Hotspot content generation and flow operation system based on large model and automation tool

Abstract

The invention belongs to the technical field of social platform management, and provides a hotspot content generation and flow operation system based on a large model and an automatic tool, which comprises a hotspot data acquisition module, a content generation module, an automatic login and release module, a timing release control module, a data analysis and feedback module and an advertisement access and optimization module. According to the method, a plurality of modules such as hotspot data acquisition, automatic content generation, automatic release, timed release control, data analysis and feedback and advertisement access and optimization are integrated, and flow automation is realized by means of a continuous integration tool and programming codes, so that the management efficiency of a social platform is greatly improved, the workload of operators can be reduced, and the social network service quality is improved. The method enables the users to focus on content creativity and strategy planning, optimizes the user experience and the advertisement effect, and improves the flow utilization rate and the advertisement conversion rate.

Description

Technical Field

[0001] The present invention belongs to the technical field of social platform management, and more specifically, is a hot content generation and traffic operation system based on a large model and automation tools. Background Art

[0002] With the development of Internet technology, social media platforms have become an important channel for information dissemination and traffic aggregation. However, manually managing and operating multiple social platform accounts, especially quickly generating and publishing content for hot events, is a very challenging task for operators.

[0003] Although there are some content generation tools and social platform management tools in the existing technology, they often have single functions and lack full-process automated solutions from hot spot collection to content generation, publishing, data analysis and advertising optimization.

[0004] To this end, those skilled in the art have proposed a hot content generation and traffic operation system based on a large model and automated tools to solve the problems raised by the background technology. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a hot content generation and traffic operation system based on a big model and automation tools to solve the problem that although there are some content generation tools and social platform management tools in the prior art, they often have single functions and lack a full-process automated solution from hot spot collection to content generation, publishing, data analysis and advertising optimization.

[0006] Hot content generation and traffic operation system based on big models and automation tools, including:

[0007] Hotspot data collection module, used to collect hotspot data from Xiaohongshu, WeChat or other social platforms, and then process the collected raw data;

[0008] The content generation module is connected to the big model to automatically generate articles based on hot data and preset platform templates;

[0009] The automated login and publishing module uses automated tools or logged-in session information to automatically log in to various social platforms and publish generated content;

[0010] The timed release control module allows users to set specific release time nodes to achieve timed delivery;

[0011] The data analysis and feedback module is used to collect and analyze the attention information, comment information and data information generated by the published content, store it in the local database, and provide data analysis results;

[0012] Advertisement access and optimization module: based on data analysis results, automatically accesses advertisements when traffic reaches a certain threshold, and tests ad placement to optimize user experience and advertising effectiveness;

[0013] The system integration and calling module uses continuous integration tools to connect various modules to achieve process automation, and can directly call large models through programming codes.

[0014] Preferably, in the content generation module, a content generation optimization algorithm (generative pre-training model) is introduced. The generative pre-training model is based on the Transformer architecture, generates text through self-attention mechanism and position encoding, and automatically generates articles according to hot data and preset platform templates;

[0015] At the same time, reinforcement learning algorithms are used to evaluate and optimize the quality of the generated content to ensure that the generated articles are both in line with hot trends and attractive.

[0016] Preferably, the content generation module also includes a sensitive content filtering function for automatically avoiding sensitive content before generating an article.

[0017] Preferably, a traffic prediction and scheduling algorithm is introduced into the timed release control module, that is, the release time node of the content is intelligently adjusted according to the predicted traffic trend and the user's settings to maximize the utilization of traffic.

[0018] Preferably, an advertisement display optimization algorithm is introduced into the advertisement access and optimization module to improve user experience and advertisement effect.

[0019] Preferably, the data analysis and feedback module further includes a hotspot prediction function, which predicts possible future hotspot trends based on historical data, and the hotspot prediction function predicts hotspot trends by adopting a hotspot prediction algorithm.

[0020] Preferably, the advertisement access and optimization module supports dynamic adjustment of advertisement display strategy, and automatically optimizes advertisement position and display frequency according to user feedback and data analysis results.

[0021] A method for generating hot content and operating traffic based on a big model and an automated tool, using the hot content generation and traffic operation system based on a big model and an automated tool, includes:

[0022] S1. Collect hotspot data through hotspot data collection module;

[0023] S2. Use the content generation module to generate articles based on hot data and templates, and filter sensitive content;

[0024] S3. Log in to the social platform and publish articles through the automated login and publishing module;

[0025] S4, sending articles according to the setting of the scheduled publishing control module;

[0026] S5, data analysis and feedback module collects and analyzes data and provides optimization suggestions;

[0027] S6. When the traffic reaches the threshold, the advertisement access and optimization module automatically accesses the advertisement and optimizes the display strategy.

[0028] A processor is configured to execute the above-mentioned hot content generation and traffic operation system based on a big model and an automation tool.

[0029] A computer-readable and writable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned hot content generation and traffic operation system based on a large model and automation tools.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The present invention builds a full-process automated hot content generation and traffic operation system by integrating multiple modules such as hot data collection, automatic content generation, automated publishing, scheduled publishing control, data analysis and feedback, and advertising access and optimization; it greatly improves the management efficiency of the social platform, reduces the workload of operators, and enables them to focus more on content creativity and strategy planning.

[0032] 2. The present invention introduces advanced generative pre-training models and reinforcement learning algorithms in the content generation module, which can automatically generate high-quality articles that conform to hot trend based on hot data and preset platform templates; at the same time, the sensitive content filtering function ensures the compliance and security of the generated content.

[0033] 3. The present invention uses the traffic prediction and scheduling algorithm in the timed release control module to intelligently adjust the content release time nodes according to the predicted traffic trend and user settings, thereby maximizing the use of traffic, which not only improves the content exposure rate, but also enhances user interaction and participation.

[0034] 4. The present invention provides a comprehensive data analysis function in the data analysis and feedback module, including the collection and analysis of attention information, comment information and data information, as well as a hot spot prediction function, which provides powerful data support for operators, enabling them to more accurately understand user needs and market trends, thereby formulating more effective operation strategies.

[0035] 5. The present invention introduces an advertising display optimization algorithm in the advertising access and optimization module, which can automatically optimize the advertising position and display frequency according to user feedback and data analysis results, thereby improving user experience and advertising effect; at the same time, the function of dynamically adjusting the advertising display strategy makes the advertising content more in line with user needs, thereby improving the conversion rate and return on investment of the advertisement. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a framework diagram of the hot content generation and traffic operation system based on a large model and automation tools of the present invention. DETAILED DESCRIPTION

[0037] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0038] Embodiment: The present invention provides a hot content generation and traffic operation system based on a large model and an automated tool, such as Figure 1 As shown, it includes a hot data collection module, a content generation module, an automatic login and publishing module, a timed publishing control module, a data analysis and feedback module, and an advertisement access and optimization module. The hot data collection module, the content generation module, the automatic login and publishing module, the timed publishing control module, the data analysis and feedback module, and the advertisement access and optimization module are electrically connected in sequence:

[0039] The hot data collection module is used to collect hot data from Xiaohongshu, WeChat or other social platforms, and then process the collected raw data; the data processing includes but is not limited to missing value processing, outlier processing, duplicate value processing, data conversion, etc.:

[0040] The content generation module is connected to the big model to automatically generate articles based on hot data and preset platform templates;

[0041] The automated login and publishing module uses automated tools or logged-in session information to automatically log in to various social platforms and publish generated content;

[0042] The timed release control module allows users to set specific release time nodes to achieve timed delivery;

[0043] The data analysis and feedback module is used to collect and analyze the attention information, comment information and data information generated by the published content, store it in the local database, and provide data analysis results;

[0044] Advertisement access and optimization module: based on data analysis results, automatically accesses advertisements when traffic reaches a certain threshold, and tests ad placement to optimize user experience and advertising effectiveness;

[0045] The system integration and calling module uses continuous integration tools to connect various modules to achieve process automation, and can directly call large models through programming codes.

[0046] From the above, we can see that by integrating multiple modules such as hot data collection, automatic content generation, automated publishing, scheduled publishing control, data analysis and feedback, and advertising access and optimization, and using continuous integration tools and programming codes to automate processes, the management efficiency of social platforms has been greatly improved, which can reduce the workload of operators and enable them to focus more on content creativity and strategy planning, while optimizing user experience and advertising effectiveness, and improving traffic utilization and advertising conversion rates.

[0047] Furthermore, in the content generation module, a content generation optimization algorithm (generative pre-training model) is introduced. The generative pre-training model is based on the Transformer architecture, generates text through self-attention mechanism and position encoding, and automatically generates articles based on hot data and preset platform templates; the formula of the content generation optimization algorithm is as follows:

[0048] h t =T′(h t-1 ,x t );

[0049] Among them, h t is the hidden state at time t, x t is the input at time t, and T′ represents the self-attention mechanism and feedforward neural network in the Transformer architecture;

[0050] At the same time, the reinforcement learning algorithm is used to evaluate and optimize the quality of the generated content to ensure that the generated articles are both in line with hot trends and attractive. The formula of the reinforcement learning algorithm is as follows:

[0051]

[0052] Among them, θ is the policy parameter; π θ (as) represents the probability of selecting action a in state s; R represents the cumulative reward; J(θ) represents the objective function.

[0053] From the above, we can see that by introducing the generative pre-training model and reinforcement learning algorithm based on the Transformer architecture, the system can automatically generate articles that are both in line with hot trends and attractive. This content generation optimization algorithm not only improves the efficiency and quality of article generation, but also continuously evaluates and optimizes the content quality through reinforcement learning, thereby ensuring the high quality and attractiveness of the output content, and effectively improving the user engagement and content dissemination effect of the social platform.

[0054] Furthermore, the content generation module also includes a sensitive content filtering function for automatically avoiding sensitive content before generating an article.

[0055] From the above, we can see that in the content generation module, the sensitive content filtering function added can automatically identify and avoid sensitive content before the article is generated, ensuring the compliance and security of the output content, effectively avoiding the legal risks and user complaints that may be caused by the release of inappropriate content, and providing social platform operators with a more reliable and secure content generation solution.

[0056] Furthermore, in the scheduled release control module, a traffic prediction and scheduling algorithm is introduced, that is, according to the predicted traffic trend and user settings, the content release time node is intelligently adjusted to maximize the use of traffic; the traffic prediction and scheduling algorithm uses random forest for traffic prediction, and the algorithm of random forest is as follows:

[0057]

[0058] Among them, Y * is the predicted value, N is the number of decision trees, T i (X) is the prediction result of the i-th decision tree for input X.

[0059] From the above, it can be seen that the traffic prediction and scheduling algorithm based on random forest introduced in the scheduled release control module has significant beneficial effects; the algorithm can predict future traffic trends by analyzing historical traffic data, and intelligently adjust the release time nodes of the content in combination with the user's settings; specifically, the random forest algorithm improves the accuracy of traffic prediction by integrating the prediction results of multiple decision trees, thereby ensuring that content can be released in the time period with the highest user activity, maximizing the use of traffic, and helping to improve the exposure and dissemination effect of content, thereby enhancing the user stickiness and activity of the social platform.

[0060] Furthermore, in the advertisement access and optimization module, an advertisement display optimization algorithm is introduced to improve user experience and advertisement effect. The formula of the advertisement display optimization algorithm is as follows:

[0061]

[0062] Where D is the difference in the display effects of the two versions of the advertisement, n A and n B are the number of impressions of version A and version B, r i A and r i B They are the revenue (such as number of clicks) of version A and version B in each impression.

[0063] From the above, we can see that in the advertising access and optimization module, the introduction of advertising display optimization algorithm can accurately measure the differences in the number of impressions and revenue per impression between version A and version B of the advertisement through quantitative analysis of the display effects of different versions of advertisements, thereby helping operators make scientific decisions, optimize advertising display strategies, and thus improve user experience and advertising effects.

[0064] Furthermore, the data analysis and feedback module further includes a hotspot prediction function, which predicts possible hotspot trends in the future based on historical data. The hotspot prediction function predicts hotspot trends by using a hotspot prediction algorithm. The formula of the hotspot prediction algorithm is as follows:

[0065]

[0066] Among them, Y t is the value of the time series at time t, c is a constant term, φ i and θ j are the coefficients of autoregression and moving average, ∈ t is the error term.

[0067] From the above, we can see that by adopting the hot spot prediction algorithm to predict hot spot trends, we can capture the patterns and trends in time series based on historical data through precise mathematical models (such as autoregression and moving average models), thereby accurately predicting possible hot spot trends in the future. It provides operators with strong data support, enabling them to plan ahead, accurately grasp market dynamics, and formulate more effective content creation and operation strategies, thereby improving content exposure and user engagement.

[0068] Furthermore, the advertising access and optimization module supports dynamic adjustment of advertising display strategies, automatically optimizing advertising position and display frequency based on user feedback and data analysis results.

[0069] From the above, we can see that in the advertising access and optimization module, it supports dynamic adjustment of advertising display strategies, can respond to user feedback and data analysis results in real time, and automatically optimize advertising position and display frequency, so as to ensure that the advertising content is more in line with user needs, reduce user disgust and neglect, and increase the attractiveness and click-through rate of advertising; it helps to improve user experience, and can also effectively improve advertising conversion rate and return on investment, bringing more commercial value and benefits to social platform operators.

[0070] Working principle: By integrating multiple modules, the whole process of hot content generation and traffic operation is automated, which specifically includes: first, collecting hot data on social platforms through the hot data collection module; then, the content generation module uses large models and preset templates to automatically generate articles and filter sensitive content; then, the automatic login and publishing module logs in to the social platform and publishes the generated content; the scheduled publishing control module adjusts the publishing time according to traffic forecasts and user settings; the data analysis and feedback module collects and analyzes data and provides optimization suggestions; finally, when the traffic reaches a certain threshold, the advertising access and optimization module automatically accesses advertisements and optimizes display strategies to improve user experience and advertising effectiveness; the entire system realizes process automation through continuous integration tools and calls large models through programming codes, which greatly improves the management efficiency and operation effect of social platforms.

[0071] Furthermore, the hot content generation and traffic operation system based on the large model and automation tool of the embodiment is compared with the content generation tool and social platform management tool in the current prior art (comparative example), and the following table is obtained:

[0072]

[0073]

[0074] It can be seen from the above table that the hot content generation and traffic operation system based on large models and automation tools in the embodiment is superior to the content generation tools and social platform management tools in the current prior art in terms of comprehensive functions, degree of automation, content generation quality, sensitive content filtering, traffic utilization, data analysis and feedback, advertising optimization effect and user experience.

[0075] A method for generating hot content and operating traffic based on a big model and an automated tool, using the hot content generation and traffic operation system based on a big model and an automated tool, includes:

[0076] S1. Collect hotspot data through hotspot data collection module;

[0077] S2. Use the content generation module to generate articles based on hot data and templates, and filter sensitive content;

[0078] S3. Log in to the social platform and publish articles through the automated login and publishing module;

[0079] S4, sending articles according to the setting of the scheduled publishing control module;

[0080] S5, data analysis and feedback module collects and analyzes data and provides optimization suggestions;

[0081] S6. When the traffic reaches the threshold, the advertisement access and optimization module automatically accesses the advertisement and optimizes the display strategy.

[0082] Application example: This system is written in Coze, and its applications include:

[0083] 1) Deploy the agent completed by Coze to the intranet service, including the following steps:

[0084] Step 1. Confirm technical feasibility: First, ensure that the intranet environment has the conditions to support the operation of the intelligent agent, including sufficient hardware resources, such as servers, storage, etc., as well as the corresponding software environment and network configuration to ensure the stable operation of the intelligent agent;

[0085] Step 2. Prepare the deployment package: After the creation, configuration, optimization and testing of the agent are completed on the Coze platform, a deployable compressed package is generated according to the export function provided by the platform, which contains all necessary runtime components such as the agent's code, configuration files, and dependent libraries;

[0086] Step 3. Install and deploy on the intranet server: Transfer the deployment package to the intranet server, unzip it to the specified directory, and complete the installation and configuration of related software dependencies on the intranet server according to the guidance of the official documentation, such as specific runtime environment, database, etc., to ensure that the intelligent agent can start and run normally;

[0087] Step 4. Configure network access: Configure the network so that other devices in the intranet can access the agent service through a specific address and port. At the same time, pay attention to setting access rights and security policies to prevent unauthorized access.

[0088] Step 5. Testing and optimization: After deployment, conduct a comprehensive test on the agent in the intranet environment to check whether all functions are normal and whether the performance meets the requirements. Perform targeted optimization and adjustments based on the test results to ensure the stable and efficient operation of the agent in the intranet service.

[0089] Step 6. Monitoring and maintenance: Establish an effective monitoring mechanism to monitor the operating status and performance indicators of the intelligent body in real time, promptly discover and solve possible problems, and formulate regular maintenance plans to ensure the long-term stable operation of the intelligent body.

[0090] 2) Deploy the AI ​​application written by Coze to the intranet, including the following steps:

[0091] S1. Preparation

[0092] -Confirm that the intranet environment has sufficient hardware resources, such as servers, storage, etc., as well as the corresponding software environment and network configuration to support the operation of AI applications;

[0093] - Generate a deployable compressed package based on the export function of the Coze platform, which should contain all necessary runtime components of the AI ​​application, including code, configuration files, and dependent libraries;

[0094] S2. Deployment of applications

[0095] -Transfer the deployment package to the intranet server and decompress it to the specified directory;

[0096] -Complete the installation and configuration of relevant software dependencies on the intranet server according to official documents, such as specific runtime environment, database, etc., to ensure that the AI ​​application can start and run normally;

[0097] S3. Configure network access

[0098] -Configure the network so that other devices in the intranet can access the AI ​​application service through specific addresses and ports;

[0099] -Set up access rights and security policies to prevent unauthorized access;

[0100] S4. Testing and Optimization

[0101] - Comprehensively test AI applications in the intranet environment to check whether various functions are normal and whether performance meets requirements, and make targeted optimizations and adjustments based on the test results;

[0102] S5. Monitoring and maintenance

[0103] -Establish an effective monitoring mechanism to monitor the operating status and performance indicators of AI applications in real time, and promptly identify and resolve potential problems;

[0104] -Develop regular maintenance plans to ensure the long-term stable operation of AI applications.

[0105] The embodiment of the present application provides an electronic device, which is applicable to the above-mentioned hot content generation and traffic operation system based on a large model and an automation tool, including:

[0106] Memory, used to protect computer programs and data;

[0107] Processor, used to run system programs.

[0108] An embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned hot content generation and traffic operation system based on a large model and automation tools, and performs hierarchical confidentiality management on the above-mentioned system and data in accordance with confidentiality management requirements.

[0109] Those skilled in the art will appreciate that the embodiments of the present application may be provided as a system or a computer program product. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of the devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer readable and writable memory capable of directing a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable and writable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0113] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0114] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0115] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0116] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, commodity or device including the elements.

[0117] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A hot content generation and traffic operation system based on a large model and automated tools, characterized by: include: Hotspot data collection module, used to collect hotspot data from Xiaohongshu, WeChat or other social platforms, and then process the collected raw data; The content generation module is connected to the big model to automatically generate articles based on hot data and preset platform templates; The automated login and publishing module uses automated tools or logged-in session information to automatically log in to various social platforms and publish generated content; The timed release control module allows users to set specific release time nodes to achieve timed delivery; The data analysis and feedback module is used to collect and analyze the attention information, comment information and data information generated by the published content, store it in the local database, and provide data analysis results; Advertisement access and optimization module: based on data analysis results, automatically accesses advertisements when traffic reaches a certain threshold, and tests ad placement to optimize user experience and advertising effectiveness; The system integration and calling module uses continuous integration tools to connect various modules to achieve process automation, and can directly call large models through programming codes.

2. The hot content generation and traffic operation system based on a large model and automated tools as claimed in claim 1, characterized in that: In the content generation module, a content generation optimization algorithm is introduced to automatically generate articles based on hot data and preset platform templates; At the same time, reinforcement learning algorithms are used to evaluate and optimize the quality of the generated content to ensure that the generated articles are both in line with hot trends and attractive.

3. The hot content generation and traffic operation system based on a large model and an automated tool as claimed in claim 1, characterized in that: The content generation module also includes a sensitive content filtering function for automatically avoiding sensitive content before generating an article.

4. The hot content generation and traffic operation system based on a large model and automated tools as claimed in claim 1, characterized in that: In the scheduled release control module, a traffic prediction and scheduling algorithm is introduced, that is, the content release time node is intelligently adjusted according to the predicted traffic trend and user settings.

5. The hot content generation and traffic operation system based on a large model and an automated tool as claimed in claim 1, characterized in that: In the advertising access and optimization module, an advertising display optimization algorithm is introduced.

6. The hot content generation and traffic operation system based on a large model and an automated tool as claimed in claim 1, characterized in that: The data analysis and feedback module further includes a hotspot prediction function, which predicts possible future hotspot trends based on historical data. The hotspot prediction function predicts hotspot trends by using a hotspot prediction algorithm.

7. The hot content generation and traffic operation system based on a large model and automated tools as claimed in claim 1, characterized in that: The advertisement access and optimization module supports dynamic adjustment of advertisement display strategies and automatically optimizes advertisement positions and display frequencies according to user feedback and data analysis results.

8. A method for generating hot content and operating traffic based on a large model and automated tools, characterized by: A hot content generation and traffic operation system based on a large model and an automated tool according to any one of claims 1 to 7, comprising: S1. Collect hotspot data through hotspot data collection module; S2. Use the content generation module to generate articles based on hot data and templates, and filter sensitive content; S3. Log in to the social platform and publish articles through the automated login and publishing module; S4, sending articles according to the setting of the scheduled publishing control module; S5, data analysis and feedback module collects and analyzes data and provides optimization suggestions; S6. When the traffic reaches the threshold, the advertisement access and optimization module automatically accesses the advertisement and optimizes the display strategy.

9. A processor, characterized in that: The system is configured to execute a hot content generation and traffic operation system based on a large model and an automation tool according to any one of claims 1 to 7.

10. A computer-readable and writable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, a hot content generation and traffic operation system based on a large model and an automation tool as described in any one of claims 1 to 7 is implemented.

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