Intelligent resource scheduling method for optimizing television post-production efficiency
The intelligent resource scheduling system has solved the problem of unreasonable resource usage in television post-production, achieving efficient resource utilization and a smooth production process, thereby improving project efficiency and quality.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-23
- Publication Date
- 2026-04-03
AI Technical Summary
In current television post-production processes, high-quality resources such as core hardware and key software licenses are often occupied by low-priority tasks, while high-value and urgent tasks are congested due to insufficient resources, resulting in low project progress efficiency. Traditional scheduling methods are unable to identify potential problems such as plugin call conflicts and rendering node overload in advance, causing resource waste and schedule delays.
A smart resource scheduling system is constructed to achieve precise resource matching and conflict prevention through resource information collection, task requirement analysis, conflict identification and handling, dynamic resource allocation and real-time monitoring. Combined with entropy weight TOPSIS priority evaluation and dynamic adjustment, resource allocation is optimized.
It has achieved efficient use of resources, avoided low-priority tasks occupying high-quality resources, reduced task delays and interruptions, improved production efficiency and resource turnover efficiency, reduced manual intervention costs, formed a standardized resource configuration template, and improved project delivery quality.
Smart Images
Figure CN121792809A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of post-production optimization technology, and in particular to an intelligent resource scheduling method for optimizing the efficiency of television post-production. Background Technology
[0002] Television post-production is the complete process of transforming scattered raw footage into finished content that meets broadcast standards and is both entertaining and communicable after the filming of a television program. This process involves a series of technical and creative steps, such as editing, color grading, sound effects synthesis, special effects production, subtitle addition, and material organization. It spans the entire chain from material import to final output. Post-production resource scheduling involves the overall coordination and dynamic management of various core resources required during the production process. This includes hardware equipment (such as rendering workstations and storage devices), software licenses (such as editing and color grading software and plugins), material resources (such as original materials and special effects resources in various formats), and network bandwidth. The core objective is to achieve the rational allocation and efficient use of resources, ensuring the orderly progress of various production tasks and avoiding conflicts and waste. In current television post-production processes, high-quality resources such as core hardware and key software licenses are often occupied by low-priority tasks, while high-value and urgent tasks are stuck in a waiting state due to insufficient resources, which seriously affects the efficiency of project progress. At the same time, traditional scheduling methods are unable to identify potential problems such as plugin call conflicts, rendering node overload, and material storage path occupation in advance. They are often dealt with passively after conflicts occur and tasks are interrupted, which not only causes a lot of time and resources to be wasted, but may also lead to a chain reaction such as material damage and progress delays. Summary of the Invention
[0003] The purpose of this invention is to provide an intelligent resource scheduling method for optimizing the efficiency of television post-production. This method addresses the problem in the existing television post-production process described above, where high-quality resources such as core hardware and key software licenses are often occupied by low-priority tasks, while high-value and urgent tasks are hampered by resource shortages and become congested, severely impacting project progress. Furthermore, traditional scheduling methods struggle to identify potential problems such as plugin call conflicts, rendering node overload, and material storage path occupancy in advance, often only addressing them passively after conflicts occur or tasks are interrupted. This not only wastes a significant amount of time and resources but may also lead to a chain reaction of problems such as material damage and schedule delays.
[0004] To achieve the above objectives, the present invention adopts the following technical solution: A smart resource scheduling method for optimizing television post-production efficiency includes the following steps: S1. In the early deployment and benchmark construction stage, basic parameters of hardware, software and materials are collected through resource information collection terminals and a three-dimensional resource library is established. Historical project data is accumulated based on the benchmark database of similar projects. The personalized scheduling threshold generation unit uses a weighted average algorithm to calculate the exclusive resource load threshold. S2. In the real-time acquisition stage of multi-source data, the core information of the task is extracted and the task requirement index is generated through the task requirement acquisition device. The dynamic data of hardware, software and network are collected at a set frequency using the resource status acquisition terminal. Resource usage conflicts are detected and conflict levels are marked through the conflict risk identification unit. S3, the task and resource intelligent matching stage, calculates the comprehensive priority score of the task through the entropy weight TOPSIS priority evaluation unit, and completes the resource allocation through the dynamic resource allocation engine based on the score and resource status. For the identified conflicts, the corresponding resolution strategy is executed through the conflict intelligent resolution unit. S4. During the execution monitoring and intelligent adjustment phase, the task execution progress is captured by the task progress monitoring terminal and the remaining time is predicted. When the resource load suddenly increases or the transmission rate is not up to standard, the resource load dynamic adjustment engine optimizes the resource configuration. After identifying task execution abnormalities, the automatic abnormal handling unit triggers the recovery mechanism and records the abnormalities. S5. In the data archiving and algorithm iteration stage, a distributed data storage system is used to classify and store the entire chain of data. Based on the archived data, the algorithm parameter iteration tool is used to periodically adjust the scheduling threshold and weight parameters using the gradient descent method. It also includes the following modules and components: The module includes: preliminary deployment and benchmark building module, multi-source data real-time acquisition module, task and resource intelligent matching module, execution monitoring and intelligent adjustment module, and data archiving and algorithm iteration module. The preliminary deployment and benchmark construction module includes: a resource information collection terminal, a benchmark database for similar projects, and a personalized scheduling threshold generation unit; The multi-source data real-time acquisition module includes: a task requirement acquisition device, a resource status acquisition terminal, and a conflict risk identification unit; The intelligent task and resource matching module includes: an entropy weight TOPSIS priority evaluation unit, a dynamic resource allocation engine, and an intelligent conflict resolution unit. The execution monitoring and intelligent adjustment module includes: a task progress monitoring terminal, a resource load dynamic adjustment engine, and an automatic exception handling unit; The data archiving and algorithm iteration module includes: a distributed data storage system and an algorithm parameter iteration tool.
[0005] As a further improvement to this technical solution: the resource information acquisition terminal integrates hardware testing tools, software licensing scanners, and material attribute parsers to collect basic parameters of hardware, software, and materials, and establish a three-dimensional resource library; the benchmark database for similar projects stores historical project resource consumption data, task time statistics, and scheduling optimization cases; the personalized scheduling threshold generation unit: based on the resource library data and the benchmark database, it uses a weighted average algorithm to calculate a specific resource load threshold, the core formula of which is: ,in =0.4、 =0.3、 =0.3, This is the hardware industry standard load threshold. To set a reasonable threshold for software usage, Resource usage thresholds for tasks with different levels of urgency.
[0006] As a further improvement to this technical solution: the task requirement acquisition device extracts task type, production standard, deadline, and material dependencies through a software API interface to generate a task requirement index (TRI); the resource status acquisition terminal integrates hardware monitoring sensors, software status monitoring tools, and a network speed test module, and routinely collects hardware, software, and network status data every 10 seconds; when resource load fluctuations exceed 15%, it triggers real-time acquisition every 1 second; the conflict risk identification unit detects scenarios such as plugin call conflicts, rendering node load conflicts, and material storage path occupancy through a rule engine, and marks the conflict level according to the scope of impact.
[0007] As a further improvement to this technical solution: the entropy-weighted TOPSIS priority evaluation unit calculates the objective weight of the indicators through information entropy, and combines it with the TOPSIS method to quantify task priority. The information entropy formula is: The weighting formula is: The priority scoring formula is: ,in , .
[0008] As a further improvement to this technical solution: the dynamic resource allocation engine is based on Score and resource status are used to allocate resources. ≥8 points will be prioritized for allocation of hardware and dedicated software licenses with high idle rates, 5 points and below will be prioritized for allocation. <8 tasks are split into multiple nodes for parallel processing. <5 points staggered resource allocation; the intelligent conflict resolution unit triggers authorization rotation when there is a software authorization conflict, and calls backup nodes or reduces the resource usage of low-priority tasks when there is a hardware load conflict.
[0009] As a further improvement to this technical solution: the task progress monitoring terminal captures execution progress data through a software interface, combines it with historical data to predict the remaining time, and triggers an early warning when the predicted timeout rate is ≥20%; the resource load dynamic adjustment engine: when the resource node load suddenly increases (CPU ≥90% or memory ≥85%), it migrates low-priority tasks or pauses and releases resources; when the material transmission rate is lower than the threshold, it switches the storage path or compresses the transmission format; the automatic anomaly handling unit identifies anomalies such as rendering crashes, material corruption, and plugin errors, automatically restarts the task and allocates redundant resources, and records the cause of the anomaly in the fault database.
[0010] As a further improvement to this technical solution: the distributed data storage system adopts a hybrid architecture of MySQL and MongoDB, with MySQL storing structured data and MongoDB storing unstructured data. Data is retained for one year and supports multi-dimensional retrieval. The algorithm parameter iteration tool adjusts the scheduling threshold and weights monthly based on the archived data of the past 30 days using the gradient descent method. The core formula is: ,in =0.001, loss function L=1 The on-time completion rate of tasks is updated when the on-time completion rate increases by ≥3%.
[0011] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves precise problem-solving by constructing a full-process intelligent scheduling system. Relying on real-time multi-source data acquisition and intelligent matching mechanisms, the system can dynamically perceive task requirements and resource status. Combined with objective and quantifiable priority evaluation logic, it allocates various resources such as hardware, software, and materials to high-value tasks, avoiding the waste of efficiency caused by low-priority tasks occupying core resources. At the same time, the built-in intelligent conflict resolution module can identify and handle various conflict scenarios such as plugin calls, rendering node load, and material storage paths in advance, reducing problems such as task lag and interruption from the source. With the real-time monitoring and dynamic adjustment mechanism, the system can respond promptly to sudden changes in resource load and execution anomalies, automatically triggering optimization configurations or recovery strategies to ensure a smooth and continuous production process. This not only maximizes the utilization of various resources but also reduces the time and resource losses caused by process interruptions and rework, significantly improving the production efficiency and resource turnover efficiency of single projects.
[0012] 2. This invention continuously archives and schedules data across the entire process. The algorithm parameters can be iteratively optimized periodically to dynamically adapt to changes in scenarios such as equipment updates, project type upgrades, and adjustments to production standards. It maintains high-precision scheduling capabilities over the long term, eliminating the need for frequent manual rule adjustments. The fully automated scheduling significantly reduces the cost of human intervention, minimizes subjective biases and operational errors when allocating resources and handling conflicts, and frees up team energy from tedious resource coordination work, allowing them to focus on creative production itself. In addition, the scheduling experience and project data accumulated during system operation can form standardized resource configuration templates, providing direct reference for subsequent similar projects and helping teams establish an efficient production process system. Overall, this solution not only solves the current efficiency bottlenecks in production but also steadily improves the team's overall project delivery quality and large-scale production capabilities through experience accumulation and continuous optimization.
[0013] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description
[0014] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 A schematic diagram illustrating an intelligent resource scheduling method for optimizing the efficiency of television post-production; Figure 2 A schematic diagram of the system structure for an intelligent resource scheduling method to optimize the efficiency of television post-production. Detailed Implementation
[0015] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention. The invention is described more specifically in the following paragraphs by way of example with reference to the accompanying drawings. It should be noted that the drawings are in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.
[0016] Please see Figures 1-2 In this embodiment of the invention, an intelligent resource scheduling method for optimizing television post-production efficiency includes the following steps: S1. In the early deployment and benchmark construction stage, basic parameters of hardware, software and materials are collected through resource information collection terminals and a three-dimensional resource library is established. Historical project data is accumulated based on the benchmark database of similar projects. The personalized scheduling threshold generation unit uses a weighted average algorithm to calculate the exclusive resource load threshold. S2. In the real-time acquisition stage of multi-source data, the core information of the task is extracted and the task requirement index is generated through the task requirement acquisition device. The dynamic data of hardware, software and network are collected at a set frequency using the resource status acquisition terminal. Resource usage conflicts are detected and conflict levels are marked through the conflict risk identification unit. S3, the task and resource intelligent matching stage, calculates the comprehensive priority score of the task through the entropy weight TOPSIS priority evaluation unit, and completes the resource allocation through the dynamic resource allocation engine based on the score and resource status. For the identified conflicts, the corresponding resolution strategy is executed through the conflict intelligent resolution unit. S4. During the execution monitoring and intelligent adjustment phase, the task execution progress is captured by the task progress monitoring terminal and the remaining time is predicted. When the resource load suddenly increases or the transmission rate is not up to standard, the resource load dynamic adjustment engine optimizes the resource configuration. After identifying task execution abnormalities, the automatic abnormal handling unit triggers the recovery mechanism and records the abnormalities. S5. In the data archiving and algorithm iteration stage, a distributed data storage system is used to classify and store the entire chain of data. Based on the archived data, the algorithm parameter iteration tool is used to periodically adjust the scheduling threshold and weight parameters using the gradient descent method. It also includes the following modules and components: The module includes: preliminary deployment and benchmark building module, multi-source data real-time acquisition module, task and resource intelligent matching module, execution monitoring and intelligent adjustment module, and data archiving and algorithm iteration module. The initial deployment and benchmark construction module includes: resource information collection terminal, benchmark database of similar projects, and personalized scheduling threshold generation unit; The multi-source data real-time acquisition module includes: a task requirement acquisition device, a resource status acquisition terminal, and a conflict risk identification unit; The task and resource intelligent matching module includes: an entropy weight TOPSIS priority evaluation unit, a dynamic resource allocation engine, and an intelligent conflict resolution unit; The execution monitoring and intelligent adjustment module includes: a task progress monitoring terminal, a resource load dynamic adjustment engine, and an automatic exception handling unit; The data archiving and algorithm iteration module includes: a distributed data storage system and an algorithm parameter iteration tool; Specifically, the steps are explained as follows: S1 Preliminary Deployment and Benchmark Building: By collecting basic resource data, accumulating historical project experience, and calculating specific thresholds, data and rule benchmarks are established for subsequent scheduling; S2 multi-source real-time data acquisition: comprehensively captures task requirements and dynamic resource status, providing real-time data input for scheduling decisions and avoiding scheduling inaccuracies caused by data lag; S3 intelligent task and resource matching: Based on priority ranking, it achieves precise resource allocation, resolves conflicts, and ensures that resources are tilted towards high-value tasks; S4 execution monitoring and intelligent adjustment: Track task progress and resource status, dynamically optimize configuration, respond to unexpected anomalies, and ensure smooth processes; S5 Data Archiving and Algorithm Iteration: Accumulate full-link scheduling data and continuously optimize scheduling rules through algorithm iteration to adapt to dynamic scene changes; The role of modules and components: Each module and component provides an execution vehicle for the corresponding step, forming a complete closed loop of deployment, collection, matching, monitoring and iteration, ensuring that the scheduling method can be implemented and optimized.
[0017] The resource information acquisition terminal integrates hardware testing tools, software licensing scanners, and material attribute parsers to collect basic parameters of hardware, software, and materials, establishing a 3D resource library. A benchmark database for similar projects stores historical project resource consumption data, task time statistics, and scheduling optimization cases. A personalized scheduling threshold generation unit calculates a dedicated resource load threshold based on the resource library data and the benchmark database, using a weighted average algorithm. The core formula is: ,in =0.4、 =0.3、 =0.3, This is the hardware industry standard load threshold. To set a reasonable threshold for software usage, Resource usage thresholds for tasks of different urgency levels; Specifically, the resource information acquisition terminal integrates hardware detection tools, software license scanners, and material attribute parsers to collect basic parameters of hardware (CPU / GPU performance, storage capacity), software (plugin version, number of licenses), and materials (format, size, storage path). Ultimately, it establishes a three-dimensional resource library of hardware, software, and materials to ensure a clear understanding of the resource inventory during scheduling. Benchmark database for similar projects: Stores resource consumption data of historical projects (such as CPU usage of 10 minutes of 1080P rendering), task time statistics, and scheduling optimization cases (such as past conflict resolution solutions), providing a reference for threshold calculation and resource prediction; Personalized scheduling threshold generation unit: Based on 3D resource library data and benchmark database, it calculates a unique resource load threshold using a weighted average algorithm, avoiding the adaptability defects of general thresholds; core formulas and annotations: , : Specific scheduling thresholds for a certain type of task (such as CPU load thresholds for rendering tasks, authorization usage thresholds for plugin calls). =0.4: Hardware load weight (hardware performance is the core constraint of scheduling and has the highest weight). =0.3: Software weight (software licensing and plugin compatibility directly affect task execution); =0.3: Task urgency weight (urgent tasks should have some thresholds relaxed for priority execution); Hardware industry standard load thresholds (e.g., CPU safe load 80%, GPU safe load 85%). : Reasonable software usage threshold (e.g., the maximum number of licenses that a single plugin can call simultaneously); Resource usage thresholds for tasks of different urgency levels (e.g., the CPU load threshold for urgent tasks can be relaxed to 90%); Formula function: By weighted integration of hardware, software, and task factors, a unique threshold is generated that fits the team's equipment configuration and project type, improving the accuracy of resource allocation.
[0018] The task requirement acquisition device extracts task type, production standards, deadline, and material dependencies through a software API interface, generating a Task Requirement Index (TRI). The resource status acquisition terminal integrates hardware monitoring sensors, software status monitoring tools, and a network speed test module, routinely collecting hardware, software, and network status data every 10 seconds. When resource load fluctuations exceed 15%, it triggers real-time acquisition every 1 second. The conflict risk identification unit detects scenarios such as plugin call conflicts, rendering node load conflicts, and material storage path occupancy through a rule engine, and marks the conflict level according to the scope of impact. Specifically, the task requirement acquisition device automatically extracts core task information (task type: editing, color grading, rendering; production standards: resolution, frame rate, effects complexity; deadline; material dependencies) through the API interfaces of editing software such as Premiere Pro, After Effects, and DaVinci Resolve, and generates a task requirement index. This enables a quantitative comparison of task requirements; Resource Status Acquisition Terminal: Integrates hardware monitoring sensors, software status monitoring tools, and network speed testing modules to achieve dynamic acquisition of resource status. Acquisition frequency: 10 seconds / acquisition in normal scenarios, balancing data real-time performance and resource consumption; when resource load fluctuations exceed 15% (e.g., CPU utilization suddenly increases from 60% to 80%), it automatically triggers a real-time acquisition mode of 1 second / acquisition to accurately capture sudden resource changes; Acquisition content: Hardware (CPU, GPU utilization, memory usage, remaining storage space, temperature), software (plugin running status, number of licenses available, crash records), network (material transmission rate, latency, packet loss rate); Core function: Ensures that scheduling decisions are based on the latest resource status, avoiding overload or waste caused by allocating resources based on old data; Conflict Risk Identification Unit: Through the rule engine's preset conflict detection logic, it automatically identifies scenarios such as plugin call conflicts (the same plugin is called by multiple tasks at the same time), rendering node load conflicts (a single node undertakes an overloaded task), and material storage path occupancy (the same material is read and written by multiple tasks at the same time). It also marks the conflict level according to the scope of impact (high, medium, low) to provide targeted targets for subsequent conflict resolution.
[0019] The TOPSIS priority evaluation unit calculates the objective weights of indicators using information entropy, and quantifies task priority using the TOPSIS method. The information entropy formula is as follows: The weighting formula is: The priority scoring formula is: ,in , ; Specifically, the Entropy Weighted TOPSIS Priority Evaluation Unit calculates objective weights for indicators using information entropy, and combines this with the TOPSIS method to quantify the overall task priority, avoiding subjective bias from manual ranking. The information entropy formula is: Parameter annotation: Let m be the information entropy of the j-th evaluation metric, where j=1: urgency, j=2: complexity, j=3: resource dependency; and m be the total number of tasks to be scheduled. The normalized value of the j-th metric for the i-th task. The original value is used as the formula's function: the smaller the entropy value, the stronger the indicator's ability to distinguish task priorities, providing an objective basis for subsequent weight allocation; Parameter annotation: Let be the objective weight of the j-th indicator, with a total weight of 1; the formula's function is to assign weights based on the information entropy result, with indicators having greater discriminative power having larger weights, thus ensuring the scientific nature of priority assessment. The priority scoring formula is: Parameter annotation: The overall priority score (0-1 points, higher score means higher priority) for the i-th task is given, where The Euclidean distance between the task and the optimal task. The maximum value of the j-th indicator. The Euclidean distance between the worst-case and worst-case scenarios. The minimum value of the j-th indicator; the formula's function: by calculating the distance between the task and the optimal and worst states, it quantifies task priority and provides a clear basis for resource allocation.
[0020] Dynamic resource allocation engine based on Score and resource status are used to allocate resources. ≥8 points will be prioritized for allocation of hardware and dedicated software licenses with high idle rates, 5 points and below will be prioritized for allocation. <8 tasks are split into multiple nodes for parallel processing. <5 points for staggered resource allocation; the intelligent conflict resolution unit triggers authorization rotation when there is a software authorization conflict, and calls on backup nodes or reduces the resource usage of low-priority tasks when there is a hardware load conflict; Specifically, the dynamic resource allocation engine: based on Scores and real-time resource status are used to allocate resources through a load balancing algorithm. The core rules are as follows: high-priority tasks... ≥8: Prioritize allocating hardware resources and dedicated software licenses with an idle rate of ≥70%, and enabling high-speed transmission channels (e.g., using more than 50% of bandwidth to transmit materials); medium priority tasks 5≤ <8 points: Employ task splitting and parallel processing strategies, dividing time-consuming tasks such as rendering and compositing into multiple idle resource nodes (e.g., a 1-hour rendering task split into 2 nodes, completed in 30 minutes); low-priority tasks <5 points: Staggered resource allocation avoids peak periods for high-priority tasks (such as automatic execution at night) and prevents the occupation of core resources; Core function: to achieve precise matching of resources and tasks, and improve resource utilization and task execution efficiency; Intelligent Conflict Resolution Unit: Executes corresponding resolution strategies for conflict points identified by Priority 3: Software Authorization Conflict: Triggers "Authorization Rotation", allocates temporary authorization according to task priority, low-priority tasks enter the waiting queue, and execute automatically after authorization is released; Hardware Load Conflict: Calls backup resource nodes (such as idle workstations) or temporarily reduces the resource usage of low-priority tasks (such as reducing the number of CPU cores allocated); Core Function: Prevents conflicts from causing task lag or failure, ensuring a smooth production process.
[0021] The task progress monitoring terminal captures execution progress data through a software interface and predicts the remaining time based on historical data. It triggers an alert when the predicted timeout rate is ≥20%. The resource load dynamic adjustment engine migrates low-priority tasks or pauses and releases resources when the resource node load suddenly increases to ≥90% CPU or ≥85% memory. When the material transmission rate is lower than the threshold, it switches the storage path or compresses the transmission format. The automatic anomaly handling unit identifies anomalies such as rendering crashes, material corruption, and plugin errors, automatically restarts the task and allocates redundant resources, and records the cause of the anomaly to the fault database. Specifically, the task progress monitoring terminal: captures task execution data (such as editing completion rate and rendering percentage) in real time through the software interface, and predicts the remaining time through linear regression by combining historical data; when the predicted timeout rate is ≥20% (such as a task planned to be completed in 3 hours being predicted to take more than 3.6 hours), an alert is automatically triggered and pushed to the administrator to remind them to intervene; Resource Load Dynamic Adjustment Engine: Based on real-time load data and progress alerts, dynamically optimizes resource configuration: Hardware load surge: When the CPU utilization of a resource node is ≥90% or the memory usage is ≥85%, automatically migrate low-priority tasks on that node to idle nodes, or pause low-priority tasks to release resources; Transmission rate not up to standard: When the material transmission rate is lower than the threshold (e.g., 10Mbps), automatically switch to an alternative storage path (local high-speed storage to cloud storage) or compress the transmission format (lossless to H.264) to improve transmission efficiency; Core function: To cope with sudden changes in resource status and avoid task delays caused by local overload; Automatic anomaly handling unit: It has preset anomaly recognition rules and automatically identifies scenarios such as rendering crashes, material damage, and plugin errors. After an anomaly is triggered, it automatically restarts the task and allocates redundant resources (such as adding a spare rendering node), while recording the cause of the anomaly to the fault database to provide data support for subsequent algorithm iterations.
[0022] The distributed data storage system adopts a hybrid architecture of MySQL and MongoDB. MySQL stores structured data, while MongoDB stores unstructured data. Data is retained for one year and supports multi-dimensional retrieval. The algorithm parameter iteration tool adjusts the scheduling threshold and weights monthly based on the archived data of the past 30 days using gradient descent. The core formula is: ,in =0.001, loss function L=1 On-time completion rate of tasks; update parameters when the on-time completion rate increases by ≥3%. Specifically, the distributed data storage system adopts a hybrid architecture of MySQL and MongoDB, storing and classifying the entire scheduling data: MySQL stores structured data (task priority scores, resource configuration parameters, scheduling adjustment records, and early warning information); MongoDB stores unstructured data (fault logs, task execution video clips, and resource status screenshots); data is retained for one year and supports multi-dimensional retrieval by project, time, and task type, providing data support for retrospective analysis. Algorithm parameter iteration tool: Based on archived data, it dynamically adjusts the scheduling threshold and weight parameters using gradient descent to achieve continuous algorithm optimization; Core formula: Parameter annotation: For the weights of the iterative indicators (such as personalized scheduling thresholds) , Priority assessment ; The current weights before the iteration; =0.001 is the learning rate (to control the iteration step size and avoid excessive weight fluctuations); Let L be the gradient of the loss function, where L = 1. On-time task completion rate: On-time task completion rate = Number of tasks completed on time / Total number of tasks; Iteration logic: One iteration is performed monthly based on the full archived data of the past 30 days; If the on-time task completion rate increases by ≥3% after the iteration, the algorithm parameters are updated; otherwise, the original parameters are retained to ensure the effectiveness of optimization; Core function: To enable the scheduling system to iterate and upgrade with usage scenarios (equipment updates, changes in project types, and improvements in production standards), and maintain high adaptability in the long term.
[0023] The method of use and working principle of this invention are as follows: Usage: Before use, preliminary deployment and baseline construction must be completed. Basic information on hardware, software, and materials should be collected through the corresponding terminal to establish a resource library. Determine the exclusive scheduling threshold based on historical project data. After the system is started, it automatically collects task requirements and resource dynamic status in real time, detects resource usage conflicts and marks their levels. The system calculates task priorities based on preset algorithms, automatically allocates tasks according to priority and resource status, and resolves identified conflicts. During task execution, progress and resource load are tracked in real time, resource configuration is dynamically optimized, and a recovery mechanism is automatically triggered and relevant information is recorded when an anomaly is encountered. During system operation, full-link scheduling data is automatically archived, and scheduling algorithm parameters are periodically iterated and optimized based on this data. No continuous manual intervention is required in the core process; only necessary manual intervention is required when a warning is received.
[0024] Working Principle: Operating within a closed-loop logic of deployment, data collection, matching, monitoring, and iteration, the core relies on the collaborative efforts of various modules to achieve intelligent scheduling of television post-production resources. The initial deployment and benchmark building module establishes the data and rule foundation, providing a basis for subsequent scheduling decisions. The multi-source real-time data acquisition module comprehensively captures dynamic information about tasks and resources, providing real-time data support for scheduling decisions. The intelligent task and resource matching module quantifies task priorities through algorithms and combines them with resource status to achieve precise allocation and conflict resolution, ensuring resources are tilted towards high-value tasks. The execution monitoring and intelligent adjustment module tracks task and resource status in real time, dynamically responding to sudden load changes and execution anomalies to ensure smooth workflow. The data archiving and algorithm iteration module stores end-to-end data and continuously optimizes algorithm parameters to adapt the system to dynamically changing production scenarios, constantly improving scheduling accuracy and efficiency, ultimately optimizing television post-production efficiency.
[0025] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the description and drawings above. However, any modifications, alterations, and variations made by those skilled in the art without departing from the scope of the present invention using the disclosed technical content are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, and variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.
Claims
1. A smart resource scheduling method for optimizing the efficiency of television post-production, characterized in that, Includes the following steps: S1. In the early deployment and benchmark construction stage, basic parameters of hardware, software and materials are collected through resource information collection terminals and a three-dimensional resource library is established. Historical project data is accumulated based on the benchmark database of similar projects. The personalized scheduling threshold generation unit uses a weighted average algorithm to calculate the exclusive resource load threshold. S2. In the real-time acquisition stage of multi-source data, the core information of the task is extracted and the task requirement index is generated through the task requirement acquisition device. The dynamic data of hardware, software and network are collected at a set frequency using the resource status acquisition terminal. Resource usage conflicts are detected and conflict levels are marked through the conflict risk identification unit. S3, the task and resource intelligent matching stage, calculates the comprehensive priority score of the task through the entropy weight TOPSIS priority evaluation unit, and completes the resource allocation through the dynamic resource allocation engine based on the score and resource status. For the identified conflicts, the corresponding resolution strategy is executed through the conflict intelligent resolution unit. S4. During the execution monitoring and intelligent adjustment phase, the task execution progress is captured by the task progress monitoring terminal and the remaining time is predicted. When the resource load suddenly increases or the transmission rate is not up to standard, the resource load dynamic adjustment engine optimizes the resource configuration. After identifying task execution abnormalities, the automatic abnormal handling unit triggers the recovery mechanism and records the abnormalities. S5. In the data archiving and algorithm iteration stage, a distributed data storage system is used to classify and store the entire chain of data. Based on the archived data, the algorithm parameter iteration tool is used to periodically adjust the scheduling threshold and weight parameters using the gradient descent method. It also includes the following modules and components: The module includes: preliminary deployment and benchmark building module, multi-source data real-time acquisition module, task and resource intelligent matching module, execution monitoring and intelligent adjustment module, and data archiving and algorithm iteration module. The preliminary deployment and benchmark construction module includes: a resource information collection terminal, a benchmark database for similar projects, and a personalized scheduling threshold generation unit; The multi-source data real-time acquisition module includes: a task requirement acquisition device, a resource status acquisition terminal, and a conflict risk identification unit; The intelligent task and resource matching module includes: an entropy weight TOPSIS priority evaluation unit, a dynamic resource allocation engine, and an intelligent conflict resolution unit. The execution monitoring and intelligent adjustment module includes: a task progress monitoring terminal, a resource load dynamic adjustment engine, and an automatic exception handling unit; The data archiving and algorithm iteration module includes: a distributed data storage system and an algorithm parameter iteration tool.
2. The intelligent resource scheduling method for optimizing television post-production efficiency according to claim 1, characterized in that, The resource information acquisition terminal integrates hardware testing tools, software licensing scanners, and material attribute parsers to collect basic parameters of hardware, software, and materials, and establish a three-dimensional resource library; the benchmark database for similar projects stores historical project resource consumption data, task time statistics, and scheduling optimization cases; The personalized scheduling threshold generation unit calculates the dedicated resource load threshold based on resource library data and a benchmark database using a weighted average algorithm. The core formula is: ,in =0.4、 =0.3、 =0.3, This is the hardware industry standard load threshold. To set a reasonable threshold for software usage, Resource usage thresholds for tasks with different levels of urgency.
3. The intelligent resource scheduling method for optimizing television post-production efficiency according to claim 1, characterized in that, The task requirement acquisition device extracts task type, production standard, deadline, and material dependency relationship through a software API interface to generate a task requirement index (TRI). The resource status acquisition terminal integrates hardware monitoring sensors, software status monitoring tools, and network speed test modules, and collects hardware, software, and network status data every 10 seconds. When resource load fluctuations exceed 15%, real-time data collection is triggered every 1 second. The conflict risk identification unit detects scenarios such as plugin call conflicts, rendering node load conflicts, and material storage path occupancy through the rule engine, and marks the conflict level according to the scope of impact.
4. The intelligent resource scheduling method for optimizing television post-production efficiency according to claim 3, characterized in that, The entropy-weighted TOPSIS priority evaluation unit calculates the objective weights of indicators using information entropy, and quantifies task priority using the TOPSIS method. The information entropy formula is: The weighting formula is: The priority scoring formula is: ,in , .
5. The intelligent resource scheduling method for optimizing television post-production efficiency according to claim 4, characterized in that, The dynamic resource allocation engine is based on Score and resource status are used to allocate resources. ≥8 points will be prioritized for allocation of hardware and dedicated software licenses with high idle rates, 5 points and below will be prioritized for allocation. <8 tasks are split into multiple nodes for parallel processing. <5 points staggered resource allocation; the intelligent conflict resolution unit triggers authorization rotation when there is a software authorization conflict, and calls backup nodes or reduces the resource usage of low-priority tasks when there is a hardware load conflict.
6. The intelligent resource scheduling method for optimizing television post-production efficiency according to claim 1, characterized in that, The task progress monitoring terminal captures execution progress data through a software interface and predicts the remaining time based on historical data. It triggers an alert when the predicted timeout rate is ≥20%. The resource load dynamic adjustment engine migrates low-priority tasks or pauses and releases resources when the resource node load suddenly increases to ≥90% CPU or ≥85% memory. When the material transmission rate is below the threshold, it switches the storage path or compresses the transmission format. The automatic anomaly handling unit identifies anomalies such as rendering crashes, material corruption, and plugin errors, automatically restarts the task, allocates redundant resources, and records the cause of the anomaly in the fault database.
7. The intelligent resource scheduling method for optimizing television post-production efficiency according to claim 1, characterized in that, The distributed data storage system adopts a hybrid architecture of MySQL and MongoDB. MySQL stores structured data, while MongoDB stores unstructured data. Data is retained for one year and supports multi-dimensional retrieval. The algorithm parameter iteration tool adjusts the scheduling threshold and weights monthly based on archived data from the past 30 days using gradient descent. The core formula is: ,in =0.001, loss function L=1 The on-time completion rate of tasks is updated when the on-time completion rate increases by ≥3%.