Advertising data placement matching method and device based on location information

Abstract

This application provides a method and device for matching advertisement data placement based on location information, which relates to the field of computer technology. The method includes: determining a high-value area distribution map; determining the location and moving speed of the current advertisement placement device; determining the second advertisement type information of the current advertisement placement device based on the location of the current advertisement placement device and the high-value area distribution map; and performing advertisement placement matching based on the second advertisement type information and the moving speed of the current advertisement placement device. It can perform precise matching and advertisement placement according to multi-dimensional information such as the user's real-time location, motion state, value, and user characteristics, improving the pertinence and effectiveness of advertisement placement; and optimizing the advertisement placement strategy to continuously improve the advertisement placement effect.

Description

Technical Field

[0001] This application relates to the field of information technology. Specifically, it relates to a method and device for matching advertisement data placement based on location information. Background Art

[0002] With the rapid development of mobile Internet and location service technologies, advertisement placement based on location information has become an important means for advertisers to conduct precision marketing. Existing advertisement placement methods mainly rely on users' geographical location information, but there are certain limitations. They cannot perform precise matching and placement based on multi-dimensional information such as users' real-time location, movement state, trajectory, navigation information, and user characteristics, resulting in poor advertisement placement effects. Summary of the Invention

[0003] The purpose of the embodiments of this application is to provide a method and device for matching advertisement data placement based on location information to solve the problems existing in the prior art.

[0004] In a first aspect, this application provides a method for matching advertisement data placement based on location information, where the advertisement is to be placed on an advertisement placement device located on an outdoor moving vehicle; the method includes:

[0005] Determine a high-value area distribution map, where the high-value area distribution map includes multiple high-value areas, and each high-value area corresponds to first advertisement type information and location information. The first advertisement type information of each high-value area is used to indicate the advertisement types with relatively high first revenue scores corresponding to the high-value area and the first revenue scores corresponding to each advertisement type, and the location information of each high-value area is used to indicate the area range corresponding to the high-value area;

[0006] Determine the location and moving speed of the current advertisement placement device;

[0007] Based on the location of the current advertisement placement device and the high-value area distribution map, determine the second advertisement type information of the current advertisement placement device, where the second advertisement type information of the current advertisement placement device is used to indicate the advertisement types with relatively high second revenue scores corresponding to the current advertisement placement device and the second revenue scores corresponding to each advertisement type; the second revenue score is determined based on the distance between the current advertisement placement device and a first high-value area, and the first advertisement type information of the location of the high-value area. The first high-value area belongs to one or more high-value areas that are the closest to the current advertisement placement device;

[0008] Perform advertisement placement matching based on the second advertisement type information and the moving speed of the current advertisement placement device.

[0009] In a second aspect, the present application provides an advertising data placement matching device based on location information, where the advertisement is for placement on an advertising placement device located on an outdoor moving vehicle; the device includes:

[0010] A first determination module, configured to determine a high-value area distribution map, the high-value area distribution map including multiple high-value areas, each high-value area corresponding to first advertisement type information and location information, the first advertisement type information of each high-value area being used to indicate the advertisement types with relatively high first revenue scores corresponding to the high-value area and the first revenue scores corresponding to each advertisement type, and the location information of each high-value area being used to indicate the area range corresponding to the high-value area;

[0011] A second determination module, configured to determine the location and moving speed of the current advertising placement device;

[0012] A third determination module, configured to determine the second advertisement type information of the current advertising placement device based on the location of the current advertising placement device and the high-value area distribution map, where the second advertisement type information of the current advertising placement device is used to indicate the advertisement types with relatively high second revenue scores corresponding to the current advertising placement device and the second revenue scores corresponding to each advertisement type; the second revenue score is determined based on the distance between the current advertising placement device and a first high-value area and the first advertisement type information of the location of the high-value area, and the first high-value area belongs to one or more high-value areas closest to the current advertising placement device;

[0013] A matching module, configured to perform advertising placement matching based on the second advertisement type information and the moving speed of the current advertising placement device.

[0014] Compared with the prior art, the present application provides an advertising data placement matching method and device based on location information, having the following beneficial effects:

[0015] It can perform precise matching and placement of advertisements based on multi-dimensional information such as the user's real-time location, motion state, value, and user characteristics, improving the targeting and effectiveness of advertising placement; and optimizing the advertising placement strategy to continuously improve the advertising placement effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can also be obtained based on these drawings without creative efforts.

[0017] Figure 1 Schematic flowchart of an advertising data placement matching method based on location information provided by an embodiment of the present application;

[0018] Figure 2 Schematic structural diagram of an advertising data placement matching device based on location information provided by an embodiment of the present application;

[0019] Figure 3 Schematic structural diagram of an advertising placement system provided by an embodiment of the present application;

[0020] Figure 4 Schematic structural diagram of a mobile phone provided by an embodiment of the present application. Detailed implementation manners

[0021] Next, the technical solutions in the embodiments of the present application will be described with reference to the accompanying drawings in the embodiments of the present application.

[0022] Figure 1 Schematic flowchart of an advertising data placement matching method based on location information provided by an embodiment of the present application. Here, the advertisement is used to be placed on an advertisement placement device of an outdoor moving vehicle. In the embodiment of the present application, the outdoor moving vehicle is an electric bicycle, and the advertisement placement device is powered by the power supply of the electric bicycle. The advertisement placement device includes a short-range communication module, and the short-range communication module is used to communicate with a mobile phone. This method can be applied to the mobile phone, and the short-range communication module includes a Bluetooth module or a WiFi (Wireless Fidelity) module. As Figure 1 shown, the method includes:

[0023] S110. Determine a high-value area distribution map, which includes multiple high-value areas. Each high-value area corresponds to first advertisement type information and location information. The first advertisement type information of each high-value area is used to indicate the advertisement types with relatively high first revenue scores corresponding to the high-value area and the first revenue scores corresponding to each advertisement type, and the location information of each high-value area is used to indicate the area range corresponding to the high-value area.

[0024] Among them, the high-value areas can be obtained based on historical data statistics and can be determined according to data such as the number of people and urban planning. To simplify the calculation amount, the location information of the high-value area can be determined by a center point and a radius, or can be delineated based on multiple broken lines.

[0025] For example, around business districts, transportation hubs or scenic spots, the boundaries of high-value areas can be comprehensively demarcated by integrating mobile device GPS (Global Positioning System) trajectories, public transportation card swiping records, and the density of surrounding commercial POIs (Points of Interest). For circular area modeling, a radius threshold can be preset to automatically generate a radial coverage area; while the polygon delineation scheme supports importing a sequence of geofence coordinates and accurately outlines irregular administrative boundaries through vector overlay algorithms. The dynamic weight model can periodically update the area value rating by integrating real-time pedestrian flow heat maps. When a new subway station or commercial complex is detected, the system will trigger an adaptive expansion mechanism for the area range. This flexible demarcation method not only accommodates the static characteristics of urban planning but also captures the dynamic variables of urban development.

[0026] The high-value area distribution map can be city-based, that is, the high-value area distribution map can be the distribution map of high-value areas in a city.

[0027] For high-value areas, the suitable types of advertisements are also different.

[0028] For example, in specific business district scenarios, pre-sale information flow advertisements for luxury new products can be targeted and pushed; around transportation hubs, online car-hailing coupons and mobile LBS (Location-Based Services) advertisements for hotel reservations are suitable; within the radiation range of scenic spots, AR interactive advertisements for travel guides and limited-time discounts for special dining restaurants are emphasized. The system analyzes the POI label features of the area through a semantic analysis engine, combines data on dimensions such as stay duration and consumption frequency in the user portrait library, and uses the Bayesian classification algorithm to automatically match the optimal advertisement form. When it is detected that a new commercial settlement has formed around a subway station, the advertising decision tree will trigger the bidding and placement authority for brand owners in the cultural and entertainment category and simultaneously adjust the time slot exposure weight coefficient of the information flow advertisement.

[0029] The type of this advertisement can be determined in advance.

[0030] First revenue score Determined based on the per capita GDP (Gross Domestic Product) of the high-value area, the pedestrian flow in the high-value area, the POI score of the high-value area, and the POI type.

[0031] ;

[0032] : The contribution weight of per capita GDP to revenue; can be obtained from the official statistical bureau;

[0033] :The contribution weight of pedestrian flow to revenue; can be determined based on base station signaling data + vehicle-mounted camera counting;

[0034] :The contribution weight of the comprehensive POI score to revenue; can be determined according to Dianping.com / Meituan;

[0035] :The advertising type adaptability weight; can be output by a pre-trained model;

[0036] :Per capita GDP of the region (10,000 yuan / year); can be obtained from the official statistical bureau;

[0037] Pedestrian flow density: Pedestrian flow per unit area (persons / km²); Analysis of base station heat map and GPS trajectory density;

[0038] :Comprehensive POI score; can be obtained through a big data platform; for example, obtained by combining business, social and environmental scores;

[0039] :Advertising type adaptation coefficient (e.g., catering = 0.9, retail = 0.7); Training with business rule library + user click feedback.

[0040] Among them, the advertising types with the top-ranked first revenue scores corresponding to the high-value areas can be the top-ranked specified number of advertising types, and the number of the specified types can be an empirical value and can be set in advance.

[0041] The calculation of HVRS (High-Value Region Revenue Score) consists of 4 linear weighting parameters, and the specific calculation logic is as follows:

[0042] Per capita GDP component ( ) can be obtained from the annual report of per capita GDP at the sub-street / business district level (unit: 10,000 yuan / year), =(Total regional GDP × fiscal contribution coefficient) / permanent population.

[0043] Example: For a certain CBD (Central Business District) with a per capita GDP of 215,000 yuan and α = 0.25, the component value = 0.25 × 21.5 = 5.375;

[0044] Pedestrian flow density component ( ) can estimate the real-time population density through the number of cellular network connections. For example, the number of pedestrians can be counted based on YOLOv5 object detection.

[0045] Calculation method: Pedestrian flow density = ∑(base station heat map grid value × 0.8 + visual statistical value × 0.2) / regional area (km²);

[0046] Example: When the population density of a business district = 8,500 people / km² and β = 0.35, the component value = 0.35 × ln(1 + 8,500) = 0.35 × 9.05 = 3.1675.

[0047] The comprehensive score of POI ( ) can be determined through a multi-dimensional scoring system:

[0048] Commercial value: store rental level × 0.6 + brand level × 0.4;

[0049] Social popularity: average stay duration obtained from the review API (Application Programming Interface) × 0.5 + check-in density × 0.5;

[0050] Environmental quality: air quality index × 0.3 + green coverage rate × 0.7;

[0051] Normalization processing: = 0.4 × commerce + 0.4 × social + 0.2 × environment;

[0052] Example: A certain complex = 8.2 / 10, when γ = 0.3, the component value = 0.3 × 8.2 = 2.46.

[0053] Type adaptation coefficient ( ) can be generated through dynamic weights:

[0054] Basic rule library: preset industry weights (catering 0.9, finance 0.85, retail 0.7);

[0055] Reinforcement learning update: adjust weights based on click-through rate feedback;

[0056] Example: For a catering advertisement in a shopping mall scenario, when δ = 0.1, the component value = 0.1 × 0.9 = 0.09;

[0057] Comprehensive calculation example:

[0058] Assume the parameters of a certain area are:

[0059] α = 0.25, GDP per = 21.5;

[0060] β = 0.35, population density = 8,500;

[0061] γ = 0.3, POI score = 8.2;

[0062] δ = 0.1, Type weight = 0.9;

[0063] Then HVRS = 5.375 + 3.1675 + 2.46 + 0.09 = 11.0925.

[0064] Sorting rule:

[0065] Adopt the Top-K screening mechanism (in information retrieval and recommendation systems, Top-K refers to the top K results selected according to a certain sorting rule). When the empirical threshold K = 3, select the top three advertising types with the highest HVRS scores (such as catering, luxury goods, finance) as the preferred placement types. The weight parameters need to be verified through grid search. The typical optimization goal is to maximize the advertising eCPM (effective Cost Per Mille, the effective cost per thousand impressions), and the constraint condition is ∑(α + β + γ + δ) = 1.

[0066] S120, determine the location and moving speed of the current advertising placement device.

[0067] Among them, the location of the advertising placement device can be determined based on GPS satellite positioning; the intersection positioning of cellular base station signal strengths; the comparison and positioning of Wi-Fi hotspot fingerprint databases; inertial navigation units and other technologies. At this time, the advertising placement device or the mobile phone communicating with the advertising placement device needs to have hardware that supports the above functions. For the determination of speed, it can be based on the GPS coordinate time series differential calculation; the Doppler effect frequency shift analysis; the reading of dynamic parameters of vehicle-mounted systems; the double integration deduction of accelerometers.

[0068] The location of the advertising placement device can be based on satellite hybrid positioning. By resolving coordinates through GPS / Beidou satellite signals, combining cellular base station triangulation positioning (error < 10 meters when the base station density in urban areas > 3 per square kilometer) and Wi-Fi hotspot fingerprint database comparison (accuracy reaches ±3 meters when there are more than 50 pre-stored hotspots in commercial areas), centimeter-level real-time positioning can be achieved.

[0069] Inertial-aided calibration can also be adopted. Equipped with a six-axis MEMS (Micro-Electro-Mechanical Systems) sensor (including a three-axis gyroscope with a range of ±2000 dps), it can maintain dead reckoning for 30 seconds when satellite signals are interrupted, which is applicable to scenarios such as underground passages, and the cumulative error is controlled within 5% of the moving distance.

[0070] The moving speed can be measured through multi-source fusion. Based on GPS carrier phase time series differential (speed error ±0.1 m / s at a 1 Hz sampling rate) and Doppler frequency shift analysis (a 1575.42 MHz signal in the L1 band with a ±4.2 kHz frequency shift corresponding to 30 m / s), an accuracy of ±0.5 km / h can be achieved on open roads.

[0071] It is possible to directly read vehicle-mounted data. Analyze the CAN bus message of the electric vehicle controller (the 0x2A1 frame contains the motor speed parameter), and combine it with the calibrated wheel diameter value (the circumference of a common 14-inch wheel is 1.85 meters) to convert the true speed and avoid the satellite signal occlusion error.

[0072] S130. Determine the second advertisement type information of the current advertisement placement device based on the position of the current advertisement placement device and the high-value area distribution map. Among them, the second advertisement type information of the current advertisement placement device is used to indicate the advertisement types with relatively high second revenue scores corresponding to the current advertisement placement device and the second revenue scores corresponding to each advertisement type; the second revenue score is determined based on the distance between the current advertisement placement device and the first high-value area and the first advertisement type information of the location of the high-value area, and the first high-value area belongs to one or more high-value areas that are the closest to the current advertisement placement device.

[0073] Among them, the advertisement types with relatively high second revenue scores corresponding to the current advertisement placement device can be a specified number of advertisement types with relatively high rankings, and the number of the specified types can be an empirical value and can be set in advance.

[0074] Among them, the second revenue score DAS is calculated based on the following formula:

[0075] ;

[0076] Among them, : The first revenue score of the i-th first high-value area;

[0077] : The number of adjacent high-value areas;

[0078] : The standard deviation of the Gaussian kernel;

[0079] : The shortest distance from the vehicle to area i;

[0080] : The angle between the vehicle driving direction and the center line of area i;

[0081] : The speed gain coefficient;

[0082] : The moving speed of the current advertisement placement device.

[0083] It can be determined according to factors such as the moving range of the vehicle and the requirements of advertisement placement. For example, within a certain range of vehicle driving, select the top areas with the highest revenue scores as adjacent high-value areas. can be a natural number.

[0084] It can be used to control the width of the Gaussian kernel function, thereby affecting the degree to which distance impacts the revenue score. The larger the value of [[ID=]], the smaller the impact of distance on the revenue score; The smaller the value of [[ID=]], the greater the impact of distance on the revenue score. This value is usually determined according to the requirements of the actual application scenario.

[0085] It can be obtained through technical means such as map service APIs and GPS positioning. For example, the straight-line distance between the current position of the vehicle and the center of a high-value area is the shortest distance.

[0086] It can be calculated based on the driving trajectory of the vehicle and the location information of the high-value area. For example, the included angle is calculated by the direction vector of the vehicle's driving direction and the center line of the area.

[0087] It can be determined according to the requirements of advertising placement. For example, if it is desired to encourage the vehicle to move quickly to cover more high-value areas, a larger speed gain coefficient can be set.

[0088] It can be obtained through technical means such as GPS positioning and vehicle speed sensors. For example, the instantaneous speed of the vehicle at the current moment is obtained through GPS positioning technology.

[0089] By integrating the above parameters, the formula DAS can comprehensively consider factors such as the revenue score of the high-value area, the distance from the vehicle to the area, the included angle between the driving direction and the center line of the area, and the moving speed of the vehicle, and calculate the second revenue score, thereby providing a decision-making basis for advertising placement.

[0090] S140, perform advertising placement matching based on the second advertising type information and moving speed of the current advertising placement device.

[0091] As an example, one or more advertisements most suitable for the current advertising placement device can be determined based on the following formula and placed on the current advertising placement device:

[0092] ;

[0093] where is the matching score of the current type of advertisement with the current advertising placement device;

[0094] : the inherent weights of different advertisement types;

[0095] : Normalize the real-time speed to the interval [0, 1];

[0096] : The adaptability score of the real-time speed and the advertisement display mode;

[0097] : The decay rate of the advertisement validity period;

[0098] : The time interval since the last advertisement update.

[0099] It can be a preset or dynamically adjustable weight, such as a higher weight for food and beverage advertisements.

[0100] Example: Food and beverage category = 0.8.

[0101] Maximum speed It can be an empirical value. For example, for an electric bicycle, it is 25 km / h.

[0102] It can be calculated according to the adaptability of the advertisement display mode and the speed.

[0103] Example: At high speed, the score of a dynamic video advertisement is high (such as Style score = 0.9), and the score of a static picture is low (such as 0.6).

[0104] It can be dynamically adjusted according to the speed, such as a higher update frequency at high speed.

[0105] Example: When v = 30 km / h, λ(v) = 0.1 (decaying 10% per minute).

[0106] It can be determined according to the timestamp of the last update of the recorded advertisement.

[0107] Example: t = 10 minutes. If λ(v) = 0.05 (decaying 5% per minute), then e 0.05×10 = e 0.5 ≈ 0.607.

[0108] In some embodiments, a first advertisement resource is stored on the mobile phone, and a second advertisement resource is stored in the cloud. When the matching degree of the second advertisement type information and the moving speed of the current advertisement delivery device with the first advertisement resource is relatively low, the second advertisement type information and the moving information of the current advertisement delivery device are sent to the cloud, so that the cloud matches and updates the advertisement resource in the second advertisement resource and returns it to the mobile phone.

[0109] The current advertising device stores third advertising resources, and the attribute data of the third advertising resources is stored on the mobile phone. When the overall matching degree of the second advertising type information and the moving speed of the current advertising device with the third advertising resources exceeds the threshold, a display strategy is generated based on the third advertising resources, and the display strategy is sent to the current advertising device so that the current advertising device can display advertisements based on the display strategy.

[0110] When the overall matching degree of the second advertising type information and the moving speed of the current advertising device with the third advertising resources is lower than the threshold, the third advertising resources are updated based on the first advertising resources, a display strategy is generated based on the updated third advertising resources, and the display strategy and the updated third advertising resources are sent to the current advertising device so that the current advertising device can display advertisements based on the display strategy.

[0111] That is to say, in the embodiment of the present application, the advertising resources adopt a distributed storage architecture. A first advertising resource package is pre-stored in the local memory of the mobile phone, and at the same time, a dynamically updated second advertising resource library is maintained on the cloud server. When the advertising placement engine real-time monitors that the comprehensive matching score of the second advertising type information and the moving speed parameter of the current advertising device with the local first advertising resource package is lower than the preset threshold (usually set to 65%), the system will automatically trigger the cloud collaboration mechanism. Specifically, the advertising placement engine will transmit the second advertising type metadata (including industry classification, product tags, etc.) and the moving information vector (including speed value, acceleration, geographical location, etc.) of the device to the cloud server through an encrypted channel. The cloud server performs multi-dimensional feature matching in the second advertising resource library based on the reinforcement learning matching algorithm, and finally returns the optimized updated advertising resource package to the mobile terminal.

[0112] The current advertising device is embedded with a third type of dynamic advertising resource cache area, and its attribute metadata (including material size, interaction protocol, validity period, etc.) is persistently stored in the secure storage partition of the mobile phone. When the advertising placement engine detects that the comprehensive value of the matching degree of the second advertising type feature vector and the moving speed parameter of the device with the third advertising resource cache exceeds the dynamic threshold (this threshold is adaptively adjusted in the range of 70 - 85% according to the network status), the resource scheduling module will activate the policy generator. The policy generator combines user portrait data and environmental context information to generate a display policy matrix including parameters such as priority sorting, display frequency control, and interactive hot zone configuration, and pushes it to the advertising rendering engine through a low-latency channel to ensure that the device can perform precise advertisement display according to the policy matrix.

[0113] When the real-time matching degree evaluation shows that the device parameters do not match the third advertising resource well enough, i.e., the matching degree is lower than the critical value, the system will start the resource iteration process. First, the resource updater filters the basic materials that match the device hardware characteristics from the local first advertising resource package, generates enhanced advertising resources through an intelligent synthesis algorithm, and simultaneously updates the version identifier and validity certificate of the third advertising resource cache. The policy engine will recalculate the delivery parameters based on the updated resource characteristics and generate a display policy package that includes a transition animation plan and a phased display logic. Finally, the system synchronizes the policy package and the updated resources to the advertising delivery device through a bulk transfer protocol to ensure visual coherence and interactive responsiveness during the resource replacement process for the advertisement display.

[0114] In some embodiments, when the updated advertising resources returned by the cloud include multimedia content, the mobile device will start the preprocessing module to decode and optimize the advertisement materials, and reduce the mobile device's computing load through hardware acceleration rendering technology. The updated third advertising resources will be synchronized to the local buffer, and an LRU eviction mechanism will be established to maintain the storage space. When the cache hit rate is lower than the preset metric, an incremental update process will be automatically triggered.

[0115] To further improve the matching efficiency, the built-in decision engine will periodically collect environmental feature parameters, including base station positioning offset, gyroscope attitude data, and the distribution map of surrounding Wi-Fi hotspots. After these parameters are converted into 32-dimensional vectors by the feature encoder, a cosine similarity calculation is performed with the advertising resource tags to form a dynamic weight evaluation matrix. When the eigenvalue fluctuation of the matrix exceeds ±15%, the secondary retrieval mechanism of the advertising resource library will be automatically triggered.

[0116] When implementing the mobile speed threshold determination, a sliding window algorithm can be used to process GPS positioning data, calculate the moving average with a 10-second cycle, and combine a Kalman filter to eliminate signal jitter. When it is detected that the user enters a signal shielding area such as a subway tunnel, the system will automatically switch to the inertial navigation mode and use a gait recognition model to compensate for the missing position information to ensure the continuous effectiveness of the geographical fence determination for advertising delivery.

[0117] For the scenario of parallel loading of multiple advertising resources, a priority queue manager can be configured to dynamically adjust the bandwidth allocation ratio according to the advertising master service level agreement (SLA). For AR augmented reality advertising resources, the environmental recognition sensors on the device side will be synchronously activated to capture planar feature points in real time and establish a three-dimensional space coordinate system to ensure that the lighting parameters of the virtual advertising content are consistent with the physical environment.

[0118] In the embodiments of this application, it is possible to perform precise matching and advertisement delivery based on multi-dimensional information such as the user's real-time location, motion state, value, and user characteristics, improving the targeting and effectiveness of advertisement delivery; and optimizing the advertisement delivery strategy to continuously enhance the advertisement delivery effect.

[0119] Figure 2 The figure is a schematic structural diagram of an advertising data placement matching device based on location information provided by an embodiment of the present application. The advertisement is for placement on an advertisement placement device located on an outdoor moving vehicle; as Figure 2 shown, the device includes:

[0120] A first determination module 201, configured to determine a high-value area distribution map, the high-value area distribution map includes multiple high-value areas, each high-value area corresponds to first advertisement type information and location information, the first advertisement type information of each high-value area is used to indicate the advertisement types with the top-ranked first revenue scores corresponding to the high-value area and the first revenue scores corresponding to each advertisement type, and the location information of each high-value area is used to indicate the area range corresponding to the high-value area;

[0121] A second determination module 202, configured to determine the location and moving speed of the current advertisement placement device;

[0122] A third determination module 203, configured to determine the second advertisement type information of the current advertisement placement device based on the location of the current advertisement placement device and the high-value area distribution map, wherein the second advertisement type information of the current advertisement placement device is used to indicate the advertisement types with the top-ranked second revenue scores corresponding to the current advertisement placement device and the second revenue scores corresponding to each advertisement type; the second revenue score is determined based on the distance between the current advertisement placement device and the first high-value area, and the first advertisement type information of the location of the high-value area, and the first high-value area belongs to one or more high-value areas that are the closest to the current advertisement placement device;

[0123] A matching module 204, configured to perform advertisement placement matching based on the second advertisement type information and the moving speed of the current advertisement placement device.

[0124] In some embodiments, the first revenue score is determined based on the per capita GDP of the high-value area, the pedestrian flow of the high-value area, the POI score of the high-value area, and the POI type. Specifically, reference can be made to the calculation process provided in the foregoing method embodiments, and they can be understood by referring to each other.

[0125] In some embodiments, the second revenue score DAS can be referred to the calculation process provided in the foregoing method embodiments, and they can be understood by referring to each other.

[0126] In some embodiments, reference can be made to the calculation process provided in the foregoing method embodiments to determine one or more advertisements that are most suitable for the current advertisement placement device, and perform placement on the current advertisement placement device.

[0127] In some embodiments, the outdoor mobile vehicle is an electric bicycle, and the advertising device is powered by the power source of the electric bicycle.

[0128] In some embodiments, the advertising device includes a short-range communication module for communicating with a mobile phone. The method is applied to the mobile phone. There is a first advertising resource stored on the mobile phone and a second advertising resource stored in the cloud. When the overall matching degree of the second advertising type information and the moving speed of the current advertising device with the first advertising resource is relatively low, the second advertising type information and the moving information of the current advertising device are sent to the cloud, so that the cloud can match and update the advertising resource with the second advertising type information and the moving information of the current advertising device in the second advertising resource and return it to the mobile phone.

[0129] In some embodiments, the short-range communication module includes a Bluetooth module or a WiFi module.

[0130] In some embodiments, there is a third advertising resource stored on the current advertising device, and the attribute data of the third advertising resource is stored on the mobile phone. When the overall matching degree of the second advertising type information and the moving speed of the current advertising device with the third advertising resource exceeds a threshold, a display strategy is generated based on the third advertising resource and sent to the current advertising device, so that the current advertising device can display advertisements based on the display strategy.

[0131] In some embodiments, when the overall matching degree of the second advertising type information and the moving speed of the current advertising device with the third advertising resource is lower than the threshold, the third advertising resource is updated based on the first advertising resource, a display strategy is generated based on the updated third advertising resource, and the display strategy and the updated third advertising resource are sent to the current advertising device, so that the current advertising device can display advertisements based on the display strategy.

[0132] The device provided in the embodiments of the present application has the same implementation principle and the same technical effects as those in the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding content in the foregoing method embodiments.

[0133] As Figure 3 shown, the embodiments of the present application provide an advertising system, which includes a cloud 301, a mobile phone 302, and an advertising device 303. Among them, the cloud 301 can provide advertising services for multiple mobile phones 302 and multiple advertising devices 303, and the mobile phone 302 can interact with one or more advertising devices 303.

[0134] The cloud 301 can be a kind of server. Specifically, the server includes a processor and a storage device; a computer program is stored on the storage device, and when the computer program is run by the processor, it executes the cloud-related method described in any one of the above-described embodiments.

[0135] The advertisement placement device 303 can include a processor, a storage device, a communication device, a display device, and a power supply device; a computer program is stored on the storage device, and when the computer program is run by the processor, it executes the advertisement placement device-related method described in any one of the above-described embodiments.

[0136] The display is used to display advertisements. The display can be a digital billboard (Digital Signage) specially designed for public places. The advertisement display can display various forms of advertisements such as static images, videos, and dynamic content to attract the attention of the audience and convey marketing information.

[0137] Figure 4 A schematic structural diagram of a mobile phone provided by an embodiment of the present application. The mobile phone 302 includes: a processor 40, a memory 41, a bus 42, and a communication interface 43. The processor 40, the communication interface 43, and the memory 41 are connected through the bus 42; the processor 40 is used to execute an executable module stored in the memory 41, such as a computer program.

[0138] Among them, the memory 41 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 43 (which can be wired or wireless), a communication connection between this system network element and at least one other network element is realized, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0139] The bus 42 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 4 only a bidirectional arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0140] Among them, the memory 41 is used to store a program. After the processor 40 receives an execution instruction, it executes the program. The method executed by the device defined by the flow process disclosed in any one of the above embodiments of the present application can be applied to the processor 40 or implemented by the processor 40.

[0141] The processor 40 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 40 or the instructions in the form of software. The above-mentioned processor 40 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 41, and the processor 40 reads the information in the memory 41 and combines its hardware to complete the steps of the above method.

[0142] The computer program product of the readable storage medium provided by the embodiments of the present application includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the method described in the foregoing method embodiments. For the specific implementation, reference can be made to the foregoing method embodiments and will not be elaborated here.

[0143] If the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0144] Finally, it should be noted that the above-mentioned embodiments are only specific implementation manners of this application, used to illustrate the technical solution of this application, rather than limiting it. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by this application can still modify the technical solutions recorded in the foregoing embodiments or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of this application and should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for advertising data delivery and matching based on location information, characterized in that: The advertisement is used to be placed on an advertisement placement device located in an outdoor mobile vehicle; the method includes: Determine a high-value area distribution map, the high-value area distribution map including a plurality of high-value areas, each high-value area corresponding to first advertisement type information and location information, the first advertisement type information of each high-value area being used to indicate an advertisement type with a high first revenue score corresponding to the high-value area and a first revenue score corresponding to each advertisement type, and the location information of each high-value area being used to indicate an area range corresponding to the high-value area; Determine the current location and movement speed of the advertising device; determining second advertisement type information of the current advertisement delivery device based on the current location of the advertisement delivery device and the high-value area distribution map, wherein the second advertisement type information of the current advertisement delivery device is used to indicate an advertisement type with a high second revenue score corresponding to the current advertisement delivery device and a second revenue score corresponding to each advertisement type; the second revenue score is determined based on a distance between the current advertisement delivery device and a first high-value area, and the first advertisement type information of the location of the high-value area, wherein the first high-value area belongs to one or more high-value areas closest to the current advertisement delivery device; Performing advertisement delivery matching based on the second advertisement type information and the moving speed of the current advertisement delivery device; The second revenue score DAS corresponding to one of the advertisement types is calculated based on the following formula: ； in, : the first revenue score of the i-th first high-value area corresponding to one of the advertisement types; : Number of adjacent high-value areas; : Gaussian kernel standard deviation; : The shortest distance from the vehicle to the i-th first high-value area; : The angle between the vehicle's driving direction and the center line of the i-th first high-value area; : speed gain coefficient; : The current moving speed of the advertising device; One or more advertisements that best match the current advertisement delivery device are determined based on the following formula, and delivered to the current advertisement delivery device: ； in, Scoring the match between the current type of advertisement and the current advertisement delivery device; : The inherent weight of different ad types; : Normalize the real-time speed v to the interval [0,1]; : Real-time speed and the adaptability score of the advertising display method; : The decay rate of the advertisement validity period; : The time interval since the last ad update.

2. The method according to claim 1, characterized in that The first revenue score is determined based on the GDP per capita of the high-value area, the passenger flow of the high-value area, the POI score of the high-value area, and the POI type.

3. The method according to claim 1, characterized in that The outdoor mobile vehicle is an electric bicycle, and the advertising placement device is powered by a power supply of the electric bicycle.

4. The method according to claim 3, characterized in that The advertising delivery device includes a short-range communication module, which is used to communicate with a mobile phone. The method is applied to the mobile phone; a first advertising resource is stored on the mobile phone, and a second advertising resource is stored in the cloud. When the second advertising type information and movement speed of the current advertising delivery device have a low overall matching degree with the first advertising resource, the second advertising type information and movement information of the current advertising delivery device are sent to the cloud, so that the second advertising type information and movement information of the current advertising delivery device in the cloud are matched in the second advertising resource, and the updated advertising resource is returned to the mobile phone.

5. The method according to claim 4, characterized in that The short-range communication module includes a Bluetooth module or a WiFi module.

6. The method according to claim 4, characterized in that A third advertising resource is currently stored on the advertising delivery device, and the attribute data of the third advertising resource is stored on the mobile phone. When the second advertising type information and the moving speed of the current advertising delivery device and the total matching degree of the third advertising resource exceed a threshold, a display strategy is generated based on the third advertising resource, and the display strategy is sent to the current advertising delivery device, so that the current advertising delivery device displays advertisements based on the display strategy.

7. The method according to claim 6, characterized in that When the total matching degree between the second advertisement type information and the moving speed of the current advertisement delivery device and the third advertisement resource is lower than a threshold, the third advertisement resource is updated based on the first advertisement resource, and a display strategy is generated based on the updated third advertisement resource. The display strategy and the updated third advertisement resource are sent to the current advertisement delivery device so that the current advertisement delivery device displays advertisements based on the display strategy.

8. An advertising data delivery and matching device based on location information, characterized in that: The advertisement is used to be placed on an advertisement placement device located in an outdoor mobile vehicle; the device includes: A first determination module is configured to determine a high-value area distribution map, the high-value area distribution map including a plurality of high-value areas, each high-value area corresponding to first advertisement type information and location information, the first advertisement type information of each high-value area being used to indicate an advertisement type with a high first revenue score corresponding to the high-value area and a first revenue score corresponding to each advertisement type, and the location information of each high-value area being used to indicate an area range corresponding to the high-value area; A second determination module is used to determine the current position and moving speed of the advertisement delivery device; a third determination module, configured to determine second advertisement type information of the current advertisement delivery device based on the current location of the advertisement delivery device and the high-value area distribution map, wherein the second advertisement type information of the current advertisement delivery device is used to indicate an advertisement type with a high second revenue score corresponding to the current advertisement delivery device and a second revenue score corresponding to each advertisement type; the second revenue score is determined based on a distance between the current advertisement delivery device and a first high-value area, and the first advertisement type information of the location of the high-value area, wherein the first high-value area belongs to one or more high-value areas closest to the current advertisement delivery device; A matching module, configured to perform advertisement delivery matching based on the second advertisement type information and the moving speed of the current advertisement delivery device; The second revenue score DAS corresponding to each ad type is calculated based on the following formula: ； in, : The first income score of the i-th first high value area ; The first revenue score corresponding to each advertising type; : Number of adjacent high-value areas; : Gaussian kernel standard deviation; : The shortest distance from the vehicle to the i-th first high-value area; : The angle between the vehicle's driving direction and the center line of the i-th first high-value area; : speed gain coefficient; : The current moving speed of the advertising device; The matching module is specifically configured to determine one or more advertisements that best match the current advertisement delivery device based on the following formula, and deliver the advertisements to the current advertisement delivery device: ； in, Scoring the match between the current type of advertisement and the current advertisement delivery device; : The inherent weight of different ad types; : Normalize the real-time speed v to the interval [0,1]; : Real-time speed and the adaptability score of the advertising display method; : The decay rate of the advertisement validity period; : The time interval since the last ad update.

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