A non-embedded metaverse multiple automatic roaming performance testing method based on blockchain and digital human replication technology

Abstract

The present invention discloses a non-embedded metaverse multiple automatic roaming performance testing method based on blockchain and digital human replication technology, including metaverse map scene abstraction, digital human creation, digital human test scene creation, multiple roaming algorithm settings, image acquisition settings, test data chaining, and image performance test analysis report generation. The present invention uses the blockchain platform as the architecture of the test environment, deploys multiple blockchain nodes, and uses digital human roaming as a medium to perform non-embedded automated performance test data collection. The data is preliminarily processed and formatted according to preset standards to generate standardized data records. The data records are protected by encryption, and then the encrypted records are stored on the chain through smart contracts to ensure the integrity and non-tamperability of the data. Finally, a complete metaverse system image performance test analysis report is generated.

Description

Technical Field

[0001] The present invention relates to the field of virtual technology, and in particular to a non-embedded metaverse multiple automatic roaming performance testing method based on blockchain and digital human replication technology. Background Art

[0002] With the development of Metaverse technology, the demand for Metaverse system image performance testing is growing. However, traditional testing methods have many shortcomings. First, each individual roaming algorithm has certain limitations, making it difficult to fully cover all test scenarios and map features, and difficult to simulate the complex and changeable image rendering and application scenarios in the Metaverse, resulting in limitations in the test results. Secondly, the accuracy of the test results is often questioned. Due to the limitations of the test methods and tools, it is difficult to accurately evaluate the true level of Metaverse system image performance. Furthermore, the test efficiency is not high, and traditional testing methods often require a lot of manpower and time investment, which cannot meet the needs of rapid iteration of Metaverse technology. Finally, traditional testing methods are slow to respond to emerging technical problems, making it difficult to promptly discover and solve new problems in the image performance of the Metaverse system.

[0003] In addition, the storage and management of traditional test data lacks sufficient security and is vulnerable to the risk of tampering and leakage, which poses a threat to the credibility and fairness of the test results. Blockchain technology, with its decentralized and tamper-proof characteristics, provides a new solution for data storage and verification, making the test results more fair and reliable. Therefore, applying blockchain technology to digital human performance testing ensures the security and reliability of data and provides a solid guarantee for the further development of digital human technology.

[0004] In response to the above problems, the present invention provides a non-embedded metaverse multiple automatic roaming performance testing method with high security performance based on blockchain and digital human replication technology. Summary of the invention

[0005] The present invention provides a highly secure non-embedded metaverse multiple automatic roaming performance testing method based on blockchain and digital human replication technology.

[0006] The purpose of the present invention is to provide a non-embedded metaverse multiple automatic roaming performance testing method based on blockchain and digital human replication technology. The testing method includes building a hardware testing environment, metaverse map modeling, digital human creation, creating a digital human test plan template, running a test scenario, uploading test data to the chain, and generating and analyzing image test results.

[0007] Furthermore, the specific steps for building a hardware test environment are as follows:

[0008] Step 1: Hardware preparation: Prepare computer test equipment equipped with high-performance CPU, large-capacity memory, and high-performance graphics card according to test requirements;

[0009] Step 2: Software installation: Install and configure Metaverse image performance testing tools, data analysis tools, the Metaverse system under test, and the blockchain system software environment;

[0010] Step 3: Environmental testing: Run the Metaverse system, testing tools, analysis tools, and blockchain system under test.

[0011] Furthermore, the specific steps for modeling the Metaverse map are as follows:

[0012] Step 1: Scenario analysis and node confirmation: Analyze the specific scenarios of the Metaverse map, including cities, mountains, and buildings, and mark key points as nodes, including buildings and intersections;

[0013] Step 2: Panorama creation: Use 3D modeling software to build a panoramic 3D model of the metaverse, ensuring that the details and proportions of the model are consistent with the real scene. Navigation structure definition: For each node, use an algorithm to generate a complete binary tree with the node as the root. Each node in the tree represents a reachable location, and the edges represent possible movement paths.

[0014] Furthermore, the specific steps for creating a digital human are as follows:

[0015] Step 1: Create a digital human according to the default template; set the action speed and jump height of the digital human to a fixed value range;

[0016] Step 2: Interactive behavior design: Set the action combination of the digital human, and choose any combination from walking, running, and jumping;

[0017] Step 3: Design the digital human dialogue system: set dialogue trigger conditions and multiple preset dialogue contents. The trigger conditions include clicking on the NPC or automatically triggering when being close to the NPC.

[0018] Furthermore, the specific steps for creating a digital human test plan template are as follows:

[0019] Step 1: Select a representative map from the Metaverse map as the test scene and set a fixed test duration, such as 1 hour or 30 minutes: the test scene contains a variety of terrains and buildings to comprehensively test the performance of the Metaverse system in multiple scenarios;

[0020] Step 2: Copy the digital human, set the number of copied digital humans, and copy multiple digital human instances in the test scenario; configure different initial positions and states for each digital human.

[0021] Furthermore, the specific steps for running the test scenario are as follows:

[0022] Step 1: Set up automatic roaming algorithm: including predefined path roaming, random path roaming, and map navigation roaming;

[0023] Step 2: Set the image quality of the Metaverse system, select a quality from high, medium, or low, and select one or more performance indicators to be collected: including frame rate, memory usage, network latency, frame time, etc.

[0024] Step 3: Run the test: The digital human roams according to the established algorithm and scene settings, and the background automatically records performance indicator data and collects related abnormal error information.

[0025] Furthermore, the specific steps for testing data on-chain are as follows:

[0026] Step 1: After the test, the non-embedded SDK performs preliminary processing and formatting on the collected performance indicator data, and automatically packages the processed data into transactions and sends them to the blockchain network and stores them on the blockchain through smart contracts;

[0027] Step 2: Verify the validity of the transaction and ensure it is successfully recorded on the blockchain.

[0028] Furthermore, the specific steps of generating and analyzing image test results are as follows:

[0029] Step 1. Report generation: The performance test data analysis tool reads the performance indicator data processed by the non-embedded SDK from the chain; automatically generates a full-scene image performance test report containing charts and statistical data. The report contains three parts: test data, result analysis, and optimization suggestions. Test data: including the frame rate changes of the Metaverse system under different image qualities and resolutions, including the average frame rate AvgFPS, minimum frame rate MinFPS, maximum frame rate MaxFPS, median frame rate MedianFPS, average frame time AvgFTime, CPU utilization includes the unnormalized CPU utilization of the whole machine TotalCPU, the unnormalized CPU utilization of the process AppCPU, the normalized CPU utilization of the whole machine TotalNormalizedCPU, and the normalized CPU utilization of the process AppNormalizedCPU. Memory utilization includes physical memory usage RealMemoryUsage, virtual memory usage VirtualMemoryUsage, and whole machine available memory usage. Use the remaining memory AvailableMemory, network conditions including the number of upstream network bytes UploadBytes, the number of downstream network bytes DownloadBytes, the number of upstream network packets UploadPackets, the number of downstream network packets DownloadPackets, and the server IP delay ServerIPdelaytime; Result analysis: Based on the test baseline including the core indicators of frame rate, CPU utilization, memory utilization, and network delay, compare and analyze the test data to point out performance bottlenecks and problem areas; compare with the industry average to evaluate the overall performance of the system; Optimization suggestions: Propose targeted optimization suggestions based on the analysis results, including improving algorithms and optimizing resource allocation;

[0030] Step 2: Feedback the test report to the development team for subsequent performance improvement work.

[0031] The present invention has the following advantages: The present invention creatively combines three path roaming methods, namely, predefined path roaming, random path roaming, and map navigation roaming, to realize the automatic roaming technology of digital humans. Predefined path roaming simulates situations of specific behaviors or scenarios. The random path roaming algorithm increases the unpredictability of roaming, allowing digital humans to freely explore in the test scenario, thereby covering a wider area, helping to discover potential problems, and improving the comprehensiveness and effectiveness of the test. The map navigation roaming algorithm automatically plans the shortest and optimal path from the starting point to the end point, and moves autonomously to the target, improving roaming efficiency and accuracy through intelligent planning, and reducing unnecessary movements and repeated paths. The combination of the above algorithms enables the digital human roaming algorithm to flexibly switch roaming strategies according to different scenarios and needs, thereby achieving better roaming effects. At the same time, the optimization and adjustment of the algorithm also take into account the balance between system performance and user experience, ensuring stability and reliability in various situations.。 This technology not only allows testers to get rid of tedious manual path planning, but also uses intelligent navigation algorithms to analyze map features in real time, dynamically generate and optimize the movement path of digital humans. This capability ensures that digital humans can move freely in complex and changing virtual environments without being restricted by the environment, thereby greatly improving the efficiency and accuracy of the testing process. In addition, it reduces the time waste and test deviation caused by improper manual path planning, significantly shortens the test cycle, and allows testers to focus more on in-depth analysis of test data and optimization of conclusive work.

[0032] The present invention uses efficient digital human automatic roaming technology to help testers test the image performance of the metaverse system more efficiently and accurately. Through the navigation algorithm, it can dynamically generate and adjust the digital human movement path according to the map characteristics, ensuring that the digital human can move freely in a changing environment. It also improves the efficiency of roaming testing, reduces time waste caused by improper manual path planning, shortens the test execution time, and allows testers to focus more on conclusive work such as test data analysis.

[0033] The present invention realizes a comprehensive automated test of the image performance of the Metaverse system. First, non-embedded automated performance test data collection is realized. The Metaverse system under test does not need to integrate a software development kit (SDK). The non-embedded SDK used in the test supports real-time capture and recording of performance data such as overall frame rate, memory usage, network latency, etc. at different resolutions and image qualities, provides exception capture, reporting, and analysis functions, and can also be scheduled from the central processing unit (CPU) to the call stack to locate function-level and module-level performance bottlenecks. The SDK has a low impact on the CPU performance of the test equipment and has no impact on the frame rate. It can collect more realistic performance data, and this automatic data collection method ensures the accuracy and consistency of the test data and avoids errors that may be caused by human operations. At the same time, the generated test report covers multi-dimensional and multi-level data information, including frame rate (average frame rate AvgFPS, minimum frame rate MinFPS, maximum frame rate MaxFPS, median frame rate MedianFPS, average frame time AvgFTime), CPU utilization (total CPU utilization of the whole machine, AppCPU, total normalized CPU utilization of the whole machine, AppNormalizedCPU, physical memory utilization RealMemoryUsage, virtual memory utilization VirtualMemoryUsage, available remaining memory of the whole machine AvailableMemory), network (uplink network bytes UploadBytes, downlink network bytes DownloadBytes, uplink network packets UploadPackets, downlink network packets DownloadPackets, server IP delay ServerIPdelaytime), etc. These detailed data not only intuitively demonstrate the stability and response speed of the system under different resolutions and scenarios, but also provide strong data support for the development team of the Metaverse system, assisting them in making more accurate performance analysis and optimization decisions.

[0034] Another highlight of the present invention is the application of digital human replication technology. Through this technology, we can easily create multiple completely identical digital human instances and start them at the same time for parallel testing. This parallel testing method greatly improves the test efficiency, allowing more test scenarios and conditions to be verified in the same time. At the same time, using digital human replication technology, we can also simulate system performance under different load conditions, such as high concurrent access, large-scale user interaction and other scenarios. Load testing provides an important guarantee for the stability and reliability of the Metaverse system, ensuring that the system can run stably under various extreme conditions.

[0035] The present invention uses blockchain technology to deploy the test environment and save test data, which has significant advantages. The decentralized nature of blockchain ensures the security of the test environment and data, prevents tampering, and enhances system stability. At the same time, blockchain ensures data transparency and traceability, and enhances the credibility of test data. This technology provides an innovative solution for test management, adapts to diverse test needs, and promotes test process optimization.

[0036] The present invention uses blockchain technology to deploy the test environment and save test data, which has many advantages. First, the decentralized nature of blockchain ensures the high security of the test environment and data, prevents the risk of single point failure and data tampering, and improves the reliability and stability of the overall system. Secondly, blockchain technology ensures the transparency and traceability of data. All data changes are recorded on the blockchain that cannot be tampered with. Anyone can view and verify the integrity and accuracy of the data, which enhances the trust and credibility of the data. Most importantly, blockchain technology provides a new solution for the management of test environment and data, which can adapt to test requirements of different scales and complexities, provides strong support for the optimization and improvement of the test process, and promotes the innovation and upgrading of the test management model. In summary, the use of blockchain technology for test environment deployment and test data preservation can not only ensure the security and credibility of data, but also improve test efficiency and management level, laying a solid foundation for the efficient development and continuous improvement of test work. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flowchart of the present invention;

[0038] Figure 2 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0039] The present invention provides a non-embedded metaverse multiple automatic roaming performance testing method based on blockchain and digital human replication technology. The testing method includes building a hardware testing environment, metaverse map modeling, digital human creation, creating a digital human test solution template, running a test scenario, uploading test data to the chain, and generating and analyzing image test results.

[0040] In this embodiment, the specific steps for setting up the hardware test environment are as follows:

[0041] Step 1: Hardware preparation: Prepare computer test equipment equipped with high-performance CPU, large-capacity memory, and high-performance graphics card according to test requirements;

[0042] Step 2: Software installation: Install and configure Metaverse image performance testing tools, data analysis tools, the Metaverse system under test, and the blockchain system software environment;

[0043] Step 3: Environmental testing: Run the Metaverse system, testing tools, analysis tools, and blockchain system under test.

[0044] In this embodiment, the specific steps of Metaverse map modeling are as follows:

[0045] Step 1: Scenario analysis and node confirmation: Analyze the specific scenarios of the Metaverse map, including cities, mountains, and buildings, and mark key points as nodes, including buildings and intersections;

[0046] Step 2: Panorama creation: Use 3D modeling software to build a complete 3D model of the metaverse, ensuring that the details and proportions of the model are consistent with the real scene. Navigation structure definition: For each node, use an algorithm to generate a complete binary tree with the node as the root. Each node in the tree represents a reachable location, and the edges represent possible movement paths.

[0047] In this embodiment, the specific steps of creating a digital human are as follows:

[0048] Step 1: Create a digital human according to the default template; define the digital human's action speed as a fixed value range; set the dynamic attribute of the jumping height;

[0049] Step 2: Interactive behavior design: Set the action combination of the digital human, and choose any combination from walking, running, and jumping;

[0050] Step 3: Design the digital human dialogue system: set dialogue trigger conditions and multiple preset dialogue contents. The trigger conditions include clicking on the NPC or automatically triggering when being close to the NPC.

[0051] In this embodiment, the specific steps for creating a digital human test solution template are as follows:

[0052] Step 1: Select a representative map from the Metaverse map as the test scene and set a fixed test duration, such as 1 hour or 30 minutes: the test scene contains a variety of terrains and buildings to comprehensively test the performance of the Metaverse system in multiple scenarios;

[0053] Step 2: Copy the digital human, set the number of copied digital humans, and copy multiple digital human instances in the test scenario; configure different initial positions and states for each digital human.

[0054] In this embodiment, the specific steps of running the test scenario are as follows:

[0055] Step 1: Predefined path roaming mode: including predefined path roaming, random path roaming, and map navigation roaming mode;

[0056] Step 2: Set the selected automatic roaming algorithm: including predefined path roaming, random path roaming, and map navigation roaming;

[0057] Step 3. Set the quality of the Metaverse system, select a quality from high, medium, or low, and select one or more performance indicators to be collected: including frame rate, memory usage, network latency, frame time, etc.

[0058] Step 4: Run the test: The digital human roams according to the established path and scene settings, and the background automatically records performance indicator data and collects relevant abnormal error information.

[0059] In this embodiment, the specific steps of uploading test data to the chain are as follows:

[0060] Step 1: After the test, the non-embedded SDK performs preliminary processing and formatting on the collected performance indicator data, and automatically packages the processed data into transactions and sends them to the blockchain network and stores them on the blockchain through smart contracts;

[0061] Step 2: Verify the validity of the transaction and ensure it is successfully recorded on the blockchain.

[0062] In this embodiment, the specific steps of generating and analyzing image test results are as follows:

[0063] Step 1. Report generation: The performance test data analysis tool reads the performance indicator data processed by the non-embedded SDK from the chain; automatically generates a full-scene image performance test report containing charts and statistical data. The report contains three parts: test data, result analysis, and optimization suggestions. Test data: including the frame rate changes of the Metaverse system under different image qualities and resolutions, including the average frame rate AvgFPS, minimum frame rate MinFPS, maximum frame rate MaxFPS, median frame rate MedianFPS, average frame time AvgFTime, CPU utilization includes the unnormalized CPU utilization of the whole machine TotalCPU, the unnormalized CPU utilization of the process AppCPU, the normalized CPU utilization of the whole machine TotalNormalizedCPU, and the normalized CPU utilization of the process AppNormalizedCPU. Memory utilization includes physical memory usage RealMemoryUsage, virtual memory usage VirtualMemoryUsage, and whole machine available memory usage. Use the remaining memory AvailableMemory, network conditions including the number of upstream network bytes UploadBytes, the number of downstream network bytes DownloadBytes, the number of upstream network packets UploadPackets, the number of downstream network packets DownloadPackets, and the server IP delay ServerIPdelaytime; Result analysis: Based on the test baseline including the core indicators of frame rate, CPU utilization, memory utilization, and network delay, compare and analyze the test data to point out performance bottlenecks and problem areas; compare with the industry average to evaluate the overall performance of the system; Optimization suggestions: Propose targeted optimization suggestions based on the analysis results, including improving algorithms and optimizing resource allocation;

[0064] Step 2: Feedback the test report to the development team for subsequent performance improvement work.

[0065] In this embodiment, the specific implementation of predefined path roaming is: a series of points are determined in advance in the map of the metaverse, and these predefined path points are stored in a list in order, so as to form a clear path to guide the digital human how to move, ensure that the digital human can visit these points in sequence according to the order in the list, and add interactive instructions at specific path points, such as talking with NPCs, triggering special effects, etc. By adjusting the position and order of the path points, different roaming scenarios can be simulated.

[0066] In this embodiment, the specific implementation of random path roaming is as follows: determine a roaming area, which can be a part of the entire metaverse map or a specific area. Through a random number generation algorithm (such as a uniform distribution or normal distribution algorithm), select a number of path points, and store the generated random path points in a list to create a path. When adding path points, check whether the new point is within a certain distance from the last point in the list to ensure the connectivity of the path. The digital human will move according to the random points in the path. During the movement, interactive instructions can be added at specific path points, such as talking to NPCs or triggering special effects. At the same time, in order to ensure that the digital human does not collide with obstacles in the virtual environment, obstacle avoidance logic needs to be designed.

[0067] In this embodiment, the specific implementation of the map navigation algorithm is as follows: the A* map navigation algorithm is integrated. First, a target point is set, and the digital human needs to automatically plan a path to the location. Next, the metaverse map is converted into a graph model, in which nodes represent walkable locations, edges represent paths, and different weights are set for each edge to reflect the walking cost. During the path planning process, a suitable heuristic function (such as Manhattan distance or Euclidean distance) is selected to estimate the cost from the current node to the target point, thereby optimizing the search efficiency. The algorithm starts from the current position, gradually evaluates adjacent nodes, and selects the next node through calculation until the target point is found. During the path search process, the A* algorithm will avoid collisions, consider obstacle information, and ensure that the generated path is feasible. Once the path planning is completed, the digital human will automatically and accurately move to the target location along the calculated path.

[0068] In this embodiment, there are three optional roaming modes in the running test scenario, and the remaining steps are the same.

[0069] In this embodiment, by establishing a map panorama, nodes in the map form a browsing path of the map through mutual links. The algorithm first establishes a complete binary tree structure for each node with the current node as the root node. By analyzing the browsing history of the digital human and the association between nodes, a data foundation is established. Finally, different algorithms are selected, such as path roaming, random path roaming and map navigation algorithm, to realize the automatic roaming of the digital human in the virtual scene.

[0070] In this embodiment, the digital human automatically collects image performance indicators in the virtual scene during the roaming process. By testing different resolutions, performance indicators under different resolution conditions are collected, including frame rate (frame per second, fps), memory usage (MB), network delay (ms), frame consumption (ms), etc., and the system monitors the quality and speed of image acquisition in real time and records relevant data.

[0071] In this embodiment, blockchain technology is used to deploy multiple blockchain nodes using the blockchain platform as the test environment architecture. The digital human collects image performance data during roaming, and the data is preliminarily processed and formatted according to preset standards to generate standardized data records. The data records are protected by encryption, and then the encrypted records are stored on the chain through smart contracts to ensure the integrity and non-tamperability of the data. Finally, a complete image test report of the entire process is generated.

[0072] In this embodiment, intelligent navigation innovatively combines a variety of roaming paths. Combined with predefined path roaming, it can simulate specific behaviors or scenarios. The random path roaming algorithm can increase the unpredictability of roaming, allowing digital humans to freely explore in the test scenario and cover a wider area. This randomness helps to discover potential problems and improve the comprehensiveness and effectiveness of the test. The map navigation algorithm can automatically plan the shortest or optimal path from the starting point to the end point and move to the target location autonomously. This intelligent planning capability improves the efficiency and accuracy of roaming and reduces unnecessary movements and repeated paths. By combining predefined path roaming, random path roaming and map navigation algorithms, the roaming navigation algorithm can flexibly switch roaming strategies according to different scenarios and needs to achieve the best roaming effect. At the same time, the optimization and adjustment of the algorithm also takes into account the balance between system performance and user experience, ensuring stability and reliability in various situations.

[0073] In this embodiment, non-embedded (APP does not need to integrate SDK) automated performance test data collection is implemented. The SDK supports the collection of real performance data, provides exception capture, reporting and analysis, and locates function / module level performance bottlenecks from CPU scheduling to call stack. The tool itself has little impact on the CPU performance of the test device and has no impact on the frame rate.

[0074] Although the specific embodiments of the present invention are described in detail in conjunction with the accompanying drawings, it should not be understood as limiting the scope of protection of the present invention. Within the scope described in the claims, various modifications and variations that can be made by those skilled in the art without creative work still belong to the scope of protection of the present invention.

Claims

1. A non-embedded metaverse multiple automatic roaming performance testing method based on blockchain and digital human replication technology, characterized by: The test method includes setting up a hardware test environment, modeling a metaverse map, creating a digital human, creating a digital human test solution template, running a test scenario, uploading test data to the blockchain, and generating and analyzing image test results; The specific steps of creating a digital human test solution template are as follows: Step 1: Select a representative map from the Metaverse map as the test scene, set a fixed test duration, and the test scene includes a variety of terrains and buildings to comprehensively test the performance of the Metaverse system in multiple scenarios; Step 2: Copy the digital human, set the number of copied digital humans, and copy multiple digital human instances in the test scenario; configure different initial positions and states for each digital human; The specific steps of running the test scenario are as follows: Step 1: Set up automatic roaming algorithm: including predefined path roaming, random path roaming, and map navigation roaming; Step 2: Set the image quality of the Metaverse system, select a quality from high, medium, or low, and select one or more performance indicators to be collected: including frame rate, memory usage, network latency, and frame time. Step 3: Run the test: The digital human roams according to the established algorithm and scene settings, and the background automatically records the performance indicator data and collects relevant abnormal error information; The specific steps of uploading the test data to the chain are as follows: Step 1: After the test, the non-embedded SDK performs preliminary processing and formatting on the collected performance indicator data, and automatically packages the processed data into transactions and sends them to the blockchain network and stores them on the blockchain through smart contracts; Step 2: Verify the validity of the transaction and ensure it is successfully recorded on the blockchain.

2. A non-embedded metaverse multiple automatic roaming performance testing method based on blockchain and digital human replication technology as claimed in claim 1, characterized in that: The specific steps for building the hardware test environment are as follows: Step 1: Hardware preparation: Prepare computer test equipment equipped with high-performance CPU, large-capacity memory, and high-performance graphics card according to test requirements; Step 2: Software installation: Install and configure Metaverse image performance testing tools, data analysis tools, the Metaverse system under test, and the blockchain system software environment; Step 3: Environmental testing: Run the Metaverse system, testing tools, analysis tools, and blockchain system under test.

3. A non-embedded metaverse multiple automatic roaming performance testing method based on blockchain and digital human replication technology as claimed in claim 1, characterized in that: The specific steps of modeling the metaverse map are as follows: Step 1: Scenario analysis and node confirmation: Analyze the specific scenarios of the Metaverse map, including cities, mountains, and buildings, and mark key points as nodes, including buildings and intersections; Step 2: Panorama creation: Use 3D modeling software to build a panoramic 3D model of the metaverse, ensuring that the details and proportions of the model are consistent with the real scene. Navigation structure definition: For each node, use an algorithm to generate a complete binary tree with the node as the root. Each node in the tree represents a reachable location, and the edges represent possible movement paths.

4. A non-embedded metaverse multiple automatic roaming performance testing method based on blockchain and digital human replication technology as claimed in claim 1, characterized in that: The specific steps of creating a digital human are as follows: Step 1: Create a digital human according to the default template; set the action speed and jump height of the digital human to a fixed value range; Step 2: Interactive behavior design: Set the action combination of the digital human, and choose any combination from walking, running, and jumping; Step 3: Design the digital human dialogue system: set dialogue trigger conditions and multiple preset dialogue contents. The trigger conditions include clicking on the NPC or automatically triggering when being close to the NPC.

5. A non-embedded metaverse multiple automatic roaming performance testing method based on blockchain and digital human replication technology as claimed in claim 1, characterized in that: The specific steps of generating and analyzing the image test results are as follows: Step 1. Report generation: The performance test data analysis tool reads the performance indicator data processed by the non-embedded SDK from the chain; automatically generates a full-scene image performance test report containing charts and statistical data. The report contains three parts: test data, result analysis, and optimization suggestions. Test data: including the frame rate changes of the Metaverse system under different image quality and resolution, including the average frame rate AvgFPS, minimum frame rate MinFPS, maximum frame rate MaxFPS, median frame rate MedianFPS, average frame time AvgFTime, CPU utilization includes the unnormalized CPU utilization of the whole machine TotalCPU, the unnormalized CPU utilization of the process AppCPU, the normalized CPU utilization of the whole machine TotalNormalizedCPU, and the normalized CPU utilization of the process AppNormaliz edCPU, memory utilization includes physical memory usage RealMemoryUsage, virtual memory usage VirtualMemoryUsage, and available memory AvailableMemory of the whole machine; network conditions include upstream network bytes UploadBytes, downstream network bytes DownloadBytes, upstream network packets UploadPackets, downstream network packets DownloadPackets, and server IP delay ServerIPdelaytime; Result analysis: Based on the test baseline including frame rate, CPU utilization, memory utilization, and network delay core indicators, compare and analyze the test data to point out performance bottlenecks and problem areas; compare with the industry average to evaluate the overall performance of the system; Optimization suggestions: Propose targeted optimization suggestions based on the analysis results, including improving algorithms and optimizing resource allocation; Step 2: Feedback the test report to the development team for subsequent performance improvement work.

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