Short-video full-process customer acquisition system based on agent collaboration

Abstract

The invention provides a short video full-process customer acquisition system based on agent collaboration, and relates to the technical field of artificial intelligence. An input layer of the system is used for receiving task data input by a user; the scheduling layer adopts an agent scheduling engine, and the agent scheduling engine is used for determining at least one subtask and a subtask execution sequence according to the task data, distributing the subtasks based on a publishing and subscribing mode and distributing computing power resources based on a weighted polling algorithm; the function layer comprises an account positioning module, a benchmarking account disassembling module, an account basic building module, a topic selection base building module, a video creation module and an editing and publishing module which are independently packaged in an intelligent agent form; the account number positioning module, the benchmarking account number disassembling module, the account number basic building module, the topic selection base building module, the video creation module and the editing and publishing module execute corresponding subtasks. According to the invention, automatic short video creation guest obtaining can be realized, and the efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and more specifically, to a short video end-to-end customer acquisition system based on intelligent agent collaboration. Background Technology

[0002] Short video creation for customer acquisition refers to the process of attracting target audiences and ultimately converting them into customers through the entire short video workflow (including account positioning, topic planning, content creation, video shooting and editing, publishing and operation, data review and optimization). Its core is reaching potential customers with high-quality short video content and continuously improving customer acquisition efficiency through data optimization. It is a crucial way for businesses and individual entrepreneurs to acquire traffic and achieve commercial success.

[0003] In related technologies, all aspects of short video creation and customer acquisition (topic selection, copywriting, shooting and framing, editing and packaging, selection of release time, and data interpretation) are mainly operated manually. However, manual operation is unstable, making it difficult to promote each aspect of short video creation and customer acquisition in a timely and effective manner, resulting in low efficiency. Summary of the Invention

[0004] The problem this invention addresses is how to improve the efficiency of acquiring customers through video creation.

[0005] To address the aforementioned issues, this invention provides a short video end-to-end customer acquisition system based on intelligent agent collaboration, comprising an input layer, a scheduling layer, and a functional layer; The input layer is used to receive task data input by the user; The scheduling layer employs an intelligent agent scheduling engine, which is used to determine at least one subtask and the execution order of the subtasks based on the task data, distribute the subtasks based on a publish-subscribe model, and allocate computing resources based on a weighted round-robin algorithm. The functional layer includes an account positioning module, a benchmark account decomposition module, an account basic construction module, a topic selection library construction module, a video creation module, and an editing and publishing module, all independently encapsulated in the form of intelligent agents. Based on the execution order of the sub-tasks and the computing resources, the account positioning module, the benchmark account decomposition module, the account basic construction module, the topic selection library construction module, the video creation module, and the editing and publishing module execute the corresponding sub-tasks.

[0006] Optionally, the agent scheduling engine includes a task dispatcher, a resource manager, and an exception handler; The task dispatcher is used to parse the subtasks of the task data, and uses an asynchronous pipeline scheduling algorithm to determine the subtask priority and subtask dependency of the subtask. Based on the subtask dependency, it determines the execution order of the subtasks, and distributes the subtasks to each agent in the functional layer based on the publish-subscribe pattern. The resource manager is used to monitor resource usage and, based on the resource usage, allocate computing resources according to the subtask priority. The exception handler is based on a three-level exception handling mechanism to handle task exceptions.

[0007] Optionally, the step of employing an asynchronous pipeline scheduling algorithm to determine the subtask priority and subtask dependency relationships of the subtask includes: Based on the directed acyclic graph of the task data, identify the prerequisite dependencies of each subtask and determine the subtask dependencies; The priority of the subtask is determined using a priority formula, which includes: Priority = α×Urgency + β×(1 / ResourceCost) + γ×(1 / EstimatedTime); Where Priority is the priority of the subtask, Urgency is the urgency, ResourceCost is the resource cost, EstimatedTime is the estimated time, and α, β and γ are all weighting coefficients.

[0008] Optionally, the three-level exception handling mechanism for handling task exceptions includes: When the subtask fails to execute, a smart retry strategy matrix is ​​used for local retry; When the local retry fails, the failed subtask is reassigned to a backup agent via the task dispatcher. When the backup agent fails to execute, a degradation scheme is triggered, and a notification requiring manual intervention is pushed.

[0009] Optionally, the account positioning module includes a target audience identification intelligent agent, a content direction intelligent decision-making intelligent agent, and a positioning tag generation intelligent agent; The target audience identification agent is used to determine the audience profile based on the task data, wherein the audience profile includes basic attributes, interest tags, and priority ranking. The intelligent decision-making agent for content direction is used to determine a short video content direction suggestion scheme based on the acquired popular data and the audience profile; The location tag generating agent is used to determine short video location tags based on high-frequency industry terms in the task data.

[0010] Optionally, the video creation module includes a text-based intelligent agent and a voice-over digital human intelligent agent; The copywriting AI is used to extract popular copywriting from popular short videos and generate innovative copywriting in different styles based on the popular copywriting. The spoken digital human agent is used to analyze the semantics of the innovative copy, break down the innovative copy into spoken segments, add SSML voice tags, facial expressions or action commands, generate a structured script that can be parsed by the digital human platform, determine a digital human image that matches the semantics of the copy and audience preferences, and, based on the semantics of the copy, the structured script and the digital human image, call the digital human platform API to generate a spoken digital human video.

[0011] Optionally, the functional layer further includes a data analysis and optimization module, which is used to analyze the key data indicators of the short video after the video is published by the editing and publishing module, and to obtain the reasons for the success and failure of the short video.

[0012] Optionally, the functional layer also includes a data review agent, which is used to generate a review report based on the key data indicators of the short video.

[0013] Optionally, the short video customer acquisition system based on intelligent agent collaboration also includes a support layer, which includes a knowledge base and a model library. The knowledge base stores industry rules and competitor data, and the model library integrates NLP models, ASR models, and digital human models.

[0014] Optionally, the short video customer acquisition system based on intelligent agent collaboration further includes a storage layer, which uses a distributed database to store various data from the input layer, the scheduling layer, and the functional layer.

[0015] The beneficial effects of the short video end-to-end customer acquisition system based on intelligent agent collaboration of the present invention are: By receiving user-input task data at the input layer, a complete and accurate data foundation is provided for subsequent task processing from the very beginning of the entire process, ensuring the continuity of the initial data flow. Based on this, the intelligent agent scheduling engine employed in the scheduling layer can accurately determine at least one subtask and its execution order according to the input task data. Tasks are published to a message queue using a publish-subscribe model, allowing each intelligent agent in the functional layer to subscribe to tasks according to its own capabilities, achieving decoupling and dynamic expansion. Simultaneously, based on a weighted round-robin algorithm, computing resources are dynamically allocated, effectively solving the problems of chaotic and uneven resource allocation in traditional manual task allocation. This not only ensures the orderly execution of tasks but also improves resource utilization, providing core scheduling support for the efficient execution of subtasks by each module in the functional layer. Furthermore, the functional layer is independently encapsulated in the form of intelligent agents. The system comprises modules for account positioning, account benchmarking, account infrastructure, topic library building, video creation, and editing and publishing. These modules collaboratively execute sub-tasks based on their execution order and allocated computing resources. The account positioning module accurately identifies the target audience and content direction to improve customer acquisition accuracy. The account benchmarking module generates analysis reports and optimization suggestions. The topic library building module dynamically updates the topic library. The video creation module automatically generates video scripts and digital voiceover videos. The editing and publishing module intelligently edits and selects the optimal publishing time. The seamless integration of these modules avoids the instability inherent in manual processes, driving a shift in short video customer acquisition from a traditional, inefficient, manually-driven model to an automated, intelligent, industrialized production line model, thereby improving the efficiency of video creation and customer acquisition. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of a short video customer acquisition system based on intelligent agent collaboration, provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0018] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0019] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0020] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0021] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0022] To address the problems existing in the aforementioned related technologies, embodiments of the present invention provide a short video end-to-end customer acquisition system based on intelligent agent collaboration.

[0023] like Figure 1 As shown in the figure, the present invention provides a short video customer acquisition system based on intelligent agent collaboration, which includes an input layer, a scheduling layer and a functional layer. The input layer is used to receive task data input by the user.

[0024] Specifically, the input layer is used to receive task data input by the user, namely, task introduction, product details, video links and account parameters, and to adapt to input in various formats such as text, links and files, so as to ensure that all kinds of data can smoothly enter the scheduling layer and the function layer.

[0025] The scheduling layer employs an intelligent agent scheduling engine, which determines at least one subtask and its execution order based on the task data, distributes the subtasks based on a publish-subscribe model, and allocates computing resources based on a weighted round-robin algorithm.

[0026] Specifically, the intelligent agent scheduling engine decomposes complex task requirements in task data into at least one subtask and determines the execution order of the subtasks to facilitate the sequential execution of all customer acquisition processes, namely topic selection, copywriting, shooting and framing, editing and packaging, selection of release time, and data interpretation. The publish-subscribe model refers to publishing each subtask to a message queue. The executing intelligent agent subscribes to the corresponding task type based on its own set capabilities, thereby receiving subtasks and executing them according to the subtask execution order. Weighted round-robin algorithms are commonly used in scenarios such as load balancing, task scheduling, and network forwarding. Their core objective is to allocate tasks or requests proportionally. Through weighted round-robin algorithms, the system's computing resources can be rationally allocated, thereby improving the system's computational efficiency.

[0027] The functional layer includes an account positioning module, a benchmark account decomposition module, an account basic construction module, a topic selection library construction module, a video creation module, and an editing and publishing module, all independently encapsulated in the form of intelligent agents. Based on the execution order of the sub-tasks and the computing resources, the account positioning module, the benchmark account decomposition module, the account basic construction module, the topic selection library construction module, the video creation module, and the editing and publishing module execute the corresponding sub-tasks.

[0028] Specifically, each module in the functional layer is built using one or more intelligent agents. Each agent in a module can achieve its corresponding function through orchestration and debugging. The account positioning module receives task data from users, such as business introductions and product details, to clarify the account's target audience, content direction, and positioning tags. For example, for a cake shop, the account's theme could be determined as "creative cake making and sharing," targeting young people who love desserts. Meanwhile, the account positioning module can deeply analyze the creator's background, help clarify the target audience profile, achieve content differentiation positioning, and provide specific content planning suggestions; the benchmark account decomposition module is used to decompose benchmark accounts, generate decomposition reports and account optimization suggestions. The workflow covers nodes such as obtaining benchmark account information, batch obtaining video lists, extracting video scripts, generating decomposition reports, generating reference suggestions, and writing to Lark documents, and finally storing the report in Lark; the account basic construction module is used to generate multiple account basic construction schemes based on account positioning and the system's knowledge base, covering the design of personal profile pages such as avatar, nickname, and introduction, the improvement of basic information such as geographical location and contact information, and providing suggestions for pinned videos; the topic selection library construction module is used to collect competitor content, posting frequency and themes in real time, analyze interaction data, gain insights into content trends, keep abreast of industry dynamics and competitor movements, and mine content. The creation of content and the selection of topics can be achieved through processes such as streamlining workflows, setting character personas and logic, binding workflows, setting triggers, and testing releases. The topic selection module can include a hotspot monitoring agent and a topic planning agent. The hotspot monitoring agent is used to promptly acquire trending videos in the field, helping to grasp market trends, learn creation techniques, discover business opportunities, and understand competitors. Its construction process is similar to that of the benchmark account monitoring agent, which collects hot videos through a specific workflow. The topic planning agent is used to automatically generate a dynamically updated topic library by combining hotspot monitoring data and benchmark account content. The video creation module can imitate the scripts of existing popular short videos and generate new short videos. The editing and publishing module is used to edit the videos created in the video creation module and select the best release time based on data analysis.

[0029] For example, the input layer, scheduling layer and functional layer of this application can be built using the button platform or other intelligent agent platforms.

[0030] In this embodiment, by receiving user-inputted task data through the input layer, a complete and accurate data foundation is provided for subsequent task processing from the very beginning of the entire process, ensuring the continuity of the initial data flow. Based on this, the intelligent agent scheduling engine employed in the scheduling layer can accurately determine at least one subtask and its execution order according to the input task data. Tasks are published to a message queue using a publish-subscribe model, allowing each intelligent agent in the functional layer to subscribe to tasks according to its own capabilities, achieving decoupling and dynamic expansion. Simultaneously, based on a weighted round-robin algorithm, computing resources are dynamically allocated, effectively solving the problems of chaotic and uneven resource allocation in traditional manual task allocation. This not only ensures the orderly execution of tasks but also improves resource utilization, providing core scheduling support for the efficient execution of subtasks by each module in the functional layer. Furthermore, the intelligent agents in the functional layer... The system comprises a comprehensive module for account positioning, account benchmarking, account infrastructure building, topic library building, video creation, and editing and publishing. It can collaboratively execute corresponding sub-tasks based on the execution order and allocated computing resources. The account positioning module accurately identifies the target audience and content direction to improve customer acquisition accuracy. The account benchmarking module generates analysis reports and optimization suggestions. The topic library building module dynamically updates the topic library. The video creation module automatically generates video scripts and digital voiceover videos. The editing and publishing module intelligently edits and selects the optimal publishing time. The seamless integration of these modules avoids the instability inherent in manual processes, driving the transformation of short video customer acquisition from a traditional, inefficient, manually-driven model to an automated, intelligent, industrialized production line model, thereby improving the efficiency of video creation and customer acquisition.

[0031] Optionally, the agent scheduling engine includes a task dispatcher, a resource manager, and an exception handler; The task dispatcher is used to parse the subtasks of the task data, and uses an asynchronous pipeline scheduling algorithm to determine the subtask priority and subtask dependency of the subtask. Based on the subtask dependency, it determines the execution order of the subtasks, and distributes the subtasks to each agent in the functional layer based on the publish-subscribe pattern. The resource manager is used to monitor resource usage and, based on the resource usage, allocate computing resources according to the subtask priority. The exception handler is based on a three-level exception handling mechanism to handle task exceptions.

[0032] Specifically, the agent scheduling engine includes a task dispatcher, a resource manager, and an exception handler. The task dispatcher first decomposes complex tasks in the task data into at least one subtask and uses an asynchronous pipeline scheduling algorithm to determine the subtask priority and dependencies. Based on these dependencies, it determines the execution order of the subtasks, thus allocating parallelizable subtasks to different agents to improve processing efficiency. Based on a publish-subscribe model, it distributes subtasks to various agents in the functional layer. The resource manager monitors resource usage, including CPU, memory, and GPU resources, and analyzes task resource requirements. Then, based on a weighted round-robin algorithm and historical resource usage data, it dynamically calculates the load weight for each agent and allocates computing resources according to subtask priority, resource requirements, and load weight, avoiding resource overload or idleness and reserving necessary computing resources for high-priority tasks to ensure fairness and efficiency in resource allocation. The exception handler monitors the execution status of each agent, promptly detects anomalies, and handles them through a three-level exception handling mechanism to ensure the normal operation of the system.

[0033] For example, the resource requirements analysis steps of File Explorer include: The system identifies the resource types of subtasks, including CPU-intensive, memory-intensive, I / O-intensive, and GPU-intensive tasks. CPU-intensive tasks include video transcoding, model inference, and data analysis; memory-intensive tasks include large file processing and batch data loading; I / O-intensive tasks include file downloading, database reading and writing, and network requests; and GPU-intensive tasks include deep learning model training and image processing. Then, resource consumption is quantified by analyzing historical data and past task resource usage patterns. Based on resource consumption formulas, resource performance data is determined, taking into account resource usage fluctuations during task execution and the estimated delay in resource release after task completion. Finally, resource conflict detection is performed to identify situations where multiple subtasks simultaneously contend for the same resource, pinpointing key resources limiting overall system performance. Resource utilization is then determined using resource consumption formulas, including: ResourceCost=BaseLoad+TaskComplexity×ScaleFactor; Where ResourceCost is the resource consumption data, BaseLoad is the basic load, TaskComplexity is the task complexity, and ScaleFactor is the scaling factor.

[0034] The formula for resource utilization rate includes: ResourceUtilization=UsedResource / TotalResource×100%; Among them, ResourceUtilization is the resource utilization rate, UsedResource is the used resources, and TotalResource is the total resources.

[0035] For example, the task dispatcher may employ an optimized scheduling strategy for scheduling optimization, which includes: A comprehensive priority formula is used to determine the overall priority, prioritizing tasks on the critical path or combinations of tasks with high resource utilization. The comprehensive priority formula includes: Priority1=α×CriticalPathWeight+β×ResourceEfficiency+γ×TaskUrgency; Among them, Priority1 is the overall priority, CriticalPathWeight is the critical task weight, ResourceEfficiency is the resource efficiency, TaskUrgency is the task urgency, and α, β and γ are all weight coefficients.

[0036] For example, the fault analysis steps of an exception handler include: Fault signal acquisition, i.e., abnormal code analysis: classifying faults based on returned HTTP status codes and error codes; monitoring execution time: detecting whether task execution time exceeds preset thresholds; resource status check: monitoring the usage of resources such as CPU, memory, and network; dependent service status: checking external dependencies such as third-party APIs and database connections.

[0037] Root cause analysis includes analyzing network faults such as network latency, connection timeouts, and DNS resolution failures; resource insufficiency such as memory overflow, CPU overload, and insufficient disk space; service faults such as third-party API rate limiting, service unavailability, and authentication failures; data anomalies such as incorrect input format, corrupted data, and invalid parameters; and system faults such as process crashes, system restarts, and insufficient permissions.

[0038] Fault impact assessment: Fault severity levels are categorized as Critical (system-level), High (module-level), Medium (task-level), and Low (negligible); Impact scope analysis: Single task impact, module impact, and system impact; Recovery time estimation: Predicting the time required for fault recovery based on historical data.

[0039] Optionally, the step of employing an asynchronous pipeline scheduling algorithm to determine the subtask priority and subtask dependency relationships of the subtask includes: Based on the directed acyclic graph of the task data, identify the prerequisite dependencies of each subtask and determine the subtask dependencies; The priority of the subtask is determined using a priority formula, which includes: Priority = α×Urgency + β×(1 / ResourceCost) + γ×(1 / EstimatedTime); Where Priority is the priority of the subtask, Urgency is the urgency, ResourceCost is the resource cost, EstimatedTime is the estimated time, and α, β and γ are all weighting coefficients.

[0040] Specifically, the asynchronous pipeline scheduling algorithm is based on task dependency analysis using a directed acyclic graph (DAG), combined with priority queues and resource constraints, to achieve parallel scheduling and execution of tasks. The core idea of ​​the algorithm is to transform a serial business process into a parallel task pipeline, maximizing system throughput and improving operational efficiency. The specific steps of the asynchronous pipeline scheduling algorithm are as follows: First, construct a directed acyclic graph, G=(V, E), where V is the set of task nodes and E is the set of dependency edges. Then, calculate the in-degree and out-degree of each task node to identify the set of tasks that can be executed in parallel (nodes with an in-degree of 0), and perform task dependency analysis to obtain sub-task dependencies. Next, use a priority formula to calculate priorities. The weight coefficients in the priority formula can be dynamically adjusted according to the business scenario. Urgency represents the urgency of the sub-task, resource cost represents the resource cost required to complete the sub-task, and estimated time represents the estimated time to complete the sub-task. Finally, perform resource matching and allocation. Scan the priority queue, select the task with the highest priority whose required resources can be met by the current system, and... The system checks the resource availability of the target agent (CPU, memory, concurrency limits) to ensure it is capable of handling tasks and prevents execution failures due to resource overload. After confirming task execution and agent availability in the first two steps, the system proceeds to actual resource allocation. It allocates the necessary resource quotas to the task (e.g., allocating 2 CPU cores and 4GB of memory to the "AI editing" task) and locks these resources (to prevent them from being occupied by other tasks). The system resource management table is marked as "A certain agent has occupied XX resources," and resource usage data is synchronized in real time (e.g., GPU utilization increases from 30% to 50%) to achieve "exclusive allocation" of resources, avoiding resource contention among multiple tasks. Simultaneously, the scheduling engine monitors resource status in real time, providing accurate data for the next scheduling iteration. Asynchronous task scheduling is then performed, encapsulating tasks into asynchronous execution units and submitting them to the corresponding agent's execution queue. The system continuously monitors task execution status and resource usage. When resource imbalance is detected, task allocation is dynamically adjusted. Furthermore, predictive scheduling can be performed: predicting future resource needs based on historical data and optimizing scheduling in advance. Anomaly detection is performed during the execution of tasks by the intelligent agent. When the task execution time exceeds the expectation, resource enhancement or task splitting is initiated. When system resources are insufficient, task queuing or degradation processing is initiated. When the current task fails, a retry mechanism or backup plan is initiated.

[0041] For example, the steps of task dependency analysis include: Topological analysis is performed on the directed acyclic graph to identify the pre-dependencies of each subtask, determine which task outputs are inputs to other tasks, and identify the longest dependency chain affecting the overall execution time to ensure the task flow is free of deadlock risk. Pre-dependencies are categorized as strong, weak, and conditional dependencies. A strong dependency means task B must wait for task A to be fully completed before it can begin (e.g., text extraction depends on video download completion). A weak dependency means task B can begin after task A is partially completed (e.g., video editing can begin after some materials are downloaded). A conditional dependency means that the execution of task B depends on the result of task A (e.g., deciding whether to publish based on content review results). Parallelism is then identified, meaning tasks without mutual dependencies can be executed simultaneously. The theoretical maximum parallelism is then determined using the formula, while ensuring that parallel tasks do not generate data conflicts. The theoretical maximum parallelism formula is: Max_Parallel = Total_Tasks / Critical_Path_Length; Where Max_Parallel is the theoretical maximum parallelism, Total_Tasks is the total number of tasks, and Critical_Path_Length is the critical path length.

[0042] Optionally, the three-level exception handling mechanism for handling task exceptions includes: When the subtask fails to execute, a smart retry strategy matrix is ​​used for local retry; When the local retry fails, the failed subtask is reassigned to a backup agent via the task dispatcher. When the backup agent fails to execute, a degradation scheme is triggered, and a notification requiring manual intervention is pushed.

[0043] Specifically, when a subtask fails, a smart retry strategy matrix is ​​used for local retries. This matrix includes exponential backoff retries (suitable for network failures and temporary service unavailability), fixed-interval retries (suitable for resource contention and queue overload), immediate retries (suitable for transient failures and data anomalies), and delayed retries (suitable for system maintenance and timed failures). Exponential backoff retries refer to retry intervals of 1 second, 2 seconds, 4 seconds, 8 seconds, or 16 seconds, or a retry interval determined by a formula. The maximum number of retries is 5. Applicable scenarios include network timeouts, API rate limiting, and temporary service failures. Fixed-interval retries... Retry with intervals of 30 seconds and a maximum of 3 retries is applicable to scenarios such as insufficient resources, full queues, and concurrency conflicts. It also includes a condition check, i.e., checking resource availability before each retry. Immediate retries have a 0-second retry interval and a maximum of 2 retries. They are applicable to scenarios such as data format errors, abnormal parameters, and instantaneous network jitter. If consecutive failures occur, a degradation solution is immediately implemented. Delayed retries have retry intervals of 5 minutes, 10 minutes, or 30 minutes and a maximum of 3 retries. They are applicable to scenarios such as system maintenance windows and scheduled task conflicts, and intelligent scheduling is implemented to avoid known system maintenance times. When a local retry fails, the failed subtask is reassigned to a backup agent via the task dispatcher. Backup agents are deployed in the functional layer, with each module having its own backup agent. When a backup agent fails, a degradation scheme is triggered, and a notification requiring manual intervention is pushed. Degradation schemes include Level 1, Level 2, and Level 3 degradation. Level 1 degradation is functional degradation, which can reduce output quality (e.g., lower video resolution, reduce effects), simplify processing (e.g., skip non-critical steps), use cached data, or adopt historical successful results. Level 2 degradation is service replacement, which can switch to a backup service (e.g., use a backup third-party service for local processing, convert cloud processing to local processing), require manual intervention (e.g., mark tasks requiring manual intervention). Level 3 degradation is task suspension, which can add failed tasks to a delay queue, send fault notifications to the administrator, or allow manual restart after system recovery. The retry interval formula includes: RetryInterval = BaseInterval×2^(RetryCount-1); Where RetryInterval is the retry interval, BaseInterval is the baseline interval, and RetryCount is the number of retries.

[0044] For example, an exception handling strategy can be determined by a dynamic strategy selection algorithm, which includes: Fault feature extraction specifically includes: mapping specific error codes generated during system operation to a pre-defined fault type system to achieve preliminary fault classification; analyzing the execution effects of historical cases with similar fault characteristics under different handling strategies by searching the historical fault handling database to extract effective handling experience; and constructing a multi-dimensional fault feature vector by combining contextual information such as the real-time load status of the current system and the traffic characteristics of the current time period to provide a comprehensive basis for subsequent strategy selection. Strategy matching and decision-making specifically includes: based on the extracted fault feature vector, using a decision tree algorithm to search the pre-defined retry strategy library to initially screen candidate strategies suitable for the current fault scenario; calculating the comprehensive score of each candidate strategy using the pre-defined formula "Success_Rate×0.4+Recovery_Time×0.3+Resource_Cost×0.3", where Success_Rate is the historical success rate, Recovery_Time is the average recovery time, and Resource_Cost is the resource consumption cost, selecting the strategy with the highest score as the optimal execution strategy; and dynamically adjusting the weight coefficients in the above formula based on real-time feedback data during strategy execution to ensure that strategy selection adapts to changes in system state. Retry execution and monitoring specifically include: during policy execution, real-time tracking of the execution status, result data, and time consumption information for each retry to form a complete retry process record; statistical analysis of the actual success rate of different policies under various fault scenarios based on the correspondence between fault types and policy types, and updating the policy effect evaluation database; and adaptive optimization of the policy library based on the above statistical data, including eliminating low success rate policies, adding combined policies, and adjusting policy parameters, so that the policy selection mechanism can continuously adapt to the long-term operating characteristics of the system.

[0045] For example, after handling anomalies, fault learning and optimization can be performed, a fault mode library can be built to record common fault types and solutions, a success case library can be built to save successful recovery handling strategies, failure case analysis can be performed to analyze the root causes of retry failures, and predictive fault handling can be performed. Potential faults can be predicted based on system indicators, degradation strategies can be proactively enabled before a fault occurs, and resource configuration can be optimized based on fault statistics.

[0046] Optionally, the account positioning module includes a target audience identification intelligent agent, a content direction intelligent decision-making intelligent agent, and a positioning tag generation intelligent agent; The target audience identification agent is used to determine the audience profile based on the task data, wherein the audience profile includes basic attributes, interest tags, and priority ranking. The intelligent decision-making agent for content direction is used to determine a short video content direction suggestion scheme based on the acquired popular data and the audience profile; The location tag generating agent is used to determine short video location tags based on high-frequency industry terms in the task data.

[0047] Specifically, the target audience identification agent uses the TF-IDF algorithm to extract core keywords from the task description based on the task data, performs industry category identification based on a Naive Bayes classifier, analyzes product characteristics using a Word2Vec model, constructs a task feature vector containing core keywords, industry category, and product characteristics, and establishes a user tag system including dimensions such as age, gender, interests, and spending power. The cosine similarity algorithm is used to calculate the matching degree between the task feature vector and the user tags, where age has a weight of 0.25, interest has a weight of 0.35, spending power has a weight of 0.25, and behavior has a weight of 0.15. Then, the audience priority is calculated according to the audience priority formula to determine the core audience and potential audience, thus obtaining an audience profile containing basic attributes, interest tags, and priority ranking. The audience priority formula includes: Priority_Score=Audience_Size×0.3+Conversion_Rate×0.4+Competition_Index×0.3; Among them, Priority_Score is the priority score, Audience_Size is the audience size, Conversion_Rate is the conversion probability, and Competition_Index is the competition intensity.

[0048] The content direction intelligent decision-making agent is used to determine short video content direction suggestions based on acquired popular data and audience profiles. The steps include: content trend analysis, specifically: collecting popular content data from various target platforms through web crawling technology, covering multiple dimensions such as views, likes, shares, and comments; modeling the collected historical data using the ARIMA model in time series analysis to identify the changing patterns of content popularity and predict future trends; automatically classifying massive amounts of content topics using the K-means clustering algorithm to aggregate them into topic groups with similar characteristics; and calculating the popularity score of each topic based on the preset formula "Heat_Score=View_Count×0.4+Like_Count×0.3+Share_Count×0.2+Comment_Count×0.1", where Heat_Score is the popularity score, View_Count is the number of views, Like_Count is the number of likes, Share_Count is the number of shares, and Comment_Count is the number of comments, thereby quantifying the degree of attention received by the content. The competitive analysis algorithm specifically includes: using text similarity algorithms (such as cosine similarity) to extract and compare features of competitors' published content, analyzing their core content strategies, topic preferences, and presentation styles; identifying content gaps and differentiated opportunities in the market by comparing and analyzing the characteristics of one's own business with those of competitors' content; and calculating the value score of each differentiated opportunity based on the formula "Opportunity_Score=Market_Gap×0.5+Trend_Alignment×0.3+Resource_Match×0.2", where Market_Gap represents the market gap, Trend_Alignment represents the degree of alignment with trends, and Resource_Match represents the degree of fit with one's own resources, thereby assessing the feasibility and potential value of each opportunity. Content direction recommendation specifically includes: constructing a multi-dimensional decision tree model based on core business characteristics, target audience preference tags, and market opportunities identified in the above steps to initially screen potential content directions; using a collaborative filtering algorithm combined with historical success case data to accurately recommend and rank the screened content directions, generating a list of candidate directions with the highest suitability; calculating the confidence level of each recommended direction using the formula "Confidence = Historical_Success_Rate × 0.6 + Algorithm_Score × 0.4", where Confidence is the confidence level, Historical_Success_Rate is the historical success rate of similar directions, and Algorithm_Score is the comprehensive algorithm score, finally outputting high-confidence content direction suggestions and execution priorities.

[0049] The positioning tag generation agent is used to determine short video positioning tags based on high-frequency industry terms in task data. The steps include: generating a tag candidate set, specifically: performing word segmentation on the task description text in the input task data and counting the frequency of each word to extract high-frequency industry terms as initial tag candidate terms; using the Word2Vec semantic vector model to semantically expand the initial tag candidate terms to generate extended terms with related meanings, enriching the range of candidate tags; matching the above terms with a pre-built industry tag library, importing general industry tags and subdivided field tags to supplement professional dimension candidate tags; calculating the comprehensive score of each candidate tag based on the preset formula "Tag_Score=Frequency×0.4+Relevance×0.4+Uniqueness×0.2", where Tag_Score is the comprehensive score of the candidate tag, Frequency is the frequency of the word in the business description, Relevance is the semantic relevance to the core business features, and Uniqueness is the uniqueness of the tag (avoiding excessive repetition with general tags), forming a preliminary tag candidate set. The tag optimization and selection process includes: calculating the text similarity between candidate tags using the edit distance algorithm, deduplicating tags with similarity exceeding a preset threshold, and retaining tags with more accurate semantics; calculating the weight value of each tag in the business description and industry context based on the TF-IDF algorithm, and sorting them by importance from high to low weight; using a genetic algorithm to optimize the combination of tags, aiming at "maximizing coverage + minimizing redundancy", and iteratively selecting complementary tag combinations; and finally selecting the Top-K tags (K is a configurable parameter) that best represent the core characteristics of the business and have both industry recognition and uniqueness, based on business scenario requirements and comprehensive tag scores, to form the final tag set.

[0050] For example, the account positioning module also includes a creator background deep analysis algorithm and a large model integration optimization scheme. The steps of the creator background deep analysis algorithm include: structuring background information, specifically including: using the Named Entity Recognition (NER) algorithm to extract key information from the personal background description text provided by the creator, extracting core elements such as work experience, skills, industry fields, and educational background; sorting the extracted experience information in chronological order to construct a complete timeline of the creator's career development, clearly presenting their growth trajectory; converting unstructured skill descriptions into standardized tags based on a preset skill system to achieve a unified representation of skill information; and constructing a creator feature vector based on the above structured data. Creator_Vector=[Experience_Years,Skill_Level,Industry_Background,Education_Level], where Creator_Vector is the creator's feature vector, Experience_Years is the number of years of experience, Skill_Level is the skill level, Industry_Background is the industry background classification value, and Education_Level is the quantitative value of education level, thus completing the digital conversion of background information. Advantage ability identification specifically includes: obtaining the matching degree between the creator's skills and the target industry by calculating "Skill_Match=Σ(Skill_i×Industry_Relevance_i)", where Skill_Match is the matching degree, Skill_i is the proficiency score of the i-th skill, and Industry_Relevance_i is the relevance weight of the skill in the target industry; evaluating the experience value based on the formula "Experience_Value=Years×Depth_Factor×Relevance_Factor", where Experience_Value is the experience value, Years is the number of years of experience in the corresponding field, and Depth... _Factor is the experience depth coefficient, and Relevance_Factor is the correlation coefficient with the current business. The creator's skills and experience data are compared and analyzed with the industry average and competitor data to identify their unique competitive advantages. The comprehensive ability score is calculated by "Capability_Score=Technical_Skill×0.4+Experience×0.3+Creativity×0.3", where Capability_Score is the comprehensive ability score, Technical_Skill is the technical skill score, Experience is the experience score, and Creativity is the innovation ability score, which quantitatively represents the creator's core ability level.The personalized positioning strategy specifically includes: matching and calculating the creator's feature vector with a pre-set persona type model to select the persona type that best matches the creator's background and abilities; recommending suitable content presentation styles (such as professional and rigorous, light and humorous, etc.) based on the creator's language style, skill characteristics, and target audience preferences; generating a quantitative evaluation result of the differentiation strategy through the formula "Differentiation_Strategy=Unique_Advantage×Market_Gap×Execution_Ability", where Differentiation_Strategy is the evaluation result, Unique_Advantage is the unique advantage coefficient, Market_Gap is the market gap, and Execution_Ability is the execution capability score, ultimately outputting a personalized positioning scheme that includes persona positioning, content style, and competitive strategy.

[0051] The large-scale model integration and optimization scheme comprises a three-layer structure: The first layer uses traditional algorithms (such as statistical analysis and rule matching) to perform basic preprocessing, feature extraction, and structured transformation of the input data, ensuring data standardization and integrity; the second layer introduces large-scale models (such as natural language processing models and multimodal understanding models) to perform deep semantic understanding, contextual analysis, and creative content generation on the pre-processed data, improving the system's ability to process complex information; the third layer uses a rule engine to perform compliance verification, logical consistency verification, and optimization adjustments on the results output by the large-scale models, ensuring that the output results meet business scenario requirements and industry standards, forming a three-layer collaborative processing mechanism of "basic processing - deep understanding - result optimization". The specific application strategies for large-scale models include: using pre-trained language models such as BERT and GPT to enhance semantic understanding of unstructured text (such as business descriptions and user comments), improving the accuracy of parsing ambiguous text and implicit intent; leveraging the generative capabilities of large-scale models to automatically generate personalized account positioning suggestions, content creation solutions, and other non-standardized outputs by combining user business characteristics and target audience preferences; constructing a multimodal analysis framework to perform correlation analysis between text information and multimedia data such as images and videos, achieving cross-modal content feature extraction and trend prediction; and establishing a model iteration mechanism based on user feedback to continuously fine-tune the parameters of large-scale models by collecting data on the actual application effects of the output results, thereby improving the model's adaptability to specific business scenarios. The advantages of the hybrid algorithm architecture are specifically reflected in the following aspects: It ensures the accuracy of basic data processing and feature extraction through traditional algorithms, while leveraging large models to enhance the understanding of complex semantics and implicit relationships, achieving a synergistic improvement in overall processing accuracy; it adopts a resource allocation strategy of "traditional algorithms handling routine tasks + large models focusing on key aspects," ensuring the effectiveness of core functions while reducing overall computing resource consumption and response latency, thus achieving effective cost control; it utilizes traditional algorithms to provide clear decision logic and feature weights, solving the "black box" problem of large models and providing interpretable decision paths for output results; it ensures the system's real-time response capability by handling high-concurrency, low-complexity routine tasks with traditional algorithms, while large models focus on handling low-frequency, high-complexity deep analysis tasks, achieving a balance between efficiency and effectiveness.

[0052] Optionally, the video creation module includes a text-based intelligent agent and a voice-over digital human intelligent agent; The copywriting AI is used to extract popular copywriting from popular short videos and generate innovative copywriting in different styles based on the popular copywriting. The spoken digital human agent is used to analyze the semantics of the innovative copy, break down the innovative copy into spoken segments, add SSML voice tags, facial expressions or action commands, generate a structured script that can be parsed by the digital human platform, determine a digital human image that matches the semantics of the copy and audience preferences, and, based on the semantics of the copy, the structured script and the digital human image, call the digital human platform API to generate a spoken digital human video.

[0053] Specifically, the video creation module includes a copywriting AI agent and a voice-over digital human AI agent. The copywriting AI agent consists of a video copywriting extraction AI agent, a viral video decomposition AI agent, a marketing AI agent, and a copywriting imitation AI agent. It extracts popular copywriting from popular short videos and generates innovative copywriting in different styles based on these popular copywritings. The construction process of the video copywriting extraction AI agent is as follows: This AI agent takes the video link as input, integrates speech recognition technology and natural language processing algorithms, and realizes automatic transcription of the audio content in the video and extraction of text copywriting. The AI ​​agent is built based on the LangGraph framework, which specifically includes defining core state nodes such as downloading the video, extracting copywriting, and outputting copywriting. Through preset state transition logic between nodes (such as automatically triggering copywriting extraction after the video is downloaded, and triggering the output process after the copywriting is extracted), a complete AI agent workflow is formed. The structured copywriting it outputs can be directly used as the basic material for subsequent content creation stages. The construction process of the viral video decomposition agent is as follows: This agent focuses on the text feature analysis of viral short videos, aiming to extract reusable creative rules and techniques. The agent is built based on the LangGraph framework, setting up video download nodes, text extraction nodes, model analysis nodes, and result output nodes. The collaborative linkage of each node is realized through the graph state management mechanism (such as automatically activating the model analysis node after the text extraction is completed), thus completing the full-process logic configuration of the agent. The construction process of the marketing agent is as follows: This agent uses the DeepSeek model to generate prompts adapted to specific marketing scenarios. Based on the LangGraph framework, the agent's workflow is built. By configuring style definition nodes, prompt generation nodes, and copywriting creation nodes, parameterized settings for the target style (such as the "Golden Gun Uncle" style) are achieved. The style definition node receives style feature descriptions input by the user, the prompt generation node generates precise model input instructions based on the style features, and the copywriting creation node calls a large language model to generate marketing copy that meets the style requirements. The construction process of the copywriting imitation agent is as follows: This agent takes the target account's historical copywriting as input and extracts its linguistic features (such as sentence structure, word preferences, and sentiment) through a text style analysis algorithm. Based on the LangGraph framework, a closed-loop workflow of "analysis-imitation-optimization" is established. Style analysis nodes, copywriting generation nodes, and optimization iteration nodes are set up. By configuring trigger conditions between nodes (such as automatically entering the optimization stage after the initial imitation is completed), the agent achieves autonomous iteration and ultimately outputs new copywriting that conforms to the target account's style.

[0054] The voice-over digital human intelligent agent, through deep integration of the intelligent agent system and the digital human anchor creation platform, has successfully constructed a fully automated generation chain from "text input" to "digital voice-over video output." Its core operational logic is as follows: First, the innovative text undergoes semantic analysis, breaking it down into paragraphs suitable for voice-over, and SSML voice markers, facial expressions, and action commands are added to generate a structured script that the digital human platform can parse; second, based on the text semantics and audience preferences, a digital human image is accurately matched; finally, combining the text semantics, the structured script, and the selected digital human image, the voice-over video is generated by calling the digital human platform API. The voice-over digital human intelligent agent is built through three core steps: First, selecting interface plugins compatible with different digital human platforms to prepare text materials and digital human image resources; second, building an intelligent agent workflow based on the LangGraph framework, setting core state nodes such as text parsing, image matching, speech synthesis, and video generation, and defining the trigger logic and data flow rules between nodes; third, after the intelligent agent is created, it can automatically receive text input, complete digital human matching, speech synthesis, and video rendering, and finally output a voice-over video that can be directly published, effectively solving the core pain points of creators such as "low willingness to appear on camera and high shooting costs". The fully automated generation chain includes intelligent text analysis and preprocessing, intelligent digital human image matching, intelligent voice matching and synthesis, automated workflow orchestration, and platform integration and API calls. The steps of intelligent text analysis and preprocessing are as follows: A large language model is used to perform deep semantic analysis on the input text, identifying its sentiment (e.g., positive, neutral, negative), tone style (e.g., serious, lively, friendly), and professional level (e.g., academic, general). Text classification algorithms are used to determine the text type, distinguishing between educational, marketing, entertainment, and professional explanation scenarios. Keyword extraction algorithms (e.g., TF-IDF) are used to extract industry-specific vocabulary, sentiment markers, and style identifiers from the text. Natural language processing techniques are combined to analyze the linguistic features of the text, determining the appropriate speaking speed range, pause rhythm, and emphasis. The stress position provides basic parameters for subsequent speech synthesis; the long text is segmented based on semantic logic, dividing it into independent segments suitable for narration, with each segment's length adapted to the digital human's single-sentence expression needs; SSML (Speech Synthesis Markup Language) markers are added to the segmented text, including speech control commands such as speech rate, pitch, and pauses; the final video duration is calculated and estimated based on the text's word count and preset speech rate parameters; based on the text's sentiment analysis results, digital human facial expression control commands (such as smiling, serious, surprised, etc.) are generated and associated with the corresponding text segments to achieve synchronization of emotions and expressions.

[0055] The steps for intelligent matching of digital human images are as follows: Digital human resources are categorized and labeled in multiple dimensions, including age (youth, middle-aged, elderly), gender, occupation (e.g., teacher, salesperson, expert), and style (e.g., formal, casual, cartoonish). Each digital human is associated with applicable scenario tags, specifying the suitable content type (e.g., instructional videos, product promotion) and industry sector (e.g., education, finance, retail). A digital human image scoring system is established, calculating a comprehensive score using the formula "Appearance_Score=Professionalism×0.4+Affinity×0.3+Authority×0.3", where Professionalism... `lism` is used to score professionalism, `affinity` to score approachability, and `authority` to score authority, forming a quantifiable image evaluation standard. Content matching is calculated using "Content_Match=Σ(Keyword_i×Avatar_Relevance_i)", where `Keyword_i` is the weight value of the copywriting keywords, and `Avatar_Relevance_i` is the relevance between the avatar and the keyword. The consistency between the copywriting style vector and the avatar style vector is evaluated using the vector dot product algorithm to calculate "Style_Consistency=Text_Style_Vector•Avatar_Style_Vector". Audience fit is calculated based on the formula "Audience_Fit=Target_Age_Match×0.4+Gender_Preference×0.3+Industry_Match×0.3", where `Target_Age_Match` is the match with the target audience's age, `Gender_Preference` is the gender preference fit, and `Industry_Match` is the industry fit. Finally, "Final_Score=Content_Fit" is used to calculate the final score. The algorithm calculates a comprehensive matching score using "ent_Match×0.4+Style_Consistency×0.35+Audience_Fit×0.25" and selects the digital human with the highest score as the optimal choice. It establishes a mapping relationship between the text's emotional tone and the digital human's facial expressions, dynamically triggering corresponding expressions based on the emotional tendency of the text paragraphs (e.g., a smiling expression triggers positive content). Based on key words and logical nodes in the text, it automatically generates appropriate gestures (e.g., triggering gestures to emphasize data). Through an eye-control algorithm, it simulates the eye movements of a real anchor, achieving eye contact with the virtual audience and enhancing the video's interactivity.

[0056] The steps of intelligent voice matching and synthesis are as follows: The voice library is categorized by timbre into sweet, magnetic, professional, approachable, and authoritative types. A suitable speech rate range (120-180 words / minute) is set based on the complexity of the text content (e.g., density of technical terms, logical structure). Pitch variation is controlled using the formula "Pitch_Range=Base_Pitch±Emotion_Modifier," where Base_Pitch is the base pitch and Emotion_Modifier is the pitch adjustment value based on emotion. Voice quality is comprehensively scored from three dimensions: clarity, naturalness, and emotional expressiveness, selecting high-quality voice resources. Initial screening is conducted based on content type matching rules (e.g., clear and professional timbre is prioritized for teaching content, while approachable and sweet timbre is prioritized for marketing content). Feature comparison ensures that the voice matches the digital human's appearance, age, and temperament (e.g., a youthful voice is matched to a young digital human). Finally, the voice is tailored to the account's brand. Positioning (e.g., high-end, approachable, technological) and selecting a voice style that aligns with the brand's tone; calculating the overall voice matching score using "Voice_Score=Content_Fit×0.4+Avatar_Harmony×0.3+Brand_Alignment×0.3", where Content_Fit represents content fit, Avatar_Harmony represents harmony with the digital human image, and Brand_Alignment represents brand tone matching; based on the copywriting sentiment analysis results, adding corresponding emotional rendering parameters during speech synthesis (e.g., raising the pitch and lowering the speech rate when expressing joy); adding appropriate pauses before and after key information (e.g., core selling points, conclusions) to enhance information delivery; highlighting important words and key data through accent processing algorithms; and post-processing the synthesized speech, including noise reduction, volume equalization, and sound quality enhancement, to improve the naturalness and listenability of the speech.

[0057] The steps for automated workflow orchestration are as follows: The system includes a text input node to receive user-inputted text and verify its formatting and content completeness; an analysis and processing node to perform semantic analysis, feature extraction, and structured processing tasks in parallel; a matching decision node to comprehensively evaluate the digital human's image and voice based on a preset algorithm and select the optimal combination; an integrated synthesis and generation node to complete video rendering and generation by calling the digital human platform API; a quality detection node to automatically detect quality indicators such as image clarity and voice synchronization in the generated video; and an automatic alternative recommendation mechanism to provide suboptimal matching results for the user to choose from when the digital human or voice matching score falls below a preset threshold. The system also includes API call handling. For failures, video generation timeouts, and other anomalies, an exponential backoff retry strategy is adopted, and a degradation plan (such as switching to the basic digital human model) is initiated after multiple failed retries. When quality inspection finds obvious defects in the video (such as audio-visual desynchronization), the regeneration process is automatically triggered, or a manual intervention notification is pushed if multiple repairs are ineffective. The system connects to the platform's data interface to collect performance metrics such as video views, interaction rate, and completion rate. Based on the performance data, a feedback model is built to dynamically adjust the weight parameters in the matching algorithm (such as increasing the matching priority of high-interaction-rate combinations). By recording users' preferences for digital humans and voices, a personalized recommendation model is trained to make subsequent matching results more in line with user needs.

[0058] The steps for platform integration and API calls are as follows: Design a unified API interface layer to encapsulate the underlying calling logic of different digital human platforms, achieving interface standardization for upper-layer applications; establish a parameter conversion mechanism to automatically convert standardized parameters within the system (such as text features and image requirements) into the private parameter formats required by each platform; implement load balancing (avoiding single-platform request overload) and concurrency control (adjusting request frequency according to platform limitations) through interface call optimization strategies; deploy a platform status monitoring module to monitor the availability and service quality of each digital human platform in real time through heartbeat detection, response time statistics, etc.; select the optimal service platform based on the platform's current load, historical response speed, and call cost through intelligent scheduling algorithms; establish a failover mechanism to automatically switch to a backup platform within 10 seconds when the main platform experiences service anomalies, ensuring the continuity of the video generation process.

[0059] Optionally, such as Figure 1 As shown, the functional layer also includes a data analysis and optimization module, which is used to analyze the key data indicators of the short video after the video is published by the editing and publishing module, and to obtain the reasons for the success and failure of the short video.

[0060] Specifically, the data analysis and optimization module comprehensively collects key data indicators from the short videos published by the editing and publishing modules in terms of dissemination, interaction, and conversion. It conducts in-depth data mining and intelligent analysis to identify successful experiences and shortcomings in content operation. The module generates visual reports to intuitively present the analysis results and outputs targeted content iteration suggestions and optimization strategies, providing creators with data-driven operational decision support. The indicator collection steps include: This system integrates with multiple data sources, including short video platform open APIs, user behavior tracking systems, and third-party statistical tools, to comprehensively collect video playback data, user interaction data, conversion behavior data, and account operation data. It establishes a standardized data source processing mechanism, unifying the format, field definitions, and time granularity of data from different sources, eliminating data silos, and forming a complete data source system. Playback-level metrics include: play count (cumulative number of plays), completion rate (percentage of users who watched the entire video), replay rate (percentage of users who watched the same video multiple times), and share rate (percentage of users who shared the video). Interaction-level metrics include: like rate (percentage of users who liked the video), comment rate (percentage of users who commented on the video), forwarding rate (percentage of users who forwarded the video), and favorite rate (percentage of users who favorited the video). Conversion-level metrics include: follow conversion rate (percentage of users who followed the account after watching), private message conversion rate (percentage of users who sent a private message after watching), and referral conversion rate (percentage of users who jumped to external links through the video). Content-level metrics include: video duration, publication time, hashtags, and content type. The system utilizes attributes to construct user-dimensional metrics, including fan growth rate, user profile characteristics (age, gender, region, etc.), user activity (average daily interactions), and user retention rate (the percentage of users interacting for multiple consecutive days). In the data acquisition layer of the data analysis and optimization module, a Kafka message queue is deployed to enable streaming reception and temporary storage of real-time user behavior data and video playback data, supporting high-concurrency writing of tens of thousands of data entries per second. In the data storage layer of the data analysis and optimization module, a ClickHouse time-series database is used for efficient storage and compression of massive amounts of time-series metric data, meeting the long-term retention requirements of historical data. In the data processing layer of the data analysis and optimization module, the Spark Streaming distributed computing framework is used to perform real-time cleaning (duplicate removal, missing value completion), format conversion, and metric calculation (such as real-time cumulative playback volume and instantaneous calculation of interaction rate) on the collected data. In the data service layer of the data analysis and optimization module, Redis is used to cache hot data (such as the playback volume of the top 10 videos of the day and the real-time number of fans), providing millisecond-level query responses for upper-layer applications and improving the interactive experience of the reporting system.

[0061] The steps involved in deep data mining and intelligent analysis by large AI models include: The ARIMA time series analysis model is used to perform trend fitting and periodic analysis on core indicators (such as play count and number of followers) to identify the long-term growth trend and periodic fluctuation characteristics of the data. The correlation strength between indicators is calculated by Pearson correlation coefficient (such as analyzing the positive correlation between play completion rate and like rate) to uncover the linkage patterns behind the indicators. The K-means clustering algorithm is used to automatically classify content (based on features such as play count and interaction rate) and users (based on features such as behavioral preferences and interaction frequency) to identify high-value content types and core user groups. An anomaly detection model is built based on the isolated forest algorithm to quickly identify abnormal fluctuations in the data (such as a sudden drop in play count or a sudden increase in conversion rate) and potential opportunities (such as an abnormally high interaction rate for a certain type of niche content). The RandomForest model is trained to predict the playback range and potential dissemination scope of newly released videos based on basic video attributes (duration, release time, tags) and the performance of similar historical content. An LSTM (Long Short-Term Memory) neural network is used, combined with user historical interaction sequences, to predict the probability of user interaction with different types of content (e.g., likes, comments). The Prophet prediction model is used to analyze the changing trends of hot topics in the content field and predict the outbreak time of potential viral topics. An XGBoost gradient boosting model is constructed to predict the attention conversion and traffic conversion effects of different content based on content features, user profiles, and historical conversion data. Key factors influencing video success are identified through feature importance analysis (such as the Gini coefficient of random forests) (e.g., the decisive role of the "first 3 seconds completion rate" in overall viewership); low-performing content (e.g., videos with below-average viewership) is compared and analyzed with high-performing content to diagnose the root causes of failure from dimensions such as content format, release time, and user relevance; new content opportunities and market gaps are explored based on data gap analysis (e.g., insufficient content supply for a certain user group); and quantifiable optimization suggestions are generated by combining historical best-case data with industry benchmarks (e.g., "keeping the video length under 60 seconds can increase the completion rate by 20%").

[0062] The steps to generate a visual report include: The dynamic dashboard design specifically includes: developing a real-time monitoring panel that displays the changing trends and current values ​​of core metrics (views, engagement rate, number of followers) in real time through line charts, number cards, etc., and supports automatic refresh; designing multi-dimensional comparative analysis charts, including bar charts and radar charts comparing metrics for different time periods (e.g., this week vs. last week), different content types (e.g., tutorials vs. reviews), and different accounts (e.g., this account vs. benchmark accounts); generating user behavior heatmaps (showing the density of user interaction at different times of the video) and content performance heatmaps (showing the distribution of content performance by tags, duration, etc.); and constructing a conversion funnel analysis chart to intuitively present the complete user path from "exposure → play → interaction → conversion" and the churn rate at each stage. It supports data drill-down functionality, allowing users to drill down from overall metrics (such as total account views) to detailed data for specific videos (such as the source of views for a single video and user profiles). It offers multi-dimensional filtering capabilities, allowing users to flexibly filter data based on time range (such as the last 7 days), content type (such as marketing), and hashtags (such as #foodtutorials). A customizable report module allows users to select analysis dimensions (such as by publication time + content type), metric combinations (such as views + follower conversion rate), and chart types to generate personalized analysis reports. Report export functionality is supported, allowing analysis results to be exported to PDF, Excel, and other formats for offline viewing and further processing. A responsive design ensures the reporting system adapts to different screen sizes on mobile devices (such as phones and tablets), guaranteeing complete chart display and smooth interaction. On mobile interfaces, core metric cards (such as daily views and follower growth) are prioritized, highlighting key information. A data push mechanism is implemented, sending real-time notifications to mobile devices when core metrics show significant changes (such as a 50% surge in views) or anomalies (such as a 30% drop in conversion rate), ensuring users are promptly informed of account dynamics.

[0063] For example, the data analysis and optimization module also includes AI large-scale model intelligent analysis, the steps of which include: This study employs a BERT pre-trained language model to perform semantic analysis on video text, titles, and comments, identifying sentiment, core themes, and underlying intentions. Computer vision techniques are used to analyze video elements, extracting objects, scenes, and visual styles. Audio feature extraction algorithms are used to analyze the style of background music and the emotional tone of speech, assessing their impact on user experience. A multimodal fusion analysis model is constructed to perform cross-modal correlation analysis, integrating text semantics, visual elements, and audio features. Features are extracted from high-viewership, high-interaction viral videos to summarize their common attributes. A content preference model for target users is built based on user interaction data and content features. Replicable creative methodologies are extracted from historical success stories. By comparing the content features of this account with those of competitors, unique success factors differentiating the account from competitors are identified. Correlation analysis based on content features and performance data provides specific directions for content improvement. Combining user activity time distribution and historical posting data, optimal posting times, frequencies, and platform combination strategies are recommended. Specific strategies for fan interaction and community operation are provided. Based on user profiles and behavioral data, monetization suggestions are offered.

[0064] For example, this embodiment provides three specific application examples to help describe the functions of the data analysis and optimization module: Example 1: Data analysis and optimization for a food blogger, specifically targeting the account "Xiao Wang's Kitchen" (500,000 followers, average 80,000 views in the past 30 days, 3.2% interaction rate, mainly focusing on home-style cooking tutorials). Data analysis revealed that videos posted between 7-9 PM received 40% more views than those posted at other times; videos tagged "10-minute quick dishes" received 60% more views; 70% of the followers were working women aged 25-35, who preferred simple and easy-to-learn recipes; and videos with cost information had higher interaction rates compared to similar bloggers. Based on these findings, the data analysis and optimization module provided the following suggestions: adjust the posting time to 7:30 PM daily; add a "Quick Dishes for Office Workers" series, with each dish labeled with preparation time and cost; add keywords such as "10 minutes" and "essential for lazy people" to the titles; and proactively respond to users' questions in the comments section. The optimization yielded significant results: average views increased to 120,000 (a 50% increase), interaction rate increased to 4.8% (a 50% increase), follower growth rate increased by 35%, and inquiries for business collaborations increased by 80%.

[0065] Example 2: Conversion optimization for paid knowledge accounts, specifically targeting the "Career Growth Academy" account (300,000 followers, course conversion rate of 1.2%, average order value of 299 yuan, focusing on sharing workplace skills). Data analysis showed that: the jump rate from videos to course pages was only 15%; case study videos had a conversion rate 3 times higher than theoretical videos; users spent an average of only 30 seconds on the course introduction page; and the conversion rate of courses priced at 199 yuan was 80% higher than that of courses priced at 299 yuan. Based on this, the data analysis and optimization module suggested: increasing the sharing of real case studies and reducing pure theoretical explanations; adding a "free trial" segment to videos to improve the jump rate; redesigning the course introduction page to highlight student success stories; and launching a 199 yuan introductory course as a lead-generating product. After optimization, the course conversion rate increased to 2.8% (an increase of 133%), the jump rate increased to 35% (an increase of 133%), monthly revenue increased from 180,000 yuan to 420,000 yuan (an increase of 133%), and user satisfaction rating increased from 4.2 to 4.7.

[0066] Example 3: E-commerce live-streaming account ROI optimization, specifically for the "Fashion Styling Assistant" account (800,000 followers, ROI of 1:3.5, monthly sales of 1.5 million RMB, mainly selling women's clothing and accessories). Data analysis revealed that: products with an average order value of 200-500 RMB had the highest conversion rate; sales from videos released on weekends were 60% higher than on weekdays; 65% of users were from second- and third-tier cities and were highly price-sensitive; and videos featuring real-person try-on had a conversion rate 5 times higher than those showing pictures. Data analysis and optimization module suggestions: Focus on promoting best-selling items in the 200-500 RMB price range; schedule main live-streaming content for Friday evenings and weekends; increase the proportion of real-person try-on and styling tutorials; negotiate exclusive coupons with brands to improve price competitiveness. After optimization, the ROI increased to 1:5.2 (a 49% increase), monthly sales increased to 2.2 million RMB (a 47% increase), user repurchase rate increased by 40%, and brand cooperation price increased by 30%.

[0067] Optionally, such as Figure 1 As shown, the functional layer also includes a data review agent, which is used to generate a review report based on the key data indicators of the short video.

[0068] Specifically, the review agent aims to address the core pain points of creators, namely "difficulty in interpreting data and unclear optimization direction." By building a standardized data review workflow and integrating multi-dimensional operational data with AI large-scale model analysis capabilities, it enables quantitative evaluation of content quality, in-depth mining of audience preferences, and outputs actionable creative strategy optimization solutions. After deployment, the agent can be widely used in scenarios such as special reviews of single videos, periodic account operation reviews, and optimization testing of new content strategies, forming a closed-loop support of "data collection-analysis-review-optimization".

[0069] Optionally, such as Figure 1 As shown, the short video customer acquisition system based on intelligent agent collaboration also includes a support layer, which includes a knowledge base and a model library. The knowledge base stores industry rules and competitor data, and the model library integrates NLP models, ASR models, and digital human models.

[0070] Specifically, the support layer provides knowledge and algorithm support for each functional module of the system, including a structured knowledge base and a model library. The knowledge base is used to systematically collect various knowledge information related to customer acquisition throughout the entire short video process, covering industry rules (such as content publishing specifications of various platforms and short video creation methodologies), competitor data (such as benchmark account operation data and characteristics of popular content in the industry), and business-specific knowledge (such as audience preference tags and content direction templates for different industries), forming a reusable knowledge resource pool. The model library is used to centrally integrate various AI models that support the functions of the system, including natural language processing (NLP) models (such as BERT and GPT series models, used for text semantic analysis and content generation), automatic speech recognition (ASR) models (used for video speech transcription and text extraction), digital human-driven models (used for voice-over digital human image control and action generation), and computer vision models (used for video image feature analysis), providing algorithmic support for intelligent agent scheduling and content creation. It can perform knowledge graph construction, transforming unstructured and semi-structured knowledge in the knowledge base into a structured knowledge graph through steps such as entity extraction, relation definition, and attribute annotation. This enables associative storage and efficient retrieval of knowledge, improving the accuracy of knowledge retrieval. Simultaneously, it can promote lightweight model deployment by reducing the computational resource consumption and runtime latency of AI models through techniques such as model compression (e.g., pruning and quantization) and model distillation. Furthermore, combined with model service encapsulation (e.g., providing model invocation capabilities via API interfaces), it ensures that each functional module can quickly call the required models, ultimately achieving a dual improvement in system operating efficiency and knowledge utilization. This provides underlying technical support for the stable and efficient operation of the short video end-to-end customer acquisition system.

[0071] Optionally, such as Figure 1 As shown, the short video customer acquisition system based on intelligent agent collaboration also includes a storage layer. The storage layer uses a distributed database to store various data from the input layer, the scheduling layer, and the functional layer.

[0072] Specifically, the storage layer enables unified management of various types of data generated during system operation. This includes centralized storage of user-input task data (such as enterprise or individual business descriptions, product parameters, target audience information, etc.), intermediate result data generated during the execution of each functional module (such as subtask decomposition results, content feature extraction data, temporary analysis reports, etc.), and final output structured result data (such as account positioning reports, finished video metadata, debriefing analysis reports, etc.). It also supports exporting the above data to common formats such as PDF, Excel, and CSV according to user needs, meeting the requirements for offline viewing, secondary processing, and integration with external systems. The key technical aspects of this solution lie in its distributed database architecture. Data sharding and multi-replica backup mechanisms ensure data security and reliability under high-concurrency access scenarios. A distributed file system enables cross-node data sharing, ensuring efficient access to required data for various intelligent agent modules (such as account location agents and data analysis agents). Database index optimization and query caching mechanisms further enhance data read / write efficiency. Ultimately, this solution meets the data usage needs of multiple scenarios within the short video customer acquisition system, including user data management, intermediate process traceability, and results report archiving, providing data storage support for the overall stable operation of the system.

[0073] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0074] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A short video end-to-end customer acquisition system based on intelligent agent collaboration, characterized in that, It includes an input layer, a scheduling layer, and a functional layer; The input layer is used to receive task data input by the user; The scheduling layer employs an intelligent agent scheduling engine, which is used to determine at least one subtask and the execution order of the subtasks based on the task data, distribute the subtasks based on a publish-subscribe model, and allocate computing resources based on a weighted round-robin algorithm. The functional layer includes an account positioning module, a benchmark account decomposition module, an account basic construction module, a topic selection library construction module, a video creation module, and an editing and publishing module, all independently encapsulated in the form of intelligent agents. Based on the execution order of the sub-tasks and the computing resources, the account positioning module, the benchmark account decomposition module, the account basic construction module, the topic selection library construction module, the video creation module, and the editing and publishing module execute the corresponding sub-tasks.

2. The short video end-to-end customer acquisition system based on intelligent agent collaboration according to claim 1, characterized in that, The intelligent agent scheduling engine includes a task dispatcher, a resource manager, and an exception handler; The task dispatcher is used to parse the subtasks of the task data, and uses an asynchronous pipeline scheduling algorithm to determine the subtask priority and subtask dependency of the subtask. Based on the subtask dependency, it determines the execution order of the subtasks, and distributes the subtasks to each agent in the functional layer based on the publish-subscribe pattern. The resource manager is used to monitor resource usage and, based on the resource usage, allocate computing resources according to the subtask priority. The exception handler is based on a three-level exception handling mechanism to handle task exceptions.

3. The short video end-to-end customer acquisition system based on intelligent agent collaboration according to claim 2, characterized in that, The asynchronous pipeline scheduling algorithm is used to determine the subtask priority and subtask dependency relationship of the subtask, including: Based on the directed acyclic graph of the task data, identify the prerequisite dependencies of each subtask and determine the subtask dependencies; The priority of the subtask is determined using a priority formula, which includes: Priority = α×Urgency + β×(1 / ResourceCost) + γ×(1 / EstimatedTime); Where Priority is the priority of the subtask, Urgency is the urgency, ResourceCost is the resource cost, EstimatedTime is the estimated time, and α, β and γ are all weighting coefficients.

4. The short video end-to-end customer acquisition system based on intelligent agent collaboration according to claim 2, characterized in that, The three-level exception handling mechanism for handling task exceptions includes: When the subtask fails to execute, a smart retry strategy matrix is ​​used for local retry; When the local retry fails, the failed subtask is reassigned to a backup agent via the task dispatcher. When the backup agent fails to execute, a degradation scheme is triggered, and a notification requiring manual intervention is pushed.

5. The short video end-to-end customer acquisition system based on intelligent agent collaboration according to claim 1, characterized in that, The account positioning module includes a target audience identification intelligent agent, a content direction intelligent decision-making intelligent agent, and a positioning tag generation intelligent agent; The target audience identification agent is used to determine the audience profile based on the task data, wherein the audience profile includes basic attributes, interest tags, and priority ranking. The intelligent decision-making agent for content direction is used to determine a short video content direction suggestion scheme based on the acquired popular data and the audience profile; The location tag generating agent is used to determine short video location tags based on high-frequency industry terms in the task data.

6. The short video end-to-end customer acquisition system based on intelligent agent collaboration according to claim 1, characterized in that, The video creation module includes a text-based intelligent agent and a voice-over digital human intelligent agent. The copywriting AI is used to extract popular copywriting from popular short videos and generate innovative copywriting in different styles based on the popular copywriting. The spoken digital human agent is used to analyze the semantics of the innovative copy, break down the innovative copy into spoken segments, add SSML voice tags, facial expressions or action commands, generate a structured script that can be parsed by the digital human platform, determine a digital human image that matches the semantics of the copy and audience preferences, and, based on the semantics of the copy, the structured script and the digital human image, call the digital human platform API to generate a spoken digital human video.

7. The short video end-to-end customer acquisition system based on intelligent agent collaboration according to claim 1, characterized in that, The functional layer also includes a data analysis and optimization module, which is used to analyze the key data indicators of the short video after it is published by the editing and publishing module, and to obtain the reasons for the success and failure of the short video.

8. The short video end-to-end customer acquisition system based on intelligent agent collaboration according to claim 7, characterized in that, The functional layer also includes a data review agent, which is used to generate a review report based on the key data indicators of the short video.

9. The short video end-to-end customer acquisition system based on intelligent agent collaboration according to claim 1, characterized in that, It also includes a support layer, which includes a knowledge base and a model library. The knowledge base stores industry rules and competitor data, and the model library integrates NLP models, ASR models, and digital human models.

10. The short video end-to-end customer acquisition system based on intelligent agent collaboration according to claim 1, characterized in that, It also includes a storage layer, which uses a distributed database to store various data from the input layer, the scheduling layer, and the functional layer.

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