Cross-platform animation resource intelligent management and distribution system

Abstract

The invention relates to a cross-platform animation resource intelligent management and distribution system. The system comprises a request module used for receiving an animation resource request; obtaining the context of a playing device based on the animation resource request, and obtaining an animation resource ID; the retrieval module is used for retrieving a corresponding animation resource version in an animation resource index based on the animation resource ID to obtain a candidate resource version list; the scoring module is used for calculating a comprehensive score for each animation resource version in the candidate resource version list based on the context; determining the animation resource version with the highest comprehensive score as a selected result; and the distribution module is used for selecting a network transmission path and a protocol based on the network condition of the playing equipment to obtain a distribution strategy of the selected result. By adopting the system, the overhead of the animation on different devices can be calculated, and the optimal transmission path and strategy for distribution can be found.

Description

Technical Field

[0001] This invention belongs to the field of intelligent management and transmission, and in particular relates to a cross-platform intelligent management and distribution system for animation resources. Background Technology

[0002] With the rapid development of mobile internet and cross-platform application technologies, animation resources have become a core element in enhancing user experience. To ensure smooth animation playback across different devices and network environments, content delivery network (CDN) technology has emerged, effectively reducing access latency. Traditionally, animation resource distribution and management typically employ static configuration. Developers pre-prepare a few quality versions of the animation based on simple device types or network bandwidth thresholds. When a user requests a resource, the system performs simple matching and transmission based on limited rules. This approach was adequate in the early stages when platform types were simple and network environments were stable. However, with the diversification of user device performance and the dynamic changes in network conditions, the current static configuration method has revealed significant problems: it cannot accurately perceive real-time network fluctuations, device performance load, and battery status, leading to frequent issues such as animation stuttering, slow loading, or excessive consumption of user data; rigid rules also struggle to adapt to complex and ever-changing business scenarios, failing to achieve a dynamic optimal balance between user experience and resource costs. Summary of the Invention

[0003] Therefore, it is necessary to provide a cross-platform intelligent management and distribution system for animation resources that can calculate the overhead of animation on different devices and find the optimal transmission path to address the aforementioned technical problems.

[0004] Firstly, this application provides a cross-platform intelligent management and distribution system for animation resources, including:

[0005] The request module is used to receive animation resource requests; and obtain the context of the playback device based on the animation resource request to get the animation resource ID; the context includes network status parameters, device capability parameters, system status parameters, and user preference parameters;

[0006] The retrieval module is used to retrieve the corresponding animation resource version from the animation resource index based on the animation resource ID, and obtain a list of candidate resource versions.

[0007] The scoring module is used to calculate a comprehensive score for each animation resource version in the candidate resource version list based on context; and to determine the animation resource version with the highest comprehensive score as the selected result.

[0008] The distribution module is used to select a network transmission path and protocol based on the network conditions of the playback device, and obtain a distribution strategy for the selected result; the distribution strategy is used to instruct the system to distribute the selected result to the playback device.

[0009] Furthermore, the system also includes a management module for:

[0010] The integrity of the original animation files is verified, and damaged original animation files are removed; the basic technical parameters of the retained original animation files are extracted to obtain the basic animation resource library;

[0011] Calculate the technical complexity level for each original animation file in the basic animation resource library;

[0012] Based on the level of technical complexity, the original animation files are converted into a format to obtain an animation resource version library; the animation resource version library includes derivative animations of different versions of the original animation files.

[0013] Detailed technical metadata is extracted for each derived animation in the animation resource version library, and the technical metadata is added to the animation resource version library to obtain the animation resource technical metadata database;

[0014] Based on the animation resource metadata database, a resource index is constructed to obtain the animation resource index.

[0015] Furthermore, the management module is also used for:

[0016] Based on the basic technical parameters in the basic animation resource library, derived parameters are calculated to obtain the basic technical parameter table; the derived parameters include the total number of pixels, data density, and frame data volume;

[0017] Based on the basic technical parameter table, the complexity score for each dimension is calculated using the following formula:

[0018]

[0019]

[0020]

[0021] in, The resolution complexity score. This represents the actual number of pixels. Based on the number of pixels, The frame rate complexity score. This is the actual frame rate. As the baseline frame rate, This is the density complexity score. For actual data density, As the baseline data density;

[0022] The complexity scores of each dimension are weighted and summed to obtain a comprehensive score; then, the comprehensive score is adjusted based on specific factors to obtain a technical complexity score.

[0023] Based on a preset complexity grading system, the technical complexity score is mapped to the corresponding level to obtain the technical complexity level of the original animation file.

[0024] Furthermore, the system also includes a feedback module for:

[0025] Calculate the playback performance metrics of the playback device for the selected result to obtain the playback monitoring dataset;

[0026] Key performance indicators (KPIs) are calculated based on the playback monitoring dataset; and the distribution strategy is evaluated based on the KPIs to obtain a distribution strategy evaluation report; the KPIs include recording performance indicators, playback quality indicators, and user experience indicators.

[0027] Based on the distribution strategy evaluation report, root cause analysis is performed to obtain a root cause analysis report.

[0028] Based on the root cause analysis report and the distribution strategy evaluation report, the weight parameters of the distribution module and the scoring module are adjusted to obtain a distribution strategy optimization suggestion.

[0029] Furthermore, the scoring module is also used for:

[0030] Based on current bandwidth, latency, and stability data, the network condition is classified, and a classification report is obtained.

[0031] Based on the classification, animation resource versions that do not meet the preset network conditions are removed from the candidate resource version list, resulting in a candidate version list suitable for the network.

[0032] Based on the context, the animation resource versions in the candidate version list are scored from multiple dimensions to obtain a multi-dimensional scored version list; the multi-dimensional scores include network adaptability score, device performance matching score, and user experience value score.

[0033] The weighted sum of each animation resource version in the multi-dimensional scoring version list is used to obtain the comprehensive score.

[0034] Furthermore, the distribution module is also used for:

[0035] Based on the newly acquired network conditions, search for CDN nodes within a preset range around the playback device to obtain candidate CDN nodes; and remove CDN nodes that are already at full load from the candidate CDN nodes to obtain available CDN nodes;

[0036] Based on network conditions, calculate the overall quality score of each available CDN node to obtain a path quality report;

[0037] Based on the path quality report, select the transmission protocol and transmission path; and based on the file technical characteristics of the selected results, formulate a file chunking strategy.

[0038] By integrating the transmission protocol, transmission path, and file chunking strategy, a distribution strategy is obtained.

[0039] Furthermore, the retrieval module is also used for:

[0040] Using the animation resource ID as the primary key, the corresponding animation resource is searched in the animation resource index; and all available versions of the animation resource are retrieved to obtain the current version set.

[0041] Based on the behavior prediction model, animation resource versions with high correlation to animation resource requests are identified, resulting in a preloading candidate set. This preloading candidate set is then added to the current version set to obtain the complete version set. The preloading candidate set is used for preloading in the distribution module.

[0042] Based on device capabilities, animation resource versions that do not meet the device platform requirements are removed from the complete version set to obtain the supported version set;

[0043] Based on device capabilities, animation resource versions with resolutions exceeding the physical screen of the playback device are removed; and animation resource versions with file sizes exceeding the available space threshold of the playback device are also removed, resulting in a candidate resource version list.

[0044] Secondly, this application also provides a cross-platform intelligent management and distribution method for animation resources, including:

[0045] Receive animation resource requests; and obtain the playback device context based on the animation resource requests to get the animation resource ID; the context includes network status parameters, device capability parameters, system status parameters, and user preference parameters;

[0046] Based on the animation resource ID, the corresponding animation resource version is retrieved from the animation resource index to obtain a list of candidate resource versions;

[0047] Based on the context, calculate a comprehensive score for each animation resource version in the candidate resource version list; and determine the animation resource version with the highest comprehensive score as the selected result.

[0048] Based on the network conditions of the playback device, a network transmission path and protocol are selected to obtain a distribution strategy for the selected result; the distribution strategy is used to instruct the system to distribute the selected result to the playback device.

[0049] Thirdly, this application also provides a computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform any of the steps performed by the system provided in the first aspect of this application.

[0050] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs any of the steps performed by the system provided in the first aspect of this application.

[0051] The aforementioned cross-platform intelligent management and distribution system for animation resources includes a request module for receiving animation resource requests and obtaining the context of the playback device based on the request to obtain the animation resource ID. The context includes network status parameters, device capability parameters, system status parameters, and user preference parameters. A retrieval module is used to retrieve the corresponding animation resource version from the animation resource index based on the animation resource ID, obtaining a candidate resource version list. A scoring module is used to calculate a comprehensive score for each animation resource version in the candidate resource version list based on the context, and determine the animation resource version with the highest comprehensive score as the selected result. A distribution module is used to select a network transmission path and protocol based on the network status of the playback device, obtaining a distribution strategy for the selected result. The distribution strategy instructs the system to distribute the selected result to the playback device. This system introduces artificial intelligence into traditional content distribution networks, balancing objectives such as network adaptability score, device performance matching score, user experience value score, and cost-effectiveness score to find the animation resource with the highest score. Different dimensions have different weights in different scenarios, reflecting the priorities of different scenarios, achieving an optimal balance among multiple objectives, and realizing an adaptive overall balance management and distribution system. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram of the structure of a cross-platform intelligent management and distribution system for animation resources provided in an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of a cross-platform intelligent management and distribution method for animation resources provided in an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0056] In one embodiment, such as Figure 1As shown, a cross-platform intelligent management and distribution system for animation resources 100 is provided. This embodiment illustrates the system's application to a terminal, but it is understood that the method can also be applied to a server, and can be implemented through interaction between the terminal and the server. In this embodiment, the system includes the following structure:

[0057] The request module 101 is used to receive animation resource requests; and obtain the context of the playback device based on the animation resource request to obtain the animation resource ID; the context includes network status parameters, device capability parameters, system status parameters and user preference parameters.

[0058] An animation resource request is an instruction initiated by a user or application to play a specific animation. The core information is the animation resource ID, used to uniquely identify the requested animation content. The playback device context refers to the comprehensive state information of the playback device's internal and external environment at the time the request occurs. It is a collection of information, specifically including: network status (the device's current network connectivity data, determining whether data can be transmitted quickly and stably); device capabilities (the device's hardware and software performance parameters, determining whether the device can smoothly decode and render animations of specific specifications); system status (the device's current real-time operating status, determining whether the device has sufficient resources to handle high-load animation playback tasks); and user preferences (personalized choices actively set by the user or learned by the system, reflecting the user's subjective desires regarding image quality, data consumption, etc.). The terminal listens for request interfaces from clients. When a playback request arrives, it captures and parses the key information, calling the device's system API (Application Programming Interface) or using a dedicated detection service to obtain the above four types of context information in real time. For example, network conditions can be assessed through a speed test service, device capabilities can be queried through a device information database, power and load information can be read through a system API, and preference settings can be read from a user profile.

[0059] The retrieval module 102 is used to retrieve the corresponding animation resource version from the animation resource index based on the animation resource ID, and obtain a list of candidate resource versions.

[0060] Specifically, the animation resource index is a pre-built, library-like, fast-search database that records the storage location and key characteristics of all available versions under each animation resource ID (unique code) for rapid retrieval. The candidate resource version list is a list containing all available animation file versions of different specifications found in the animation resource index based on the currently requested animation resource ID. The terminal receives the animation resource ID from upstream, uses the ID as the query condition to query the animation resource index database, quickly finds all associated, pre-transcoded versions of different animation files, and collects the metadata information of these files.

[0061] The scoring module 103 is used to calculate a comprehensive score for each animation resource version in the candidate resource version list based on the context; and to determine the animation resource version with the highest comprehensive score as the selected result.

[0062] Specifically, the overall score is a quantifiable numerical value used to comprehensively evaluate the suitability of a particular animation resource version in the current context. A higher score indicates that the version is considered the better choice after weighing multiple competitive objectives such as loading speed, playback smoothness, power consumption, and user experience. The selected result refers to the unique animation resource version with the highest overall score calculated by the scoring algorithm, which will be ultimately determined by the system and prepared for distribution to the user. For example, the terminal iterates through each version in the candidate resource version list and performs multi-dimensional evaluation based on rich environmental information in the context. Based on the current bandwidth, it calculates the time required to load the version and determines whether it will cause stuttering; based on the device's decoding capabilities and the current system load, it determines whether the device can smoothly decode the version and whether it will cause overheating or excessive power consumption; combining user preferences and the version's inherent image quality, it evaluates the viewing experience it can provide, assigning scores to each version's performance in the above dimensions, and merging these scores into a final overall score using a weighted algorithm. After calculating the overall scores for all versions, a simple sorting and selection operation is performed, marking the version with the highest score as the final selected result.

[0063] The distribution module 104 is used to select a network transmission path and protocol based on the network conditions of the playback device, and obtain a distribution strategy for the selected result; the distribution strategy is used to instruct the system to distribute the selected result to the playback device.

[0064] Among these, network transmission path and protocol are two key network choices. The transmission path refers to the specific network route data takes from the server to the user device, typically involving selecting which CDN (Content Delivery Network) node to use as the data source to ensure the shortest path and lowest latency. The transmission protocol refers to the rules and format followed by data transmission; different protocols have advantages and disadvantages in connection efficiency, transmission speed, and resistance to network jitter. The distribution strategy is a specific action plan that integrates the selected network transmission path and transmission protocol, including other optimization instructions. This strategy directly guides the underlying transmission system on how to efficiently deliver the selected results to the playback device. The terminal re-examines the network conditions in the context and, combined with the file characteristics of the selected results, makes a second intelligent decision. Based on the user's IP address (Internet Protocol Address), it selects the geographically nearest CDN node with the lowest current load as the transmission path. If the network is unstable, it selects a transmission protocol with stronger jitter resistance. These choices are integrated into an executable plan, the distribution strategy, ensuring that the optimal animation content is transmitted to the user device in the most efficient and stable way.

[0065] This embodiment provides a cross-platform intelligent management and distribution system for animation resources. It includes a request module for receiving animation resource requests and obtaining the context of the playback device based on the request to obtain the animation resource ID. The context includes network status parameters, device capability parameters, system status parameters, and user preference parameters. A retrieval module retrieves the corresponding animation resource version from the animation resource index based on the animation resource ID, obtaining a candidate resource version list. A scoring module calculates a comprehensive score for each animation resource version in the candidate resource version list based on the context and determines the animation resource version with the highest comprehensive score as the selected result. A distribution module selects a network transmission path and protocol based on the network status of the playback device to obtain a distribution strategy for the selected result. The distribution strategy instructs the system to distribute the selected result to the playback device. Through this structure, artificial intelligence is introduced into the traditional content distribution network, balancing multiple scores to find the animation resource with the highest score. Different dimensions have different weights in different scenarios, reflecting the priorities in different scenarios, achieving an optimal balance among multiple objectives, and realizing an adaptive overall balance management and distribution system.

[0066] In one embodiment, the system further includes a management module for:

[0067] Step 201: Verify the integrity of the original animation files, remove any damaged original animation files, and extract the basic technical parameters of the retained original animation files to obtain the basic animation resource library.

[0068] Specifically, raw animation files are initial, unprocessed animation files provided by content creators. These are typically high-resolution, high-bitrate master files and serve as the source for all derivative versions. Integrity verification confirms that the files have not been corrupted or lost during transmission or storage. Basic technical parameters are a set of core technical data describing the fundamental characteristics of an animation file, including resolution, frame rate, duration, encoding format, bitrate, and file size. These are key indicators for understanding the basic features of an animation file. The basic animation resource library is a selected collection of high-quality raw animation files, each of which has passed integrity verification, and its basic technical parameters have been extracted and recorded. It serves as the raw material repository for all subsequent processing operations. The terminal iterates through all raw animation files to be processed, performs a verification operation on each file, and once a file fails the verification, it is determined to be a corrupted file and is isolated or deleted to ensure that subsequent processing is not based on erroneous data. For the intact files that are retained, the system calls media information analysis tools to read the file header information or parse the file content, thereby extracting the aforementioned basic technical parameters, and associating the intact files with their corresponding basic technical parameters to form a structured and queryable resource library.

[0069] Step 202: Calculate the technical complexity level for each original animation file in the basic animation resource library.

[0070] The technical complexity level is a label used to categorize the computational, storage, and bandwidth resources required by the original animation file. It is a comprehensive evaluation result that aggregates the multidimensional technical parameters of the file into a simple, easy-to-understand, and easy-to-use indicator. Based on the basic technical parameters extracted from each file in the basic animation resource library, the terminal calculates a quantified complexity score using a preset algorithm. According to a preset score threshold range, this score is mapped to a specific level, transforming complex technical parameters into an intuitive level label, enabling the system to make intelligent decisions based on this level.

[0071] Step 203: Based on the level of technical complexity, the original animation file is converted to a new format to obtain an animation resource version library; the animation resource version library includes derivative animations of different versions of the original animation file.

[0072] Specifically, format conversion, also known as transcoding or transformation, refers to the process of re-encoding the original animation file to generate a new file with different parameters such as resolution, bitrate, and encoding format. Derivative animations are new animation files generated from the original animation file through format conversion operations; one original file can generate multiple versions with different specifications. The animation resource repository is a collection containing all derivative animation files corresponding to all original files. The terminal formulates a conversion strategy for each original file based on the calculated level of technical complexity. For example, for a high-complexity original file, it decides to generate multiple derivative versions with medium and low complexity; while for a low-complexity original file, only one or two even lower-complexity versions need to be generated. The transcoding engine is then invoked to perform the actual conversion operation according to the predetermined parameter settings.

[0073] Step 204: Extract detailed technical metadata for each derived animation in the animation resource version library, and add the technical metadata to the animation resource version library to obtain the animation resource technical metadata database.

[0074] Detailed technical metadata, which is more comprehensive than basic technical parameters, describes the technical characteristics of derived animation files. Besides basic parameters, it includes in-depth information such as color space, encoding level, suggested decoder, and file size. The animation resource technical metadata database is an enhanced database built upon the animation resource version library, with each derived animation file in the library associated with its corresponding detailed technical metadata. The terminal then uses media analysis tools to perform a deeper analysis of each derived animation file in the animation resource version library, extracting the aforementioned detailed technical metadata. This metadata is then used as attributes and bound to the corresponding file records, thereby enriching the information in the database.

[0075] Step 205: Based on the animation resource metadata database, construct a resource index to obtain the animation resource index.

[0076] The resource index is a database structure whose core function is to provide a fast mapping from animation resource IDs to all related animation file versions and their detailed technical metadata. Built upon the animation resource technical metadata database, the animation resource index is an optimized index structure for query speed and serves as the direct data source for the retrieval module to operate quickly. The terminal reads records from the animation resource technical metadata database, uses the animation resource ID as the primary key, organizes all versions under that ID, and establishes fast search relationships using database indexing technology or a search engine.

[0077] The version implementation constructs an animation resource index. When the retrieval module receives a request with an animation resource ID, it can directly query the animation resource index to quickly obtain a list of all candidate versions. This provides performance assurance for subsequent real-time decision-making and provides detailed data support for intelligent decision-making, making the resource retrieval and matching process more accurate and reliable.

[0078] In one embodiment, the management module is further configured to:

[0079] Step 301: Calculate derived parameters based on the basic technical parameters in the basic animation resource library to obtain the basic technical parameter table; the derived parameters include the total number of pixels, data density, and frame data volume.

[0080] Specifically, basic technical parameters are fundamental attributes directly extracted from the original animation files, typically including resolution, frame rate, bitrate, and duration; these are the raw observations. Derived parameters are secondary indicators calculated from the basic technical parameters, providing a more intuitive measure of computational and bandwidth pressure. These can include: total number of pixels, referring to the number of pixels contained in a single frame, directly related to the computational load required to decode and render a frame; data density, a metric for encoding efficiency, which can be approximated as the quotient of bitrate and total number of pixels, representing the average amount of data allocated to each pixel. Higher data density means richer image detail, requiring the encoder to process more information and demanding higher computational power; and frame data volume, referring to the average amount of data occupied per frame of video, directly affecting the memory bandwidth and cache required by the decoder to process each frame. The basic technical parameter table is an enhanced data table formed by adding the above-mentioned derived parameters to each original animation file, based on the basic animation resource library, providing richer dimensions for subsequent complexity assessment. The terminal reads the basic technical parameters of each animation file, calculates the total number of pixels, data density and frame data volume of each file in batches, and integrates the newly calculated derived parameters with the original basic technical parameters to form a more comprehensive data table that is more conducive to quantitative analysis.

[0081] Step 302: Based on the basic technical parameter table, calculate the complexity score for each dimension using the following formula:

[0082]

[0083]

[0084]

[0085] in, The resolution complexity score. This represents the actual number of pixels. Based on the number of pixels, The frame rate complexity score. This is the actual frame rate. As the baseline frame rate, This is the density complexity score. For actual data density, This is the baseline data density.

[0086] Specifically, the baseline value is a preset reference value representing a standard level, set based on industry standards, mainstream device capabilities, or business experience. For example, the baseline pixel count is set to the pixel count of a mainstream 1080p display, and the baseline frame rate is set to 30 frames per second. The actual value is the actual value of the derived parameters for each original animation file in the corresponding dimension. The dimensional complexity score is a standardized score between 0 and 1.0, representing the complexity of the file relative to the baseline value in a certain dimension. For each file, the terminal calculates the complexity scores for multiple dimensions based on the derived parameters using a formula, performing coarse-grained classification to drive a coarse-grained decision.

[0087] Step 303: Sum the complexity scores of each dimension by weight to obtain a comprehensive score; and adjust the comprehensive score based on special factors to obtain a technical complexity score.

[0088] Weighted summation is a method that comprehensively considers multiple factors. A weight is assigned to the score of each dimension, and a weighted sum is calculated. The weights reflect the relative importance of different dimensions to the overall complexity. Special factors are technical characteristics that cannot be fully covered by the three dimensions mentioned above but significantly affect processing complexity. For example, high dynamic range, specific advanced encoding formats, and the presence of significant dynamic blur or complex textures require additional computational resources. The technical complexity score is a quantitative score representing the overall technical complexity of the file after weight integration and adjustment for special factors. The terminal performs a weighted summation of the complexity scores for each dimension according to a preset weight configuration to obtain a preliminary comprehensive score. It then checks whether the file contains certain special factors; if so, the comprehensive score is adjusted according to preset rules.

[0089] Step 304: Based on the preset complexity classification, map the technical complexity score to the corresponding level to obtain the technical complexity level of the corresponding original animation file.

[0090] The preset complexity levels are a predefined set of mapping rules between score ranges and level labels. The technical complexity level is a simple classification label used to intuitively represent the processing difficulty of the original animation file. The terminal compares the technical complexity score with the preset level ranges to determine which range the score falls into and maps it to the corresponding level label.

[0091] This embodiment greatly simplifies decision-making logic by converting quantified scores into qualitative indicators that can be directly used in business logic. The introduction of special factor adjustments makes complexity assessment more accurate and flexible, adaptable to evolving video technologies. The capping function greatly simplifies the model, avoiding the need to design complex mathematical functions for ultra-high complexity regions and preventing future new technologies from impacting the scoring system. Resources exceeding the benchmark value are treated equally as the highest complexity category, ensuring system stability and scalability.

[0092] In one embodiment, the system further includes a feedback module for:

[0093] Step 401: Calculate the playback performance index of the playback device for the selected result to obtain the playback monitoring dataset.

[0094] Among them, playback performance metrics are quantifiable technical data generated by the playback device during the actual rendering and playback of the selected result; they are raw, fine-grained indicators. The playback monitoring dataset is a collection of data that records a series of playback performance metrics continuously collected throughout the entire session, from the start to the end of playback, providing factual evidence for subsequent analysis. Monitoring code is embedded in the player client on the terminal to track and calculate various performance data in real time. For example, it calculates the initial buffer time, average download speed, etc.; monitors the number of stutters, average stutter duration, actual playback frame rate, etc.; and records decoding errors, network interruptions, and other events, transforming the subjective experience of smooth playback into objective, analyzable data.

[0095] Step 402: Calculate key performance indicators based on the playback monitoring dataset; and evaluate the distribution strategy based on the key performance indicators to obtain a distribution strategy evaluation report; the key performance indicators include recording performance indicators, playback quality indicators, and user experience indicators.

[0096] Specifically, key performance indicators (KPIs) are core metrics extracted from playback monitoring datasets to measure the achievement of business goals. Loading performance metrics directly impact the user's perceived startup speed; playback quality metrics measure playback stability and reliability; and user experience metrics comprehensively reflect user satisfaction. The distribution strategy evaluation report is an analysis based on KPIs, used to determine the success of previously implemented distribution strategies. The report indicates in which aspects the strategy performed well and in which aspects it failed to meet expectations. The terminal aggregates, statistically analyzes, and calculates the reported playback monitoring datasets to generate KPIs, evaluates the distribution strategy, and compares the KPIs with preset target thresholds or historical baselines to obtain the distribution strategy evaluation report. This elevates fragmented performance data to a measurable and assessable strategic level.

[0097] Step 403: Based on the distribution strategy evaluation report, perform root cause analysis to obtain a root cause analysis report.

[0098] Specifically, the root cause analysis report is a diagnostic document that attempts to pinpoint the deep-seated, systemic reasons causing playback metrics to fail to meet standards. It involves cross-analyzing the playback monitoring dataset, the context at the time, and the executed distribution strategy. For example, if a high stuttering rate is found, and the playback monitoring dataset shows significant network bandwidth fluctuations, yet the distribution strategy selects a high bitrate version, the root cause might be that the weight of the network adaptability score in the scoring module is set too low, failing to effectively avoid unstable networks. Similarly, if a low actual playback resolution is found, but the context indicates strong user device performance and a good network, the root cause might be that the weight of the device performance matching score or user experience value score in the scoring module is set too low, leading to an overly conservative system.

[0099] Step 404: Based on the root cause analysis report and the distribution strategy evaluation report, adjust the weight parameters of the distribution module and the scoring module to obtain a distribution strategy optimization suggestion.

[0100] In this context, weight parameters refer to adjustable coefficients used in the decision-making algorithm to balance the importance of different dimensions, determining the system's decision preferences. The distribution strategy optimization proposal is a specific adjustment plan that clearly indicates which weight parameters need to be modified, as well as the direction and magnitude of the modifications. The terminal formulates its parameter adjustment strategy based on the conclusions of the root cause analysis report.

[0101] This embodiment optimizes the closed loop through feedback to ensure that the system does not statically execute fixed rules, but can learn from the actual operating results, dynamically adjust its decision-making logic, and ultimately achieve increasingly intelligent resource distribution and management.

[0102] In one embodiment, the scoring module 103 is further configured to:

[0103] Step 501: Based on the current bandwidth, latency, and stability data, classify the network condition to obtain a classification report.

[0104] Bandwidth refers to the data transmission rate of a network channel, measured in megabits per second, determining the maximum potential download speed. Latency refers to the round-trip time required for data to travel from source to destination, measured in milliseconds, affecting connection response speed. Stability data refers to network connection fluctuations, measured by jitter or packet loss rate, reflecting the reliability of network quality. The rating report is a comprehensive assessment of the current network condition; this rating is a discrete label that summarizes complex, multi-dimensional network data into a simple classification conclusion. The terminal acquires real-time bandwidth, latency, and stability data and maps this data to a specific rating according to preset rules or models. This multi-condition decision-making process simplifies and reduces the dimensionality of the data, transforming complex raw indicators into clear signals, greatly facilitating subsequent conditional judgments.

[0105] Step 502: Based on the classification status, remove animation resource versions from the candidate resource version list that do not meet the preset network conditions, and obtain a candidate version list suitable for the network.

[0106] Specifically, the preset network conditions are a predefined set of rules that stipulate the hard technical thresholds that animation resource versions must meet under different network levels. The candidate version list suitable for the network is a subset of the input candidate resource version list. All versions that do not meet the preset network conditions corresponding to the current classification report have been eliminated, and the versions in the list have the potential to be successfully distributed at the network level. The terminal finds the corresponding preset network conditions based on the classification report, iterates through each version in the candidate resource version list, checks its technical metadata, and if the bitrate of a version does not meet the network conditions, it is directly removed and no longer participates in subsequent calculations.

[0107] Step 503: Based on the context, perform multi-dimensional scoring on the animation resource versions in the candidate version list to obtain a multi-dimensional scoring version list; the multi-dimensional scoring includes network adaptability score, device performance matching score, and user experience value score.

[0108] Specifically, multi-dimensional scoring quantifies the value or applicability of an animation resource version in different aspects. This includes: network adaptability scoring, which quantifies how easily the version can be loaded quickly and stably in the current network environment (higher scores mean shorter loading times and lower risk of stuttering); device performance matching scoring, which quantifies the compatibility between the version and the playback device's hardware decoding and rendering capabilities, as well as the current system load (higher scores mean the device can easily and smoothly decode and play the version with more controllable power consumption); and user experience value scoring, which quantifies the subjective visual and experiential benefits that the version's technical specifications can bring to users (higher scores mean a theoretically better viewing experience). The multi-dimensional scoring version list is an enhanced list formed by adding the above three-dimensional scores to each version in the candidate list of network-suitable versions, based on the previous list. Each version now has a complete capability profile. The terminal combines rich contextual information to calculate three scores for each version in the list: a network adaptability score, which estimates loading time based on version file size and current available bandwidth, with shorter loading times resulting in higher scores; a device performance matching score, which judges decoding smoothness based on the device model's capability database and version's technical complexity, and makes fine adjustments considering current device battery level, temperature load; and a user experience value score, which is calculated based on the version's inherent quality and user preferences.

[0109] Step 504: Perform a weighted summation on each animation resource version in the multi-dimensional scoring version list to obtain a comprehensive score.

[0110] The overall score is a single numerical value representing the overall suitability of an animation resource version in the current environment, and is the sole basis for final version ranking and selection. The terminal reads the preset weight configuration and performs a weighted summation calculation on each version in the multi-dimensional scoring version list. The weight configuration reflects the strategy bias; for example, in the default mode, the three weights are balanced; in the data-saving mode, the network adaptability weight is significantly increased; and on high-performance devices, the user experience value weight is increased. Optionally, an artificial intelligence model can be built into the management module to analyze the animation content and assign emotional tags to the animation resources. In the scoring module, a contextual matching score can be added. If the system detects that a user is watching late at night or when the battery is low, and the current animation is identified as lighthearted and therapeutic, the system strategy could prioritize versions that save power and load faster, rather than solely pursuing the highest image quality, while ensuring a basic user experience, thus creating a more comfortable viewing environment.

[0111] This embodiment, by realizing network conditions, significantly reduces the number of versions that need to participate in subsequent complex calculations, lowers system overhead, speeds up response time, and avoids the inevitable failure caused by attempting to distribute extremely large files in a weak network environment, ensuring a minimum level of user experience. By adjusting the weight parameters, it can easily adapt to different business strategies and user preferences, achieving flexibility and optimizability in decision-making.

[0112] In one embodiment, the distribution module 104 is further configured to:

[0113] Step 601: Based on the newly acquired network conditions, search for CDN nodes within a preset range around the playback device to obtain candidate CDN nodes; and remove CDN nodes that are already at full load from the candidate CDN nodes to obtain available CDN nodes.

[0114] The newly acquired network status is the latest network data detected in real time at the time of distribution. This data is more accurate and real-time than the information obtained from the request module, making it crucial for selecting the best node. The preset range is a pre-defined geographical or network topology distance limit. CDN nodes are edge servers in a content delivery network, distributed globally to cache resources, allowing users to obtain data from the geographically nearest node, thereby reducing latency. Candidate CDN nodes are a preliminary search list of nodes located within the user's preset range in terms of physical location or network topology. Fully loaded CDN nodes are those whose current connection count, utilization, or outbound bandwidth has reached its processing capacity limit and can no longer effectively serve new requests. Available CDN nodes are the list obtained by removing fully loaded CDN nodes from the candidate CDN nodes; these nodes are both close to the user and currently capable of providing services. Based on the user device's IP address or geographical location information, the terminal searches for all nodes within a preset range in the CDN node map database, sends lightweight health check requests to these candidate nodes, or queries the real-time load status interface provided by the CDN network management center. Once it is found that the load index of a node exceeds the threshold, it is removed from the candidate list.

[0115] Step 602: Based on the network conditions, calculate the overall quality score of each available CDN node to obtain a path quality report.

[0116] Specifically, the overall quality score is a quantifiable numerical value used to comprehensively evaluate the quality of the entire network path from the user's device to a specific available CDN node. A higher score indicates a better expected data transmission experience through that node. The path quality report is a list or report containing each available CDN node and its corresponding overall quality score, directly prioritizing each node. Based on real-time network conditions, the terminal performs a rapid path evaluation for each available CDN node by sending probe packets. Evaluation dimensions include: latency (round-trip time for data packets to reach and return to the node); bandwidth (potential transmission speed to the node); stability (jitter and packet loss rate of the link to the node); and node current load (even if the node is not at full capacity, nodes with lower load should receive a higher score). These measurements are combined into a single overall quality score using a weighted algorithm.

[0117] Step 603: Based on the path quality report, select the transmission protocol and transmission path; and based on the file technical characteristics of the selected results, formulate a file segmentation strategy.

[0118] Specifically, the transmission path refers to the final selected available CDN nodes used for actual data transmission, derived from the node with the highest overall quality score in the path quality report. The transmission protocol is the set of rules followed when data is transmitted over a network; different protocols differ in efficiency, reliability, and characteristics. For example, TCP (Transmission Control Protocol) is reliable but slow to establish connections and susceptible to head-of-line congestion; HTTP / 2 (Hypertext Transfer Protocol / 2) improves TCP connection efficiency through multiplexing; QUIC (Quick UDP Internet Connections), based on UDP (User Datagram Protocol), establishes connections quickly and has strong resistance to packet loss and network switching. File technical characteristics refer to the technical attributes of the selected result itself, such as total file size, encoding format, and encapsulation format. The file chunking strategy determines how to divide the entire animation file into multiple parts (chunks) for transmission. The terminal selects the protocol based on the path quality report, especially network stability data and business requirements. For example, in unstable mobile networks with high latency, the QUIC protocol might be preferred; in stable, high-speed Wi-Fi, HTTP / 2 might be chosen, with the node with the highest score in the path quality report being selected as the final path. The terminal analyzes the technical characteristics of the selected file; for large files, a parallel chunking download strategy is developed, dividing the file into several smaller chunks and downloading them simultaneously from CDN nodes to fully utilize bandwidth and accelerate the overall download. The chunk size is optimized based on network conditions.

[0119] Step 604: Integrate the transmission protocol, transmission path, and file chunking strategy to obtain the distribution strategy.

[0120] The distribution strategy is a specific, executable set of instructions that clearly specifies which CDN segment will be used, which network protocol will be adopted, and how to obtain the file for this distribution task. The transmission protocol, transmission path, and file chunking strategy determined by the terminal are integrated together to form a unified policy document or set of instructions that can be understood and executed by downstream transmission systems.

[0121] This embodiment integrates scattered, technical decision points into a complete and actionable plan, ensuring a high degree of optimization and adaptability in the resource transmission process. Through intelligent segmentation, a large task is broken down into multiple parallel smaller tasks, greatly optimizing transmission efficiency and reducing user waiting time. Through load filtering, requests are avoided from being sent to overloaded nodes, preventing delays or failures caused by server-side bottlenecks from the source.

[0122] In one embodiment, the retrieval module 102 is further configured to:

[0123] Step 701: Using the animation resource ID as the primary key, search for the corresponding animation resource in the animation resource index; and retrieve all available versions of the animation resource to obtain the current version set.

[0124] The animation resource ID is a string or number that uniquely identifies a specific animation content. The animation resource index is a pre-built, fast-query database that establishes a mapping from animation resource IDs to all related animation file versions. The current version set is a list containing all available versions of the currently requested animation that can be directly found in the animation resource index using the animation resource ID; it serves as the starting point for subsequent operations. The terminal receives the animation resource ID from upstream and uses this ID as the query condition to quickly query the animation resource index database. The index system returns metadata information for all pre-transcoded versions of animation files associated with that ID, and this information is collected to form the current version set.

[0125] Step 702: Based on the behavior prediction model, identify animation resource versions that are highly correlated with animation resource requests and obtain a preloading candidate set; add the preloading candidate set to the current version set to obtain a complete version set; the preloading candidate set is used for preloading in the distribution module.

[0126] Specifically, the behavior prediction model is an algorithmic model based on machine learning or data analysis used to predict the next one or more requests a user might make after the current request. The preload candidate set is a collection of versions of other animation resources highly relevant to the current animation resource request, output by the behavior prediction model. For example, if a user requests the first episode, and the model predicts the user is likely to watch the second episode, then the versions of the second episode constitute the preload candidate set. The complete version set is a larger set formed by merging the current version set and the preload candidate set. The terminal inputs the context of the current request into the behavior prediction model, which outputs a list of one or more highly relevant animation resource IDs. Based on these predicted resource IDs, the model quickly retrieves their available versions from the animation resource index, forming the preload candidate set. The complete version set is then obtained by simply merging the preload candidate set and the current version set.

[0127] Step 703: Based on device capabilities, remove animation resource versions from the complete version set that do not meet the device platform requirements to obtain the supported version set.

[0128] Specifically, the device platform refers to the operating system and software environment of the playback device. Different platforms have different support ranges for video file container formats and decoders. The supported version set is a subset obtained by removing all versions incompatible with the current playback device's platform from the complete version set. The terminal checks the technical metadata of each version in the complete version set, especially its container format and encoding format. Based on a preset platform format support lookup table, it removes versions that are explicitly not supported by the current device platform, avoiding the transmission of unplayable file versions to subsequent modules and preventing playback errors caused by format incompatibility.

[0129] Step 704: Based on device capabilities, remove animation resource versions with resolutions exceeding the physical screen of the playback device; and remove animation resource versions with file sizes exceeding the available space threshold of the playback device, thus obtaining a candidate resource version list.

[0130] The physical screen refers to the actual number of physical pixels on the playback device's display. Playing versions exceeding this resolution is a waste of resources and does not improve image quality. The available space threshold is a safe storage space limit set to ensure stable device operation and prevent the device's storage space from being exhausted due to downloading large files. The candidate resource version list is the most concise version list obtained by further removing all versions from the supported version set whose resolution exceeds the device's physical screen and whose file size exceeds the device's available space threshold. The terminal compares the resolution of each version with the device's physical screen resolution. Any version whose resolution is greater than the screen resolution in either width or height is removed. The device's current available storage space is checked. If the file size of a version exceeds the safe threshold, it is removed to ensure that downloading does not affect the normal use of the device.

[0131] This embodiment ensures that every version that ultimately enters the scoring stage is a feasible option that can be played normally on the device without wasting resources or affecting device performance, greatly improving the efficiency and accuracy of subsequent decisions and eliminating the need to waste time on impractical versions.

[0132] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0133] Based on the same inventive concept, this application also provides a method for implementing the aforementioned cross-platform intelligent management and distribution system for animation resources. The solution provided by this method is similar to the implementation scheme described in the above system. Therefore, the specific limitations in one or more embodiments of the cross-platform intelligent management and distribution method provided below can be found in the limitations of the cross-platform intelligent management and distribution system described above, and will not be repeated here.

[0134] In one exemplary embodiment, such as Figure 2 As shown, a cross-platform intelligent management and distribution method for animation resources is provided, including:

[0135] Step 801: Receive an animation resource request; and obtain the context of the playback device based on the animation resource request to obtain the animation resource ID; the context includes network status parameters, device capability parameters, system status parameters, and user preference parameters;

[0136] Step 802: Based on the animation resource ID, retrieve the corresponding animation resource version from the animation resource index to obtain a list of candidate resource versions;

[0137] Step 803: Based on the context, calculate a comprehensive score for each animation resource version in the candidate resource version list; and determine the animation resource version with the highest comprehensive score as the selected result;

[0138] Step 804: Based on the network conditions of the playback device, select the network transmission path and protocol to obtain the distribution strategy of the selected result; the distribution strategy is used to instruct the system to distribute the selected result to the playback device.

[0139] Furthermore, before receiving animation resource requests, the process also includes:

[0140] The integrity of the original animation files is verified, and damaged original animation files are removed; the basic technical parameters of the retained original animation files are extracted to obtain the basic animation resource library;

[0141] Calculate the technical complexity level for each original animation file in the basic animation resource library;

[0142] Based on the level of technical complexity, the original animation files are converted into a format to obtain an animation resource version library; the animation resource version library includes derivative animations of different versions of the original animation files.

[0143] Detailed technical metadata is extracted for each derived animation in the animation resource version library, and the technical metadata is added to the animation resource version library to obtain the animation resource technical metadata database;

[0144] Based on the animation resource metadata database, a resource index is constructed to obtain the animation resource index.

[0145] Furthermore, calculating the technical complexity level for each original animation file in the basic animation resource library also includes:

[0146] Based on the basic technical parameters in the basic animation resource library, derived parameters are calculated to obtain the basic technical parameter table; the derived parameters include the total number of pixels, data density, and frame data volume;

[0147] Based on the basic technical parameter table, the complexity score for each dimension is calculated using the following formula:

[0148]

[0149]

[0150]

[0151] in, The resolution complexity score. This represents the actual number of pixels. Based on the number of pixels, The frame rate complexity score. This is the actual frame rate. As the baseline frame rate, This is the density complexity score. For actual data density, As the baseline data density;

[0152] The complexity scores of each dimension are weighted and summed to obtain a comprehensive score; then, the comprehensive score is adjusted based on specific factors to obtain a technical complexity score.

[0153] Based on a preset complexity grading system, the technical complexity score is mapped to the corresponding level to obtain the technical complexity level of the original animation file.

[0154] Furthermore, after selecting the network transmission path and protocol based on the network conditions of the playback device and obtaining the distribution strategy, the following steps are also included:

[0155] Calculate the playback performance metrics of the playback device for the selected result to obtain the playback monitoring dataset;

[0156] Key performance indicators (KPIs) are calculated based on the playback monitoring dataset; and the distribution strategy is evaluated based on the KPIs to obtain a distribution strategy evaluation report; the KPIs include recording performance indicators, playback quality indicators, and user experience indicators.

[0157] Based on the distribution strategy evaluation report, root cause analysis is performed to obtain a root cause analysis report.

[0158] Based on the root cause analysis report and the distribution strategy evaluation report, the weight parameters of the distribution module and the scoring module are adjusted to obtain a distribution strategy optimization suggestion.

[0159] Furthermore, based on the context, a comprehensive score is calculated for each animation resource version in the candidate resource version list, which also includes:

[0160] Based on current bandwidth, latency, and stability data, the network condition is classified, and a classification report is obtained.

[0161] Based on the classification status, animation resource versions that do not meet the preset network conditions are removed, resulting in a list of candidate versions suitable for the network.

[0162] Based on the context, the animation resource versions in the candidate version list are scored from multiple dimensions to obtain a multi-dimensional scored version list; the multi-dimensional scores include network adaptability score, device performance matching score, and user experience value score.

[0163] The weighted sum of each animation resource version in the multi-dimensional scoring version list is used to obtain the comprehensive score.

[0164] Furthermore, based on the network conditions of the playback device, the network transmission path and protocol are selected to obtain the distribution strategy, which also includes:

[0165] Based on the newly acquired network conditions, search for CDN nodes within a preset range around the playback device to obtain candidate CDN nodes; and remove CDN nodes that are already at full load from the candidate CDN nodes to obtain available CDN nodes;

[0166] Based on network conditions, calculate the overall quality score of each available CDN node to obtain a path quality report;

[0167] Based on the path quality report, select the transmission protocol and transmission path; and based on the file technical characteristics of the selected results, formulate a file chunking strategy.

[0168] By integrating the transmission protocol, transmission path, and file chunking strategy, a distribution strategy is obtained.

[0169] Furthermore, based on the animation resource ID, the corresponding animation resource version is retrieved from the animation resource index to obtain a list of candidate resource versions, including:

[0170] Using the animation resource ID as the primary key, the corresponding animation resource is searched in the animation resource index; and all available versions of the animation resource are retrieved to obtain the current version set.

[0171] Based on the behavior prediction model, animation resource versions with high correlation to animation resource requests are identified, resulting in a preloading candidate set. This preloading candidate set is then added to the current version set to obtain the complete version set. The preloading candidate set is used for preloading in the distribution module.

[0172] Based on device capabilities, animation resource versions that do not meet the device platform requirements are removed from the complete version set to obtain the supported version set;

[0173] Based on device capabilities, animation resource versions with resolutions exceeding the physical screen of the playback device are removed; and animation resource versions with file sizes exceeding the available space threshold of the playback device are also removed, resulting in a candidate resource version list.

[0174] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of a cross-platform intelligent management and distribution system for animation resources as described above.

[0175] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above system embodiments.

[0176] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0177] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A cross-platform animation resource intelligent management and distribution system, characterized in that, The system comprises: a request module configured to receive an animation resource request, obtain a context of a playback device based on the animation resource request, and obtain an animation resource ID, wherein the context comprises a network condition parameter, a device capability parameter, a system state parameter, and a user preference parameter; a retrieval module configured to retrieve a corresponding animation resource version in an animation resource index based on the animation resource ID, and obtain a candidate resource version list; a scoring module configured to calculate a comprehensive score for each animation resource version in the candidate resource version list based on the context, and determine the animation resource version with the highest comprehensive score as a selected result; a distribution module configured to select a network transmission path and a protocol based on the network condition of the playback device, and obtain a distribution strategy of the selected result, wherein the distribution strategy is used to instruct the system to distribute the selected result to the playback device.

2. The system of claim 1, wherein, The system further comprises a management module configured to: perform integrity verification on original animation files, remove damaged original animation files, and extract basic technical parameters of the remaining original animation files to obtain a basic animation resource library; calculate a technical complexity level for each original animation file in the basic animation resource library; perform format conversion on the original animation files based on the technical complexity level to obtain an animation resource version library, wherein the animation resource version library comprises derivative animations of different versions of the original animation files; extract detailed technical metadata for each derivative animation in the animation resource version library, and add the technical metadata to the animation resource version library to obtain an animation resource technical metadata database; construct a resource index based on the animation resource technical metadata database to obtain the animation resource index.

3. The system of claim 2, wherein, The management module is further configured to: calculate derivative parameters based on the basic technical parameters in the basic animation resource library to obtain a basic technical parameter table, wherein the derivative parameters comprise a total number of pixels, a data density, and a frame data volume; calculate a complexity score in each dimension based on the basic technical parameter table by using the following formula: wherein, is a resolution complexity score, is a number of actual pixels, is a number of reference pixels, is a frame rate complexity score, is an actual frame rate, is a reference frame rate, is a density complexity score, is an actual data density, is a reference data density; perform weighted summation on the complexity scores in each dimension to obtain a comprehensive score, and adjust the comprehensive score based on special factors to obtain a technical complexity score; map the technical complexity score to a corresponding level based on a preset complexity classification to obtain the technical complexity level corresponding to the original animation file.

4. The system of claim 1, wherein, The system further comprises a feedback module configured to: calculate a playback performance indicator of the playback device for the selected result to obtain a playback monitoring data set; calculate a key performance indicator based on the playback monitoring data set, evaluate the distribution strategy based on the key performance indicator to obtain a distribution strategy evaluation report, and wherein the key performance indicator comprises a recording performance indicator, a playback quality indicator, and a user experience indicator; perform root cause analysis based on the distribution strategy evaluation report to obtain a root cause analysis report; adjust weight parameters of the distribution module and the scoring module based on the root cause analysis report and the distribution strategy evaluation report to obtain a distribution strategy optimization suggestion scheme.

5. The system of claim 1, wherein, The scoring module is further configured to: grade the network condition based on current bandwidth, delay and stability data to obtain a grading report; eliminate the animation resource versions in the candidate resource version list that do not meet the preset network condition based on the graded condition to obtain a network-applicable candidate version list; perform multi-dimensional scoring on the animation resource versions in the candidate version list based on the context to obtain a multi-dimensional scoring version list; the multi-dimensional scoring includes network adaptability scoring, device performance matching scoring and user experience value scoring; perform weighted summation on each of the animation resource versions in the multi-dimensional scoring version list to obtain the comprehensive score.

6. The system of claim 1, wherein, The distribution module is further configured to: search for CDN nodes within a preset range around the playback device based on the newly obtained network condition to obtain candidate CDN nodes; and eliminate CDN nodes with full load in the candidate CDN nodes to obtain available CDN nodes; calculate the comprehensive quality score of each of the available CDN nodes based on the network condition to obtain a path quality report; select a transmission protocol and a transmission path based on the path quality report; and formulate a file blocking strategy based on the file technical features of the selected result; integrate the transmission protocol, the transmission path and the file blocking strategy to obtain the distribution strategy.

7. The system of claim 1, wherein, The retrieval module is further configured to: find the corresponding animation resource in the animation resource index with the animation resource ID as the primary key; and retrieve all available versions of the animation resource to obtain a current version set; discover the animation resource versions with high correlation to the animation resource request based on a behavior prediction model to obtain a preloading candidate set; and add the preloading candidate set to the current version set to obtain a complete version set; the preloading candidate set is used for preloading in the distribution module; eliminate the animation resource versions in the complete version set that do not meet the device platform based on the device capability to obtain a support version set; eliminate the animation resource versions with resolution exceeding the physical screen of the playback device based on the device capability; and eliminate the animation resource versions with file size exceeding the available space threshold of the playback device to obtain the candidate resource version list.

8. A cross-platform animation resource intelligent management and distribution method, characterized in that, The method comprises: receiving an animation resource request; and obtaining the context of a playback device based on the animation resource request to obtain an animation resource ID; the context includes network condition, device capability, system state and user preference; retrieving the corresponding animation resource versions in the animation resource index based on the animation resource ID to obtain a candidate resource version list; calculating the comprehensive score for each of the animation resource versions in the candidate resource version list based on the context; and determining the animation resource version with the highest comprehensive score as the selected result; selecting a network transmission path and protocol based on the network condition of the playback device to obtain a distribution strategy for the selected result; the distribution strategy is used to instruct the system to distribute the selected result to the playback device. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The computer program, when executed by the processor, implements the steps performed by the system of any of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps performed by the system of any of claims 1 to 7.

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