematical model to determine the visit ratio.

[0108] It should be noted that the implementation of step S15 involves in-depth mining and analysis of user habit data. First, it is necessary to collect the daily visit data of site users, including user flow data obtained through cameras, sensors or mobile applications of electronic station signs. At the same time, it is also necessary to collect user volume data within ten kilometers of the site, which needs to be achieved through technologies such as geographic information systems (GIS) or user location services. After these data are collected, they need to be cleaned and preprocessed to ensure the accuracy and availability of the data. Then, the formula is used to calculate the user access ratio.

[0109] It should be noted that the effect of step S15 is to provide the advertising update system with a quantitative indicator of user access behavior. By understanding the user access ratio, the system can identify which advertising content is more popular and which advertisements have poor display effects. This helps the system optimize the advertising display strategy and improve the exposure rate and user engagement of advertisements.

[0110] The calculation formula of the user access ratio is:

[0111]

[0112] Among them, P is the user access ratio, Q is the daily visits of site users, M is the number of users within ten kilometers of the site, and t is the preset time weight factor, which can be adjusted according to the importance of the time period.

[0113] Among them, the time weight factor t plays an adjustment and optimization role in the process of calculating the user access ratio. It reflects the influence of different time periods on the user access volume. The following is a detailed explanation of how the time weight factor t affects the user access ratio:

[0114] 1. Importance of time periods: Certain time periods are more critical to ad exposure and user interaction than others during the day. For example, during rush hour, there is a lot of user traffic, and the potential exposure opportunities for ads also increase accordingly. The time weight factor t allows the system to assign higher weights to these high-traffic, high-value time periods, so that user access data during these time periods accounts for a larger proportion of the total access ratio.

[0115] 2. Adjusting the user access ratio: By introducing the time weight factor t, the calculation of the user access ratio P not only considers the absolute number of user accesses, but also the time period in which the accesses occur. This means that even in periods with low user access, if the time weight factor is high, the accesses in these periods still have a significant impact on the final user access ratio.

[0116] 3. Optimize advertising delivery strategy: The introduction of the time weight factor t enables the advertising update system to dynamically adjust the advertising content according to the importance of the time period. For example, if the user interaction with the advertisement is particularly high during a certain time period, the system can increase the display frequency of a specific advertisement during that time period by increasing the time weight factor of that time period, thereby optimizing the advertising effect.

[0117] 4. Reflecting actual user behavior: The time weight factor t can also be adjusted based on actual user behavior data to reflect the actual attention users pay to ads at different time periods. If data shows that users have a higher interest in certain types of ads during certain non-peak hours, the time weight factor can be adjusted accordingly to ensure that ad delivery better meets users’ actual needs and preferences.

[0118] In summary, the time weight factor t has many effects on the user access ratio. It not only affects the weighted calculation of user access data, but also provides a flexible adjustment mechanism for advertising delivery strategies to ensure that the advertising content is consistent with the user's actual access behavior and preferences, thereby improving the effectiveness and accuracy of advertising.

[0119] In step S16, a product calculation is performed based on the advertisement tendency and the user access ratio to obtain the advertisement to be updated.

[0120] Specifically, when implementing step S16, the system multiplies the user preference level of each advertisement (output of step S14) by its corresponding user access ratio (output of step S15) to obtain a comprehensive score. This score reflects the degree of match between the advertisement content and user preferences and the popularity of the advertisement. The calculation process involves scoring and sorting each advertisement to determine which advertisements have the highest comprehensive score, that is, which advertisements best match the preferences and behavior patterns of the current user group.

[0121] It should be noted that in step S16, the system will multiply the user's preference for the advertisement obtained in step S14 and the user access ratio calculated in step S15. This calculation process can be expressed as a mathematical formula, for example: advertisement update score = user preference × user access ratio. Among them, the user preference reflects the matching degree between the advertisement and the user preference, and the user access ratio reflects the actual popularity of the advertisement. The calculated update score of each advertisement will be used to sort all candidate advertisements. The advertisement with the highest score means that it not only highly matches the user's preference, but also has a high user access volume, which indicates that the advertisement has the greatest chance of attracting the user's attention and producing the expected advertising effect. The system will sort the advertisements in descending order according to this advertisement update score, and select the advertisement with the highest score as the advertisement to be updated.

[0122] It should be noted that the effect of step S16 is that it can accurately screen out the advertising content that best meets the needs and preferences of users. Through product calculation, the system can quantify the comprehensive attractiveness of each advertisement, so as to give priority to displaying those advertisements that both meet the user's preferences and have a high access ratio. This method improves the personalization level of advertisements, makes the advertising content closer to the actual needs of users, enhances the relevance and attractiveness of advertisements, and improves the accuracy of advertisement updates.

[0123] Wherein, the product calculation formula is:

[0124] S=W·P

[0125] Among them, S is the advertisement update score, W is the user preference degree, and P is the user access ratio.

[0126] In summary, the present invention proposes an advertising update method and system for electronic bus stops, which realizes personalized and precise advertising content updates by comprehensively analyzing the user's travel habits and surrounding commercial information data. Specifically, the technical solution includes the following key steps: first, obtaining the user habit data and surrounding commercial information data of the site; second, performing a series of calculation operations based on these data to obtain user tendency data and advertising tendency data; then, calculating the user's tendency degree for advertising and the user's access ratio; finally, performing a product calculation based on the user's tendency degree for advertising and the access ratio to obtain the advertisement to be updated. This process involves the collaborative work of multiple modules such as data acquisition, tendency data calculation, tendency degree evaluation and access ratio analysis. Through the interaction of these modules, dynamic updating and optimization of advertising content is achieved.

[0127] In the derivation process, the present invention first collects data such as the user's travel mode, population structure, advertising expectations, and the proportion of people in the surrounding commercial areas, commercial advertising expectations, and other information through the data acquisition module. Then, the first calculation operation is performed using these data to obtain user tendency data. This step involves analyzing the user's travel habits and evaluating the user's expectations and preferences for advertising content. Subsequently, a second calculation operation is performed based on the user tendency data to obtain advertising tendency data. This step is mainly to quantitatively analyze the matching degree between advertising content and user preferences. Next, through the third calculation operation, the user's preference for advertising is obtained. This step is to further quantify the attractiveness of advertising and user interest. Finally, the fourth calculation operation is performed in combination with the user access ratio to obtain the final advertising update result. The entire process ensures the timeliness and accuracy of advertising updates through precise calculation and analysis.

[0128] Compared with the prior art, the beneficial effect of the present invention is that, by comprehensively analyzing the user habit data and surrounding commercial information data, the personalized and accurate update of the electronic station sign advertisement is realized. This method can not only make dynamic adjustments according to the user's travel habits, but also make the advertisement content closer to the actual needs of the user by analyzing the user's preference for advertisements and the access ratio, thereby enhancing the effectiveness of the advertisement and the user's satisfaction. In addition, by dynamically adjusting the advertising delivery strategy, it is ensured that the advertisement content always maintains a high degree of match with the user's needs.

[0129] Reference Figure 2 The second embodiment of the present invention provides an electronic station sign advertising update system, comprising:

[0130] A data acquisition module is used to acquire user habit data of the site and surrounding commercial information data of the site;

[0131] A user tendency data module, configured to perform a first calculation operation according to the user habit data and the surrounding commercial information data to obtain user tendency data;

[0132] An advertisement tendency data module, configured to perform a second calculation operation according to the user tendency data to obtain advertisement tendency data;

[0133] A tendency degree module, used to perform a third calculation operation according to the advertisement tendency data to obtain the user's tendency degree for the advertisement;

[0134] A user access ratio module, configured to perform a fourth calculation operation according to the user habit data to obtain a user access ratio;

[0135] The advertisement update result module is used to perform product calculation according to the user's advertisement preference degree and the user access ratio to obtain the advertisement update result.

[0136] In an optional implementation, the user habit data includes data on users arriving at the station by various travel modes, demographic data on the city where the station is located, data on the expectations of station users for advertisements, daily visits by station users, the number of users within ten kilometers of the station, and visit data on advertisements by station users;

[0137] The surrounding commercial information data includes the population ratio of each commercial area around the site, the expected data of commercial advertisements in the area around the site, the viewing data of commercial advertisements in the area around the site, and the annual passenger flow data of the commercial area around the site.

[0138] In an optional implementation, performing a first calculation operation based on the user habit data and the surrounding commercial information data to obtain user tendency data includes:

[0139] The user tendency data is calculated by the following formula:

[0140] T(U1,U2,U3,U4,U5)=U1·C1+U2·C2+U3·C3+U4·C4+U5·C5

[0141] Among them, T(U1, U2, U3, U4, U5) is the user tendency data, U1 is the proportion of people who take a taxi to the station, U2 is the proportion of people who take the subway to the station, U3 is the proportion of people who walk to the station, U4 is the proportion of people in nearby parks and attractions, and U5 is the proportion of people in nearby office places. C1 is the viewing time of people who take a taxi to the station, C2 is the viewing time of people who take the subway to the station, C3 is the viewing time of people who walk to the station, C4 is the viewing time of people who arrive at the station from parks and attractions, and C5 is the viewing time of people who arrive at the station from offices.

[0142] In an optional implementation, performing a second calculation operation according to the user tendency data to obtain advertisement tendency data includes:

[0143] The advertising tendency data is calculated by the following formula:

[0144] F(A1,A2,A3,A4,A5)=T(U1,U2,U3,U4,U5)·G(A1,A2,A3,A4,A5)

[0145] Among them, F(A1, A2, A3, A4, A5) is the advertising tendency data, and G(A1, A2, A3, A4, A5) is the number of people who watch the advertisement.

[0146] G(A1,A2,A3,A4,A5)=A1·C1+A2·C2+A3·C3+A4·C4+A5·C5

[0147] Among them, A1 is the proportion of people who watch the advertisement when they arrive at the station by taxi, A2 is the proportion of people who watch the advertisement when they arrive at the station by subway, A3 is the proportion of people who watch the advertisement when they arrive at the station on foot, A4 is the proportion of people who watch the advertisement when they arrive at the station from nearby parks and attractions, and A5 is the proportion of people who watch the advertisement when they arrive at the station from nearby office places.

[0148] In an optional implementation, performing a third calculation operation according to the advertisement preference data to obtain the user's preference for the advertisement includes:

[0149] The user's preference for advertisements is calculated using the following formula:

[0150] W=F(A1,A2,A3,A4,A5)·T1·T2·T3·T4

[0151] Among them, W is the user's preference for advertising, F(A1, A2, A3, A4, A5) is the advertising preference data, T1 is the expectation that users of the site like the same type of advertisements, T2 is the expectation whether the advertisement caters to the characteristics of the crowd, T3 is the expectation that the advertisement meets the preferences of the people in the site area, and T4 is the expectation that the advertisement location is reasonable.

[0152] In an optional implementation, performing a fourth formula calculation according to the user habit data to obtain the user access ratio includes:

[0153] The user access ratio is calculated by the following formula:

[0154]

[0155] Among them, P is the user access ratio, Q is the daily visits of site users, M is the number of users within ten kilometers of the site, and t is the preset time weight factor, which can be adjusted according to the importance of the time period.

[0156] In an optional implementation manner, performing a product calculation based on the advertisement tendency and the user access ratio to obtain the advertisement to be updated includes:

[0157] According to the tendency degree and the user access ratio, a product calculation is performed to obtain an advertisement update score;

[0158] The advertisements are sorted in descending order according to the values ​​of the advertisement update scores, and the advertisement with the highest ranking is determined as the advertisement to be updated.

[0159] It should be noted that the electronic signboard advertisement updating device provided in the embodiment of the present invention is used to execute all the process steps of the electronic signboard advertisement updating method in the above embodiment, and the working principles and beneficial effects of the two correspond one to one, so they will not be repeated.

[0160] The embodiment of the present invention also provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program for acquiring user habit data of a site and surrounding commercial information data of the site. When the processor executes the computer program, the steps of each of the above-mentioned electronic station sign advertising update method embodiments are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0161] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0162] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0163] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0164] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0165] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0166] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0167] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for updating advertisements of electronic bus stops, characterized in that: Executed by a computer, including: Obtain user habit data of the site and surrounding commercial information data of the site; Performing a first calculation operation according to the user habit data and the surrounding commercial information data to obtain user tendency data; Performing a second calculation operation according to the user tendency data to obtain advertisement tendency data; performing a third calculation operation according to the advertisement preference data to obtain the user's preference degree for the advertisement; performing a fourth calculation operation according to the user habit data to obtain a user access ratio; The advertisement to be updated is obtained by performing a product calculation based on the user's preference for the advertisement and the user's access ratio.

2. The method for updating advertisements of an electronic station sign according to claim 1, characterized in that: The user habit data includes data on users who travel to the station by various modes of transportation, demographic data on the city where the station is located, data on the expectations of station users for advertisements, daily visits by station users, the number of users within ten kilometers of the station, and data on visits to advertisements by station users; The surrounding commercial information data includes the population ratio of each commercial area around the site, the expected data of commercial advertisements in the area around the site, the viewing data of commercial advertisements in the area around the site, and the annual passenger flow data of the commercial area around the site.

3. The method for updating advertisements of an electronic station sign according to claim 1, characterized in that: The first calculation operation is performed according to the user habit data and the surrounding commercial information data to obtain user tendency data, including: The user tendency data is calculated by the following formula: T(U1,U2,U3,U4,U5)=U1·C1+U2·C2+U3·C3+U4·C4+U5·C5 Among them, T(U1, U2, U3, U4, U5) is the user tendency data, U1 is the proportion of people who take a taxi to the station, U2 is the proportion of people who take the subway to the station, U3 is the proportion of people who walk to the station, U4 is the proportion of people in nearby parks and attractions, U5 is the proportion of people in nearby office places, C1 is the viewing time of people who take a taxi to the station, C2 is the viewing time of people who take the subway to the station, C3 is the viewing time of people who walk to the station, C4 is the viewing time of people who arrive at the station from parks and attractions, and C5 is the viewing time of people who arrive at the station from offices.

4. The method for updating advertisements of an electronic station sign according to claim 3, characterized in that: The performing a second calculation operation according to the user tendency data to obtain advertisement tendency data includes: The advertising tendency data is calculated by the following formula: F(A1,A2,A3,A4,A5)=T(U1,U2,U3,U4,U5)·G(A1,A2,A3,A4,A5) Among them, F(A1, A2, A3, A4, A5) is the advertising tendency data, and G(A1, A2, A3, A4, A5) is the number of people who watch the advertisement. G(A1,A2,A3,A4,A5)=A1·C1+A2·C2+A3·C3+A4·C4+A5·C5 Among them, A1 is the proportion of people who watch the advertisement when they arrive at the station by taxi, A2 is the proportion of people who watch the advertisement when they arrive at the station by subway, A3 is the proportion of people who watch the advertisement when they arrive at the station on foot, A4 is the proportion of people who watch the advertisement when they arrive at the station from nearby parks and attractions, and A5 is the proportion of people who watch the advertisement when they arrive at the station from nearby office places.

5. The method for updating advertisements of an electronic station sign according to claim 1, characterized in that: The performing a third calculation operation according to the advertisement preference data to obtain the user's preference degree for the advertisement includes: The user's preference for advertisements is calculated using the following formula: W=F(A1,A2,A3,A4,A5)·T1·T2·T3·T4 Among them, W is the user's preference for advertising, F(A1, A2, A3, A4, A5) is the advertising preference data, T1 is the expectation that users of the site like the same type of advertisements, T2 is the expectation whether the advertisement caters to the characteristics of the crowd, T3 is the expectation that the advertisement meets the preferences of the people in the site area, and T4 is the expectation that the advertisement location is reasonable.

6. The method for updating advertisements of an electronic station sign according to claim 1, characterized in that: The fourth formula is calculated according to the user habit data to obtain the user access ratio, including: The user access ratio is calculated by the following formula: Among them, P is the user access ratio, Q is the daily visits of site users, M is the number of users within ten kilometers of the site, and t is the preset time weight factor, which can be adjusted according to the importance of the time period.

7. The method for updating advertisements of an electronic station sign according to claim 1, characterized in that: The step of performing a product calculation based on the advertisement tendency and the user access ratio to obtain the advertisement to be updated includes: According to the tendency degree and the user access ratio, a product calculation is performed to obtain an advertisement update score; The advertisements are sorted in descending order according to the values ​​of the advertisement update scores, and the advertisement with the highest ranking is determined as the advertisement to be updated.

8. An electronic station sign advertising update system, characterized in that: include: A data acquisition module is used to acquire user habit data of the site and surrounding commercial information data of the site; A user tendency data module, configured to perform a first calculation operation according to the user habit data and the surrounding commercial information data to obtain user tendency data; An advertisement tendency data module, configured to perform a second calculation operation according to the user tendency data to obtain advertisement tendency data; A tendency degree module, used to perform a third calculation operation according to the advertisement tendency data to obtain the user's tendency degree for the advertisement; A user access ratio module, configured to perform a fourth calculation operation according to the user habit data to obtain a user access ratio; The advertisement update result module is used to perform product calculation according to the user's advertisement preference degree and the user access ratio to obtain the advertisement update result.

9. An electronic device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, an advertisement updating method for an electronic bus stop sign as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the advertisement updating method of an electronic bus stop sign according to any one of claims 1 to 7.

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