Automatic broadcasting method and system for vehicle-mounted advertisement publishing platform programs

Abstract

The invention discloses an automatic broadcasting method and system for a vehicle-mounted advertisement publishing platform program. The method comprises the following steps: collecting advertisement publishing related information submitted by a client; judging the main type of the current person in the vehicle according to the collected vehicle position; a machine learning algorithm is adopted to train an automatic broadcasting model, the processed parameters and historical broadcasting data are used as first input during training, and the position information and the time period of the vehicle and the number and the type of people in the vehicle are used as second input, so that the automatic broadcasting model can broadcast according to the advertisement release related information; meanwhile, dynamic adjustment is carried out according to the current requirements of people in the vehicle; and the automatic broadcasting model automatically generates a detailed advertisement broadcasting plan according to parameter requirements of clients. According to the invention, the vehicle-mounted advertisement broadcasting efficiency can be improved, and broadcasting adjustment can be carried out in real time according to the type of the current person in the vehicle, so that the currently played advertisement can meet the needs of the current person in the vehicle, and the advertisement putting accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of advertising scheduling technology, specifically to an automatic scheduling system and method for programs on an in-vehicle advertising platform. Background Technology

[0002] In the field of converged media broadcasting, content scheduling has always been a complex and crucial task. Traditional scheduling methods rely on manual experience and require consideration of numerous factors, such as release time, type, method, and strategy. This not only consumes a significant amount of manpower and time but is also prone to human error, leading to unsatisfactory release results. With the rapid development of converged media services, the diversity of content, and the increase in distribution channels, the complexity of scheduling has further increased.

[0003] Traditional vehicle advertising scheduling relies on manual planning, which presents numerous problems. On one hand, the scheduling logic is complex, requiring consideration of multiple parameters such as release time, playback type, and priority, making manual operation prone to errors. On the other hand, it is inefficient; when faced with a large number of advertising orders and complex playback rules, manual scheduling often consumes significant time and effort, making it difficult to meet rapidly changing market demands. Furthermore, the accuracy of manual scheduling is difficult to guarantee, easily leading to advertising time conflicts and priority confusion, resulting in poor advertising performance and increased labor costs. Existing scheduling systems in the broadcasting and media convergence field also suffer from the inability to perform targeted scheduling based on vehicle location, time period, and the characteristics of passengers (such as age and occupation), resulting in low efficiency, a lack of intelligent decision-making, and insufficient flexibility. Summary of the Invention

[0004] In view of the above problems, this application provides an automatic scheduling method and system for in-vehicle advertising platform programs to solve the technical problems of poor advertising scheduling efficiency and accuracy.

[0005] To achieve the above objectives, this application provides an automatic scheduling method for programs on an in-vehicle advertising platform, comprising the following steps:

[0006] Collect advertising placement information submitted by customers, including placement period, playback type and playback level, as well as historical data of in-vehicle advertising playback, status information of in-vehicle devices, vehicle location information, current time period, and the number and characteristics of people in the vehicle, including age and gender;

[0007] The collected parameters are cleaned and standardized to convert different types of playback types and playback levels into numerical features that the model can recognize; based on the collected vehicle location information, current time period, and characteristics of people in the vehicle, the main types of people in the vehicle are determined.

[0008] An automatic scheduling model is trained using machine learning algorithms. During training, the processed parameters and historical scheduling data are used as the first input, and the vehicle's location information, time period, and the number and type of people in the vehicle are used as the second input. This enables the trained automatic scheduling model to schedule advertisements based on the relevant information and dynamically adjust according to the current needs of the people in the vehicle.

[0009] The system obtains the vehicle's current location, time period, and the characteristics of the people inside the vehicle in real time and inputs them into the trained automatic advertising scheduling model. The automatic advertising scheduling model automatically generates a detailed advertising scheduling plan based on the customer's parameter requirements, including the playback time, playback frequency, and playback order of each advertisement.

[0010] Monitor the execution of the advertising schedule in real time, record the actual playback data of the advertisements, compare it with the schedule, and promptly identify and handle any abnormalities that occur during playback.

[0011] Furthermore, the automatic scheduling model automatically generates a detailed advertising scheduling plan based on the customer's parameter requirements, including:

[0012] A first container and a second container are preset. The second container includes two or more sub-containers, and each sub-container is set with different audience tags.

[0013] The automatic scheduling model places scheduled advertisements in the first container according to the playback type and playback level, and distributes non-scheduled advertisements to different sub-containers according to their target audience.

[0014] When generating the advertising schedule, the automatic scheduling model obtains multiple first advertisements from the first container and sorts them to generate a first schedule for fixed-time playback based on the first advertisements; and according to the main types of people in the vehicle during different time periods, obtains multiple second advertisements from the corresponding sub-containers and inserts the second advertisements into the first schedule according to time periods, so that the second advertisements played match the types of people in the vehicle at the moment.

[0015] Furthermore, it also includes the following steps:

[0016] Based on the collected characteristic information of the people inside the vehicle, it is determined whether there is a significant difference between the people inside the vehicle at the current time and location in the historical data. If so, the broadcast schedule is adjusted according to the determination result.

[0017] Furthermore, the playback type includes one or more of loop, timed, fixed-point, interstitial, and emergency, and is marked with a value of 1-5 respectively; the playback level includes one or more of append and overlay, and is marked with 1 and 2 respectively.

[0018] Furthermore, the machine learning algorithm includes one or more of neural network algorithms and decision tree algorithms.

[0019] To address the aforementioned technical problems, this application also provides another technical solution:

[0020] An automatic scheduling system for programs on an in-vehicle advertising platform includes:

[0021] The data collection module is used to collect advertising publication-related information submitted by customers, including publication period, playback type and playback level, as well as historical data of in-vehicle advertising playback, status information of in-vehicle devices, vehicle location information, current time period, and the number and characteristics of people in the vehicle, including age and gender.

[0022] The data processing module is used to clean and standardize the collected parameters, converting different types of playback types and playback levels into numerical features that the model can recognize; and to determine the main type of people currently in the vehicle based on the collected vehicle location information, current time period, and characteristics of people in the vehicle.

[0023] The training module is used to train the automatic scheduling model using machine learning algorithms. During training, the processed parameters and historical scheduling data are used as the first input, and the vehicle's location information, time period, and the number and type of people in the vehicle are used as the second input. This enables the trained automatic scheduling model to schedule advertisements based on the relevant information and to dynamically adjust according to the current needs of the people in the vehicle.

[0024] The scheduling module is used to obtain the vehicle's current location, time period, and the characteristics of the people currently in the vehicle in real time. The automatic scheduling model is then used to automatically generate a detailed advertising schedule based on the customer's parameter requirements, including the playback time, frequency, and order of each advertisement.

[0025] The monitoring module is used to monitor the execution of the advertising schedule in real time, record the actual playback data of the advertisements, compare it with the schedule, and promptly detect and handle any abnormalities that occur during playback.

[0026] Furthermore, the automatic scheduling model automatically generates a detailed advertising scheduling plan based on the customer's parameter requirements, including:

[0027] A first container and a second container are preset. The second container includes two or more sub-containers, and each sub-container is set with different audience tags.

[0028] The automatic scheduling model places scheduled advertisements in the first container according to the playback type and playback level, and distributes non-scheduled advertisements to different sub-containers according to their target audience.

[0029] When generating the advertising schedule, the automatic scheduling model obtains multiple first advertisements from the first container and sorts them to generate a first schedule for fixed-time playback based on the first advertisements; and according to the main types of people in the vehicle during different time periods, obtains multiple second advertisements from the corresponding sub-containers and inserts the second advertisements into the first schedule according to time periods, so that the second advertisements played match the types of people in the vehicle at the moment.

[0030] Furthermore, it also includes an adjustment module, which is used to determine whether there is a significant difference between the current people in the vehicle and the people in the historical data at the current time period or location, based on the collected characteristic information of the people in the vehicle. If so, the broadcast schedule is adjusted according to the judgment result.

[0031] Furthermore, the playback type includes one or more of loop, timed, fixed-point, interstitial, and emergency, and is marked with a value of 1-5 respectively; the playback level includes one or more of append and overlay, and is marked with 1 and 2 respectively.

[0032] Furthermore, the machine learning algorithm includes one or more of neural network algorithms and decision tree algorithms.

[0033] Unlike existing technologies, the aforementioned automatic scheduling method and system for in-vehicle advertising platforms significantly improves advertising scheduling efficiency through an automatic scheduling model. Furthermore, during training, the automatic scheduling model schedules ads based on client-submitted advertising information, vehicle location information, current time period, and in-vehicle occupant characteristics. Therefore, advertising scheduling not only meets client requirements but also allows for real-time adjustments based on the type of occupants, ensuring that currently playing ads align with their needs and greatly enhancing the accuracy of ad delivery.

[0034] The above description of the invention is merely an overview of the technical solution of this application. In order to enable those skilled in the art to better understand the technical solution of this application and to implement it based on the description and drawings, and to make the above-mentioned objectives and other objectives, features and advantages of this application easier to understand, the following description is provided in conjunction with the specific embodiments and drawings of this application. Attached Figure Description

[0035] The accompanying drawings are only used to illustrate the principles, implementation methods, applications, features, and effects of specific embodiments of the present invention and other related contents, and should not be considered as limitations on this application.

[0036] In the accompanying drawings of the instruction manual:

[0037] Figure 1A flowchart illustrating the automatic scheduling method for programs on the in-vehicle advertising platform as described in a specific implementation;

[0038] Figure 2 A flowchart illustrating the automatic generation of a detailed advertising scheduling plan as described in the specific implementation method;

[0039] Figure 3 A block diagram of the automatic scheduling system for the in-vehicle advertising platform program, as described in a specific implementation;

[0040] Figure 4 A block diagram of the automatic scheduling system for the in-vehicle advertising platform program, as described in a specific implementation;

[0041] The reference numerals used in the above figures are explained as follows:

[0042] 300. Automatic scheduling system for in-vehicle advertising platform programs; 301. Data collection module;

[0043] 302. Data processing module; 303. Training module; 304. Scheduling module; 305. Monitoring module; 306. Adjustment module. Detailed Implementation

[0044] To illustrate the possible application scenarios, technical principles, implementable specific solutions, and achievable objectives and effects of this application in detail, the following description, in conjunction with the listed specific embodiments and accompanying drawings, provides a detailed explanation. The embodiments described herein are merely illustrative of the technical solutions of this application and are therefore intended to limit the scope of protection of this application.

[0045] In this document, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The term "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment, nor does it specifically limit its independence or connection with other embodiments. In principle, in this application, as long as there are no technical contradictions or conflicts, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0046] Unless otherwise defined, the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the use of related terms herein is merely for the purpose of describing particular embodiments and is not intended to limit this application.

[0047] In the description of this application, the term "and / or" is used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and A and B exist simultaneously. Additionally, the character " / " in this document generally indicates that the preceding and following objects have an "or" logical relationship.

[0048] In this application, terms such as “first” and “second” are used only to distinguish one entity or operation from another, and do not necessarily require or imply any actual quantity, hierarchy or order relationship between these entities or operations.

[0049] Without further limitations, the use of terms such as “comprising,” “including,” “having,” or other similar open-ended expressions in this application is intended to cover non-exclusive inclusion, which does not exclude the presence of additional elements in a process, method, or product that includes the stated elements, such that a process, method, or product that includes a list of elements may include not only those defined elements but also other elements not expressly listed, or elements inherent to such a process, method, or product.

[0050] Similar to the understanding in the Examination Guidelines, in this application, expressions such as "greater than," "less than," and "exceeding" are understood to exclude the stated number; expressions such as "above," "below," and "within" are understood to include the stated number. Furthermore, in the description of the embodiments in this application, "multiple" means two or more (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups" and "multiple times," unless otherwise explicitly specified.

[0051] In the description of the embodiments of this application, the space-related expressions used, such as "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "vertical," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," indicate the orientation or positional relationship based on the orientation or positional relationship shown in the specific embodiments or drawings. They are only for the purpose of describing the specific embodiments of this application or for the reader's understanding, and do not indicate or imply that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0052] Unless otherwise expressly specified or limited, the terms "installation," "connection," "linking," "fixing," and "setting," as used in the description of the embodiments of this application, should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two components or the interaction between two components. For those skilled in the art to which this application pertains, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0053] Please see Figure 1 This embodiment provides an automatic scheduling method for programs on an in-vehicle advertising platform. This method can be applied to the scheduling of advertisements on in-vehicle displays on buses, where the displays play advertisements according to a scheduled order. In this embodiment, the automatic scheduling method for programs on an in-vehicle advertising platform includes the following steps:

[0054] S101. Collect advertising placement information and vehicle data submitted by the client; including: collecting advertising placement information submitted by the client, including placement period, playback type and playback level, as well as historical data of in-vehicle advertising playback, status information of in-vehicle equipment, vehicle location information, current time period, and the number and characteristics of people in the vehicle, wherein the personnel characteristics include age and gender; the playback type includes one or more of loop, timed, fixed-point, interstitial, and emergency, and is marked with a value of 1-5 respectively; the playback level includes one or more of supplement and overlay, and is marked with 1 and 2 respectively.

[0055] S102. The collected parameters are cleaned and standardized, including: cleaning and standardizing the collected parameters, converting different types of playback types and playback levels into numerical features that the model can recognize; and determining the main type of the current occupants based on the collected vehicle location information, the current time period, and the characteristics of the occupants.

[0056] S103. Training an automatic scheduling model using machine learning algorithms; including: training an automatic scheduling model using machine learning algorithms, taking processed parameters and historical scheduling data as the first input during training, and taking vehicle location information, time period, and the number and type of people in the vehicle as the second input, so that the trained automatic scheduling model can schedule advertisements according to the relevant information of the advertisement release, and dynamically adjust according to the needs of the people in the vehicle at present; the machine learning algorithm includes one or more of neural network algorithms and decision tree algorithms.

[0057] S104. Use an automatic scheduling model to generate a detailed advertising scheduling plan; including: real-time acquisition of the vehicle's current location, time period, and current personnel characteristics in the vehicle, inputting these into a trained automatic scheduling model, wherein the automatic scheduling model automatically generates a detailed advertising scheduling plan based on the customer's parameter requirements, including the playback time, playback frequency, and playback order of each advertisement.

[0058] S105. Monitor the execution of the advertising schedule in real time, record the actual playback data of the advertisements, compare it with the schedule, and promptly detect and handle any abnormal situations that occur during playback.

[0059] like Figure 2 As shown, in this embodiment, the automatic scheduling model automatically generates a detailed advertising scheduling plan based on the customer's parameter requirements, including:

[0060] S201. A first container and a second container are preset, wherein the second container includes two or more sub-containers, and each sub-container is set with a different audience label;

[0061] S202, pre-allocation of advertisements, including: the automatic scheduling model places timed advertisements in the first container according to the playback type and playback level, and allocates non-timed advertisements to different sub-containers according to their target audience;

[0062] S203. Generate a first playback schedule with fixed times, and insert the second advertisement into the first playback schedule to ensure that the second advertisement played matches the type of the current passenger in the vehicle. This includes: when generating the advertisement playback plan, the automatic playback model obtains multiple first advertisements from the first container, sorts them, and generates a first playback schedule with fixed times based on the first advertisements; and according to the main types of passengers in the vehicle during different time periods, obtains multiple second advertisements from the corresponding sub-containers, and inserts the second advertisements into the first playback schedule according to time periods to ensure that the second advertisement played matches the type of the current passenger in the vehicle.

[0063] In this embodiment, during the parameter setting process in step S101, the customer sets the advertising publishing parameters on the in-vehicle advertising publishing platform, including the publishing start time, end time, playback type (e.g., selecting timed playback and setting specific playback time points), playback level (e.g., selecting coverage level), etc.

[0064] In step S102, during model training, historical scheduling data is collected, including past ad release parameters and actual playback effects. The parameter processing module processes this data to generate a training set. In step S103, the training set is used to train the automatic scheduling model, for example, using a neural network algorithm to iteratively optimize the model's weights and biases, enabling the model to accurately predict the optimal scheduling strategy. During model training, the model training module incorporates vehicle location information, time, and the number and type of people inside the vehicle. Therefore, after training, the model can schedule ads based on the vehicle's current location, time, and real-time collected occupant characteristic information. This allows the trained model to further combine the ad release parameters with the occupant type to schedule ads, thus meeting scheduling requirements while accurately targeting different audiences with appropriate ads.

[0065] In step S104, during the scheduling plan generation, the scheduling planning module automatically generates a scheduling plan based on the parameters set by the customer and the trained model. For example, for timed advertisements, the model determines the optimal playback time based on historical data and the current playback schedule of the in-vehicle device to avoid conflicts with other advertisements. Furthermore, it schedules the playback based on the vehicle's location, time, and the number of people in the vehicle. For interstitial advertisements, the model determines the timing and order of insertion based on the playback level and real-time conditions.

[0066] In step S105, the broadcast schedule is sent to the vehicle-mounted device, which then executes the advertisement playback according to the schedule. Simultaneously, the execution monitoring module collects real-time advertisement playback data, such as actual playback time and number of plays, and compares it with the broadcast schedule. If any anomalies are detected, such as an advertisement failing to play at the scheduled time, the execution monitoring module will promptly issue an alarm and take corresponding remedial measures, such as adjusting the playback order of subsequent advertisements, to ensure the completion of the advertisement delivery task.

[0067] In this embodiment, the automatic scheduling method for programs on the in-vehicle advertising platform further includes the following steps:

[0068] Based on the collected characteristic information of the people inside the vehicle, it is determined whether there is a significant difference between the people inside the vehicle at the current time and location in the historical data. If so, the broadcast schedule is adjusted according to the determination result.

[0069] In this embodiment, based on the collected characteristic information of the people inside the vehicle, it is determined whether there is a significant difference between the current people inside the vehicle and the people in the historical data at the current time period or location. If there is a significant difference, the real-time adjustment module triggers the scheduling planning module to regenerate the scheduling plan. The scheduling planning module uses the trained model combined with the current personnel characteristics to regenerate the scheduling plan to adapt to changes in the people inside the vehicle and achieve more accurate advertising delivery.

[0070] In the above-described automatic scheduling method for in-vehicle advertising platforms, automated scheduling significantly reduces manual intervention, greatly improving scheduling efficiency and enabling the rapid processing of large numbers of advertising orders to meet the market's demand for rapid response. Automatic scheduling of in-vehicle advertisements, combining vehicle location, time, and in-vehicle occupant characteristics, improves scheduling efficiency and accuracy, reduces labor costs, and achieves precise advertising delivery.

[0071] like Figure 3 As shown, in another embodiment, an automatic scheduling system 300 for in-vehicle advertising platform programs is provided. This automatic scheduling system 300 for in-vehicle advertising platform programs includes: a data collection module 301, a data processing module 302, a training module 303, a scheduling module 304, and a monitoring module 305.

[0072] The data collection module 301 is used to collect advertising publication-related information submitted by the customer, including the publication period, playback type, and playback level, as well as historical data of in-vehicle advertising playback, status information of the in-vehicle device, vehicle location information, current time period, and the number and characteristics of people in the vehicle, including age and gender. The playback type includes one or more of loop, timed, fixed-point, interstitial, and emergency, and is marked with a value of 1-5 respectively; the playback level includes one or more of append and overlay, and is marked with 1 and 2 respectively.

[0073] The data processing module 302 is used to clean and standardize the collected parameters, convert different types of playback types and playback levels into numerical features that the model can recognize; and determine the main type of the current occupants based on the collected vehicle location information, current time period and occupant characteristics.

[0074] The training module 303 is used to train an automatic scheduling model using machine learning algorithms. During training, the processed parameters and historical scheduling data are used as the first input, and the vehicle's location information, time period, and the number and type of people in the vehicle are used as the second input. This enables the trained automatic scheduling model to schedule advertisements based on the relevant information and dynamically adjust according to the current needs of the people in the vehicle. The machine learning algorithm includes one or more of neural network algorithms and decision tree algorithms.

[0075] The scheduling module 304 is used to obtain the vehicle's current location, time period, and current personnel characteristics in the vehicle in real time and input them into the trained automatic scheduling model. The automatic scheduling model automatically generates a detailed advertising scheduling plan based on the customer's parameter requirements, including the playback time, playback frequency, and playback order of each advertisement.

[0076] The monitoring module 305 is used to monitor the execution of the advertising schedule in real time, record the actual playback data of the advertisements, compare it with the schedule, and promptly detect and handle any abnormal situations that occur during the playback process.

[0077] The automatic scheduling model automatically generates a detailed advertising scheduling plan based on the customer's parameter requirements, including: a preset first container and a second container, wherein the second container includes two or more sub-containers, and each sub-container is set with different audience tags.

[0078] The automatic scheduling model places scheduled advertisements in the first container according to the playback type and playback level, and distributes non-scheduled advertisements to different sub-containers according to their target audience.

[0079] When generating the advertising schedule, the automatic scheduling model obtains multiple first advertisements from the first container and sorts them to generate a first schedule for fixed-time playback based on the first advertisements; and according to the main types of people in the vehicle during different time periods, obtains multiple second advertisements from the corresponding sub-containers and inserts the second advertisements into the first schedule according to time periods, so that the second advertisements played match the types of people in the vehicle at the moment.

[0080] like Figure 4 As shown in the full implementation, the automatic scheduling system 300 for the in-vehicle advertising platform also includes an adjustment module 306. The adjustment module 306 is used to determine, based on the collected characteristic information of the people in the vehicle, whether there is a significant difference between the current people in the vehicle and the people in the historical data at the current time period or location. If so, the scheduling plan is adjusted according to the determination result.

[0081] Finally, it should be noted that although the above embodiments have been described in the text and drawings of this application, this should not limit the scope of patent protection of this application. Any technical solutions that are based on the essential concept of this application and utilize the content described in the text and drawings of this application, resulting in equivalent structural or procedural substitutions or modifications, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of patent protection of this application.

Claims

1. An automatic scheduling method for programs on an in-vehicle advertising platform, characterized in that, Includes the following steps: Collect advertising placement information submitted by customers, including placement period, playback type and playback level, as well as historical data of in-vehicle advertising playback, status information of in-vehicle devices, vehicle location information, current time period, and the number and characteristics of people in the vehicle, including age and gender; The collected parameters are cleaned and standardized to convert different types of playback types and playback levels into numerical features that the model can recognize. Based on the collected vehicle location information, current time period, and characteristics of the people inside the vehicle, determine the main types of people currently inside the vehicle; An automatic scheduling model is trained using machine learning algorithms. During training, the processed parameters and historical scheduling data are used as the first input, and the vehicle's location information, time period, and the number and type of people in the vehicle are used as the second input. This enables the trained automatic scheduling model to schedule advertisements based on the relevant information and dynamically adjust according to the current needs of the people in the vehicle. The system obtains the vehicle's current location, time period, and the characteristics of the people inside the vehicle in real time and inputs them into the trained automatic advertising scheduling model. The automatic advertising scheduling model automatically generates a detailed advertising scheduling plan based on the customer's parameter requirements, including the playback time, playback frequency, and playback order of each advertisement. Monitor the execution of the advertising schedule in real time, record the actual playback data of the advertisements, compare it with the schedule, and promptly identify and handle any abnormalities that occur during playback.

2. The automatic scheduling method for programs on the vehicle-mounted advertising platform according to claim 1, characterized in that, The automatic scheduling model automatically generates a detailed advertising scheduling plan based on the client's parameter requirements, including: A first container and a second container are preset. The second container includes two or more sub-containers, and each sub-container is set with different audience tags. The automatic scheduling model places scheduled advertisements in the first container according to the playback type and playback level, and distributes non-scheduled advertisements to different sub-containers according to their target audience. When generating the advertising schedule, the automatic scheduling model obtains multiple first advertisements from the first container and sorts them to generate a first schedule for fixed-time playback based on the first advertisements; and according to the main types of people in the vehicle during different time periods, obtains multiple second advertisements from the corresponding sub-containers and inserts the second advertisements into the first schedule according to time periods, so that the second advertisements played match the types of people in the vehicle at the moment.

3. The automatic scheduling method for programs on the vehicle-mounted advertising platform according to claim 1, characterized in that, It also includes the following steps: Based on the collected characteristic information of the people inside the vehicle, it is determined whether there is a significant difference between the people inside the vehicle at the current time and location in the historical data. If so, the broadcast schedule is adjusted according to the determination result.

4. The automatic scheduling method for in-vehicle advertising platform programs according to claim 1 or 2, characterized in that, The playback type includes one or more of loop, timed, fixed-point, interstitial, and emergency, and is marked with a value of 1-5 respectively; the playback level includes one or more of append and overwrite, and is marked with 1 and 2 respectively.

5. The automatic scheduling method for programs on an in-vehicle advertising platform according to claim 1 or 2, characterized in that, The machine learning algorithm includes one or more of the following: neural network algorithm and decision tree algorithm.

6. An automatic scheduling system for programs on a vehicle-mounted advertising platform, characterized in that, include: The data collection module is used to collect advertising publication-related information submitted by customers, including publication period, playback type and playback level, as well as historical data of in-vehicle advertising playback, status information of in-vehicle devices, vehicle location information, current time period, and the number and characteristics of people in the vehicle, including age and gender. The data processing module is used to clean and standardize the collected parameters, converting different types of playback types and playback levels into numerical features that the model can recognize. Based on the collected vehicle location information, current time period, and characteristics of the people inside the vehicle, determine the main types of people currently inside the vehicle; The training module is used to train the automatic scheduling model using machine learning algorithms. During training, the processed parameters and historical scheduling data are used as the first input, and the vehicle's location information, time period, and the number and type of people in the vehicle are used as the second input. This enables the trained automatic scheduling model to schedule advertisements based on the relevant information and to dynamically adjust according to the current needs of the people in the vehicle. The scheduling module is used to obtain the vehicle's current location, time period, and the characteristics of the people in the vehicle in real time and input them into the trained automatic scheduling model. The automatic scheduling model automatically generates a detailed advertising scheduling plan based on the customer's parameter requirements, including the playback time, playback frequency, and playback order of each advertisement. The monitoring module is used to monitor the execution of the advertising schedule in real time, record the actual playback data of the advertisements, compare it with the schedule, and promptly detect and handle any abnormalities that occur during playback.

7. The automatic scheduling system for in-vehicle advertising platform programs according to claim 6, characterized in that, The automatic scheduling model automatically generates a detailed advertising scheduling plan based on the client's parameter requirements, including: A first container and a second container are preset. The second container includes two or more sub-containers, and each sub-container is set with different audience tags. The automatic scheduling model places scheduled advertisements in the first container according to the playback type and playback level, and distributes non-scheduled advertisements to different sub-containers according to their target audience. When generating the advertising schedule, the automatic scheduling model obtains multiple first advertisements from the first container and sorts them to generate a first schedule for fixed-time playback based on the first advertisements; and according to the main types of people in the vehicle during different time periods, obtains multiple second advertisements from the corresponding sub-containers and inserts the second advertisements into the first schedule according to time periods, so that the second advertisements played match the types of people in the vehicle at the moment.

8. The automatic scheduling system for in-vehicle advertising platform programs according to claim 6, characterized in that, It also includes an adjustment module, which is used to determine whether there is a significant difference between the current people in the vehicle and the people in the historical data at the current time or location, based on the collected characteristic information of the people in the vehicle. If so, the broadcast schedule is adjusted according to the judgment result.

9. The automatic scheduling system for in-vehicle advertising platform programs according to claim 6, characterized in that, The playback type includes one or more of loop, timed, fixed-point, interstitial, and emergency, and is marked with a value of 1-5 respectively; the playback level includes one or more of append and overwrite, and is marked with 1 and 2 respectively.

10. The automatic scheduling system for in-vehicle advertising platform programs according to claim 6, characterized in that, The machine learning algorithm includes one or more of the following: neural network algorithm and decision tree algorithm.

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