Multi-task-oriented animation production process intelligent scheduling method and system

Abstract

The application discloses a multi-task-oriented animation production process intelligent scheduling method and system, relates to the technical field of animation production project process management, collects animation production whole-process task data and disassembles minimum task units, extracts task dependency relationship, duration requirement, delivery node resource demand core features, constructs a multi-project multi-task unified resource pool and labels resource parameters, establishes a scheduling constraint system, generates an initial scheduling scheme by adopting a multi-objective optimization algorithm, identifies risk deviation by collecting execution data in real time, dynamically iteratively optimize scheduling, and outputs a final scheduling scheme and supporting charts after evaluating the effect. The application realizes fine disassembly and resource overall arrangement of animation tasks, quantifies task priority, dynamically optimizes the scheduling scheme, improves multi-task collaboration and resource utilization efficiency, adapts to various animation production scenes, and improves the intelligent and standardized level of scheduling.

Description

Technical Field

[0001] This invention relates to the field of animation production project workflow management technology, and in particular to an intelligent scheduling method and system for multi-task animation production workflow. Background Technology

[0002] Animation production is a creative production process characterized by multi-stage collaboration, parallel task execution, and strong workflow dependencies. A complete animation production workflow covers three main stages: pre-production planning, mid-production, and post-production compositing. Each stage contains multiple sub-production steps, with strict sequential dependencies and parallel collaborative relationships between these steps. A single project often includes hundreds of sub-task units, and multi-project parallel production is common in the industry. Traditional animation production scheduling relies heavily on the manual experience of production managers. The formulation of scheduling plans depends heavily on the managers' professional experience and project control capabilities, leading to significant differences in scheduling plans created by different managers, resulting in insufficient stability and standardization of the scheduling results. Manual scheduling makes it difficult to comprehensively consider the dependencies, resource requirements, and time constraints of all tasks. When multiple projects are running concurrently, problems such as resource allocation conflicts, overlapping task sequences, and delivery node conflicts easily arise. For the frequent review and modification stages in animation production, manual scheduling often fails to adequately allow for buffer periods, easily causing overall project delays due to multiple rounds of modifications, and also making it difficult to achieve time optimization and balanced resource allocation among multiple tasks.

[0003] Existing general-purpose project scheduling management tools and methods are mostly geared towards standardized engineering projects or production process designs, and cannot adapt to the creative attributes and non-linear production characteristics of animation production processes. Their task decomposition rules are disconnected from the actual stages of animation production, and they fail to construct a scheduling topology network that aligns with the task characteristics and dependencies of each stage of animation production. The scheduling constraint systems of these tools are relatively simplistic, mostly only able to implement basic schedule constraints and task dependency settings. They cannot simultaneously cover core constraints in the animation production process, such as resource load, review and modification reservations, multi-project resource priority, and requirement change compatibility. They also lack multi-objective collaborative optimization capabilities, failing to balance multiple optimization objectives such as schedule, resource load, delay risk, and change compatibility. The generated initial scheduling schemes often deviate significantly from the actual execution scenario of animation production, requiring frequent manual adjustments during implementation.

[0004] Current animation production scheduling solutions lack a standardized task priority quantification and grading system and a scheduling evaluation system. The division of task importance and urgency relies entirely on manual judgment, failing to accurately categorize tasks based on their inherent attributes and cross-stage / cross-project impacts, thus compromising the rationality of resource allocation. Progress monitoring and risk identification during scheduling execution exhibit significant lag, hindering real-time collection of task execution and resource status data for deviation identification. Furthermore, there is a lack of standardized dynamic optimization strategies to address issues such as requirement changes, schedule delays, and resource overload during production, resulting in insufficient timeliness and rationality in scheduling adjustments. Simultaneously, existing solutions lack a unified multi-project resource pool management mechanism, leading to both resource contention and idle resources during concurrent production, resulting in low overall resource utilization. They also fail to adapt to the differentiated scheduling needs of various animation types, such as film, game, and short video animation, demonstrating significant shortcomings in overall scenario adaptability and intelligence. Summary of the Invention

[0005] The present invention proposes an intelligent scheduling method and system for multi-task animation production workflow to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent scheduling method for multi-task animation production workflow, comprising the following steps: Collect task data from the entire animation production process, break down each production stage into its smallest task unit, and extract core features such as task dependencies, schedule requirements, delivery nodes, and resource requirements. Build a unified resource pool for multiple projects and tasks, integrating four core resources: production personnel, hardware equipment, production venues, and time cycles, and labeling resources with skill tags, available time periods, load limits, and priority rules; Establish an animation production task scheduling constraint system, setting four core constraints: pre-task dependency constraints, resource load constraints, delivery time constraints, and review and modification reservation constraints. Based on the task characteristics and constraint system, a multi-objective optimization algorithm is used to generate an initial scheduling scheme, and to allocate the execution time, responsible resources and delivery nodes of each task unit. Real-time collection of task execution progress data, resource status data, and requirement change data; dynamic identification of scheduling deviations, resource conflicts, and task delay risks. To address the identified risks and deviations, the scheduling plan is dynamically iterated and optimized, adjusting the task execution sequence, resource allocation, and node settings to mitigate resource conflicts and delay risks. Conduct a comprehensive evaluation of the scheduling plan's execution effectiveness, and compile statistics on key indicators such as on-time task delivery rate, resource utilization rate, and response efficiency to demand changes. Output the final optimized animation production multi-task scheduling plan, and simultaneously generate a schedule execution Gantt chart, resource allocation table and risk warning list.

[0007] Furthermore, it also includes a step of quantifying and classifying the comprehensive weight of tasks, which integrates the task's own attributes and related influence dimensions to calculate the comprehensive weight value of the task. The calculation formula is as follows: ;in This represents the dimensionless overall weight value for the task. The total number of core attribute dimensions of the task. The dimensionless weight coefficient of the core attribute of item a, Let a be the dimensionless standardized value of the core attribute of item a. The total number of dimensions affected by task association. Let b be the dimensionless weight coefficient of the correlation influence dimension. For the dimensionless standardized value of the correlation and influence dimension of item b, the weights of tasks in the entire animation production process are ranked and sorted. Combining the urgency, importance, and complexity of the task itself with the correlation characteristics of cross-project and cross-stage dependence, influence, and resource matching, three priority levels are divided into core key tasks, important support tasks, and routine auxiliary tasks.

[0008] Furthermore, it also includes a comprehensive suitability assessment step for the scheduling scheme, which quantitatively calculates the feasibility of the scheduling scheme and the degree of matching with requirements from multiple dimensions. The calculation formula is as follows: ;in This represents the overall fit value of the dimensionless scheduling scheme. This represents the total number of dimensions for matching the project duration. Let c be the dimensionless weighting coefficient for the c-th dimension of project duration matching. Let c be the dimensionless deviation value of the project duration matching dimension. This represents the total number of resource load dimensions. Let d be the dimensionless weight coefficient for the resource load dimension. Let d be the dimensionless overload value of the resource load dimension. The total number of dimensions with controllable risk. Let e ​​be the dimensionless weight coefficient of the e-th dimension of risk controllability. For the dimensionless risk value of the controllable risk dimension (e), perform a fit ranking on multiple initial scheduling schemes and select the scheme with the highest fit as the implementation scheme.

[0009] Furthermore, the steps of collecting task data for the entire animation production process and breaking it down into the smallest task units adopt a standardized WBS hierarchical decomposition method. The entire animation production process is divided into three major levels: pre-production planning, mid-production, and post-production compositing. The pre-production planning level is further divided into scriptwriting, storyboard design, art setting, and dynamic storyboard task units. The mid-production level is divided into original drawing design, scene creation, character rigging, animation production, and special effects production task units. The post-production compositing level is divided into image rendering, audio-visual compositing, color grading and correction, review and modification, and final output task units. Each smallest task unit is marked with a unique identifier, its project, its stage, the relationship between preceding and succeeding tasks, the standard timeframe, the delivery standard, and the type and quantity of required resources. This establishes the serial and parallel relationships between tasks, forming a complete animation production task topology network.

[0010] Furthermore, the steps for constructing a unified resource pool for multiple projects and tasks adopt a tag-based classification management approach. Production personnel resources are tagged with skill tags according to their professional directions such as concept art, animation, special effects, rendering, compositing, and voice-over, while also tagging skill levels, project experience, available working hours, and maximum daily workload parameters. Hardware equipment resources are tagged with performance parameters, available time periods, and concurrent usage limits according to the type of graphics workstation, render farm, recording equipment, and motion capture equipment. Production venue resources are tagged with the number of people that can be accommodated, available time periods, and usage rules parameters according to their function type. Time cycle resources are tagged with available time periods and prohibited time periods according to project delivery nodes, statutory holidays, and production cycles. The resource pool updates the status data and load of all resources in real time, while also setting priority rules for multi-project resource allocation.

[0011] Furthermore, the initial scheduling scheme generation process employs an improved non-dominated sorting multi-objective optimization algorithm. The algorithm sets four main optimization objectives: shortest total project duration, highest resource load balancing, lowest task delay risk, and highest compatibility with requirement changes. Boundary conditions are defined by task dependency constraints, resource load constraints, delivery time constraints, and review / modification reservation constraints. During algorithm iteration, the optimization direction is adjusted based on the comprehensive task weight values. A fixed buffer period is set for the review / modification stage in the animation production process. Iterative scheduling reservation windows are set for tasks with multiple rounds of modifications. Resource conflict avoidance rules are set for parallel tasks across projects. The algorithm outputs multiple Pareto-optimal scheduling schemes. The final scheme selection is completed by combining the comprehensive suitability values ​​of the scheduling schemes, generating a standardized scheduling plan covering all tasks across the entire project.

[0012] Furthermore, the steps for dynamically identifying scheduling risks and implementing iterative scheduling optimization adopt a rolling periodic update mechanism. Data on actual task execution progress, actual resource load, and changes in requirements are collected at fixed intervals. By comparing these data with the schedule plan's set progress thresholds, resource load thresholds, and delivery node thresholds, four types of scheduling deviations are identified: schedule lag, resource overload, node conflict, and requirement change. Corresponding optimization strategies are matched for different types of deviations. For schedule lag deviations, optimization methods include parallel task splitting, resource supplementation and allocation, and fine-tuning of project nodes. For resource overload deviations, optimization methods include task timing shifting, load balancing, and calling up backup resources. For node conflict deviations, optimization methods include buffer period calls and postponement of non-core tasks. For requirement change deviations, optimization methods include task addition and splitting, and schedule reallocation. After each round of optimization, the scheduling constraints and the suitability of the solution are re-verified.

[0013] Furthermore, it includes the following modules: The animation task data acquisition and decomposition module is used to collect task data throughout the entire animation production process, complete task hierarchical decomposition and feature extraction, and construct a task topology association network. The multi-project resource pool management module is used to integrate all types of animation production resources, complete resource tagging management and real-time status updates, and establish a dynamic resource allocation mechanism; The scheduling constraint system construction module is used to set multi-dimensional constraints for animation production scheduling and to clarify constraint boundaries and rule parameters. The initial scheduling intelligent generation module is used to generate multiple initial scheduling schemes based on task data, resource data and constraint system, and to complete scheme selection and output; The task progress dynamic monitoring module is used to collect task execution progress, resource status and requirement change data in real time, and identify scheduling deviations and various risks. The scheduling scheme iteration and optimization module is used to dynamically adjust and optimize the scheduling scheme in response to identified deviations and risks, so as to resolve resource conflicts and delay risks. The scheduling effectiveness evaluation module is used to conduct a comprehensive quantitative evaluation of the implementation effectiveness of the scheduling plan and to collect statistics on core operational indicators. The scheduling results output module is used to output the final scheduling plan and simultaneously generate a Gantt chart, resource allocation table, and risk warning list.

[0014] Furthermore, the system adopts a distributed microservice architecture and a modular collaborative scheduling mechanism. The animation task data acquisition and decomposition module and the multi-project resource pool management module establish a real-time data transmission channel. Task data and resource data are synchronously updated to the scheduling constraint system construction module and the initial scheduling intelligent generation module. Real-time data collected by the task progress dynamic monitoring module is synchronously pushed to the scheduling scheme iteration optimization module and the scheduling effect evaluation module. The system supports parallel scheduling management of multiple projects, allocating scheduling tasks of different projects to independent service nodes for parallel computing. The calculation results of each node are summarized to the global scheduling unit in real time. The system has a built-in hierarchical permission management system, dividing operation permissions according to the roles of production management, project responsibility, production execution, and review and supervision. Different roles correspond to different data viewing, scheme editing, and scheduling adjustment permissions. The system supports multi-terminal data synchronization, and the scheduling scheme and updated content are synchronized to the corresponding terminals in real time, completing the collaborative work of all participants in the animation production process.

[0015] Furthermore, the system is configured with scenario-based adaptation and customizable extension components. These components include four standardized scheduling templates: film and television animation, game animation, short video animation, and commercial advertising animation. Each template corresponds to different task breakdown rules, scheduling constraints, weight allocation parameters, and optimization goals. The system can automatically match the appropriate template based on the animation production project type. The components support user-defined scheduling rules, constraints, evaluation indicators, and weight parameters. They can adjust the task breakdown granularity, resource allocation rules, and optimization strategies according to actual project needs. The system features standardized open interfaces that can connect to animation production software, project management systems, render farm platforms, and financial accounting systems, enabling bidirectional synchronization and interoperability of scheduling data with third-party systems. The components can also iterate and expand their functionality based on changes in animation production industry processes and requirements.

[0016] Compared with existing technologies, the beneficial effects of this invention are: This invention utilizes a standardized Worksheet-Based Structure (WBS) hierarchical decomposition method to achieve refined breakdown and feature extraction of tasks throughout the entire animation production process. It establishes serial and parallel relationships between tasks, forming a complete animation production task topology network. This provides a standardized and regulated task data foundation for scheduling calculations, aligning with the characteristics of each stage and task dependency in the entire animation production process. A tag-based classification management approach is employed to construct a unified resource pool for multiple projects and tasks, enabling the integration and dynamic management of all types of animation production resources. Clear resource allocation priority rules are defined, improving resource scheduling efficiency and load balancing in multi-project parallel production scenarios.

[0017] This invention quantifies and grades tasks by comprehensive weighting, combining task attributes with related influence dimensions to prioritize tasks and achieve differentiated and precise allocation of scheduling resources, matching the different scheduling needs of core and auxiliary stages of animation production. An improved multi-objective optimization algorithm is used to generate an initial scheduling plan, simultaneously considering multiple optimization objectives such as time, resources, risk, and change compatibility. A buffer period and reserved window are set for the review and modification stage, aligning with the non-linear production characteristics of animation and improving the feasibility and adaptability of the scheduling plan.

[0018] This invention employs a rolling, periodic update mechanism to collect real-time data on task execution, resource status, and requirement changes. It accurately identifies scheduling deviations and various risks, and matches corresponding optimization strategies to different types of deviations, enabling dynamic iterative optimization of the scheduling plan and reducing the probability of task delays and resource conflicts. Through a comprehensive suitability assessment of the scheduling plan, it provides a unified standard for initial plan selection and dynamic optimization, ensuring that schedule adjustments always match the core constraints of the project and reducing secondary problems caused by schedule adjustments.

[0019] This invention employs a distributed microservice architecture and a modular collaborative scheduling mechanism, supporting parallel scheduling management of multiple projects and collaborative work among all participants throughout the process. A hierarchical permission management system adapts to the operational needs of different roles in animation production, and multi-terminal data synchronization capabilities enhance the efficiency of scheduling scheme execution. It features scenario-based adaptation and customizable extension components, built-in standardized scheduling templates for various animation production types, and supports user-defined scheduling rules and parameters. Standardized open interfaces allow for integration with software and systems related to the entire animation production process, improving the system's scenario adaptability and long-term availability. Overall, it achieves standardized, intelligent, and refined management of animation production process scheduling. Attached Figure Description

[0020] Figure 1 This is a schematic block diagram of the overall flowchart of the intelligent scheduling of animation production process proposed in this invention; Figure 2 This is a schematic diagram of the WBS hierarchical breakdown diagram for the animation production task standardization proposed in this invention; Figure 3 This is a diagram of the unified resource pool management architecture for multiple projects and multiple tasks proposed in this invention; Figure 4 The intelligent generation logic diagram for the initial scheduling scheme proposed in this invention; Figure 5 This is the closed-loop diagram of the rolling scheduling dynamic monitoring and iterative optimization proposed in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Reference Figures 1 to 5 A smart scheduling method for multi-task animation production workflow, comprising the following steps: Collect task data from the entire animation production process, break down each production stage into its smallest task unit, and extract core features such as task dependencies, schedule requirements, delivery nodes, and resource requirements. Build a unified resource pool for multiple projects and tasks, integrating four core resources: production personnel, hardware equipment, production venues, and time cycles, and labeling resources with skill tags, available time periods, load limits, and priority rules; Establish an animation production task scheduling constraint system, setting four core constraints: pre-task dependency constraints, resource load constraints, delivery time constraints, and review and modification reservation constraints. Based on the task characteristics and constraint system, a multi-objective optimization algorithm is used to generate an initial scheduling scheme, and to allocate the execution time, responsible resources and delivery nodes of each task unit. Real-time collection of task execution progress data, resource status data, and requirement change data; dynamic identification of scheduling deviations, resource conflicts, and task delay risks. To address the identified risks and deviations, the scheduling plan is dynamically iterated and optimized, adjusting the task execution sequence, resource allocation, and node settings to mitigate resource conflicts and delay risks. Conduct a comprehensive evaluation of the scheduling plan's execution effectiveness, and compile statistics on key indicators such as on-time task delivery rate, resource utilization rate, and response efficiency to demand changes. Output the final optimized animation production multi-task scheduling plan, and simultaneously generate a schedule execution Gantt chart, resource allocation table and risk warning list.

[0023] This invention also includes a step of quantifying and classifying the comprehensive weight of tasks, which integrates the task's own attributes and related influence dimensions to calculate the comprehensive weight value of the task, providing a quantitative basis for scheduling priority division and resource allocation. The calculation formula is as follows: ;in This represents the dimensionless overall weight value for the task. The total number of core attribute dimensions of the task. The dimensionless weight coefficient of the core attribute of item a, Let a be the dimensionless standardized value of the core attribute of item a. The total number of dimensions affected by task association. Let b be the dimensionless weight coefficient of the correlation influence dimension. For the dimensionless standardized value of the correlation and influence dimension of item b, the tasks in the entire animation production process are weighted and ranked. Combining the urgency, importance, and complexity of the tasks themselves with the correlation characteristics of cross-project and cross-stage dependence, influence, and resource matching, three priority levels are divided into core key tasks, important support tasks, and routine auxiliary tasks. The scheduling process prioritizes the supply of resources and the reservation of time for high-weight tasks, so as to achieve differentiated and precise allocation of scheduling resources and match the different scheduling needs of core and auxiliary stages in the animation production process.

[0024] This invention also includes a comprehensive suitability assessment step for scheduling schemes, which quantitatively calculates the feasibility and demand matching degree of the scheduling schemes from multiple dimensions, providing a judgment standard for initial scheme selection and dynamic optimization. The calculation formula is as follows: ;in This represents the overall fit value of the dimensionless scheduling scheme. This represents the total number of dimensions for matching the project duration. Let c be the dimensionless weighting coefficient for the c-th dimension of project duration matching. Let c be the dimensionless deviation value of the project duration matching dimension. This represents the total number of resource load dimensions. Let d be the dimensionless weight coefficient for the resource load dimension. Let d be the dimensionless overload value of the resource load dimension. The total number of dimensions with controllable risk. Let e ​​be the dimensionless weight coefficient of the e-th dimension of risk controllability. For the dimensionless risk value of the controllable risk dimension of item e, multiple initial scheduling schemes are sorted by adaptability, and the scheme with the highest adaptability is selected as the implementation scheme. During the dynamic optimization of the schedule, the adaptability value of the adjusted scheme is calculated simultaneously to maintain the matching degree between the optimized scheme and the core constraints of the animation production project, and reduce the secondary resource conflicts and schedule risks caused by the schedule adjustment.

[0025] In this invention, the steps of collecting task data for the entire animation production process and breaking it down into the smallest task units adopt a standardized WBS hierarchical decomposition method. The entire animation production process is divided into three levels: pre-production planning, mid-production, and post-production compositing. The pre-production planning level is further divided into scriptwriting, storyboard design, art setting, and dynamic storyboard task units. The mid-production level is divided into original drawing design, scene creation, character rigging, animation production, and special effects production task units. The post-production compositing level is divided into image rendering, audio-visual compositing, color grading, review and modification, and final output task units. Each smallest task unit is marked with a unique identifier, its project, its stage, the preceding and succeeding tasks, the standard duration, the delivery standard, and the type and quantity of required resources. This establishes serial and parallel relationships between tasks, forming a complete animation production task topology network, providing a standardized task data foundation for subsequent scheduling calculations.

[0026] In this invention, the steps of constructing a unified resource pool for multiple projects and tasks adopt a tag-based classification management approach. Production personnel resources are tagged with skill tags according to their professional directions such as concept art, animation, special effects, rendering, compositing, and voice-over, while also tagging skill levels, project experience, available working hours, and maximum daily workload parameters. Hardware equipment resources are tagged with performance parameters, available time periods, and concurrent usage limits according to the type of graphics workstation, render farm, recording equipment, and motion capture equipment. Production venue resources are tagged with the number of people that can be accommodated, available time periods, and usage rules parameters according to their function type. Time cycle resources are tagged with available time periods and prohibited time periods according to project delivery nodes, statutory holidays, and production cycles. The resource pool updates the status data and load of all resources in real time, establishes a dynamic management mechanism for resource occupation and release, and sets priority rules for multi-project resource allocation, clarifying the resource call permissions and allocation order for projects of different levels.

[0027] In this invention, the initial scheduling scheme is generated using an improved non-dominated sorting multi-objective optimization algorithm. The algorithm aims to achieve four main optimization objectives: shortest total project duration, highest resource load balancing, lowest task delay risk, and highest compatibility with requirement changes. Boundary conditions are defined by task dependency constraints, resource load constraints, delivery time constraints, and review / modification reservation constraints. During algorithm iteration, the optimization direction is adjusted based on the comprehensive weight values ​​of the tasks, prioritizing the scheduling needs of high-weight tasks. A fixed buffer period is set for the review / modification stage in the animation production process. An iterative scheduling reservation window is set for tasks with multiple rounds of modifications. Resource conflict avoidance rules are set for parallel tasks across projects. The algorithm outputs multiple Pareto-optimal scheduling schemes. The final scheme is selected by combining the comprehensive suitability values ​​of the scheduling schemes, generating a standardized scheduling plan covering all tasks across the entire project.

[0028] In this invention, the steps of dynamically identifying scheduling risks and performing iterative optimization of the schedule adopt a rolling periodic update mechanism. Data on actual task execution progress, actual resource load, and changes in requirements are collected at fixed intervals. These data are compared with the schedule plan's set progress thresholds, resource load thresholds, and delivery node thresholds to identify four types of scheduling deviations: schedule lag, resource overload, node conflicts, and requirement changes. Corresponding optimization strategies are matched to different types of deviations. For schedule lag deviations, optimization methods include parallel task splitting, resource supplementation and allocation, and fine-tuning of project nodes. For resource overload deviations, optimization methods include task timing shifting, load balancing, and calling up backup resources. For node conflict deviations, optimization methods include buffer period calls and postponement of non-core tasks. For requirement change deviations, optimization methods include task addition and decomposition, and schedule reallocation. After each round of optimization, the scheduling constraints and the suitability of the solution are re-verified, completing the rolling update and synchronous distribution of the scheduling plan.

[0029] This invention includes the following modules: The animation task data acquisition and decomposition module is used to collect task data throughout the entire animation production process, complete task hierarchical decomposition and feature extraction, and construct a task topology association network. The multi-project resource pool management module is used to integrate all types of animation production resources, complete resource tagging management and real-time status updates, and establish a dynamic resource allocation mechanism; The scheduling constraint system construction module is used to set multi-dimensional constraints for animation production scheduling and to clarify constraint boundaries and rule parameters. The initial scheduling intelligent generation module is used to generate multiple initial scheduling schemes based on task data, resource data and constraint system, and to complete scheme selection and output; The task progress dynamic monitoring module is used to collect task execution progress, resource status and requirement change data in real time, and identify scheduling deviations and various risks. The scheduling scheme iteration and optimization module is used to dynamically adjust and optimize the scheduling scheme in response to identified deviations and risks, so as to resolve resource conflicts and delay risks. The scheduling effectiveness evaluation module is used to conduct a comprehensive quantitative evaluation of the implementation effectiveness of the scheduling plan and to collect statistics on core operational indicators. The scheduling results output module is used to output the final scheduling plan and simultaneously generate a Gantt chart, resource allocation table, and risk warning list.

[0030] In this invention, the system adopts a distributed microservice architecture and a modular collaborative scheduling mechanism. The animation task data acquisition and decomposition module and the multi-project resource pool management module establish a real-time data transmission channel. Task data and resource data are synchronously updated to the scheduling constraint system construction module and the initial scheduling intelligent generation module. Real-time data collected by the task progress dynamic monitoring module is synchronously pushed to the scheduling scheme iteration optimization module and the scheduling effect evaluation module. The system supports parallel scheduling management of multiple projects, allocating scheduling tasks of different projects to independent service nodes for parallel computing. The calculation results of each node are summarized to the global scheduling unit in real time. The system has a built-in hierarchical permission management system, dividing operation permissions according to the roles of production management, project responsibility, production execution, and review and supervision. Different roles correspond to different data viewing, scheme editing, and scheduling adjustment permissions. The system supports multi-terminal data synchronization, and the scheduling scheme and updated content are synchronized to the corresponding terminals in real time, realizing collaborative work of all participants in the animation production process.

[0031] In this invention, the system is configured with scenario-based adaptation and customizable extension components. These components include four standardized scheduling templates: film and television animation, game animation, short video animation, and commercial advertising animation. Each template corresponds to different task breakdown rules, scheduling constraints, weight allocation parameters, and optimization goals. The system can automatically match the appropriate template based on the animation production project type. The components support user-defined scheduling rules, constraints, evaluation indicators, and weight parameters. The task breakdown granularity, resource allocation rules, and optimization strategies can be adjusted according to actual project needs. The system features standardized open interfaces that can connect to animation production software, project management systems, rendering farm platforms, and financial accounting systems, enabling bidirectional synchronization and interoperability of scheduling data with third-party systems. The components can undergo functional iteration and expansion based on updates to animation production processes and changes in requirements, enhancing the system's scenario adaptability and long-term availability.

[0032] The following two examples further illustrate the specific implementation of this system: Example 1

[0033] This embodiment is applied to a multi-task intelligent scheduling scenario for feature-length film and animation projects. It primarily serves the entire production scheduling management of a single feature-length film or animation project, covering the complete stages of pre-production planning, mid-production, and post-production compositing. The project involves a large number of tasks, complex dependencies, and long production cycles, requiring high levels of scheduling stability and resource coordination capabilities. The system's hardware environment includes a central management server, multi-terminal operating devices, and data storage devices. The software environment includes process management components, intelligent optimization calculation components, and data interaction components, comprehensively supporting large-scale task data processing and dynamic scheduling adjustments.

[0034] After system startup, the first step is to collect and decompose task data through the animation task data acquisition and decomposition module. A standardized Worksheet Structure (WBS) hierarchical decomposition method is used to divide feature-length film and animation projects into three main levels: pre-production planning, mid-production, and post-production compositing. Pre-production planning is further broken down into scriptwriting, storyboard design, art direction, and dynamic storyboarding task units, clearly defining the delivery standards and timelines for each unit. Mid-production is broken down into keyframe design, scene creation, character rigging, animation production, and special effects production task units, outlining the sequential and parallel relationships between these units. Post-production compositing is broken down into image rendering, audio-visual compositing, color grading, review and revision, and final output task units, with multiple iteration buffer cycles set for the review and revision stages. Each smallest task unit is labeled with a unique identifier, its corresponding stage, preceding and succeeding tasks, standard timeline, delivery standards, and required resource types and quantities, establishing a complete task topology network and providing a standardized data foundation for subsequent scheduling.

[0035] The multi-project resource pool management module synchronously constructs a unified resource pool, integrating four categories of resources—production personnel, hardware equipment, production venues, and timeframes—using a tag-based classification management approach. Production personnel are tagged with skill labels for keyframes, animation, special effects, rendering, compositing, and voice-over, with synchronized labeling of skill level, project experience, available working hours, and maximum daily workload. Hardware equipment is tagged with performance parameters, available hours, and concurrent usage limits for graphics workstations, render farms, recording equipment, and motion capture equipment. Production venues are tagged with capacity, available hours, and usage rules based on their function type. Timeframes are tagged with available hours and prohibited hours based on project delivery deadlines, statutory holidays, and the overall production cycle. The resource pool updates resource status and load data in real time, establishing a dynamic management mechanism for resource occupancy and release, setting resource allocation priority rules specific to film and animation projects, and clearly defining resource access permissions for core production stages.

[0036] The scheduling constraint system construction module establishes four core constraints: pre-task dependency constraints, resource load constraints, delivery time constraints, and review / modification reservation constraints. This constraint system clearly defines the order of task execution, maximum resource load thresholds, overall delivery milestones, and review / modification reservation periods, thus defining clear boundaries for schedule generation. The initial schedule intelligent generation module loads task data, resource data, and constraints, employing an improved non-dominated sorting multi-objective optimization algorithm. The optimization objectives are to minimize the total project duration, maximize resource load balance, minimize task delay risk, and maximize compatibility with requirement changes. The algorithm adjusts the optimization direction based on the comprehensive weighting of tasks, prioritizing the scheduling needs of high-weight tasks. The algorithm sets a fixed buffer period for the review / modification stage, an iterative scheduling reservation window for tasks with multiple rounds of modifications, and resource conflict avoidance rules for parallel tasks, outputting multiple optimal scheduling schemes. The optimal scheme is selected through a comprehensive suitability evaluation of the scheduling schemes, generating a standardized initial schedule plan covering all tasks.

[0037] The task progress dynamic monitoring module initiates rolling periodic monitoring, collecting data on actual task execution progress, actual resource load, and requirement changes at fixed intervals. It compares this data with the schedule's progress thresholds, resource load thresholds, and delivery node thresholds to identify four types of deviations: schedule lag, resource overload, node conflicts, and requirement changes. The scheduling scheme iterative optimization module executes corresponding optimization strategies for different deviations. Schedule lag deviations are handled through parallel task splitting, resource replenishment and allocation, and fine-tuning of project milestones. Resource overload deviations are handled through task timing shifting, load balancing, and the use of backup resources. Node conflict deviations are handled through buffered periodic calls and the postponement of non-core tasks. Requirement change deviations are handled through task addition and decomposition, and schedule reallocation. After each round of optimization, the constraints and scheme adaptability are re-verified, and the schedule is updated and synchronously distributed.

[0038] The scheduling effectiveness evaluation module conducts a comprehensive quantitative assessment, statistically analyzing key indicators such as on-time delivery rate, resource utilization rate, and response efficiency to requirement changes, and generating an evaluation report. The scheduling results output module outputs the final optimized scheduling plan, simultaneously generating a schedule execution Gantt chart, resource allocation table, and risk warning list, which are then distributed to each execution terminal. The system adopts a distributed microservice architecture and a modular collaborative scheduling mechanism. Each module establishes a real-time data transmission channel, supports multi-terminal data synchronization, and categorizes operation permissions according to production management, project responsibility, production execution, and review and supervision, enabling collaborative work across the entire process. The system is configured with a dedicated scheduling template for film and animation, matching the task rules, constraints, and optimization goals of film and animation production. It supports custom parameter adjustments and can interface with animation production software, project management systems, and rendering farm platforms to achieve bidirectional data synchronization.

[0039] Table 1 Comparison of the scheduling effects of film and animation projects

[0040] Table 1 presents the scheduling effects of this invention on feature-length film and animation projects through five core indicators. The data comes from comparative measurements across multiple consecutive film and animation projects. Improved task breakdown completeness stems from standardized WBS hierarchical decomposition, covering the smallest task units throughout the entire process and establishing clear dependencies. Reduced resource conflict rate relies on a unified resource pool and balanced allocation algorithm to achieve rational resource scheduling. Enhanced delay risk control relies on multi-objective optimization and buffer period settings to reserve adjustment space in advance. Accelerated dynamic adjustment response speed relies on rolling monitoring and automatic iterative optimization to shorten the deviation handling cycle. Improved multi-task collaboration fluency relies on clear task sequence and resource coordination to strengthen the efficiency of each link, comprehensively meeting the scheduling needs of stable production of feature-length film and animation.

[0041] Example 2

[0042] This embodiment is applied to a scenario of parallel intelligent scheduling for multiple short video animations and commercial advertising animations. It mainly serves animation production scenarios where multiple projects are progressing simultaneously, production cycles are short, tasks iterate rapidly, and requirements change frequently. The system needs to have the capabilities of rapid scheduling, dynamic adaptation, and multi-project coordination. The system's hardware environment adopts a combination of cloud servers and local terminals, while the software environment is equipped with a lightweight scheduling engine, multi-project management components, and real-time data synchronization components, balancing rapid calculation and flexible scheduling.

[0043] After system startup, it simultaneously collects task data from multiple short video animation and commercial advertising animation projects. The animation task data collection and decomposition module performs unified task decomposition across projects. Following standardized Work Breakdown Structure (WBS) rules, each project is broken down into pre-production, mid-production, and post-production task units, simplifying the granularity of non-core processes, strengthening core production and rapid review processes, and establishing intra-project task dependencies and cross-project resource sharing relationships. All task units are uniformly labeled with identifiers, timelines, delivery standards, and resource requirements, forming a unified task topology network across multiple projects, achieving unified management of cross-project tasks.

[0044] The multi-project resource pool management module constructs a unified cross-project resource pool, integrating all production personnel, hardware equipment, production facilities, and time-bound resources. It employs a tag-based management approach to label resource attributes and availability, sets priority rules for multi-project resource allocation, and prioritizes resource usage based on project level, delivery urgency, and commercial importance. The resource pool synchronizes resource usage data across projects in real time, dynamically updates resource availability, and establishes a cross-project temporary resource allocation mechanism to improve overall resource utilization.

[0045] The scheduling constraint system construction module establishes constraints adapted to short videos and commercial advertising animations, strengthening constraints on rapid delivery, high-frequency changes, and short-cycle iterations. It maintains the basic framework of pre-task dependency constraints and resource load constraints, compresses the overall production cycle reserve ratio, and improves the iterative response requirements of the review and modification process. The initial scheduling intelligent generation module employs an improved multi-objective optimization algorithm, aiming to achieve the highest overall delivery efficiency across multiple projects, the most balanced resource load, and the fastest change response. It quickly generates parallel initial scheduling schemes for multiple projects by combining the comprehensive weighting and grading results of tasks. The optimal scheme is selected through comprehensive adaptability evaluation to meet the needs of rapid scheduling.

[0046] The task progress dynamic monitoring module performs rolling monitoring at a higher frequency, collecting real-time data on the progress of multiple project tasks, resource load, and requirement changes, and quickly identifying various scheduling deviations. The scheduling scheme iteration and optimization module performs lightweight and rapid optimization tailored to the characteristics of short video projects. When progress is lagging, it directly activates backup resources and parallel splitting strategies; when resources are overloaded, it directly implements task off-peaking and load transfer; and when requirements change, it directly performs rapid rescheduling. Each round of optimization maintains the compliance of constraints and adaptability, achieving second-level response and adjustment of the scheduling scheme.

[0047] The scheduling effectiveness evaluation module simultaneously assesses the scheduling execution effectiveness of multiple projects, compiling statistics on on-time delivery rate, overall resource utilization rate, and change response efficiency for each project, and generating a multi-project comparative evaluation report. The scheduling results output module independently outputs a scheduling plan, Gantt chart, resource allocation table, and risk warning list for each project, while also outputting a comprehensive view of multiple projects for convenient overall management control.

[0048] The system adopts a distributed microservice architecture, supporting parallel computing and independent management of multiple projects, with each project's scheduling tasks remaining uninterrupted. A hierarchical permission management system adapts to the needs of parallel multi-project management, supporting both overall production management by the chief producer and independent responsibility for individual projects. The system is configured with dedicated scheduling templates for short video animations and commercial advertising animations, automatically matching the characteristics of rapid iteration, high-frequency changes, and short production cycles, and supports user-defined scheduling rules and parameters. Standardized open interfaces can connect to rapid production tools, material management systems, and client review platforms, achieving seamless linkage between scheduling data and production processes. Scenario-adaptive components automatically switch scheduling strategies based on project type, maintaining scheduling stability and execution efficiency under multi-project parallel operation.

[0049] Table 2 Comparison of the scheduling effects of short videos and commercial animation projects

[0050] Table 2 illustrates the advantages of this invention in multi-project parallel scenarios for short videos and commercial advertising animations through five indicators. The data comes from multiple high-frequency iterative project tests. Improved multi-project coordination capabilities rely on a unified task network and cross-project resource pools to achieve global scheduling. Faster schedule generation efficiency relies on lightweight algorithms and dedicated templates to shorten the solution generation cycle. Improved adaptability to requirement changes relies on high-frequency rolling monitoring and rapid optimization strategies to shorten the adjustment cycle. Increased overall resource utilization relies on dynamic cross-project allocation to reduce resource idleness and contention. Improved on-time project delivery rate relies on accurate scheduling and rapid correction to adapt to short-cycle, fast-iteration production requirements, comprehensively improving the management efficiency and execution effectiveness of multi-project parallel production.

[0051] Reference Figure 2This diagram details the task breakdown logic for the entire animation production process. The system employs a standardized Worksheet Breakdown (WBS) approach, dividing the production workflow into three core levels: pre-production planning, mid-production, and post-production compositing. Each level is further subdivided into specific minimum task units (such as scriptwriting, modeling, and rendering). Each task unit is labeled with core characteristics such as timeline, delivery standards, and resource requirements, forming a complete task topology network that provides a standardized data foundation for subsequent scheduling calculations.

[0052] Reference Figure 3 This diagram illustrates the resource pool's tag-based management mechanism. The system categorizes core resources in animation production into four main types: personnel, hardware, venue, and timeframe. By labeling each type of resource with skill tags, performance parameters, available time slots, and load limits, a dynamic management mechanism is established. The resource pool not only updates its status in real time but also sets multi-project priority rules to ensure that resources can be accurately and efficiently allocated based on project urgency in a multi-tasking environment.

[0053] Reference Figure 4 This diagram illustrates the initial schedule generation logic based on a multi-objective optimization algorithm. The system takes the decomposed task units, resource pool data, and four core constraints (dependencies, load, delivery time, and audit reservations) as input. Through algorithm iteration, it prioritizes high-weight critical tasks, balances schedule, resource utilization, and risk, and outputs multiple Pareto optimal solutions. Finally, based on the solution suitability evaluation, it selects the standardized initial schedule plan that best meets the current project requirements.

[0054] Reference Figure 5 This diagram illustrates the dynamic maintenance mechanism of the scheduling plan. The system employs a rolling update mechanism, periodically comparing actual progress with the schedule to identify four types of deviations: schedule lag, resource overload, node conflicts, and requirement changes. For each type of deviation, the system applies corresponding optimization strategies (such as task splitting, off-peak scheduling, or buffered calls). Constraints are re-verified after each round of optimization, thus enabling the rolling update of the scheduling plan and ensuring that the schedule always accurately guides the production process.

[0055] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent scheduling of multi-task animation production workflows, characterized in that, Includes the following steps: Collect task data from the entire animation production process, break down each production stage into its smallest task unit, and extract core features such as task dependencies, schedule requirements, delivery nodes, and resource requirements. Build a unified resource pool for multiple projects and tasks, integrating four core resources: production personnel, hardware equipment, production venues, and time cycles, and labeling resources with skill tags, available time periods, load limits, and priority rules; Establish an animation production task scheduling constraint system, setting four core constraints: pre-task dependency constraints, resource load constraints, delivery time constraints, and review and modification reservation constraints. Based on the task characteristics and constraint system, a multi-objective optimization algorithm is used to generate an initial scheduling scheme, and to allocate the execution time, responsible resources and delivery nodes of each task unit. Real-time collection of task execution progress data, resource status data, and requirement change data; dynamic identification of scheduling deviations, resource conflicts, and task delay risks. To address the identified risks and deviations, the scheduling plan is dynamically iterated and optimized, adjusting the task execution sequence, resource allocation, and node settings to mitigate resource conflicts and delay risks. Conduct a comprehensive evaluation of the scheduling plan's execution effectiveness, and compile statistics on key indicators such as on-time task delivery rate, resource utilization rate, and response efficiency to demand changes. Output the final optimized animation production multi-task scheduling plan, and simultaneously generate a schedule execution Gantt chart, resource allocation table and risk warning list.

2. The intelligent scheduling method for multi-task animation production workflow according to claim 1, characterized in that, It also includes a step of quantifying and classifying the overall task weight, which integrates the task's own attributes and related influence dimensions to calculate the overall task weight value. The calculation formula is as follows: ;in This represents the dimensionless overall weight value for the task. The total number of core attribute dimensions of the task. The dimensionless weight coefficient of the core attribute of item a, Let a be the dimensionless standardized value of the core attribute of item a. The total number of dimensions affected by task association. Let b be the dimensionless weight coefficient of the correlation influence dimension. For the dimensionless standardized value of the correlation and influence dimension of item b, the weights of tasks in the entire animation production process are ranked and sorted. Combining the urgency, importance, and complexity of the task itself with the correlation characteristics of cross-project and cross-stage dependence, influence, and resource matching, three priority levels are divided into core key tasks, important support tasks, and routine auxiliary tasks.

3. The intelligent scheduling method for multi-task animation production workflow according to claim 1, characterized in that, It also includes a comprehensive suitability assessment step for the scheduling scheme, which quantitatively calculates the feasibility of the scheduling scheme and the degree of matching with requirements from multiple dimensions. The calculation formula is as follows: ;in This represents the overall fit value of the dimensionless scheduling scheme. This represents the total number of dimensions for matching the project duration. Let c be the dimensionless weighting coefficient for the c-th dimension of project duration matching. Let c be the dimensionless deviation value of the project duration matching dimension. This represents the total number of resource load dimensions. Let d be the dimensionless weight coefficient for the resource load dimension. Let d be the dimensionless overload value of the resource load dimension. The total number of dimensions with controllable risk. Let e ​​be the dimensionless weight coefficient of the e-th dimension of risk controllability. For the dimensionless risk value of the controllable risk dimension (e), perform a fit ranking on multiple initial scheduling schemes and select the scheme with the highest fit as the implementation scheme.

4. The intelligent scheduling method for multi-task animation production workflow according to claim 1, characterized in that, The process of collecting data from the entire animation production workflow and breaking it down into its smallest task units adopts a standardized WBS hierarchical decomposition method. The entire animation production workflow is divided into three major levels: pre-production planning, mid-production, and post-production compositing. The pre-production planning level is further divided into scriptwriting, storyboard design, art direction, and dynamic storyboard task units. The mid-production level is divided into key animation design, scene creation, character rigging, animation production, and special effects production task units. The post-production compositing level is divided into image rendering, audio-visual compositing, color grading and correction, review and modification, and final output task units. Each smallest task unit is marked with a unique identifier, its project, its stage, its preceding and succeeding tasks, its standard timeframe, delivery standards, and the type and quantity of resources required. This establishes serial and parallel relationships between tasks, forming a complete animation production task topology network.

5. The intelligent scheduling method for multi-task animation production workflow according to claim 1, characterized in that, The steps for building a unified resource pool for multiple projects and tasks adopt a tag-based classification management approach. Production personnel resources are tagged with skill tags according to their professional directions such as concept art, animation, special effects, rendering, compositing, and voice-over, while also tagging skill levels, project experience, available working hours, and maximum daily workload parameters. Hardware equipment resources are tagged with performance parameters, available time periods, and concurrent usage limits according to the type of graphics workstation, render farm, recording equipment, and motion capture equipment. Production venue resources are tagged with the number of people that can be accommodated, available time periods, and usage rules parameters according to their function type. Time cycle resources are tagged with available time periods and prohibited time periods according to project delivery nodes, statutory holidays, and production cycles. The resource pool updates the status data and load of all resources in real time, and sets priority rules for resource allocation across multiple projects.

6. The intelligent scheduling method for multi-task animation production workflow according to claim 1, characterized in that, The initial scheduling scheme is generated using an improved non-dominated sorting multi-objective optimization algorithm. The four main optimization objectives are: shortest total project duration, highest resource load balancing, lowest task delay risk, and highest compatibility with requirement changes. Boundary conditions are task dependency constraints, resource load constraints, delivery time constraints, and review / modification reservation constraints. During algorithm iteration, the optimization direction is adjusted based on the comprehensive task weight values. A fixed buffer period is set for the review / modification stage in the animation production process. Iterative scheduling reservation windows are set for tasks with multiple rounds of modifications. Resource conflict avoidance rules are set for parallel tasks across projects. The algorithm outputs multiple Pareto optimal scheduling schemes. The final scheme is selected based on the comprehensive suitability value of the scheduling schemes, generating a standardized scheduling plan covering all tasks across the entire project.

7. The intelligent scheduling method for multi-task animation production workflow according to claim 1, characterized in that, The dynamic identification of scheduling risks and the execution of iterative scheduling optimization steps adopt a rolling periodic update mechanism. Data on actual task execution progress, actual resource load, and changes in requirements are collected at fixed intervals. By comparing these data with the schedule plan's set progress thresholds, resource load thresholds, and delivery node thresholds, four types of scheduling deviations are identified: schedule lag, resource overload, node conflict, and requirement change. Corresponding optimization strategies are matched for different types of deviations. For schedule lag deviations, optimization methods include parallel task splitting, resource supplementation and allocation, and fine-tuning of project nodes. For resource overload deviations, optimization methods include task timing shifting, load balancing, and calling up backup resources. For node conflict deviations, optimization methods include buffer period calls and postponement of non-core tasks. For requirement change deviations, optimization methods include task addition and decomposition and schedule reallocation. After each round of optimization, the scheduling constraints and the suitability of the solution are re-verified.

8. A multi-task-oriented intelligent scheduling system for animation production workflows, applicable to the multi-task-oriented intelligent scheduling method for animation production workflows as described in any one of claims 1-7, characterized in that, Includes the following modules: The animation task data acquisition and decomposition module is used to collect task data throughout the entire animation production process, complete task hierarchical decomposition and feature extraction, and construct a task topology association network. The multi-project resource pool management module is used to integrate all types of animation production resources, complete resource tagging management and real-time status updates, and establish a dynamic resource allocation mechanism; The scheduling constraint system construction module is used to set multi-dimensional constraints for animation production scheduling and to clarify constraint boundaries and rule parameters. The initial scheduling intelligent generation module is used to generate multiple initial scheduling schemes based on task data, resource data and constraint system, and to complete scheme selection and output; The task progress dynamic monitoring module is used to collect task execution progress, resource status and requirement change data in real time, and identify scheduling deviations and various risks. The scheduling scheme iteration and optimization module is used to dynamically adjust and optimize the scheduling scheme in response to identified deviations and risks, so as to resolve resource conflicts and delay risks. The scheduling effectiveness evaluation module is used to conduct a comprehensive quantitative evaluation of the implementation effectiveness of the scheduling plan and to collect statistics on core operational indicators. The scheduling results output module is used to output the final scheduling plan and simultaneously generate a Gantt chart, resource allocation table, and risk warning list.

9. The intelligent scheduling system for multi-task animation production workflow according to claim 8, characterized in that, The system adopts a distributed microservice architecture and a modular collaborative scheduling mechanism. The animation task data acquisition and decomposition module and the multi-project resource pool management module establish a real-time data transmission channel. Task data and resource data are synchronously updated to the scheduling constraint system construction module and the initial scheduling intelligent generation module. Real-time data collected by the task progress dynamic monitoring module is synchronously pushed to the scheduling scheme iteration and optimization module and the scheduling effect evaluation module. The system supports parallel scheduling management of multiple projects, allocating scheduling tasks of different projects to independent service nodes for parallel computing. The calculation results of each node are summarized to the global scheduling unit in real time. The system has a built-in hierarchical permission management system, dividing operation permissions according to the roles of production management, project responsibility, production execution, and review and supervision. Different roles correspond to different data viewing, scheme editing, and scheduling adjustment permissions. The system supports multi-terminal data synchronization, and the scheduling scheme and updated content are synchronized to the corresponding terminals in real time, completing the collaborative work of all participants in the animation production process.

10. The intelligent scheduling system for multi-task animation production workflow according to claim 8, characterized in that, The system is configured with scenario-based adaptation and customizable extension components. These components include four standardized scheduling templates: film and television animation, game animation, short video animation, and commercial advertising animation. Each template corresponds to different task breakdown rules, scheduling constraints, weight allocation parameters, and optimization goals. The system can automatically match the appropriate template based on the animation production project type. The components support user-defined scheduling rules, constraints, evaluation indicators, and weight parameters. Task breakdown granularity, resource allocation rules, and optimization strategies can be adjusted according to actual project needs. The system features standardized open interfaces that can connect to animation production software, project management systems, render farm platforms, and financial accounting systems, enabling bidirectional synchronization and interoperability of scheduling data with third-party systems. The components can also iterate and expand their functionality based on changes in animation production industry processes and requirements.

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