A big data-based method and system for advertising a tourism product

Abstract

The present application relates to the technical field of tourism product management, and particularly relates to a tourism product advertisement putting method and system based on big data; a tourism product that has been opened is marked as a played product, and product tags corresponding to each played product are obtained; then a currently logged-in user is marked as a current user, a current tag of the current user and customer tags of played tourists of each played product are matched to obtain a first matching value of each played product relative to the current user, a played product with the highest first matching value is marked as a reference product of the current user, and all other tourism products except the reference product are marked as candidate products of the current user, and a second matching value of other candidate products relative to the reference product is further obtained based on the reference product; then each candidate product is arranged in descending order of the second matching value and combined with the reference product to obtain a promotion sequence, so as to improve the accuracy of advertisement putting.

Description

Technical Field

[0001] This invention relates to the field of tourism product management technology, specifically to a method and system for placing tourism product advertisements based on big data. Background Technology

[0002] With the booming tourism industry, more and more people are choosing travel as their vacation option. Simultaneously, with the rise of internet advertising, various industries are leveraging online media for advertising and promotion, and tourism service providers are also using internet advertising to promote their tourism products. However, currently, most internet advertisements for tourism products employ generic targeting strategies, resulting in poor accuracy in ad placement. Summary of the Invention

[0003] The main objective of this invention is to provide a method and system for advertising tourism products based on big data, aiming to solve the problem that current internet advertising for tourism products mostly adopts a general placement strategy, resulting in poor accuracy in advertising placement.

[0004] The technical solution proposed in this invention is as follows:

[0005] A method for advertising tourism products based on big data, applied to a big data-based tourism product advertising system; the system includes a cloud server and multiple user terminals connected to the cloud server; the method includes:

[0006] The cloud server marks the tourism products that have already been offered as "visited" products and obtains the product tags corresponding to each visited product, as well as the customer tags of the visitors. The product tags include the destination, duration of visit, product price, and attraction name; the customer tags include age, gender, place of residence, and estimated duration of visit.

[0007] The user terminal marks the currently logged-in user as the current user and obtains the current user's customer tag, which is then sent to the cloud server as the current tag.

[0008] The cloud server matches the current user's current tag with the customer tags of tourists who have already played each product to obtain the first matching value of each product relative to the current user. The higher the first matching value, the higher the degree of matching between the product and the current user.

[0009] The cloud server marks the played product with the highest first matching value as the current user's baseline product, and marks all other travel products besides the baseline product as the current user's candidate products;

[0010] The cloud server matches the product tags of the benchmark product and the candidate products to obtain a second matching value for each candidate product relative to the benchmark product. The higher the second matching value, the higher the degree of matching between the candidate product and the benchmark product.

[0011] The cloud server arranges the candidate products in descending order of the second matching value and combines them with the benchmark product to obtain a promotion sequence;

[0012] The user terminal displays the advertisements according to the promotion sequence.

[0013] Preferred options also include:

[0014] The user terminal displays user tag tabs, which include an age tab, a gender tab, a place of residence tab, and an estimated travel duration tab.

[0015] The user terminal obtains the current user's age selected by the age tab, gender selected by the gender tab, actual place of residence selected by the place of residence tab, and estimated travel duration selected by the estimated travel duration tab, and uses these as the current user's user tags.

[0016] Preferably, the cloud server matches the current user's current tag with the customer tags of visitors who have already visited each visited product to obtain a first matching value for each visited product relative to the current user, including:

[0017] The cloud server will mark the age in the customer tag of the j-th visitor who has already visited the i-th product as... Gender is marked as Permanent residence marked as Estimated travel duration is marked as ;

[0018] The cloud server will label the current user's age as the current age, gender as the current gender, place of residence as the current place of residence, and estimated travel duration as the current estimated travel duration.

[0019] The cloud server traversal To determine the number of customers whose age tag matches their current age among all visitors to the i-th visited product, and mark it as the number of age matches corresponding to the i-th visited product;

[0020] The cloud server traversal To determine the number of customers whose gender label matches their current gender among all visitors to the i-th visited product, and mark it as the number of gender matches corresponding to the i-th visited product;

[0021] The cloud server traversal To determine the number of customers whose permanent residence label matches their current permanent residence among all visitors of the i-th visited product, and mark it as the permanent residence matching number corresponding to the i-th visited product;

[0022] The cloud server traversal To determine the number of tourists whose estimated travel duration tag matches the current estimated travel duration among all tourists who have visited the i-th visited product, and mark it as the estimated travel duration matching number corresponding to the i-th visited product;

[0023] The cloud server calculates the first matching value of each visited product relative to the current user based on the number of age matching, gender matching, place of residence matching, and estimated travel duration matching.

[0024] Preferably, the cloud server calculates the first matching value of each visited product relative to the current user based on the number of age matches, the number of gender matches, the number of residence matches, and the number of estimated travel duration matches using the following formula:

[0025] (1),

[0026] In the formula, Let be the first matching value of the i-th already played product relative to the current user, 1≤i≤I, where I is the total number of already played products; The number of age matches corresponding to the i-th already played product; The number of gender matches corresponding to the i-th played product; The number of locations to match for the i-th visited product; Match the number of estimated travel durations for the i-th already visited product; Let be the total number of all visitors who have visited the i-th visited product.

[0027] Preferably, the system further includes a management terminal communicatively connected to the cloud server; the method further includes:

[0028] Whenever a new tourism product is identified, the management terminal obtains the product tags that are manually entered and correspond one-to-one with the new tourism product.

[0029] The cloud server matches the product tags of the benchmark product and the candidate products to obtain a second matching value for each candidate product relative to the benchmark product, including:

[0030] The cloud server filters out preferred products and secondary products from the candidate products. The destination of the preferred product is the same as the destination of the benchmark product, and the name of the attraction in the preferred product is the same as the name of the attraction in the benchmark product. The secondary products are other tourism products in the candidate products besides the preferred products.

[0031] The cloud server sequentially obtains the absolute value of the difference between the playtime of each preferred product and the playtime of the benchmark product, and marks it as the first absolute value.

[0032] The cloud server sequentially obtains the absolute value of the difference between the product price of each preferred product and the product price of the benchmark product, and marks it as the second absolute value;

[0033] The cloud server calculates a second matching value for each candidate product relative to the benchmark product based on a first absolute value and a second absolute value.

[0034] Preferably, the cloud server calculates a second matching value for each candidate product relative to a benchmark product based on a first absolute value and a second absolute value, including:

[0035] The cloud server calculates a second matching value for each preferred product relative to the benchmark product based on a first absolute value and a second absolute value:

[0036] (2),

[0037] In the formula, Let J be the second matching value of the j-th preferred product relative to the benchmark product, 1≤j≤J, where J is the total number of preferred products and satisfies J≤I; The playtime for the j-th preferred product is in days. The playtime of the benchmark product is in days; The maximum value of the first absolute value, expressed in days; The price of the j-th preferred product is expressed in yuan. The product price is the benchmark product, in yuan. The maximum value of the second absolute value, expressed in yuan;

[0038] The cloud server sets the second matching value of the secondary product relative to the benchmark product to 0.

[0039] Preferably, the user terminal displays and delivers advertisements according to the promotion sequence, including:

[0040] After receiving the promotion sequence, the user terminal marks the tourism products in the promotion sequence as promotion products. The first product in the promotion sequence is the baseline product, and the second product is the candidate product with the largest second matching value.

[0041] The user terminal generates a display page according to the promotion sequence. The display page includes multiple advertising images that can be swiped up and down sequentially. Each advertising image is displayed in full screen on the display page, and each advertising image corresponds to a promoted product. The first advertising image displayed corresponds to the base product.

[0042] Preferably, the user terminal includes a touch screen; the display page further includes prompt text to guide the user to swipe up and down to switch advertising images; the user terminal generates the display page according to the promotion sequence, and then further includes:

[0043] The user terminal displays the display page through the touch screen and obtains the finger touch data corresponding to the user swiping to switch the advertisement image during the display page. The finger touch data includes the moment when the finger starts to touch the touch screen, the moment when the finger leaves the touch screen, and the contact area of ​​the finger on the touch screen.

[0044] The user terminal obtains the time when the user switches from the kth advertisement image to the (k+1)th advertisement image based on finger touch data, which is marked as the kth start time and the time when the finger leaves the touch screen, which is marked as the kth end time, where 1≤k≤K and K+1 is the total number of advertisement images;

[0045] The user terminal marks the interval between the k-th start time and the k-th end time as the k-th interval duration:

[0046] When the duration of the kth interval is less than the preset duration, the user terminal marks the tourism product corresponding to the kth advertisement image as an obsolete product.

[0047] The user terminal obtains the contact area of ​​each sampling time between the k-th start time and the k-th end time based on finger touch data, and determines whether the contact area of ​​each sampling time is greater than the average contact area.

[0048] If so, the user terminal will mark the tourism product corresponding to the k-th advertising image as the key product for the current user, and remove the eliminated products from the key products;

[0049] The user terminal sends key products to the management terminal.

[0050] The present invention also proposes a tourism product advertising system based on big data, and applies a tourism product advertising method based on big data; the system includes a cloud server and user terminals connected to the cloud server; the number of user terminals is multiple.

[0051] The above technical solution can achieve the following beneficial effects:

[0052] The big data-based tourism product advertising method proposed in this invention addresses the problem that current internet advertising for tourism products often employs generic targeting strategies, resulting in poor accuracy. First, previously toured tourism products are marked as "already visited" products, and corresponding product tags are obtained for each visited product. Then, the currently logged-in user is marked as the current user, and their customer tag is obtained. Next, the current user's current tag is matched with the customer tags of the already visited products to obtain a first matching value for each visited product relative to the current user. A higher first matching value indicates a higher degree of matching between the visited product and the current user. Therefore, the method with the highest first matching value is further optimized... The visited products are marked as the current user's baseline products, and all other travel products besides the baseline products are marked as the current user's candidate products. Here, the baseline products are the visited products that the current user is most interested in. Based on the baseline products, a second matching value is obtained for the other candidate products relative to the baseline products. The candidate products are arranged in descending order of their second matching values ​​and combined with the baseline products to obtain a promotion sequence. The higher the second matching value of a candidate product, the greater the probability that the current user is interested in it. Therefore, it is given priority in advertising to ensure the promotion effect. This allows for the priority placement of travel product advertisements that users are interested in on the user's terminal, improving the accuracy of advertising and avoiding network resource waste. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating the first embodiment of a tourism product advertising placement method based on big data proposed in this invention. Detailed Implementation

[0055] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0056] This invention proposes a method and system for advertising tourism products based on big data.

[0057] As attached Figure 1As shown, in the first embodiment of the big data-based tourism product advertising method proposed in this invention, this big data-based tourism product advertising method is applied to a big data-based tourism product advertising system; the system includes a cloud server and user terminals (e.g., smartphone terminals) connected to the cloud server; the number of user terminals is multiple; this embodiment includes the following steps:

[0058] Step S110: The cloud server marks the tourism products that have already been offered as "visited" products, and obtains the product tags corresponding to each visited product, as well as the customer tags of the visitors who have visited the products. The product tags include the destination, the duration of the visit (the duration of the visit determined by the tourism product itself), the product price, and the name of the attraction. The customer tags include age, gender, place of residence, and estimated duration of the visit (i.e., the duration of the visit estimated by the user).

[0059] Specifically, only products that have already been visited have corresponding visitors. By analyzing the user tags of visitors who have already visited and comparing them with the current users, we can obtain products that are more suitable for the current users. New tourism products (those that have not yet been offered) are difficult to determine whether they are of interest to the current users because there is no relevant visitor information.

[0060] Step S120: The user terminal marks the currently logged-in user as the current user, obtains the current user's customer tag, marks it as the current tag, and sends it to the cloud server.

[0061] Step S130: The cloud server matches the current user's current tag with the customer tags of the tourists who have already played each product to obtain the first matching value of each product relative to the current user. The higher the first matching value, the higher the degree of matching between the product and the current user.

[0062] Specifically, the higher the first match value, the higher the current user's level of interest in the product they have already played.

[0063] Step S140: The cloud server marks the played product with the highest first matching value as the current user's baseline product, and marks all other travel products other than the baseline product as the current user's candidate products.

[0064] Specifically, the benchmark product here is the product that the current user is most interested in and has already visited, which is obtained through the analysis of the first matching value. Therefore, the benchmark product will be used as a judgment standard to further obtain the degree of matching between other tourism products (i.e., subsequent candidate products; it is worth noting that the candidate products here can include tourism products that have not been offered as tours, as long as they have product tags) and the current user.

[0065] Step S150: The cloud server matches the product tags of the benchmark product and the candidate products to obtain a second matching value of each candidate product relative to the benchmark product. The higher the second matching value, the higher the degree of matching between the candidate product and the benchmark product.

[0066] Step S160: The cloud server arranges each candidate product in descending order of the second matching value and combines it with the benchmark product to obtain a promotion sequence.

[0067] Specifically, since a higher second matching value indicates a higher degree of matching between the candidate product and the benchmark product, the candidate product with a higher second matching value is more likely to be of interest to the current user. Therefore, it should be prioritized for advertising to ensure the promotion effect.

[0068] Step S170: The user terminal displays the advertisement according to the promotion sequence.

[0069] The big data-based tourism product advertising method proposed in this invention addresses the problem that current internet advertising for tourism products often employs generic targeting strategies, resulting in poor accuracy. First, previously toured tourism products are marked as "already visited" products, and corresponding product tags are obtained for each visited product. Then, the currently logged-in user is marked as the current user, and their customer tag is obtained. Next, the current user's current tag is matched with the customer tags of the already visited products to obtain a first matching value for each visited product relative to the current user. A higher first matching value indicates a higher degree of matching between the visited product and the current user. Therefore, the method with the highest first matching value is further optimized... The visited products are marked as the current user's baseline products, and all other travel products besides the baseline products are marked as the current user's candidate products. Here, the baseline products are the visited products that the current user is most interested in. Based on the baseline products, a second matching value is obtained for the other candidate products relative to the baseline products. The candidate products are arranged in descending order of their second matching values ​​and combined with the baseline products to obtain a promotion sequence. The higher the second matching value of a candidate product, the greater the probability that the current user is interested in it. Therefore, it is given priority in advertising to ensure the promotion effect. This allows for the priority placement of travel product advertisements that users are interested in on the user's terminal, improving the accuracy of advertising and avoiding network resource waste.

[0070] In a second embodiment of the tourism product advertising method based on big data proposed in this invention, based on the first embodiment, this embodiment further includes the following steps:

[0071] Step S210: The user terminal displays a user tag tab, which includes an age tab, a gender tab, a place of residence tab, and an estimated travel duration tab; the estimated travel duration tab has options for 1-3 days, 4-7 days, and more than 7 days.

[0072] Step S220: The user terminal obtains the current user's age selected by the age tab, the gender selected by the gender tab, the actual place of residence selected by the place of residence tab, and the estimated travel duration selected by the estimated travel duration tab, and uses them as the current user's user tags.

[0073] Specifically, this embodiment provides a method for users to determine their corresponding user tags themselves.

[0074] In the third embodiment of the tourism product advertising method based on big data proposed in this invention, based on the first embodiment, step S130 includes the following steps:

[0075] Step S310: The cloud server marks the age in the customer tag of the j-th visitor who has already played the i-th product as... Gender is marked as Permanent residence marked as Estimated travel duration is marked as .

[0076] Specifically, there are multiple products that have been visited (represented by variable i), and each visited product corresponds to multiple visitors (represented by variable j); the age in the customer tag of the j-th visitor of the i-th visited product is marked as... Gender is marked as Permanent residence marked as Estimated travel duration is marked as This is to facilitate further analysis later.

[0077] Step S320: The cloud server marks the current user's age tag as the current age, gender tag as the current gender, place of residence tag as the current place of residence, and estimated travel duration tag as the current estimated travel duration.

[0078] Specifically, the current age, current gender, current place of residence, and current estimated travel duration are the specific content of the current user's user tags.

[0079] Step S330: The cloud server is traversed To determine the number of visitors whose age tag matches their current age among all visitors to the i-th visited product, and mark it as the number of age matches corresponding to the i-th visited product.

[0080] Specifically, the more age matches the i-th played product, the more tourists in the i-th played product are of the same age as the current user, and the more the i-th played product matches the current user in terms of audience age.

[0081] Step S340: The cloud server is traversed To determine the number of customers whose gender label matches their current gender among all visitors to the i-th visited product, and mark it as the number of gender matches corresponding to the i-th visited product.

[0082] Specifically, the more gender matches the i-th played product has, the more tourists in the i-th played product have the same gender as the current user, and the more the i-th played product matches the current user in terms of audience gender.

[0083] Step S350: The cloud server is traversed To determine the number of customers whose permanent residence label matches their current permanent residence among all visitors to the i-th visited product, and mark it as the permanent residence matching number corresponding to the i-th visited product.

[0084] Specifically, the more matching places of residence the i-th visited product has, the more visitors to the i-th visited product share the same place of residence as the current user, and the more the i-th visited product matches the current user from the perspective of place of residence.

[0085] Step S360: The cloud server traversal To determine the number of tourists whose estimated travel duration tag matches the current estimated travel duration among all tourists who have visited the i-th visited product, and mark it as the estimated travel duration matching number corresponding to the i-th visited product.

[0086] Specifically, the more times the estimated travel duration matches the i-th visited product, the more tourists in the i-th visited product have the same estimated travel duration as the current user, and the more the i-th visited product matches the current user in terms of estimated travel duration.

[0087] Step S370: The cloud server calculates the first matching value of each visited product relative to the current user based on the number of age matching, the number of gender matching, the number of residence matching, and the number of estimated travel duration matching.

[0088] Specifically, in summary, by comprehensively considering the number of age matches, gender matches, place of residence matches, and estimated travel duration matches, the first matching value of each visited product relative to the current user can be quantitatively calculated.

[0089] In the fourth embodiment of the big data-based tourism product advertising method proposed in this invention, based on the third embodiment, the cloud server calculates the first matching value of each visited product relative to the current user based on the number of age matches, the number of gender matches, the number of residence matches, and the number of estimated travel duration matches using the following formula:

[0090] (1),

[0091] In the formula, Let be the first matching value of the i-th already played product relative to the current user, 1≤i≤I, where I is the total number of already played products; The number of age matches corresponding to the i-th already played product; The number of gender matches corresponding to the i-th played product; The number of locations to match for the i-th visited product; Match the number of estimated travel durations for the i-th already visited product; Let be the total number of all visitors who have visited the i-th visited product.

[0092] Specifically, this embodiment provides a formula for calculating the first matching value of each played product relative to the current user; from the formula, it can be seen that... The maximum value is 4, and The larger the value, the higher the match between the i-th already played product and the current user, and the greater the probability that the current user is interested in the i-th already played product. Therefore, the first matching value is directly set to... The largest i-th visited product is marked as the benchmark product (i.e., the travel product that best matches the current user).

[0093] In the fifth embodiment of the tourism product advertising method based on big data proposed in this invention, based on the fourth embodiment, the system further includes a management terminal (e.g., a computer terminal) communicatively connected to the cloud server; this embodiment also includes the following steps:

[0094] Step S410: Whenever a new tourism product is identified, the management terminal obtains the product tags that are manually entered and correspond one-to-one with the new tourism product.

[0095] Step S150 includes the following steps:

[0096] Step S420: The cloud server selects preferred products and secondary products from the candidate products. The destination of the preferred product is the same as the destination of the benchmark product, and the name of the attraction in the preferred product is the same as the name of the attraction in the benchmark product. The secondary products are other tourism products in the candidate products besides the preferred products.

[0097] Specifically, the candidate products are all other travel products besides the benchmark product. Therefore, it is necessary to further subdivide the matching degree of the candidate products with the current user in order to facilitate on-demand advertising in the future. Here, the candidate products are directly divided into preferred products and secondary products. Since the destination of the preferred product is the same as the destination of the benchmark product, and the name of the attraction in the preferred product is the same as the name of the attraction in the benchmark product, it can be seen that the preferred product is a better match for the current user than the secondary product. Therefore, the second matching value of the preferred product must be higher than the second matching value of the secondary product.

[0098] Step S430: The cloud server sequentially obtains the absolute value of the difference between the playtime of each preferred product and the playtime of the benchmark product, and marks it as the first absolute value.

[0099] Specifically, the unit of the first absolute value here is days. The larger the first absolute value, the greater the difference in playtime. Therefore, the greater the difference between the preferred product and the benchmark product in terms of playtime, the lower the matching degree between the preferred product and the current user. Correspondingly, the second matching value should be smaller.

[0100] Step S440: The cloud server sequentially obtains the absolute value of the difference between the product price of each preferred product and the product price of the benchmark product, and marks it as the second absolute value.

[0101] Specifically, the unit of the second absolute value here is yuan. The larger the second absolute value, the greater the price difference between the products. In this case, the greater the price difference between the preferred product and the benchmark product, the lower the matching degree between the preferred product and the current user; the corresponding second matching value should be smaller.

[0102] Step S450: The cloud server calculates the second matching value of each candidate product relative to the benchmark product based on the first absolute value and the second absolute value.

[0103] In the sixth embodiment of the tourism product advertising placement method based on big data proposed in this invention, based on the fifth embodiment, step S450 includes the following steps:

[0104] Step S610: The cloud server calculates a second matching value for each preferred product relative to the benchmark product based on the first absolute value and the second absolute value.

[0105] (2),

[0106] In the formula, Let J be the second matching value of the j-th preferred product relative to the benchmark product, 1≤j≤J, where J is the total number of preferred products and satisfies J≤I; The playtime for the j-th preferred product is in days. The playtime of the benchmark product is in days; The maximum value of the first absolute value, expressed in days; The price of the j-th preferred product is expressed in yuan. The product price is the benchmark product, in yuan. It represents the maximum value of the second absolute value, expressed in yuan.

[0107] Specifically, this embodiment provides a specific formula for calculating the second matching value of each preferred product relative to the benchmark product; in the formula... and The maximum value of all of them is 1, therefore The minimum value is 0, while the maximum value is close to 2, but cannot be equal to 2; the larger the second matching value, the higher the degree of matching between the preferred product and the benchmark product.

[0108] Step S620: The cloud server sets the second matching value of the secondary product relative to the benchmark product to 0.

[0109] Specifically, the candidate products also include secondary products. Since the secondary products are less compatible with the current user than the preferred products, the second matching value assigned to them should be smaller, so they are directly assigned a value of 0.

[0110] In the seventh embodiment of the tourism product advertising method based on big data proposed in this invention, based on the fifth embodiment, step S170 includes the following steps:

[0111] Step S710: After receiving the promotion sequence, the user terminal marks the tourism products in the promotion sequence as promotion products, wherein the first product in the promotion sequence is the baseline product and the second product is the candidate product with the largest second matching value.

[0112] Specifically, since the benchmark product is the most suitable product that the current user has already played, the benchmark product is directly used as the first product in the promotion sequence, the second product is the candidate product with the largest second matching value, and the subsequent products are arranged in descending order of the second matching value.

[0113] Step S720: The user terminal generates a display page according to the promotion sequence. The display page includes multiple advertising images that can be swiped up and down sequentially. Each advertising image is displayed in full screen on the display page, and each advertising image corresponds to a promoted product. The first advertising image displayed corresponds to the base product.

[0114] Specifically, the user terminal's display page includes multiple advertising images that can be scrolled up and down sequentially, allowing users to browse and switch between them simultaneously. This provides users with a variety of tourism product promotions, balancing the precision and diversity of advertising.

[0115] In the eighth embodiment of the tourism product advertising method based on big data proposed in this invention, based on the seventh embodiment, the user terminal includes a touch screen; the display page also includes prompt text for prompting the user to swipe up and down to switch the advertisement image (thereby guiding the user to manually swipe and switch while browsing the display page); after step S720, the following steps are also included:

[0116] Step S810: The user terminal displays the display page through the touch screen and obtains the finger touch data corresponding to the user swiping to switch the advertisement image during the display of the display page. The finger touch data includes the moment when the finger starts to touch the touch screen, the moment when the finger leaves the touch screen, and the contact area of ​​the finger on the touch screen.

[0117] Step S820: The user terminal obtains the time when the user switches from the kth advertisement image to the (k+1)th advertisement image based on the finger touch data, which is marked as the kth start time and the time when the finger leaves the touch screen, which is marked as the kth end time, where 1≤k≤K and K+1 is the total number of advertisement images.

[0118] Step S830: The user terminal marks the interval between the kth start time and the kth end time as the kth interval duration.

[0119] Specifically, the kth interval duration here is the finger swipe duration corresponding to the user switching from the kth ad image to the (k+1)th ad image.

[0120] Step S840: When the duration of the kth interval is less than the preset duration (e.g., 0.3 seconds), the user terminal marks the tourism product corresponding to the kth advertising image as an obsolete product.

[0121] Specifically, the shorter the time it takes for a user to switch between ad images by swiping their finger, the less interested the user is in the ad image that is being switched to. Therefore, in this step, the travel product corresponding to the kth interval with a duration less than the preset duration is marked as an eliminated product. Subsequently, the cloud server should reduce the promotion of eliminated products relative to the current user.

[0122] Step S850: The user terminal obtains the contact area of ​​each sampling time between the k-th start time and the k-th end time based on the finger touch data, and determines whether the contact area of ​​each sampling time is greater than the average contact area (the average contact area here is obtained by taking the average value of the finger touch data corresponding to all users operating the user terminal over a period of time (e.g., 1 year)).

[0123] In practical applications, regardless of whether it's a capacitive, resistive, or other type of touchscreen, all touchscreens are equipped with multiple touch sensors arranged in an array. These sensors divide the touchscreen into multiple touch units, enabling the detection of user touch actions at various locations on the screen and subsequently responding to these actions to output touch data. Specifically, when a user's finger touches the touchscreen, it touches multiple touch units. The touch sensor corresponding to each touch unit can detect the touch action, thus determining the location of the touch point in this embodiment. The more touch sensors triggered when a user's finger touches the touchscreen, the larger the contact area between the user's finger and the touchscreen. Therefore, the user terminal can obtain the contact area between the finger and the touchscreen by counting the number of triggered touch sensors.

[0124] Specifically, when a user switches between ad images by swiping their finger, the harder the finger presses (i.e., the larger the contact area between the finger and the touchscreen), the higher the user's attention is to the currently switched ad image. This indicates a more hesitant swipe action and greater user interest in the currently switched ad image. Therefore, the tourism product corresponding to that ad image is marked as a key product. In subsequent steps, discarded products are removed from the key products list. The remaining key products are presumed to be tourism products that the user cares about and is more interested in. Therefore, these key products are directly sent to the management terminal so that tourism product suppliers can conduct precise tracking and marketing for the current user's key products.

[0125] If so, proceed to step S860: The user terminal marks the tourism product corresponding to the kth advertising image as the key product for the current user, and removes the eliminated products from the key products.

[0126] Step S870: The user terminal sends the key products to the management terminal.

[0127] The present invention also proposes a tourism product advertising system based on big data, and applies a tourism product advertising method based on big data; the system includes a cloud server and user terminals connected to the cloud server; the number of user terminals is multiple.

[0128] The sequence 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.

[0129] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for advertising tourism products based on big data, characterized in that, An application to a tourism product advertising system based on big data; the system includes a cloud server and user terminals connected to the cloud server. The number of user terminals is multiple; the method includes: The cloud server marks the tourism products that have already been offered as "visited" products and obtains the product tags corresponding to each visited product, as well as the customer tags of the visitors. The product tags include the destination, duration of visit, product price, and attraction name; the customer tags include age, gender, place of residence, and estimated duration of visit. The user terminal marks the currently logged-in user as the current user and obtains the current user's customer tag, which is then sent to the cloud server as the current tag. The cloud server matches the current user's current tag with the customer tags of tourists who have already played each product to obtain the first matching value of each product relative to the current user. The higher the first matching value, the higher the degree of matching between the product and the current user. The cloud server marks the played product with the highest first matching value as the current user's baseline product, and marks all other travel products besides the baseline product as the current user's candidate products; The cloud server matches the product tags of the benchmark product and the candidate products to obtain a second matching value for each candidate product relative to the benchmark product. The higher the second matching value, the higher the degree of matching between the candidate product and the benchmark product. The cloud server arranges the candidate products in descending order of the second matching value and combines them with the benchmark product to obtain a promotion sequence; The user terminal displays the advertisements according to the promotion sequence.

2. The method for placing tourism product advertisements based on big data according to claim 1, characterized in that, Also includes: The user terminal displays user tag tabs, which include an age tab, a gender tab, a place of residence tab, and an estimated travel duration tab. The user terminal obtains the current user's age selected by the age tab, gender selected by the gender tab, actual place of residence selected by the place of residence tab, and estimated travel duration selected by the estimated travel duration tab, and uses these as the current user's user tags.

3. The method for placing tourism product advertisements based on big data according to claim 1, characterized in that, The cloud server matches the current user's current tag with the customer tags of visitors who have already visited each visited product to obtain a first matching value for each visited product relative to the current user, including: The cloud server will mark the age in the customer tag of the j-th visitor who has already visited the i-th product as... Gender is marked as Permanent residence marked as Estimated travel duration is marked as ; The cloud server will label the current user's age as the current age, gender as the current gender, place of residence as the current place of residence, and estimated travel duration as the current estimated travel duration. The cloud server traversal To determine the number of customers whose age tag matches their current age among all visitors to the i-th visited product, and mark it as the number of age matches corresponding to the i-th visited product; The cloud server traversal To determine the number of customers whose gender label matches their current gender among all visitors to the i-th visited product, and mark it as the number of gender matches corresponding to the i-th visited product; The cloud server traversal To determine the number of customers whose permanent residence label matches their current permanent residence among all visitors of the i-th visited product, and mark it as the permanent residence matching number corresponding to the i-th visited product; The cloud server traversal To determine the number of tourists whose estimated travel duration tag matches the current estimated travel duration among all tourists who have visited the i-th visited product, and mark it as the estimated travel duration matching number corresponding to the i-th visited product; The cloud server calculates the first matching value of each visited product relative to the current user based on the number of age matching, gender matching, place of residence matching, and estimated travel duration matching.

4. The method for placing tourism product advertisements based on big data according to claim 3, characterized in that, The cloud server calculates the first matching value of each visited product relative to the current user based on the number of age matches, gender matches, place of residence matches, and estimated travel duration matches using the following formula: （1）， In the formula, Let i be the first matching value of the i-th played product relative to the current user, 1≤i≤I, where I is the total number of played products; The number of age matches corresponding to the i-th already played product; The number of gender matches corresponding to the i-th played product; The number of locations to match for the i-th visited product; Match the number of estimated travel durations for the i-th already visited product; This represents the total number of all visitors who have already visited the i-th visited product.

5. The method for placing tourism product advertisements based on big data according to claim 4, characterized in that, The system further includes a management terminal that is communicatively connected to the cloud server; the method further includes: Whenever a new tourism product is identified, the management terminal obtains the product tags that are manually entered and correspond one-to-one with the new tourism product. The cloud server matches the product tags of the benchmark product and the candidate products to obtain a second matching value for each candidate product relative to the benchmark product, including: The cloud server filters out preferred products and secondary products from the candidate products. The destination of the preferred product is the same as the destination of the benchmark product, and the name of the attraction in the preferred product is the same as the name of the attraction in the benchmark product. The secondary products are other tourism products in the candidate products besides the preferred products. The cloud server sequentially obtains the absolute value of the difference between the playtime of each preferred product and the playtime of the benchmark product, and marks it as the first absolute value. The cloud server sequentially obtains the absolute value of the difference between the product price of each preferred product and the product price of the benchmark product, and marks it as the second absolute value; The cloud server calculates a second matching value for each candidate product relative to the benchmark product based on a first absolute value and a second absolute value.

6. The method for placing tourism product advertisements based on big data according to claim 5, characterized in that, The cloud server calculates a second matching value for each candidate product relative to a benchmark product based on a first absolute value and a second absolute value, including: The cloud server calculates a second matching value for each preferred product relative to the benchmark product based on a first absolute value and a second absolute value: （2）， In the formula, Let J be the second matching value of the j-th preferred product relative to the benchmark product, 1≤j≤J, where J is the total number of preferred products and satisfies J≤I; The playtime for the j-th preferred product is in days. The playtime of the benchmark product is in days; The maximum value of the first absolute value, expressed in days; Let be the product price of the j-th preferred product, in yuan; The product price is the benchmark product, in yuan. The maximum value of the second absolute value, expressed in yuan; The cloud server sets the second matching value of the secondary product relative to the benchmark product to 0.

7. The method for placing tourism product advertisements based on big data according to claim 5, characterized in that, The user terminal displays advertisements according to the promotion sequence, including: After receiving the promotion sequence, the user terminal marks the tourism products in the promotion sequence as promotion products. The first product in the promotion sequence is the baseline product, and the second product is the candidate product with the largest second matching value. The user terminal generates a display page according to the promotion sequence. The display page includes multiple advertising images that can be swiped up and down sequentially. Each advertising image is displayed in full screen on the display page, and each advertising image corresponds to a promoted product. The first advertising image displayed corresponds to the base product.

8. The method for placing tourism product advertisements based on big data according to claim 7, characterized in that, The user terminal includes a touch screen; the display page also includes prompt text to guide the user to swipe up and down to switch advertising images; the user terminal generates the display page according to the promotion sequence, and then includes: The user terminal displays the display page through the touch screen and obtains the finger touch data corresponding to the user swiping to switch the advertisement image during the display page. The finger touch data includes the moment when the finger starts to touch the touch screen, the moment when the finger leaves the touch screen, and the contact area of ​​the finger on the touch screen. The user terminal obtains the time when the user switches from the kth advertisement image to the (k+1)th advertisement image based on finger touch data, which is marked as the kth start time and the time when the finger leaves the touch screen, which is marked as the kth end time, where 1≤k≤K and K+1 is the total number of advertisement images; The user terminal marks the interval between the k-th start time and the k-th end time as the k-th interval duration: When the duration of the kth interval is less than the preset duration, the user terminal marks the tourism product corresponding to the kth advertisement image as an obsolete product. The user terminal obtains the contact area of ​​each sampling time between the k-th start time and the k-th end time based on finger touch data, and determines whether the contact area of ​​each sampling time is greater than the average contact area. If so, the user terminal will mark the tourism product corresponding to the k-th advertising image as the key product for the current user, and remove the eliminated products from the key products; The user terminal sends key products to the management terminal.

9. A tourism product advertising delivery system based on big data, characterized in that, The system employs a tourism product advertising method based on big data as described in any one of claims 1-8; the system includes a cloud server and user terminals connected to the cloud server; the number of user terminals is multiple.

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