An automobile promotion advertisement delivery management system

Abstract

The application relates to the technical field of advertisement delivery and discloses an automobile promotion advertisement delivery management system, which comprises a vehicle state monitoring module, an environment perception fusion module, a scene semanticization module and an advertisement value matching module. The vehicle state monitoring module is connected with a vehicle-mounted control system through a CAN bus protocol and acquires vehicle dynamic data in real time. The environment perception fusion module integrates multi-channel data sources and comprehensively perceives the environment where the vehicle is located. The scene semanticization module converts original data into calculable scene labels and calculates scene weights. The advertisement value matching module screens suitable advertisement types from an advertisement library according to the scene labels. The automobile promotion advertisement delivery management system can realize real-time collection of multi-source data, lay a foundation for accurate delivery, quantitatively generate labels and match vehicle model advertisements, improve the targeting of delivery, accurately decide and optimally select advertisements according to scene weights and constraint conditions, and reasonably select a delivery form and guarantee the core function of a vehicle machine on the premise of driving safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of advertisement delivery, and in particular to a car promotion advertisement delivery management system. BACKGROUND

[0002] At present, the car machine advertisement management system mostly adopts a delivery mode based on user portrait and fixed time period. These systems construct user portraits by collecting static data such as user age, gender and consumption habits, and push advertisements in combination with preset time and regional rules. Some systems can access vehicle basic data, but only simply associate information such as vehicle speed and geographic location, lacking deep analysis of driving scenarios. In terms of advertisement delivery form, visual pop-up advertisements are still the main form, which frequently appear during driving, ignoring the balance between driving safety and user experience, and the relevance between advertisement content and real-time vehicle state and surrounding environment is weak, making it difficult to stimulate user interest.

[0003] The existing advertisement delivery management system has certain deficiencies: firstly, the scene perception ability is insufficient, and the vehicle operating state (such as energy consumption, driving mode) and environmental information (weather, road conditions) cannot be integrated in real time, resulting in a lack of pertinence in advertisement delivery and difficulty in meeting the user's current needs; secondly, the safety mechanism is missing, and no advertisement delivery rules based on driving safety have been established, and visual advertisements are still pushed during high-speed driving or navigation, seriously interfering with driving safety. SUMMARY

[0004] The technical problem to be solved by the present application is that the existing technology lacks scene perception ability and safety mechanism, and therefore a car promotion advertisement delivery management system is proposed.

[0005] In order to achieve the above purpose, the following technical scheme is adopted: a car promotion advertisement delivery management system, comprising: a vehicle state monitoring module, which establishes stable connection with the vehicle control system through CAN bus protocol and obtains vehicle dynamic data in real time; an environmental perception fusion module, which integrates multi-channel data sources and comprehensively perceives the environment in which the vehicle is located; a scene semantic module, which converts raw data into calculable scene labels and calculates scene weights; an advertisement value matching module, which selects suitable advertisement types from the advertisement library according to scene labels; a multi-factor decision engine module, which generates advertisement delivery instructions based on scene weights and constraint conditions, the decision process being that whether the scene weight is greater than a preset threshold is first judged, if it is greater, the constraint conditions are checked, if there are constraint conditions, only the advertisement is cached, otherwise the delivery instruction is generated and the medium type is selected, and if the scene weight is not greater than the threshold, the delivery is abandoned; a competitive advertisement optimization module, which is used to solve conflicts and select the optimal advertisement when multiple advertisements trigger at the same time, and the following formula is used for calculation: , wherein is the scene matching degree; For commercial value; For user fatigue; The media arbitration module selects and delivers the advertising delivery form according to the driving state.

[0006] Preferably, the vehicle dynamic data obtained by the vehicle state monitoring module includes motion state, energy state, and whether the vehicle navigation and call are in an active state.

[0007] Preferably, the data sources integrated by the environment perception fusion module include internal rain and snow amount, light intensity, temperature and humidity data obtained by means of vehicle-mounted sensors, and external real-time road conditions and accident warning information obtained through V2X network; and calling a weather API to obtain future precipitation probability and wind force level.

[0008] Preferably, the scene label converted by the scene semantic module includes an energy emergency factor, a weather risk coefficient, and a driving state factor; the energy emergency factor is a binary variable, 1 for emergency and 0 for normal; the weather risk coefficient is calculated by multiplying the rain and snow amount intensity by its corresponding weight coefficient and adding the wind force level multiplied by its corresponding weight coefficient; the driving state factor is divided according to the vehicle speed, 0 for high speed, 0.5 for medium speed, and 1 for low speed; and the scene weight calculation formula is: , wherein is the comprehensive scene weight, representing the priority of advertising delivery under the current driving scene, is the energy state weight, is the energy emergency factor, is the weather risk weight, is the weather risk coefficient, is the driving state weight, is the driving state factor.

[0009] Preferably, the advertising value matching module establishes a matching matrix when screening the advertising type, and matches low fuel consumption vehicle type, long endurance vehicle type or fast charging technology advertising when the energy is in emergency, matches four-wheel drive system and rain wiper technology advertising in rainy weather, and matches automatic driving assistance and comfortable seat configuration advertising in congested road sections.

[0010] Preferably, the constraint conditions of the multi-factor decision engine module when generating the advertising delivery instruction are whether the car machine is in a navigation or call state, whether the vehicle speed exceeds the safe delivery standard, and whether the scene meets the advertising content adaptation requirement.

[0011] Preferably, the execution rule of the media arbitration module for placing advertisements is that when the vehicle speed is greater than 80 km / h, all visual advertisements are disabled, and voice advertisements are allowed when there is no navigation or voice call instruction; when the vehicle speed is between 20 km / h and 80 km / h, and the road is straight and the traffic density is low, the advertisements are placed in the form of simplified HUD projection, and the duration is not more than 5 seconds; when the vehicle speed is lower than 20 km / h or the vehicle is stopped, and there is no reversing or door opening operation, full-width advertisements are displayed on the center control screen.

[0012] Preferably, the system further comprises a voice priority channel module, which is used to automatically delay the playing of advertisements when there is a navigation broadcast or an incoming call, and enter an advertisement mute mode during continuous voice instructions.

[0013] Preferably, the system further comprises a biological feature monitoring module, which is used to evaluate the effect of advertisements by means of in-vehicle cameras and / or microphones.

[0014] Preferably, the system further comprises a dynamic parameter optimization module, which dynamically optimizes the preset threshold value according to the feedback data of the effect of advertisements, and calculates by using the formula , wherein is the updated threshold value, is the preset threshold value, is the historical conversion rate, is the real-time feedback, , is the learning rate.

[0015] The technical effects and advantages of the present application are as follows: in the present application, multi-source heterogeneous data is collected in real time, vehicle and environmental information is comprehensively obtained through the vehicle state monitoring module and the environmental perception fusion module, providing a data basis for accurate scene analysis, the scene semanticization module and the advertisement value matching module convert the original information into scene labels and filter suitable vehicle advertisements based on these data, realizing accurate placement of advertisements; the multi-factor decision engine module and the competitive advertisement optimization module make decisions in combination with the scene weight and multiple constraint conditions, and optimize the most suitable advertisement when multiple advertisements are triggered, ensuring the rationality of the placement; the media arbitration module sets strict placement rules according to the driving state and the vehicle function, ensuring that the core function of the vehicle takes priority, and realizing advertisement placement while ensuring driving safety. BRIEF DESCRIPTION OF DRAWINGS

[0016] The disclosed content of the present application is explained with reference to the accompanying drawings. It should be understood that the drawings are only for illustrative purposes, and are not intended to limit the scope of protection of the present application. In the drawings, the same reference signs are used to refer to the same components.

[0017] Figure 1 is the overall module flowchart of the present application; Figure 2A scenario semanticization flowchart of the present application; Figure 3 An advertisement decision flowchart of the present application; Figure 4 A closed-loop optimization flowchart of the present application. DETAILED DESCRIPTION

[0018] It is easy to understand that, according to the technical solution of the present application, a person skilled in the art can propose a plurality of structures and implementation modes that can be replaced with each other without changing the essential spirit of the present application. Therefore, the following detailed description and the accompanying drawings are only exemplary descriptions of the technical solution of the present application, and should not be regarded as the whole or as a limitation or restriction on the technical solution of the present application.

[0019] REFERENCE Figures 1-4 As shown in the figure, an automobile promotion advertisement delivery management system is composed of a data perception layer, a scenario analysis engine, an intelligent decision center, a safe execution layer, and an effect feedback loop to form a closed loop, focus on the same brand vehicle promotion, and realize safe priority and scenario-driven precise advertisement delivery.

[0020] The data perception layer specifically includes: a vehicle state monitoring module: through the CAN bus protocol, a stable connection is established with the vehicle-mounted control system, real-time and accurate vehicle dynamic data are obtained, in terms of motion state, vehicle speed, acceleration, and gear state are continuously monitored to provide a basis for judging driving conditions, in terms of energy state, oil / electricity percentage is collected with an accuracy of ±1%, and estimated cruising range is calculated to timely grasp the vehicle energy situation, and whether the vehicle-mounted navigation / talking is active is recorded, in terms of data collection frequency, data is collected every 100ms when moving at high speed to ensure the timeliness of the data, and data is collected every 1s when stationary to balance data accuracy and system resource consumption.

[0021] An environment perception fusion module: integrates multi-channel data sources to comprehensively perceive the environment in which the vehicle is located, internally obtains environmental data such as rain / snow amount, light intensity, temperature and humidity through vehicle-mounted sensors, externally obtains real-time road conditions and accident warning information through the V2X network, calls a weather API to obtain precipitation probability and wind grade in the next 2 hours, and identifies the POI type of the vehicle location, such as a business district, a school, and a charging station, to provide rich environmental background information for subsequent advertisement delivery.

[0022] The above-mentioned vehicle state monitoring and environment perception fusion modules realize real-time collection of multi-source heterogeneous data, comprehensively obtain vehicle operation and environmental information, and provide rich and accurate data support for subsequent scenario analysis and advertisement delivery, which is the basis for precise operation of the entire system.

[0023] The scene analysis engine specifically comprises a scene semantic module: the original data is converted into a scene label with practical significance and calculation, and quantitative rules are used, such as energy shortage judgment, when the oil quantity is lower than 15% or the electricity quantity is lower than 20%, it is marked as energy shortage, otherwise it is normal, and the energy shortage factor is expressed as a numerical value, specifically, the energy shortage factor is a binary variable, 1 when the oil quantity is lower than 15% or the electricity quantity is lower than 20%, and 0 when it is normal, the weather risk coefficient is calculated by multiplying the rainfall intensity by 0.8 and adding the wind force level by 0.2, wherein 0.8 is the weight coefficient of rainfall intensity and 0.2 is the weight coefficient of wind force level, and the driving state factor is divided according to the vehicle speed, greater than 80km / h is 0, 20-80km / h is 0.5, and lower than 20km / h is 1.

[0024] The scene weight is calculated by the formula , wherein The comprehensive scene weight represents the priority of the advertisement in the current driving scene, is the energy state weight, preferably 0.6, is the energy shortage factor, which is monitored by the vehicle-mounted sensor in real time, is the weather risk weight, preferably 0.3, is the weather risk coefficient, which is calculated in combination with meteorological API data, is the driving state weight, preferably 0.1, is the driving state factor, which is divided based on the real-time vehicle speed data of the CAN bus, and this module calculates the scene weight and finally outputs the scene label containing information such as energy emergency state, weather risk coefficient, and driving state.

[0025] The advertisement value matching module: according to the scene label, the suitable advertisement type is selected from the same brand vehicle advertisement library, a matching matrix is established, when the energy is in emergency, the same brand low fuel consumption vehicle, long endurance vehicle or fast charging technology advertisement is matched; in rainy and snowy weather, the same brand four-wheel drive system and excellent wiper technology advertisement is matched, in congested road section, the same brand automatic driving assistance and comfortable seat configuration advertisement is recommended, and in navigation broadcast, only the advertisement is cached, not put, to ensure that the advertisement is highly matched with the scene and focused on the same brand promotion.

[0026] The scene semantic module above converts the original data into a calculable scene label, and the advertisement value matching module selects the same brand vehicle advertisement according to the label, and the cooperation of the two makes the advertisement closely match the actual scene of the vehicle and the user, greatly improving the relevance and attractiveness of the advertisement.

[0027] The intelligent decision center specifically comprises: a multi-factor decision engine module: based on scene weight and various constraint conditions, a reasonable advertisement launching instruction is generated, wherein the constraint conditions comprise but are not limited to: whether the car machine is in a navigation broadcast or a call state; whether the vehicle speed exceeds a safe launching standard; whether the current scene meets the advertisement content adaptation requirement; the decision process is that whether the scene weight is greater than a preset threshold is first judged, if any of the above constraint conditions exists, the advertisement is cached to a temporary storage area, after the constraint condition is removed, the launching is re-evaluated, if all the constraint conditions are not triggered, the next step of generating the launching instruction and selecting a medium type is entered, if the scene weight is not greater than the threshold, the launching is abandoned, and invalid exposure is avoided.

[0028] A competitive advertisement optimization module: when multiple advertisements of the same brand and vehicle model are triggered at the same time, the conflict is solved and the optimal advertisement is selected, and the following formula is used for calculation: , wherein is a scene matching degree, which is measured by a cosine similarity between an advertisement feature and a scene label; is a business value, which is obtained based on a machine learning prediction model of advertisement owner bidding data and historical launching effect; is a user fatigue degree, which is determined according to the number of exposures of the same type of advertisement of the user within 7 days, and the advertisement with a high priority is preferentially launched.

[0029] The multi-factor decision engine generates a reasonable launching instruction based on the scene weight and the constraint condition, the competitive advertisement optimization module solves the conflict of multiple advertisements of the same brand, ensures that the advertisement launching is accurate and reasonable, and realizes efficient utilization of resources.

[0030] The safe execution layer specifically comprises: a medium arbitration module: used for reasonably selecting an advertisement launching form according to a driving state such as vehicle speed, formulating an execution rule, disabling all visual advertisements when the vehicle speed is greater than 80 km / h, allowing only voice advertisements when there is no navigation instruction within 3 seconds, launching the advertisement in a simplified HUD projection form when the vehicle speed is between 20 km / h and 80 km / h, under the condition that the road is straight and the traffic density is low, and the duration is not more than 5 seconds, displaying a full-width advertisement on the center control screen when the vehicle speed is lower than 20 km / h or the vehicle is parked, but ensuring that there is no reversing or door opening operation, and guaranteeing driving safety and user experience. A voice priority channel module is used for ensuring the priority of the core function of the car machine, realizing harmonious coexistence of the advertisement and the core function of the car machine, and adopting a preemptive scheduling design, when navigation broadcast or incoming call occurs, the advertisement playing is automatically delayed, and during continuous voice instructions, an advertisement mute mode is entered. The medium arbitration module selects a suitable advertisement launching form according to the driving state, the voice priority channel guarantees that the core function of the car machine is not disturbed, and driving safety is comprehensively guaranteed, so that the advertisement launching and driving safety can be considered.

[0031] The effect feedback loop specifically includes: a biological feature monitoring module: with the help of in-vehicle cameras and / or microphones, the advertising effect is evaluated in multiple dimensions. If there is a monitoring device in the vehicle, for visual advertisements, the eye gaze duration is monitored, and more than 1.5 seconds is considered effective exposure. For voice advertisements, the voice interruption rate is calculated, i.e. the number of times the user issues a "skip advertisement" instruction. Facial expression happiness is analyzed using CNN emotion recognition technology, and the impact of the advertisement on the user is comprehensively evaluated.

[0032] A dynamic parameter optimization module: based on advertising effect feedback data, parameters such as decision thresholds are dynamically optimized, using a Bayesian update algorithm for calculation, with the specific formula as follows: wherein is the updated threshold, is the preset threshold, is the historical conversion rate, which is specifically the historical number of ad clicks / number of displays, which is obtained by statistical analysis of the historical advertising database, is the real-time feedback, specifically the current advertising effect comprehensive score, such as eye gaze duration, voice interruption rate, etc., and , is the learning rate, which is 0.2 by default and automatically increases when the ad skip rate is greater than 30% This module continuously adjusts system parameters to make the same brand vehicle advertising strategy more user-friendly and improve advertising effectiveness.

[0033] The above biological feature monitoring module evaluates the advertising effect in multiple dimensions, and the dynamic parameter optimization module optimizes system parameters based on feedback, allowing the system to continuously adapt to changes in the market and user needs, and continuously improve the effectiveness of advertising and user satisfaction.

[0034] Through the close cooperation of each module, the system realizes a complete closed loop from data collection, scene analysis, intelligent decision-making, safe deployment, to effect feedback optimization. Under the premise of ensuring driving safety, it focuses on the promotion of the same brand vehicle, and realizes precise and efficient car promotion and advertising deployment.

[0035] The working principle of the system: the vehicle state monitoring module in the data perception layer collects the vehicle motion, energy state and navigation call situation in real time through the CAN bus protocol, the environment perception fusion module integrates the multi-source data of the vehicle-mounted sensor and V2X network to obtain the environmental information, the scene semanticization module in the scene analysis engine converts the above data into scene labels, calculates the scene weight through specific quantitative rules, the advertisement value matching module selects the adaptive advertisement from the same brand vehicle advertisement library according to the labels, the multi-factor decision engine module of the intelligent decision center judges whether to put the advertisement based on the scene weight and the safety constraint condition, the competitive advertisement optimization module calculates the priority to select the optimal advertisement when multiple advertisements are triggered, the medium arbitration module of the safety execution layer selects the advertisement putting form according to the driving state such as vehicle speed, the voice priority channel module guarantees the priority of the vehicle machine core function, the biological feature monitoring module of the effect feedback loop evaluates the advertisement effect in multiple dimensions, the dynamic parameter optimization module optimizes the preset threshold and other parameters according to the feedback by using the Bayesian updating algorithm, and the optimized parameters act on the scene semanticization module to promote the continuous optimization and iteration of the system.

[0036] The technical scope of the present application is not limited to the above description, and those skilled in the art can make various modifications and changes to the above embodiments without departing from the technical idea of the present application, and these modifications and changes should all belong to the protection scope of the present application.

Claims

1. A car promotion and advertising placement management system, characterized in that, include: The vehicle status monitoring module establishes a stable connection with the vehicle control system via the CAN bus protocol to acquire vehicle dynamic data in real time. The environmental perception fusion module integrates data from multiple channels to comprehensively perceive the vehicle's surroundings. The scene semanticization module transforms raw data into computable scene labels and calculates scene weights. The scene labels transformed by this module include: energy emergency factor, weather risk coefficient, and driving status factor. The energy emergency factor is a binary variable, equal to 1 in an emergency and 0 in a normal situation. The weather risk coefficient is calculated by multiplying the rainfall / snow intensity by its corresponding weight coefficient and then adding the wind force level multiplied by its corresponding weight coefficient. The driving status factor is categorized based on vehicle speed: 0 for high speed, 0.5 for medium speed, and 1 for low speed. The scene weight calculation formula is as follows: ,in The overall scenario weight represents the priority of ad placement in the current driving scenario. As energy state weights, As an energy crisis factor, As a weather risk weight, For weather risk factor, For driving state weights, The system includes a driving state factor; an advertising value matching module that selects suitable advertising types from the advertising library based on scene tags; a multi-factor decision engine module that generates advertising delivery instructions based on scene weights and constraints. The decision process is as follows: first, it checks if the scene weight is greater than a preset threshold. If it is, it checks the constraints. If constraints exist, it only caches the advertisement; otherwise, it generates a delivery instruction and selects the media type. If the scene weight is not greater than the threshold, it abandons the delivery. The competitive advertising optimization module is used to resolve conflicts and select the optimal advertisement when multiple advertisements are triggered simultaneously. It is calculated using the following formula: ,in , representing the scene matching degree; For commercial value; The system assesses user fatigue; the media arbitration module selects and delivers advertisements based on driving status.

2. The automobile promotion and advertising placement management system according to claim 1, characterized in that: The vehicle status monitoring module acquires vehicle dynamic data including: motion status, energy status, and whether the in-vehicle navigation and communication are active.

3. The automobile promotion and advertising placement management system according to claim 2, characterized in that: The environmental perception fusion module integrates data from the following sources: internally, it uses onboard sensors to acquire data on rainfall, snowfall, light intensity, temperature, and humidity; externally, it uses V2X networks to acquire real-time road conditions and accident warning information; and it calls meteorological APIs to obtain future precipitation probability and wind speed.

4. The automobile promotion and advertising placement management system according to claim 1, characterized in that: When the advertising value matching module filters advertising types, it establishes a matching matrix. When energy is in short supply, it matches advertisements for low-fuel-consumption vehicles, long-range vehicles, or fast-charging technology. In rainy or snowy weather, it matches advertisements for four-wheel drive systems and windshield wiper technology. In congested areas, it matches advertisements for autonomous driving assistance and comfortable seat configurations.

5. The automobile promotion and advertising placement management system according to claim 1, characterized in that: The constraints for the multi-factor decision engine module when generating ad delivery instructions are: whether the vehicle's infotainment system is in navigation or call mode, whether the vehicle speed exceeds the safe delivery standard, and whether the scene meets the requirements for ad content adaptation.

6. The automobile promotion and advertising placement management system according to claim 1, characterized in that: The execution rules for advertising placement by the media arbitration module are as follows: when the vehicle speed is greater than 80km / h, all visual advertisements are disabled, and voice advertisements are allowed when there are no navigation or voice call commands; when the vehicle speed is between 20-80km / h, on straight road sections with low traffic density, advertisements are placed in a simplified HUD projection format for a duration not exceeding 5 seconds; when the vehicle speed is less than 20km / h or when the vehicle is parked and there is no reversing or door opening operation, a full-width advertisement is displayed on the central control screen.

7. The automobile promotion and advertising placement management system according to claim 1, characterized in that: It also includes a voice priority channel module, which automatically delays the playback of advertisements when navigation is broadcast or a call comes in, and enters an advertisement silence mode during continuous voice commands.

8. The automobile advertising placement management system according to any one of claims 1-7, characterized in that: The system also includes a biometric monitoring module, which is used to evaluate the effectiveness of advertising using an in-vehicle camera and / or microphone.

9. The automobile promotion and advertising placement management system according to claim 8, characterized in that: It also includes a dynamic parameter optimization module, which dynamically optimizes preset thresholds based on advertising performance feedback data, using formulas. Perform calculations, where For the updated threshold, For the preset threshold, For historical conversion rate, For real-time feedback, , This is the learning rate.

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