Advertisement plan analysis system and method

Abstract

The invention discloses an advertisement plan analysis system and method, and the method comprises the steps: carrying out the efficient fusion and automatic data cleaning and preprocessing of multi-source data through a data collection and preprocessing unit, and carrying out the extraction, construction and analysis of original data through a feature engineering and data analysis unit; the method effectively improves the expressive force of data and the reliability of a prediction result, introduces an iterative optimization and adjustment mechanism through an optimization and adjustment unit, effectively improves the performance and adaptability of a target prediction model in application, and achieves the purpose of continuously optimizing an advertisement plan.

Description

Technical Field

[0001] The present disclosure generally relates to the field of computer technology, and more particularly to an advertising plan analysis system and method. Background Art

[0002] Advertising has a long history and rich background. With the development of business and the progress of society, enterprises and businesses are increasingly in need of effective ways to promote their products, services or brands.

[0003] In related technologies, advertising plans are usually formulated based on the experience of enterprises or merchants and market analysts, which can easily lead to wrong decisions, affect the effectiveness of advertising, and fail to effectively respond to complex market environments and changes. Summary of the invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, it is desired to provide an advertising plan analysis system and method, which can effectively reduce the reliance on experience through a data-driven scientific decision-making method, thereby providing a more objective and reliable advertising plan.

[0005] In a first aspect, an embodiment of the present application provides an advertising plan analysis system, including:

[0006] A data collection and pre-processing unit for collecting raw data related to a target advertising plan from multiple data sources;

[0007] A feature engineering and data analysis unit, configured to extract at least one candidate feature data from the raw data, perform feature processing based on at least one of the candidate feature data to obtain target feature data, and perform data analysis based on the target feature data to obtain data analysis features between the target feature data;

[0008] A model selection and training unit, configured to select a target prediction model based on the target feature data and / or the data analysis features, and train the target prediction model;

[0009] A prediction and evaluation model, used to predict and / or evaluate the life stage of the target advertising plan based on the trained target prediction model to obtain a prediction result and / or an evaluation result;

[0010] An optimization and adjustment unit is used to optimize the target advertising plan based on the prediction result and / or the evaluation result, and to perform feedback adjustment on the target prediction model.

[0011] In some embodiments, the data collection and preprocessing unit further includes:

[0012] A data collection module for collecting initial data related to the targeted advertising plan from multiple data sources;

[0013] A data cleaning module, used for cleaning the initial data;

[0014] The data preprocessing module is used to preprocess the cleaned data to obtain the original data.

[0015] In some embodiments, the feature engineering and data analysis unit includes:

[0016] A feature extraction module, used to extract at least one candidate feature data from the original data;

[0017] A feature construction module, used to construct data according to preset rules based on at least one of the candidate feature data to obtain target feature data;

[0018] The data analysis module is used to analyze the target feature data to obtain descriptive features and correlation features of the target feature data.

[0019] In some embodiments, the feature engineering and data analysis unit further includes:

[0020] The data visualization module is used to visualize the descriptive features and correlation features of the target feature data.

[0021] In some embodiments, the model selection and training unit is further used to:

[0022] The target feature data is split into a training set, a validation set and a test set, and the target prediction model is cross-trained based on the training set, the validation set and the test set.

[0023] In some embodiments, the prediction and evaluation model comprises:

[0024] A prediction module, used for predicting the life stage of the target advertising plan based on the target prediction model to obtain the prediction result;

[0025] The evaluation module is used to perform evaluation based on the prediction result to obtain an evaluation result.

[0026] In some embodiments, the optimization and adjustment unit includes:

[0027] An optimization module, used to optimize the delivery strategy of the target advertising plan according to the prediction result;

[0028] An adjustment module is used to perform feedback adjustment on the target prediction model based on the evaluation result.

[0029] In a second aspect, an embodiment of the present application provides an advertising plan analysis method, including:

[0030] Gathering raw data relevant to targeted advertising initiatives from multiple data sources;

[0031] Extracting at least one candidate feature data from the original data, performing feature processing based on at least one of the candidate feature data to obtain target feature data, and performing data analysis based on the target feature data to obtain data analysis features between the target feature data;

[0032] Selecting a target prediction model based on the target feature data and / or the data analysis features, and training the target prediction model;

[0033] Predicting and / or evaluating the life stage of the target advertising plan based on the trained target prediction model to obtain a prediction result and / or evaluation result;

[0034] The target advertising plan is optimized based on the prediction result and / or the evaluation result, and feedback adjustment is performed on the target prediction model.

[0035] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the embodiment of the present application when executing the program.

[0036] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the embodiment of the present application.

[0037] The advertising plan analysis system provided in the embodiment of the present application can utilize the data collection and preprocessing unit to efficiently fuse and automatically clean and preprocess multi-source data, utilize the feature engineering and data analysis unit to extract, construct and analyze the original data, effectively improve the expressiveness of the data and improve the reliability of the prediction results, and utilize the optimization and adjustment unit to introduce an iterative optimization and adjustment mechanism, effectively improve the performance and adaptability of the target prediction model in the application, and achieve the purpose of continuous optimization of the advertising plan.

[0038] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0040] Figure 1A schematic diagram of the structure of an advertising plan analysis system provided by an embodiment of the present application is shown;

[0041] Figure 2 A schematic diagram showing the structure of an advertising plan analysis system provided by another embodiment of the present application is shown;

[0042] Figure 3 A schematic diagram of a flow chart of an advertising plan analysis method provided by an embodiment of the present application is shown;

[0043] Figure 4 A schematic diagram of the structure of a computer system of an electronic device or server suitable for implementing an embodiment of the present application is shown. DETAILED DESCRIPTION

[0044] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0045] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] First, some nouns or terms that appear in the process of describing the embodiments of the present application are used for the following explanations:

[0047] Advertising plan: An advertising plan refers to a series of advertising strategies and actions formulated by the advertiser to promote products or services. An advertising plan usually includes elements such as target audience, delivery platform, delivery time, budget allocation, advertising content and form. Its purpose is to achieve goals such as brand promotion, user conversion and sales growth through planned advertising activities.

[0048] Model: In machine learning and data science, a model is a mathematical or computational method used to describe the relationship between data. Models can predict new data results by learning patterns and regularities in existing data. Common types of models include regression models, classification models, time series models, and deep learning models.

[0049] Lifecycle: The lifecycle refers to the entire process of an advertising plan from the beginning to the end. The various stages in the lifecycle may include ad creation, delivery, monitoring, optimization, and summary. Each stage has different goals and tasks, and the ultimate goal is to improve the overall effectiveness and return of advertising through continuous optimization and adjustment.

[0050] Delivery strategy: Delivery strategy refers to a series of methods and strategies used by advertisers when specifying advertising plans to maximize advertising effectiveness and return on investment. These strategies may include selecting appropriate delivery platforms (such as social media, search engines, display ads, etc.), determining target audiences, designing attractive advertising content, reasonably allocating budgets, determining the best delivery time period, etc. Delivery strategies need to be based on data analysis and market research to ensure that advertisements can effectively reach the target audience and achieve the desired results.

[0051] Figure 1 A schematic diagram of the structure of an advertising plan analysis system provided in an embodiment of the present application is shown.

[0052] like Figure 1 As shown, the advertising plan analysis system 10 includes:

[0053] The data collection and preprocessing unit 11 is used to collect raw data related to the target advertising plan from multiple data sources.

[0054] In a possible embodiment, if Figure 2 As shown, the data collection and preprocessing unit 11 further includes:

[0055] A data collection module is used to collect initial data related to the targeted advertising plan from multiple data sources.

[0056] The initial data includes, but is not limited to, display, click, consumption, registration, application, credit, balance, delivery time, etc. The initial data can be automatically obtained through the embedding point in the product page where the target advertising plan is located.

[0057] The data cleaning module is used to clean the initial data.

[0058] It should be noted that data cleaning is mainly used to deal with data anomalies, such as missing data, data anomalies, etc. Data cleaning can improve the quality and integrity of the original data, thereby improving the accuracy of subsequent predictions and evaluations.

[0059] Among them, missing data can be processed by filling or deleting. Data outlier processing can be done by identifying the initial data, determining the outliers in the data, and then correcting them according to the outlier correction methods corresponding to each type of data, thereby ensuring the reliability of the data.

[0060] The data preprocessing module is used to preprocess the cleaned data to obtain the original data.

[0061] It should be noted that data preprocessing is to make data from different sources comparable in value, including but not limited to scaling the data to between 0 and 1, or making it have zero mean and unit variance. In the embodiment of the present application, preferably, the initial data are scaled to between 0 and 1 to improve the convergence speed and performance of the subsequent model prediction.

[0062] The feature engineering and data analysis unit 12 is used to extract at least one candidate feature data from the original data, perform feature processing based on the at least one candidate feature data to obtain target feature data, and perform data analysis based on the target feature data to obtain data relationships between the target feature data.

[0063] In a feasible embodiment, the feature engineering and data analysis unit 12 includes:

[0064] The feature extraction module is used to extract at least one candidate feature data from the original data.

[0065] It should be noted that candidate feature data can be selected according to analysis requirements. For example, in order to obtain the click-through rate, the number of clicks and the number of impressions can be set as candidate features for extraction. In order to obtain the conversion rate, the number of registrations and the number of clicks can be set as candidate features for extraction. You can also set relevant features such as the number of applicants, balance, budget, and delivery time.

[0066] The feature construction module is used to construct data according to preset rules based on at least one candidate feature data to obtain target feature data.

[0067] Among them, constructing the target feature data is to combine at least one candidate feature into one feature data based on the prediction needs, for example, combining multiple time features into combined features of longer time periods such as daily features, weekly features and monthly features, and using them as target feature data.

[0068] It should be understood that the embodiment of the present application combines (compounds) multiple independent features with correlation into a combined (compound) feature with a larger span through a feature construction module, which can effectively improve the data expressiveness of the target feature data.

[0069] The data analysis module is used to analyze the target feature data to obtain the descriptive features and correlation features of the target feature data.

[0070] Optionally, the data analysis module can perform descriptive analysis and correlation analysis on the target feature data. Descriptive analysis is to calculate or analyze a certain descriptive parameter using multiple target feature data, such as mean, variance, median and other statistics; correlation analysis is to analyze the correlation between multiple target feature data, such as the correlation between change trend and time feature, the correlation between conversion rate and click-through rate, etc.

[0071] In another feasible embodiment, the feature engineering and data analysis unit 12 further includes:

[0072] The data visualization module is used to visualize the descriptive features and correlation features of the target feature data.

[0073] Specifically, the data visualization module can use visualization methods such as charts to display the descriptive features and correlation features obtained by the data analysis module.

[0074] It should be understood that the analysis and display of descriptive features and correlation features through the data analysis module can better demonstrate the changing trends between target data features and the correlation between different features, thereby providing a reliable data basis for subsequent model adjustments.

[0075] The model selection and training unit 13 is used to select a target prediction model based on target feature data and / or data analysis features, and train the target prediction model.

[0076] In a feasible embodiment, the model selection and training unit 13 includes:

[0077] The model selection module is used to select a prediction model that matches the target feature data as the target prediction model.

[0078] It should be noted that the candidate prediction models may include multiple types, such as time series models, regression models, and deep learning models, etc. Optionally, a prediction model matching the target data features may be selected as the target prediction model based on the correlation features between the multiple target data features analyzed by the data analysis module. For example, a time series model may be selected as the prediction model corresponding to the target data features having correlation with time.

[0079] Optionally, when there are multiple target feature data or multiple correlations, multiple target prediction models can be selected. For scenarios where multiple target prediction models are selected, the multiple target prediction models can be used together for separate predictions, or they can be used in combination to obtain comprehensive prediction results. This application does not make specific limitations.

[0080] The model training module is used to split the target feature data into a training set, a validation set, and a test set, and train the target prediction model based on the training set.

[0081] The cross-validation module is used to cross-validate the trained target prediction model using the validation set and the test set.

[0082] The model evaluation module is used to evaluate the performance of the trained target prediction model.

[0083] The evaluation method can be a mathematical method, such as calculating the mean square error (MSE), mean absolute error (MAE), R 2 wait.

[0084] It should be understood that in the embodiment of the present application, cross-validation is performed through a cross-validation module, which can effectively evaluate the performance of the target prediction model, avoid overfitting, and improve the generalization ability of the model. At the same time, the model evaluation module is used to evaluate the model performance, thereby achieving multiple evaluations of the target prediction model and improving the reliability of the target prediction model.

[0085] The prediction and evaluation model 14 is used to predict and / or evaluate the life stage of the target advertising plan based on the trained target prediction model to obtain prediction results and / or evaluation results.

[0086] In a feasible embodiment, the prediction and evaluation model 14 includes:

[0087] The prediction module is used to predict the life stage of the target advertising plan based on the target prediction model to obtain the prediction result.

[0088] It should be noted that the prediction result of the prediction module is to predict the current life stage of the target advertising plan, such as the growth stage, decline stage, etc., based on the original data related to the target advertising plan.

[0089] The evaluation module is used to evaluate based on the prediction results and obtain evaluation results.

[0090] Specifically, the evaluation module is used to evaluate the prediction performance of the target prediction model based on the prediction result to obtain the evaluation result. That is, in the embodiment of the present application, the evaluation module is used to evaluate the performance of the target prediction model.

[0091] The optimization and adjustment unit 15 is used to optimize the target advertising plan based on the prediction result and / or the evaluation result, and to make feedback adjustment to the target prediction model.

[0092] In a feasible embodiment, the optimization and adjustment unit 15 includes:

[0093] The optimization module is used to optimize the delivery strategy of the target advertising plan based on the prediction results.

[0094] It should be noted that the present application predicts the life cycle of a target advertising plan through a target prediction model, and then optimizes the target advertising plan through a subsequent optimization module, thereby improving the delivery effect and return of the target advertising plan.

[0095] Optionally, the optimization module is also used to optimize the iterative process of the target prediction model according to the evaluation results to improve the performance of the target prediction model, such as optimizing feature selection, optimizing feature engineering improvements, model parameter adjustment, etc.

[0096] The adjustment module is used to make feedback adjustments to the target prediction model based on the evaluation results.

[0097] Specifically, both the adjustment module and the optimization module can optimize and adjust the target prediction model. The adjustment module is used to further improve the prediction effect of the target prediction model by using more advanced adjustment techniques, such as using advanced techniques such as hyperparameter tuning and model integration to ensure the robustness and accuracy of the target prediction model in practical applications.

[0098] Specifically, hyperparameter tuning is to use grid search, random search and other methods to tune hyperparameters, and model integration is to use integrated learning methods (such as Bagging, Boosting) to adjust the model to improve model performance.

[0099] Therefore, the advertising plan analysis system provided in the embodiment of the present application can use the data collection and preprocessing unit to efficiently fuse and automatically clean and preprocess multi-source data, use the feature engineering and data analysis unit to extract, construct and analyze the original data, effectively improve the expressiveness of the data and improve the reliability of the prediction results, and use the optimization and adjustment unit to introduce an iterative optimization and adjustment mechanism, effectively improve the performance and adaptability of the target prediction model in the application, and achieve the purpose of continuous optimization of the advertising plan.

[0100] Figure 3 Shows a schematic diagram of the process of a terminal interface identification method provided in an embodiment of the present application.

[0101] like Figure 3 As shown, the advertising plan analysis method includes:

[0102] Step 301, collecting raw data related to a target advertising plan from multiple data sources.

[0103] Step 302 extracts at least one candidate feature data from the original data, performs feature processing based on at least one of the candidate feature data to obtain target feature data, and performs data analysis based on the target feature data to obtain data analysis features between the target feature data.

[0104] Step 303: Select a target prediction model based on the target feature data and / or the data analysis features, and train the target prediction model.

[0105] Step 304: predict and / or evaluate the life stage of the target advertising plan based on the trained target prediction model to obtain a prediction result and / or evaluation result.

[0106] Step 305: Optimize the target advertising plan based on the prediction result and / or the evaluation result, and perform feedback adjustment on the target prediction model.

[0107] Therefore, the advertising plan analysis method provided in the embodiment of the present application effectively improves the expressiveness of the data and the reliability of the prediction results by efficiently fusing multi-source data and automatically cleaning and preprocessing the data, as well as extracting, constructing and analyzing the original data. It introduces an iterative optimization and adjustment mechanism, effectively improves the performance and adaptability of the target prediction model in the application, and achieves the purpose of continuous optimization of the advertising plan.

[0108] It should be noted that although the operations of the method of the present invention are described in a particular order in the drawings, this does not require or imply that the operations must be performed in this particular order or that all illustrated operations must be performed to achieve desired results.

[0109] Reference below Figure 4 , Figure 4 A schematic diagram of the structure of a computer system of an electronic device or server suitable for implementing an embodiment of the present application is shown.

[0110] like Figure 4 As shown, the computer system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 into the random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation instructions of the system are also stored. The CPU 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0111] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.

[0112] In particular, according to an embodiment of the present application, the above reference flow chart Figure 2 The described process can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 409, and / or installed from the removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, the above-mentioned functions defined in the system of the present application are executed.

[0113] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium such as a computer-readable storage medium that can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0114] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operating instructions of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the aforementioned module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, the boxes represented by two connections can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operating instruction, or can be implemented with a combination of dedicated hardware and computer instructions.

[0115] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device. The above computer-readable storage medium stores one or more programs, and when the above programs are used by one or more processors to execute the advertising plan analysis method described in the present application.

[0116] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. An advertising plan analysis system, characterized in that: include: A data collection and pre-processing unit for collecting raw data related to a target advertising plan from multiple data sources; A feature engineering and data analysis unit, configured to extract at least one candidate feature data from the raw data, perform feature processing based on at least one of the candidate feature data to obtain target feature data, and perform data analysis based on the target feature data to obtain data analysis features between the target feature data; A model selection and training unit, configured to select a target prediction model based on the target feature data and / or the data analysis features, and train the target prediction model; A prediction and evaluation model, used to predict and / or evaluate the life stage of the target advertising plan based on the trained target prediction model to obtain a prediction result and / or an evaluation result; An optimization and adjustment unit is used to optimize the target advertising plan based on the prediction result and / or the evaluation result, and to perform feedback adjustment on the target prediction model.

2. The advertising plan analysis system according to claim 1, characterized in that: The data collection and preprocessing unit further includes: A data collection module for collecting initial data related to the targeted advertising plan from multiple data sources; A data cleaning module, used for cleaning the initial data; The data preprocessing module is used to preprocess the cleaned data to obtain the original data.

3. The advertising plan analysis system according to claim 1, characterized in that: The feature engineering and data analysis unit comprises: A feature extraction module, used to extract at least one candidate feature data from the original data; A feature construction module, used to construct data according to preset rules based on at least one of the candidate feature data to obtain target feature data; The data analysis module is used to analyze the target feature data to obtain descriptive features and correlation features of the target feature data.

4. The advertising plan analysis system according to claim 3, characterized in that: The feature engineering and data analysis unit further includes: The data visualization module is used to visualize the descriptive features and correlation features of the target feature data.

5. The advertising plan analysis system according to claim 1, characterized in that: The model selection and training unit is also used for: The target feature data is split into a training set, a validation set and a test set, and the target prediction model is cross-trained based on the training set, the validation set and the test set.

6. The advertising plan analysis system according to claim 1, characterized in that: The prediction and evaluation model includes: A prediction module, used for predicting the life stage of the target advertising plan based on the target prediction model to obtain the prediction result; The evaluation module is used to perform evaluation based on the prediction result to obtain an evaluation result.

7. The advertising plan analysis system according to claim 1, characterized in that: The optimization and adjustment unit comprises: An optimization module, used to optimize the delivery strategy of the target advertising plan according to the prediction result; An adjustment module is used to perform feedback adjustment on the target prediction model based on the evaluation result.

8. An advertising plan analysis method, characterized in that: include: Gathering raw data relevant to targeted advertising initiatives from multiple data sources; Extracting at least one candidate feature data from the original data, performing feature processing based on at least one of the candidate feature data to obtain target feature data, and performing data analysis based on the target feature data to obtain data analysis features between the target feature data; Selecting a target prediction model based on the target feature data and / or the data analysis features, and training the target prediction model; Predicting and / or evaluating the life stage of the target advertising plan based on the trained target prediction model to obtain a prediction result and / or evaluation result; The target advertising plan is optimized based on the prediction result and / or the evaluation result, and feedback adjustment is performed on the target prediction model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the advertising plan analysis method as described in claim 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the advertising plan analysis method as claimed in claim 8 is implemented.

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