Art resource operation information acquisition system and method

Abstract

The invention relates to an art resource operation information acquisition system and method. The system comprises a resource request analysis module for analyzing an identifier and a resource configuration file, a resource topology construction module for constructing a directed dependency topological graph, and a key node identification module for generating a resource dependency weight score of a key resource node, the performance index generation module is used for generating performance analysis indexes of the key resource nodes; and the dynamic parameter regulation and control module is used for dynamically adjusting identifiers. By adopting the system, a complete technical closed loop from operation information perception to optimization, regulation and control verification can be constructed through dynamic dependency modeling, intelligent node screening and multi-dimensional fusion evaluation, the accuracy and timeliness of operation information acquisition can be improved, and the operation and maintenance cost of art resources can be reduced.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to an art resource operation information collection system and method. Background Art

[0002] In recent years, as the scope of application applications has become increasingly widespread, the running screens of applications have also become more and more exquisite. The virtual scenes in the running screens of applications are mainly loaded from art resources. Art resources will also affect the performance of applications during operation. Therefore, the art resources in applications need to be tested. In traditional technologies, the collection of art resource operation information usually relies on manual configuration and empirical judgment. Developers need to manually set parameters such as the loading order, resolution, and model complexity of art resources. When faced with large and complex projects, it is difficult to ensure the accurate collection of art resource operation information. In addition, traditional systems also have shortcomings in handling resource dependencies. They are unable to dynamically identify key resource nodes and dependency bottlenecks, resulting in the inability to carry out resource optimization after the collection of art resource operation information, which in turn increases the optimization cycle and cost.

[0003] In the existing technology with authorization announcement number CN112052158B, by extracting the attribute information and content information of the art resources to be collected, extracting the memory information of the art resources to be collected based on the content information, and fusing the attribute information and memory information of the art resources to be collected, the operation information of the art resources to be collected is obtained. This can greatly improve the efficiency of collecting the operation information of the art resources loaded by the application at runtime. However, there are still shortcomings such as a single performance evaluation dimension, a lack of dynamic control capabilities, and poor interpretability. Summary of the Invention

[0004] Based on this, it is necessary to provide an art resource operation information collection system and method that can improve the efficiency of art resource loading, stabilize display system performance, and focus on collecting and analyzing key art resource operation information to address the above technical problems.

[0005] In a first aspect, the present application provides an art resource operation information collection system, comprising:

[0006] A resource request parsing module, configured to receive a collection request including a collection period and parse the identifier of the resource to be collected and the resource configuration file corresponding to the collection request;

[0007] The resource topology construction module is used to construct a directed dependency topology graph of the resources to be collected based on the resource configuration file;

[0008] The key node identification module is used to obtain the in-degree resource key threshold and the out-degree resource key threshold in the resource configuration file, traverse the directed dependency topology graph, and obtain the key resource nodes and resource dependency weight scores of the key resource nodes in the directed dependency topology graph based on the in-degree resource key threshold and the out-degree resource key threshold;

[0009] The performance index generation module is used to collect memory data and operation data of key resource nodes, and generate performance analysis indexes of key resource nodes based on resource dependency weight scores, memory data and operation data;

[0010] The dynamic parameter control module is used to update the identifiers of the resources to be collected based on the dynamic adjustment strategy if the performance analysis index exceeds the performance threshold. The identifiers include texture resolution identifier, model vertex number identifier, resource priority identifier and key frame sampling rate identifier.

[0011] In one embodiment, the art resource operation information collection system further includes:

[0012] The resource consumption assessment module is used to extract the dependency path data and circular reference data of key resource nodes from the directed dependency topology graph, and calculate the resource consumption weight based on the dependency path data and circular reference data in combination with the weighted path analysis algorithm, identify the resource consumption bottleneck nodes, and generate a list of resource consumption bottleneck nodes;

[0013] The resource consumption quantification module is used to obtain the corresponding resource file attribute data from the resource consumption bottleneck node list, combine the memory data and running data, apply the performance analysis model, and generate animation performance quantification index data;

[0014] The resource consumption display module is used to output a performance analysis report based on the animation performance quantification index data, which includes performance cost heat distribution data and frame rate impact comparison data.

[0015] In one embodiment, the resource consumption assessment module includes:

[0016] The topology sequence generation component is used to obtain dependency path data and circular reference data from the directed dependency topology graph, and uses the topological sorting algorithm to generate an ordered sequence of node dependency relationships of key resource nodes to obtain node dependency sequence data;

[0017] The path weight calculation component is used to extract the dependency path subset of key resource nodes based on the node dependency sequence data, and calculate the resource consumption weight of each key resource node using a weighted path analysis algorithm in combination with the circular reference data;

[0018] A bottleneck node screening component is used to mark a key resource node as a resource consumption bottleneck node if the resource consumption weight of the key resource node exceeds a preset resource consumption weight threshold, and generate a resource consumption bottleneck node list;

[0019] Among them, the expression of the weighted path analysis algorithm is:

[0020]

[0021] Where, ω i is the resource consumption weight of the i-th key resource node, α, β and γ are weighted adjustment coefficients, Sigmoid(·) is the logical activation function, L o is the initial value adjustment coefficient of the activation function, f ref is the reference frequency corresponding to the circular reference data, V res is the resource volume of the key resource node, L path The dependency path length corresponding to the dependency path data.

[0022] In one embodiment, the animation performance quantification index data includes video memory pressure index data corresponding to the performance cost thermal distribution data and animation key frame efficiency ratio index data corresponding to the frame rate impact comparison data. The resource consumption quantification module includes:

[0023] Theoretical parameter analysis component, used to obtain the actual resource volume data of each resource consumption bottleneck node in the resource consumption bottleneck node list, and generate the theoretical maximum frame number data and theoretical sampling rate data of the resource consumption bottleneck node based on the resource volume data;

[0024] Multi-source feature extraction component, used to extract actual video memory usage data, video memory threshold data, baseline resource volume data, actual rendering frame number data and actual sampling rate data from memory data and running data;

[0025] A video memory pressure analysis component, configured to calculate and generate video memory pressure index data based on video memory occupancy ratio data obtained based on actual video memory occupancy data and video memory threshold data, and resource volume ratio data obtained based on actual resource volume data and benchmark resource volume data;

[0026] The key frame efficiency analysis component is used to calculate and generate animation key frame efficiency ratio index data based on the rendering frame ratio data obtained according to the actual rendering frame data and the theoretical maximum frame data, and the sampling rate ratio data obtained according to the actual sampling rate data and the theoretical sampling rate data.

[0027] In one embodiment, the art resource operation information collection system further includes a resource allocation optimization module, which includes:

[0028] The optimization sequence analysis component is used to generate optimization priority data for resource consumption bottleneck nodes based on the animation performance quantitative index data and the optimization priority sorting model of the resource consumption bottleneck nodes;

[0029] Dependency topology optimization component, used to extract high-priority nodes from resource consumption bottleneck nodes based on optimization priority data, obtain dependency path data and circular reference data of high-priority nodes, and generate dependency relationship optimization topology subgraph data;

[0030] Adjustment data generation component, used to optimize topology subgraph data through dependency relationships, adjust resource allocation strategies, and generate optimized resource adjustment plan data;

[0031] The dependency path update component is used to generate optimized animation performance quantitative index data based on resource adjustment plan data, combined with memory data, operation data and performance analysis model. If the optimized animation performance quantitative index data meets the performance requirement judgment conditions, the topology subgraph data is optimized according to the dependency relationship to update the dependency path data and circular reference data of the high-priority node.

[0032] In one embodiment, the key node identification module includes:

[0033] Threshold configuration parsing component, used to obtain the key thresholds of in-degree resources and out-degree resources in the resource configuration file;

[0034] Node degree statistics component, used to traverse the directed dependency topology graph and calculate the in-degree and out-degree of the nodes in the directed dependency topology graph;

[0035] A key node screening component is used to screen nodes whose in-degree exceeds a preset in-degree resource key threshold and / or whose out-degree exceeds a preset out-degree resource key threshold as key resource nodes;

[0036] The node weight evaluation component is used to calculate the weight score of the key resource node based on the in-degree, out-degree, in-degree resource critical threshold and out-degree resource critical threshold.

[0037] In one embodiment, the in-degree resource critical threshold is greater than or equal to the out-degree resource critical threshold.

[0038] Secondly, this application also provides a method for collecting art resource operation information, including:

[0039] Receive a collection request including a collection period and parse the identifier of the resource to be collected and the resource configuration file corresponding to the collection request;

[0040] Build a directed dependency topology graph of the resources to be collected based on the resource configuration file;

[0041] Obtain the in-degree resource critical threshold and out-degree resource critical threshold in the resource configuration file, traverse the directed dependency topology graph, and obtain the key resource nodes and resource dependency weight scores of the key resource nodes in the directed dependency topology graph based on the in-degree resource critical threshold and out-degree resource critical threshold;

[0042] Collect memory data and operation data of key resource nodes, and generate performance analysis indexes for key resource nodes based on resource dependency weight scores, memory data, and operation data;

[0043] If the performance analysis index exceeds the performance threshold, the identifiers of the resources to be collected are updated based on the dynamic adjustment strategy. The identifiers include a texture resolution identifier, a model vertex number identifier, a resource priority identifier, and a key frame sampling rate identifier.

[0044] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in the second aspect of the present application when executing the computer program.

[0045] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the second aspect of the present application.

[0046] The above-mentioned art resource operation information collection system and method can clarify the specific goals and scope of art resource operation information collection by parsing the collection request, and determine key information such as the type of resources to be collected, the application to which they belong, and the time period for collection; by constructing a directed dependency topology graph of the resources to be collected, it can help understand the loading order and mutual influence of resources, thereby improving the efficiency of art resource management; by setting the in-degree key threshold and out-degree key threshold, it can accurately locate the nodes that play a key role in the operation of art resources, thereby improving the collection efficiency of art resource operation information and improving the optimization efficiency of art resources; by generating a performance analysis index, it can comprehensively and accurately evaluate the performance status of key resource nodes, and improve the accuracy of art resource operation information collection; by updating the identifier of the resources to be collected based on the dynamic adjustment strategy, it can adjust the art resource loading system in real time based on the art resource operation information, thereby effectively avoiding excessive use of computing resources and reducing the operating cost of the art resource loading system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 A schematic diagram of an application environment for an art resource operation information collection system provided in one embodiment of the present application;

[0049] Figure 2 A schematic diagram of the structure of an art resource operation information collection system provided in one embodiment of the present application;

[0050] Figure 3 A schematic diagram of the structure of another art resource operation information collection system provided in one embodiment of the present application;

[0051] Figure 4 A schematic diagram of the structure of a resource consumption assessment module provided in one embodiment of the present application;

[0052] Figure 5 A schematic diagram of the structure of a resource consumption quantification module provided in one embodiment of the present application;

[0053] Figure 6 A schematic diagram of the structure of a resource allocation optimization module provided in one embodiment of the present application;

[0054] Figure 7 A schematic diagram of the structure of a key node identification module provided in one embodiment of the present application;

[0055] Figure 8 A flowchart of a method for collecting art resource operation information provided in one embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0057] The art resource operation information collection system provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 can communicate with the server 101 through the network. The database 103 can store data that the server 101 needs to process. The database 103 can be integrated on the server 101, or placed on the cloud or other network servers.

[0058] Illustratively, the server 101 may collect art resource operation information of the terminal 102 and store the art resource operation information of the terminal 102 in the database 103 .

[0059] Exemplarily, the server 101 can be implemented as an independent server or a server cluster consisting of multiple servers. The terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The database 103 can be, but is not limited to, a relational database, a non-relational database, a key-value storage database, and a column family database. In addition, the database 103 can also be a distributed database, which distributes data on multiple nodes, ensures data consistency and availability through distributed algorithms, and can handle massive data storage and highly concurrent access requests, such as a distributed database system for storing user data, log data, etc.

[0060] In an exemplary embodiment, Figure 2 As shown, an art resource operation information collection system 200 is provided, which may include:

[0061] The resource request parsing module 201 may be configured to receive a collection request including a collection period and parse the identifier of the to-be-collected resource and the resource configuration file corresponding to the collection request.

[0062] The resource topology construction module 202 can be used to construct a directed dependency topology graph of the resources to be collected based on the resource configuration file.

[0063] Optionally, the nodes of the directed dependency topology graph may not be limited to nodes corresponding to texture art resources, model art resources, or animation art resources.

[0064] The key node identification module 203 can be used to obtain the in-degree resource key threshold and the out-degree resource key threshold in the resource configuration file, and traverse the directed dependency topology graph to obtain the key resource nodes and resource dependency weight scores of the key resource nodes in the directed dependency topology graph based on the in-degree resource key threshold and the out-degree resource key threshold.

[0065] Optionally, the edge weights of the directed dependency topology graph may be calculated and generated based on the reference frequency, resource volume, and resource call path length of the resources to be collected.

[0066] The performance index generation module 204 may be used to collect memory data and operation data of key resource nodes, and generate a performance analysis index of the key resource nodes based on resource dependency weight scores, memory data, and operation data.

[0067] The dynamic parameter control module 205 may be configured to update the identifier of the resource to be collected based on a dynamic adjustment strategy if the performance analysis index exceeds a performance threshold.

[0068] Specifically, the identifier may include a texture resolution identifier, a model vertex number identifier, a resource priority identifier, and a key frame sampling rate identifier.

[0069] In the above-mentioned art resource operation information collection system, by constructing a directed dependency topology graph and based on the key thresholds of in-degree and out-degree resources, it is possible to accurately identify key resource nodes and assign a resource dependency weight score to each key node. This allows for rapid focus on the art resources that have the greatest impact on system performance, enabling efficient and targeted collection of art resource operation information, thereby avoiding the potential performance waste caused by blindly collecting art resource operation information. Through quantitative analysis of resource performance, it is possible to more scientifically and objectively measure whether the art resource operation information indicates that the art resources meet the performance requirements of the application, thereby accurately quantifying the actual benefits brought by the art resource configuration optimization plan and improving the effectiveness of the art resource management strategy.

[0070] In an optional embodiment of the present application, the art resource operation information collection system 200 may further include:

[0071] The resource consumption assessment module 306 can be used to extract the dependency path data and circular reference data of key resource nodes from the directed dependency topology graph, combine with the weighted path analysis algorithm, calculate the resource consumption weight based on the dependency path data and circular reference data, identify the resource consumption bottleneck nodes, and generate a resource consumption bottleneck node list.

[0072] Optionally, the resource consumption bottleneck node list may be a structured data set containing resource consumption bottleneck nodes.

[0073] The resource consumption quantification module 307 can be used to obtain corresponding resource file attribute data from the resource consumption bottleneck node list, combine memory data and operation data, apply performance analysis model, and generate animation performance quantification index data.

[0074] Optionally, the resource file attribute data may include, but is not limited to, art resource file size data, art resource file format data, and art resource-associated animation data.

[0075] The resource consumption display module 308 can be used to output a performance analysis report including performance cost heat distribution data and frame rate impact comparison data based on the animation performance quantification index data.

[0076] In the above-mentioned art resource operation information collection system, by extracting the dependency path data and circular reference data of key resource nodes from the directed dependency topology graph, and combining it with the weighted path analysis algorithm, it is possible to accurately identify resource consumption bottleneck nodes, and then quickly focus on the art resource operation information of art resources that rank high in contribution to computing resource consumption; by generating animation performance quantitative index data, it is possible to integrate the many attributes of art resource files and actual operation performance, and realize multi-dimensional quantitative evaluation of animation performance, which helps to more accurately evaluate art resource operation information; through the performance analysis report, the performance load corresponding to the art resource operation information of different art resources can be presented in a visual manner.

[0077] In an optional embodiment of the present application, the resource consumption assessment module 307 may include:

[0078] The topology sequence generation component 401 can be used to obtain dependency path data and circular reference data from the directed dependency topology graph, and use a topology sorting algorithm to generate an ordered sequence of node dependency relationships of key resource nodes to obtain node dependency sequence data.

[0079] The path weight calculation component 402 can be used to extract the dependency path subset of key resource nodes based on the node dependency sequence data, and calculate the resource consumption weight of each key resource node using a weighted path analysis algorithm in combination with the circular reference data.

[0080] The bottleneck node screening component 403 may be used to mark a key resource node as a resource consumption bottleneck node if the resource consumption weight of the key resource node exceeds a preset resource consumption weight threshold, and generate a resource consumption bottleneck node list.

[0081] The expression of the weighted path analysis algorithm can be:

[0082]

[0083] Where, ω i is the resource consumption weight of the i-th key resource node, α, β and γ are weighted adjustment coefficients, Sigmoid(·) is the logical activation function, L o is the initial value adjustment coefficient of the activation function, f ref is the reference frequency corresponding to the circular reference data, V res is the resource volume of the key resource node, L path The dependency path length corresponding to the dependency path data.

[0084] In the above-mentioned art resource operation information collection system, based on dependency path data and circular reference data, combined with a topological sorting algorithm, an ordered sequence of node dependency relationships of key resource nodes can be obtained, which can clearly show the dependency order of key art resources, and help to more deeply understand the complex dependency relationships between art resources; by comprehensively considering multi-dimensional factors such as the reference frequency corresponding to circular reference data, the resource volume of key resource nodes, and the dependency path length corresponding to dependency path data, and using logical activation functions for fine-tuning, the calculation results of resource consumption weights can more accurately reflect the actual consumption of resource nodes, thereby improving the analytical value of art resource operation information; by screening resource consumption bottleneck nodes, the bottleneck nodes that have the greatest impact on resource consumption can be quickly and accurately screened out, thereby improving the efficiency and effectiveness of the analysis of art resource operation information.

[0085] In an optional embodiment of the present application, the animation performance quantification index data includes video memory pressure index data corresponding to the performance cost thermal distribution data and animation key frame efficiency ratio index data corresponding to the frame rate impact comparison data. The resource consumption quantification module 307 may include:

[0086] The theoretical parameter analysis component 501 can be used to obtain actual resource volume data of each resource consumption bottleneck node in the resource consumption bottleneck node list, and generate theoretical maximum frame number data and theoretical sampling rate data of the resource consumption bottleneck node based on the resource volume data.

[0087] The multi-source feature extraction component 502 can be used to extract actual video memory usage data, video memory threshold data, baseline resource volume data, actual rendering frame number data and actual sampling rate data from memory data and running data.

[0088] The memory pressure analysis component 503 can be used to calculate and generate memory pressure index data based on memory occupancy ratio data obtained from actual memory occupancy data and memory threshold data and resource volume ratio data obtained from actual resource volume data and benchmark resource volume data.

[0089] The key frame efficiency analysis component 504 can be used to calculate and generate animation key frame efficiency ratio index data based on rendering frame ratio data obtained according to actual rendering frame data and theoretical maximum frame data and sampling rate ratio data obtained according to actual sampling rate data and theoretical sampling rate data.

[0090] In the above-mentioned art resource operation information collection system, the animation performance quantification index data can be used to quantitatively evaluate the animation performance of the art resources represented by the art resource operation information from multiple dimensions such as resource volume, rendering frame number, sampling rate, etc.; through the animation key frame efficiency ratio index data, the animation efficiency of the art resources represented by the art resource operation information can be carefully evaluated.

[0091] In an optional embodiment of the present application, the art resource operation information collection system further includes a resource allocation optimization module 309. The resource allocation optimization module 309 may include:

[0092] The optimization sequence analysis component 601 can be used to generate optimization priority data of resource consumption bottleneck nodes based on the animation performance quantification index data and the resource consumption bottleneck node optimization priority ranking model.

[0093] The dependency topology optimization component 602 can be used to extract high-priority nodes from resource consumption bottleneck nodes based on optimization priority data, obtain dependency path data and circular reference data of the high-priority nodes, and generate dependency relationship optimized topology subgraph data.

[0094] Optionally, the resource consumption bottleneck nodes in the first one third of the optimization priority data in descending order among all resource consumption bottleneck nodes may be selected as high priority nodes.

[0095] The adjustment data generation component 603 can be used to optimize the topology subgraph data through dependency relationships, adjust the resource allocation strategy, and generate optimized resource adjustment solution data.

[0096] The dependency path update component 604 can be used to generate optimized animation performance quantification index data based on resource adjustment plan data, combined with memory data, operation data and performance analysis model. If the optimized animation performance quantification index data meets the performance requirement judgment conditions, the dependency path data and circular reference data of the high-priority node are updated according to the dependency relationship optimized topology subgraph data.

[0097] In the above-mentioned art resource operation information collection system, the dynamic adjustment mechanism can flexibly adjust the resource allocation strategy according to real-time performance data and resource status, ensuring that art resources can be reasonably allocated and utilized under different hardware environments and operating conditions.

[0098] In an optional embodiment of the present application, the key node identification module 203 may include:

[0099] The threshold configuration parsing component 701 can be used to obtain the key thresholds of in-degree resources and out-degree resources in the resource configuration file.

[0100] The node degree value statistics component 702 can be used to traverse the directed dependency topology graph and calculate the in-degree and out-degree of the nodes in the directed dependency topology graph.

[0101] Schematically, the in-degree of a node represents the number of times it is directly depended on by other resources in the art resource's runtime information. In a directed dependency graph, the in-degree can be the number of edges pointing to the node. Nodes with high in-degree are typically key dependency nodes, directly referenced by multiple other resources.

[0102] Schematically, the out-degree of a node represents the number of times it depends on other resources within the art resource's runtime information. In a directed dependency graph, the out-degree can be the number of edges pointing from the node to other nodes. Nodes with high out-degrees are often complex aggregation nodes that reference a large number of sub-resources.

[0103] The key node screening component 703 may be used to screen nodes whose in-degree exceeds a preset in-degree resource key threshold and / or whose out-degree exceeds a preset out-degree resource key threshold as key resource nodes.

[0104] The node weight evaluation component 704 can be used to calculate the weight score of the key resource node according to the in-degree, out-degree, in-degree resource critical threshold and out-degree resource critical threshold.

[0105] In the above-mentioned art resource operation information collection system, the node degree value statistics component traverses the directed dependency topology graph and calculates the in-degree and out-degree of each node, which can fully and accurately grasp the connection status of each node in the graph, thereby avoiding missing important nodes; the key node screening component flexibly screens out nodes with in-degree or out-degree exceeding the threshold as key resource nodes based on the preset threshold, and can make adaptive adjustments according to the needs of different applications and the characteristics of art resource operation information, thereby accurately locating art resources that have a key impact on resource management and performance optimization, thereby improving the pertinence and efficiency of optimization work.

[0106] In an optional embodiment of the present application, the in-degree resource critical threshold may be greater than or equal to the out-degree resource critical threshold.

[0107] Based on the same inventive concept, the present application also provides an art resource operation information collection method that can be used to implement the art resource operation information collection system mentioned above. The solution provided by this method is similar to the solution described in the above-mentioned system. Therefore, the specific limitations of one or more art resource operation information collection method embodiments provided below can be found in the above-mentioned limitations of the art resource operation information collection system and will not be repeated here.

[0108] In an exemplary embodiment, Figure 8 As shown, a method for collecting art resource operation information is provided, which may include:

[0109] Step S801: receiving a collection request including a collection period and parsing the identifier of the resource to be collected and the resource configuration file corresponding to the collection request.

[0110] Step S802: constructing a directed dependency topology graph of the resources to be collected based on the resource configuration file.

[0111] Step S803: Obtain the in-degree resource critical threshold and the out-degree resource critical threshold in the resource configuration file, traverse the directed dependency topology graph, and obtain the key resource nodes and resource dependency weight scores of the key resource nodes in the directed dependency topology graph based on the in-degree resource critical threshold and the out-degree resource critical threshold.

[0112] Step S804 : collecting memory data and operation data of key resource nodes, and generating a performance analysis index of the key resource nodes based on the resource dependency weight score, memory data and operation data.

[0113] Step S805: If the performance analysis index exceeds the performance threshold, the identifier of the resource to be collected is updated based on the dynamic adjustment strategy.

[0114] Specifically, the identifier includes a texture resolution identifier, a model vertex number identifier, a resource priority identifier, and a key frame sampling rate identifier.

[0115] In an optional embodiment of the present application, the art resource operation information collection method may further include:

[0116] The dependency path data and circular reference data of key resource nodes are extracted from the directed dependency topology graph. Combined with the weighted path analysis algorithm, the resource consumption weight is calculated based on the dependency path data and circular reference data, the resource consumption bottleneck nodes are identified, and a list of resource consumption bottleneck nodes is generated.

[0117] Obtain the corresponding resource file attribute data from the resource consumption bottleneck node list, combine it with memory data and running data, apply the performance analysis model, and generate animation performance quantitative index data.

[0118] Based on the animation performance quantification index data, a performance analysis report is output, which includes performance cost heat distribution data and frame rate impact comparison data.

[0119] In an optional embodiment of the present application, dependency path data and circular reference data of key resource nodes are extracted from a directed dependency topology graph, and a weighted path analysis algorithm is used to calculate resource consumption weights based on the dependency path data and circular reference data, identify resource consumption bottleneck nodes, and generate a resource consumption bottleneck node list, which may include:

[0120] Dependency path data and circular reference data are obtained from the directed dependency topology graph, and a topological sorting algorithm is used to generate an ordered sequence of node dependency relationships of key resource nodes to obtain node dependency sequence data.

[0121] According to the node dependency sequence data, the dependency path subset of key resource nodes is extracted. Combined with the circular reference data, the weighted path analysis algorithm is used to calculate the resource consumption weight of each key resource node.

[0122] If the resource consumption weight of a key resource node exceeds a preset resource consumption weight threshold, the key resource node is marked as a resource consumption bottleneck node, and a resource consumption bottleneck node list is generated.

[0123] Among them, the expression of the weighted path analysis algorithm is:

[0124]

[0125] Where, ω i is the resource consumption weight of the i-th key resource node, α, β and γ are weighted adjustment coefficients, Sigmoid(·) is the logical activation function, L o is the initial value adjustment coefficient of the activation function, f ref is the reference frequency corresponding to the circular reference data, V res is the resource volume of the key resource node, L path The dependency path length corresponding to the dependency path data.

[0126] In an optional embodiment of the present application, the animation performance quantification index data includes video memory pressure index data corresponding to the performance cost thermal distribution data and animation key frame efficiency ratio index data corresponding to the frame rate impact comparison data. The corresponding resource file attribute data is obtained from the resource consumption bottleneck node list, and the performance analysis model is applied in combination with the memory data and the running data to generate the animation performance quantification index data. The data may include:

[0127] The actual resource volume data of each resource consumption bottleneck node in the resource consumption bottleneck node list is obtained, and the theoretical maximum frame number data and theoretical sampling rate data of the resource consumption bottleneck node are generated based on the resource volume data.

[0128] Extract actual video memory usage data, video memory threshold data, benchmark resource volume data, actual rendering frame number data and actual sampling rate data from memory data and running data.

[0129] The memory pressure index data is calculated and generated based on the memory occupancy ratio data obtained according to the actual memory occupancy data and the memory threshold data and the resource volume ratio data obtained according to the actual resource volume data and the benchmark resource volume data.

[0130] Based on the rendering frame ratio data obtained according to the actual rendering frame data and the theoretical maximum frame data and the sampling rate ratio data obtained according to the actual sampling rate data and the theoretical sampling rate data, the animation key frame efficiency ratio index data is calculated and generated.

[0131] In an optional embodiment of the present application, the method for collecting art resource operation information may further include:

[0132] According to the animation performance quantitative index data, combined with the resource consumption bottleneck node optimization priority ranking model, the optimization priority data of the resource consumption bottleneck node is generated.

[0133] Based on the optimization priority data, high-priority nodes are extracted from the resource consumption bottleneck nodes, the dependency path data and circular reference data of the high-priority nodes are obtained, and the dependency optimization topology subgraph data is generated.

[0134] Optimize topology subgraph data through dependency relationships, adjust resource allocation strategies, and generate optimized resource adjustment plan data.

[0135] Based on the resource adjustment plan data, combined with memory data, operation data and performance analysis model, the optimized animation performance quantitative index data is generated. If the optimized animation performance quantitative index data meets the performance requirement judgment conditions, the topology subgraph data is optimized according to the dependency relationship to update the dependency path data and circular reference data of the high-priority nodes.

[0136] In an optional embodiment of the present application, obtaining an in-degree resource critical threshold and an out-degree resource critical threshold in a resource configuration file, traversing a directed dependency topology graph, and obtaining a key resource node and a resource dependency weight score of the key resource node in the directed dependency topology graph based on the in-degree resource critical threshold and the out-degree resource critical threshold may include:

[0137] Get the critical thresholds for in-degree resources and out-degree resources in the resource configuration file.

[0138] Traverse the directed dependency topology graph and calculate the in-degree and out-degree of the nodes in the directed dependency topology graph.

[0139] Nodes whose in-degree exceeds a preset in-degree resource critical threshold and / or whose out-degree exceeds a preset out-degree resource critical threshold are screened as key resource nodes.

[0140] The weight score of the key resource node is calculated based on the in-degree, out-degree, in-degree resource critical threshold and out-degree resource critical threshold.

[0141] In an optional embodiment of the present application, the in-degree resource critical threshold is greater than or equal to the out-degree resource critical threshold.

[0142] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0143] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method for collecting art resource operation information when executing the computer program.

[0144] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0145] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0146] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. An art resource operation information collection system, characterized in that: The system comprises: A resource request parsing module, configured to receive a collection request including a collection period and parse the identifier of the resource to be collected and the resource configuration file corresponding to the collection request; A resource topology construction module, configured to construct a directed dependency topology graph of the resources to be collected based on the resource configuration file; a key node identification module, configured to obtain an in-degree resource key threshold and an out-degree resource key threshold from the resource configuration file, traverse the directed dependency topology graph, and obtain key resource nodes and resource dependency weight scores of the key resource nodes in the directed dependency topology graph based on the in-degree resource key threshold and the out-degree resource key threshold; a performance index generation module, configured to collect memory data and operation data of the key resource node, and generate a performance analysis index of the key resource node based on the resource dependency weight score, the memory data, and the operation data; A dynamic parameter control module is used to update the identifier of the resource to be collected based on a dynamic adjustment strategy if the performance analysis index exceeds a performance threshold, the identifier including a texture resolution identifier, a model vertex number identifier, a resource priority identifier and a key frame sampling rate identifier.

2. The system according to claim 1, wherein: The system further comprises: A resource consumption assessment module is configured to extract dependency path data and circular reference data of the key resource nodes from the directed dependency topology graph, calculate resource consumption weights based on the dependency path data and the circular reference data in combination with a weighted path analysis algorithm, identify resource consumption bottleneck nodes, and generate a resource consumption bottleneck node list; A resource consumption quantification module is used to obtain corresponding resource file attribute data from the resource consumption bottleneck node list, combine the memory data and the operation data, apply a performance analysis model, and generate animation performance quantification index data; The resource consumption display module is used to output a performance analysis report including performance cost heat distribution data and frame rate impact comparison data based on the animation performance quantification index data.

3. The system according to claim 2, characterized in that The resource consumption assessment module includes: A topology sequence generation component is used to obtain the dependency path data and the circular reference data from the directed dependency topology graph, and use a topological sorting algorithm to generate an ordered sequence of node dependency relationships of the key resource nodes to obtain node dependency sequence data; A path weight calculation component is used to extract the dependency path subset of the key resource nodes based on the node dependency sequence data, and calculate the resource consumption weight of each key resource node using a weighted path analysis algorithm in combination with the circular reference data; A bottleneck node screening component is configured to mark the key resource node as the resource consumption bottleneck node if the resource consumption weight of the key resource node exceeds a preset resource consumption weight threshold, and generate a resource consumption bottleneck node list; The expression of the weighted path analysis algorithm is: Where, ω i is the resource consumption weight of the i-th key resource node, α, β and γ are weighted adjustment coefficients, Sigmoid(·) is the logical activation function, L o is the initial value adjustment coefficient of the activation function, f ref is the reference frequency corresponding to the circular reference data, V res is the resource volume of the key resource node, L path is the dependency path length corresponding to the dependency path data.

4. The system according to claim 2, wherein: The animation performance quantification index data includes video memory pressure index data corresponding to the performance cost thermal distribution data and animation key frame efficiency ratio index data corresponding to the frame rate impact comparison data. The resource consumption quantification module includes: a theoretical parameter analysis component for obtaining actual resource volume data of each resource consumption bottleneck node in the resource consumption bottleneck node list, and generating theoretical maximum frame number data and theoretical sampling rate data of the resource consumption bottleneck node based on the resource volume data; A multi-source feature extraction component is used to extract actual video memory usage data, video memory threshold data, benchmark resource volume data, actual rendering frame number data and actual sampling rate data from the memory data and the running data; a video memory pressure analysis component, configured to calculate and generate the video memory pressure index data based on video memory occupancy ratio data obtained according to the actual video memory occupancy data and the video memory threshold data, and resource volume ratio data obtained according to the actual resource volume data and the benchmark resource volume data; A key frame efficiency analysis component is used to calculate and generate the animation key frame efficiency ratio index data based on the rendering frame ratio data obtained according to the actual rendering frame data and the theoretical maximum frame data, and the sampling rate ratio data obtained according to the actual sampling rate data and the theoretical sampling rate data.

5. The system according to any one of claims 2 to 4, characterized in that: The system further includes a resource allocation optimization module, which includes: An optimization sequence analysis component is used to generate optimization priority data of the resource consumption bottleneck node based on the animation performance quantification index data and a resource consumption bottleneck node optimization priority ranking model; A dependency topology optimization component is configured to extract high-priority nodes from the resource consumption bottleneck nodes based on the optimization priority data, obtain the dependency path data and the circular reference data of the high-priority nodes, and generate dependency relationship optimized topology subgraph data; An adjustment data generation component is used to optimize the topology subgraph data through the dependency relationship, adjust the resource allocation strategy, and generate optimized resource adjustment solution data; A dependency path update component is used to generate optimized animation performance quantification index data based on the resource adjustment plan data, combined with the memory data, the operation data and the performance analysis model. If the optimized animation performance quantification index data meets the performance requirement judgment conditions, the dependency path data and the circular reference data of the high-priority node are updated according to the dependency relationship optimized topology subgraph data.

6. The system according to claim 1, wherein: The key node identification module includes: A threshold configuration parsing component, configured to obtain the in-degree resource key threshold and the out-degree resource key threshold in the resource configuration file; A node degree value statistics component, configured to traverse the directed dependency topology graph and calculate the in-degree and the out-degree of the nodes in the directed dependency topology graph; A key node screening component, configured to screen the nodes whose in-degree exceeds a preset in-degree resource key threshold and / or whose out-degree exceeds a preset out-degree resource key threshold as the key resource nodes; A node weight evaluation component is used to calculate the weight score of the key resource node according to the in-degree, the out-degree, the in-degree resource critical threshold and the out-degree resource critical threshold.

7. The system according to claim 6, characterized in that: The in-degree resource critical threshold is greater than or equal to the out-degree resource critical threshold.

8. A method for collecting art resource operation information, characterized in that: The method comprises: Receiving a collection request including a collection period and parsing an identifier of a resource to be collected and a resource configuration file corresponding to the collection request; Constructing a directed dependency topology graph of the resources to be collected based on the resource configuration file; Obtaining an in-degree resource critical threshold and an out-degree resource critical threshold in the resource configuration file, traversing the directed dependency topology graph, and obtaining a key resource node in the directed dependency topology graph and a resource dependency weight score of the key resource node based on the in-degree resource critical threshold and the out-degree resource critical threshold; Collecting memory data and operation data of the key resource node, and generating a performance analysis index of the key resource node based on the resource dependency weight score, the memory data and the operation data; If the performance analysis index exceeds a performance threshold, the identifiers of the resources to be collected are updated based on a dynamic adjustment strategy, where the identifiers include a texture resolution identifier, a model vertex number identifier, a resource priority identifier, and a key frame sampling rate identifier.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to claim 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to claim 8 is implemented.

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