Performance testing method, device, medium and product for intelligent building three-dimensional view

Abstract

The application discloses a performance test method, device, medium and product of a smart building three-dimensional view, and relates to the technical field of smart home / smart home, and the performance test method of the smart building three-dimensional view comprises the following steps: determining test scene configuration parameters of a smart building, and generating a scene description file corresponding to the smart building based on the test scene configuration parameters; constructing a three-dimensional view instance corresponding to the smart building based on the scene description file, and determining a test operation sequence for performance test; performing operation test on the three-dimensional view instance according to the test operation sequence, and recording operation test data of the three-dimensional view instance; generating a test report of the three-dimensional view instance based on the operation test data, and sending the test report to a test user. The method realizes the unity and automation of a test scene and a test process, improves test consistency, reproducibility and coverage efficiency, enhances the positioning capability of performance anomalies, and improves test efficiency.

Description

Technical Field

[0001] This application relates to the field of smart home / intelligent home, and more specifically, to a performance testing method, device, medium, and product for a three-dimensional view of a smart building. Background Technology

[0002] With the increasing popularity of smart home devices, users have ever raised their expectations for the performance of 3D home view systems, including smooth interaction, loading speed, and memory usage. Currently, front-end performance testing of 3D home views typically involves manually or semi-automatically constructing test scenarios, executing interactive operations, and collecting performance data.

[0003] However, existing performance testing solutions are highly dependent on the operation of testers and the construction of scenarios, resulting in a lack of consistency between the complexity of test scenarios and the operation process. The construction of highly complex 3D scenarios takes a long time, and the repeatability and horizontal comparability of test results are insufficient. At the same time, the performance data collected is mostly global statistics, which are difficult to accurately correspond to specific interaction steps, affecting the efficiency of performance bottleneck location.

[0004] Therefore, improving the standardization, repeatability, and accuracy of problem localization in front-end performance testing of 3D home view has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a performance testing method, device, medium, and product for intelligent building 3D views, which solves the technical problems of low standardization, poor repeatability of results, and inaccurate location of performance issues in 3D home view front-end performance testing.

[0006] Firstly, this application provides a performance testing method for a three-dimensional view of an intelligent building, including:

[0007] The test scenario configuration parameters for the intelligent building are determined, and a corresponding scenario description file for the intelligent building is generated based on the test scenario configuration parameters. The test scenario configuration parameters are used to reflect the construction complexity of the three-dimensional test scenario of the intelligent building. The scenario description file is used to construct the three-dimensional test scenario of the intelligent building.

[0008] Based on the scene description file, construct a 3D view instance corresponding to the intelligent building and determine the test operation sequence for performance testing; the test operation sequence is a set of test actions to be executed on the 3D view instance.

[0009] Perform operation tests on the 3D view instance according to the test operation sequence, and record the operation test data of the 3D view instance;

[0010] Based on the operational test data, a test report of the 3D view instance is generated and sent to the test user.

[0011] In one possible implementation, determining the test scenario configuration parameters for the smart building includes:

[0012] Obtain the scenario configuration information input by the test user. The scenario configuration information includes at least one of the following: preset complexity level and custom scenario data.

[0013] Based on the scenario configuration information, the test scenario configuration parameters for intelligent buildings are determined.

[0014] In one possible implementation, the test scenario configuration parameters for the intelligent building are determined based on scenario configuration information, including:

[0015] When the scenario configuration information is at a preset complexity level, the configuration parameter library is searched according to the preset complexity level to obtain the test scenario configuration parameters for the intelligent building.

[0016] When the scenario configuration information is custom scenario data, the custom scenario data is parsed to obtain the test scenario configuration parameters for the intelligent building;

[0017] When the scenario configuration information includes a preset complexity level and custom scenario data, the configuration parameter library is searched according to the preset complexity level to obtain the initial configuration parameters of the smart building. Based on the custom scenario data, the initial configuration parameters are corrected to obtain the test scenario configuration parameters of the smart building.

[0018] The configuration parameter library is used to store different complexity levels of 3D test scenarios for intelligent buildings and the corresponding test scenario configuration parameters for each complexity level.

[0019] In one possible implementation, the test scenario configuration parameters include: number of floors, number of rooms, number of home models, number of smart devices, and lighting configuration; before generating the scene description file corresponding to the smart building based on the test scenario configuration parameters, the method further includes:

[0020] In response to the configuration actions of test users, determine the room type, home model type, and smart device type of the smart building;

[0021] Based on the test scenario configuration parameters, a scenario description file corresponding to the smart building is generated, including:

[0022] Based on the number of floors, number of rooms, room type, furniture model type, number of furniture models, smart device type, number of smart devices, and lighting configuration, a scene description file corresponding to the smart building is generated.

[0023] In one possible implementation, a scene description file corresponding to the smart building is generated based on the number of floors, number of rooms, room type, furniture model type, number of furniture models, smart device type, number of smart devices, and lighting configuration. This file includes:

[0024] Based on the number of floors, the number of rooms, and the room type corresponding to each room, determine the preset layout template corresponding to the smart building, and generate the unit structure data corresponding to the preset layout template.

[0025] Based on the number and type of home furnishing models, select the corresponding types and quantities of preset home furnishing models from the preset model library and generate a corresponding home furnishing model list; the home furnishing model list includes the home furnishing model identifier and preset placement position of each preset home furnishing model;

[0026] Based on the number and type of smart devices, the device identifier, device binding relationship, and initial state value of each smart device are determined, and a corresponding smart device list is generated; the device binding relationship is used to characterize the association state between each smart device and the corresponding physical model;

[0027] Based on the lighting configuration, determine the number of lights in each room and the initial working status of each light, and generate the corresponding lighting configuration list;

[0028] Based on the apartment layout data, home model list, smart device list, and lighting configuration list, determine the scene description file corresponding to the smart building.

[0029] In one possible implementation, the 3D view instance is subjected to operation tests according to a test operation sequence, and the operation test data of the 3D view instance is recorded, including:

[0030] The preset test rounds are executed cyclically according to the test operation sequence to perform repeated operation tests on the 3D view instance, and the corresponding operation test data are recorded according to the preset data dimensions.

[0031] The preset data dimensions include at least one of view state, device response, and lighting effects; the operation test data includes operation time, minimum frame rate during operation, and memory increment during operation.

[0032] In one possible implementation, a test report for a 3D view instance is generated based on the operational test data, including:

[0033] Determine the performance index thresholds for different performance metrics corresponding to each test operation in the test operation sequence;

[0034] For any test operation, determine the measured value of each performance indicator, and compare the measured value of each performance indicator with the corresponding performance indicator threshold.

[0035] Based on the comparison results, generate visualization charts corresponding to the operational test data;

[0036] Based on operational test data and visualization charts, generate a test report of 3D view instances;

[0037] The visualization charts include a time-consuming bar chart, a frame rate curve chart, and a memory curve chart. The time-consuming bar chart is used to represent the operation time of each test operation; the frame rate curve chart is used to represent the frame rate timing changes corresponding to the test operation sequence; and the memory curve chart is used to represent the memory timing changes corresponding to the test operation sequence.

[0038] Secondly, this application provides a performance testing device for a three-dimensional view of an intelligent building, comprising:

[0039] The processing module is used to determine the test scenario configuration parameters of the intelligent building and generate a corresponding scenario description file for the intelligent building based on the test scenario configuration parameters. The test scenario configuration parameters are used to reflect the construction complexity of the three-dimensional test scenario of the intelligent building. The scenario description file is used to construct the three-dimensional test scenario of the intelligent building.

[0040] The processing module is also used to construct a 3D view instance corresponding to the intelligent building based on the scene description file, and determine the test operation sequence for performance testing; the test operation sequence is a set of test actions to be executed on the 3D view instance.

[0041] The processing module is also used to perform operation tests on the 3D view instance according to the test operation sequence and record the operation test data of the 3D view instance.

[0042] The processing module is also used to generate a test report of the 3D view instance based on the operation test data, and send the test report to the test user.

[0043] In one possible implementation, the performance testing device for the three-dimensional view of a smart building further includes an acquisition module.

[0044] The acquisition module is used to acquire scene configuration information input by the test user. The scene configuration information includes at least one of the preset complexity level and custom scene data.

[0045] The processing module is also used to determine the test scenario configuration parameters of the intelligent building based on the scenario configuration information.

[0046] In one possible implementation, the processing module is further configured to, when the scenario configuration information is at a preset complexity level, search the configuration parameter library according to the preset complexity level to obtain the test scenario configuration parameters of the intelligent building.

[0047] The processing module is also used to parse the custom scene data when the scene configuration information is custom scene data, so as to obtain the test scene configuration parameters of the intelligent building.

[0048] The processing module is also used to search the configuration parameter library according to the preset complexity level when the scenario configuration information includes preset complexity level and custom scenario data, to obtain the initial configuration parameters of the intelligent building, and to correct the initial configuration parameters based on the custom scenario data to obtain the test scenario configuration parameters of the intelligent building.

[0049] The configuration parameter library is used to store different complexity levels of 3D test scenarios for intelligent buildings and the corresponding test scenario configuration parameters for each complexity level.

[0050] In one possible implementation, the processing module is further configured to determine the room type, home model type, and smart device type of the smart building in response to the configuration operation of the test user.

[0051] The processing module is also used to generate scene description files corresponding to smart buildings based on the number of floors, number of rooms, room type, furniture model type, number of furniture models, smart device type, number of smart devices, and lighting configuration.

[0052] In one possible implementation, the processing module is further configured to determine the preset layout template corresponding to the smart building based on the number of floors, the number of rooms, and the room type corresponding to each room, and generate the unit structure data corresponding to the preset layout template.

[0053] The processing module is also used to filter out the corresponding types and quantities of preset home models from the preset model library according to the number and type of home models, and generate the corresponding home model list; the home model list includes the home model identifier and preset placement position of each preset home model.

[0054] The processing module is also used to determine the device identifier, device binding relationship and initial state value of each smart device based on the number and type of smart devices, and generate the corresponding smart device list; the device binding relationship is used to characterize the association state between each smart device and the corresponding physical model.

[0055] The processing module is also used to determine the number of lights in each room and the initial working status of each light based on the lighting configuration, and to generate a corresponding lighting configuration list.

[0056] The processing module is also used to determine the scene description file corresponding to the smart building based on the apartment structure data, home model list, smart device list and lighting configuration list.

[0057] In one possible implementation, the processing module is further configured to cyclically execute a preset test round according to the test operation sequence, perform repeated operation tests on the 3D view instance, and classify and record the corresponding operation test data according to a preset data dimension.

[0058] The preset data dimensions include at least one of view state, device response, and lighting effects; the operation test data includes operation time, minimum frame rate during operation, and memory increment during operation.

[0059] In one possible implementation, the processing module is further configured to determine the performance index thresholds for different performance indicators corresponding to each test operation in the test operation sequence.

[0060] The processing module is also used to determine the measured values ​​of each performance indicator for any test operation, and to compare the measured values ​​of each performance indicator with the corresponding performance indicator threshold.

[0061] The processing module is also used to generate visualization charts corresponding to the operation test data based on the comparison results.

[0062] The processing module is also used to generate test reports of 3D view instances based on operational test data and visualization charts;

[0063] The visualization charts include a time-consuming bar chart, a frame rate curve chart, and a memory curve chart. The time-consuming bar chart is used to represent the operation time of each test operation; the frame rate curve chart is used to represent the frame rate timing changes corresponding to the test operation sequence; and the memory curve chart is used to represent the memory timing changes corresponding to the test operation sequence.

[0064] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0065] The memory stores instructions that the computer executes;

[0066] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0067] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0068] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0069] This application provides a performance testing method, equipment, medium, and product for intelligent building 3D views. The method involves: acquiring scene configuration information input by a test user; determining test scene configuration parameters for the intelligent building based on the scene configuration information; responding to the test user's configuration operation, determining the room type, furniture model type, and intelligent device type of the intelligent building; generating a scene description file corresponding to the intelligent building based on the number of floors, number of rooms, room type, furniture model type, number of furniture models, intelligent device type, number of intelligent devices, and lighting configuration; constructing a 3D view instance corresponding to the intelligent building based on the scene description file, and determining a test operation sequence for performance testing; repeatedly executing a preset test round according to the test operation sequence to perform repeated operation tests on the 3D view instance, and recording the corresponding operation test data according to a preset data dimension; generating a test report for the 3D view instance based on the operation test data, and sending the test report to the test user. This method generates structured data through parameterized configuration, solving the problem of incomparable test results caused by the reliance on manual construction of test scenarios and the lack of unified standards in existing technologies. Automated interface calls improve construction efficiency, realize the unification and automation of test scenarios and test processes, improve test consistency, reproducibility and coverage efficiency, enhance the ability to locate performance anomalies, and improve test efficiency. Attached Figure Description

[0070] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0071] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 A schematic diagram of the hardware environment for the performance testing method of the intelligent building 3D view provided in this application;

[0073] Figure 2 Flowchart of the performance testing method for the intelligent building 3D view provided in this application Figure 1 ;

[0074] Figure 3 Flowchart of the performance testing method for the intelligent building 3D view provided in this application Figure 2 ;

[0075] Figure 4 A schematic diagram of the performance testing device for the intelligent building 3D view provided in this application;

[0076] Figure 5 This is a schematic diagram of the structure of an electronic device provided in this application. Detailed Implementation

[0077] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0078] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0079] According to one aspect of the embodiments of this application, a performance testing method for a three-dimensional view of a smart building is provided. This performance testing method for a three-dimensional view of a smart building is widely used in whole-house intelligent digital control application scenarios such as smart homes, smart home ecosystems, smart home device ecosystems, and intelligence house ecosystems. Optionally, in this embodiment, the above-mentioned performance testing method for a three-dimensional view of a smart building can be applied to, for example... Figure 1 The hardware environment shown consists of terminal device 102 and server 104. For example... Figure 1 As shown, server 104 is connected to terminal device 102 via a network and can be used to provide services (such as application services) to the terminal or clients installed on the terminal. A database can be set up on the server or independently of the server to provide data storage services for server 104. Cloud computing and / or edge computing services can be configured on the server or independently of the server to provide data processing services for server 104.

[0080] The aforementioned network may include, but is not limited to, at least one of the following: wired network, wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network, metropolitan area network, local area network. The aforementioned wireless network may include, but is not limited to, at least one of the following: Wi-Fi (Wireless Fidelity), Bluetooth. The terminal device 102 may not be limited to PC, mobile phone, tablet computer, smart air conditioner, smart range hood, smart refrigerator, smart oven, smart stove, smart washing machine, smart water heater, smart washing equipment, smart dishwasher, smart projector, smart TV, smart clothes rack, smart curtains, smart audio-visual equipment, smart socket, smart speaker, smart speaker box, smart fresh air equipment, smart kitchen and bathroom equipment, smart bathroom equipment, smart robot vacuum cleaner, smart window cleaning robot, smart mopping robot, smart air purifier, smart steam oven, smart microwave oven, smart water heater, smart air purifier, smart water dispenser, smart door lock, etc.

[0081] With the increasing popularity of smart home devices, users have higher and higher requirements for the performance of 3D home views, such as the smoothness of interaction, loading speed, and memory usage. Among these requirements, front-end performance testing of 3D home views is an important step in verifying the operating efficiency and stability of the smart home 3D environment in complex scenarios.

[0082] A 3D home view is a visual view obtained by digitally modeling the corresponding building space. In the front-end performance testing of a 3D home view, test users usually need to rely on the test control environment, the terminal under test, and the 3D view loading capability to verify the performance of a 3D scene composed of building layout, floor space, equipment objects, and other content.

[0083] Currently, in the front-end performance testing of 3D home view, test scenarios are typically constructed manually or semi-automatically, interactive operations are performed, and performance data is collected.

[0084] Specifically, testers pre-build architectural test scenarios, configure objects such as floors, rooms, models, and equipment, and then enter the 3D view to perform interactive actions such as switching floors, entering pages, editing or manipulating objects. At the same time, performance tools are used to record data such as frame rate, time consumption, and memory usage. The basic principle of this performance test is to artificially construct test scenarios and trigger interactive processes, and then judge the running status of the tested 3D view based on the collected results.

[0085] However, existing performance testing solutions rely heavily on manual setup and operation. Different testers often construct scenarios with varying complexity, number of objects, and operational rhythms, resulting in a lack of standardized test scenarios and difficulties in reproducing and comparing test results consistently. For complex 3D scenarios with multiple floors and large-scale objects, the construction process significantly lengthens the test preparation cycle and reduces test coverage. Furthermore, existing performance data is mostly presented as global statistics, making it difficult to accurately correlate with specific test actions. This makes it challenging to quickly pinpoint the source of performance fluctuations, impacting subsequent optimization efficiency.

[0086] In view of this, how to improve the standardization, repeatability and accuracy of problem localization in the front-end performance testing of 3D home view has become an urgent technical problem to be solved.

[0087] The performance testing method for intelligent building 3D views provided in this application aims to solve the aforementioned technical problems of existing technologies. This method abstracts the complexity of the test scenario into quantifiable parameters (such as the number of rooms, models, and devices) and generates corresponding scenario description files. Using these scenario description files, combined with automated interface calls, test view instances are generated, and the test operation sequences are scripted. Then, the 3D view instances are operated according to these test operation sequences, and the operation test data is recorded. A test report is generated based on the operation test data and sent to the test user, thereby making the performance testing process more consistent and usable. This method ensures scenario uniformity through parameterized configuration, improves construction efficiency through automated interface calls, and guarantees test repeatability through scripted operation sequences. This enables standardized testing of 3D view performance based on preset scenario complexity and test tasks, thereby improving the standardization, repeatability, and analytical value of test results, as well as increasing testing efficiency.

[0088] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0089] In the embodiments of this application, the execution subject of the following embodiments can be a 3D view performance testing system deployed on a test server, or a test control module integrated into a related platform or test tool.

[0090] Figure 2 A flowchart illustrating the performance testing method for the intelligent building 3D view provided in the embodiments of this application. Figure 1 The execution entity in this embodiment can be, for example, a 3D view performance testing system. Figure 2As shown, the performance testing method for intelligent building 3D views provided in this embodiment includes:

[0091] S201: Determine the test scenario configuration parameters for the intelligent building, and generate a corresponding scenario description file for the intelligent building based on the test scenario configuration parameters.

[0092] Among them, the test scenario configuration parameters are used to reflect the construction complexity of the 3D test scenario of the intelligent building, and the scenario description file is used to construct the 3D test scenario of the intelligent building.

[0093] Understandably, intelligent buildings refer to various types of buildings equipped with intelligent facilities. These intelligent buildings cover a variety of application forms, such as residential buildings, including detached villas, duplexes, and standard apartments; and commercial and public buildings, including office buildings, integrated parks, and commercial venues.

[0094] The test scene configuration parameters include various settings required for building a 3D scene. These test scene configuration parameters can comprehensively reflect the construction complexity of the intelligent building 3D test scene from multiple dimensions such as spatial scale, number of models, and layout structure, so as to achieve standardized description and quantitative differentiation of the complexity of the 3D scene. In specific implementation, the test scene configuration parameters can be expressed in the form of a parameter set to characterize the scene scale, number of objects, and rendering load.

[0095] The scene description file is a carrier file used to build a 3D test scene for intelligent buildings, providing standardized and unified data support for the construction of 3D test scenes. In specific implementations, the scene description file can be saved in the form of JSON (JavaScript Object Notation), XML (eXtensible Markup Language), or other structured data files, which record the scene data and related parameter information used for scene construction, as input for the subsequent automatic generation of 3D view instances.

[0096] In one possible implementation, after determining the test scenario configuration parameters, the performance testing system can verify the integrity of the parameters and generate corresponding scenario construction data according to preset rules.

[0097] In this embodiment of the application, the performance testing system has pre-built preset allocation rules for room allocation and furniture and appliance placement; at the same time, the rooms are pre-classified by type, and a unified standard apartment structure and fixed layout template are configured for each type of room, and the standard apartment structure and fixed layout template corresponding to each type of room are pre-stored in the performance testing system.

[0098] After determining the test scenario configuration parameters, the performance testing system determines the ratio of various types of rooms based on the pre-stored room allocation and furniture and appliance placement rules, combined with the currently determined number of floors and total number of rooms. Then, the system calls the corresponding apartment template to complete the spatial arrangement of various types of rooms. Then, combined with the quantity parameters of furniture, smart devices, and lighting fixtures, the corresponding placement is completed within the template and compiled into a scenario description file in a preset format.

[0099] For example, if the current test scenario configuration parameters include 4 rooms, 2 floors, 16 home models, 12 smart devices, and 10 lights, the system will complete the room type allocation and space arrangement based on preset rules, and complete the placement of various objects in the standard apartment template. Then, it will integrate all the scene information and compile and generate the corresponding scene description file according to the JSON or XML structured format.

[0100] Understandably, by transforming the scene construction process into a parameter-driven and documented definition process, the scale, number of objects, and rendering load of the 3D test scene can be described with a unified basis, thereby ensuring that the 3D view instances constructed subsequently remain consistent, solving the problem of test scenes lacking a unified standard and results being difficult to reproduce stably.

[0101] S202: Based on the scene description file, construct a 3D view instance corresponding to the intelligent building and determine the test operation sequence for performance testing.

[0102] The test operation sequence is a set of test actions performed on a 3D view instance.

[0103] Understandably, a 3D view instance is used to host an executable 3D test scenario for performance testing. Essentially, a 3D view instance is a collection of loadable scene objects generated in a 3D rendering engine based on a scene description file.

[0104] In its implementation, the performance testing system reads the scene description file determined for the current time, completes the scene object assembly based on the scene data recorded therein, initializes the corresponding display parameters, and finally generates an operable 3D view instance.

[0105] A test operation sequence is a set of test actions performed on a 3D view instance. This test operation sequence can be determined by a task sequence approach. The test operation sequence can include one or more test actions for the 3D view instance to test its performance. By performing operation tests on the 3D view instance through the test operation sequence, the test operation sequence no longer depends on temporary operations by testers, but forms a standard chain of repeatable actions.

[0106] In one possible embodiment, after constructing a 3D view instance, the performance testing system can perform an initialization check on scene availability, verify indicators such as the integrity of the test scene and the validity of parameters, and determine the corresponding test operation sequence when all indicators meet the test conditions.

[0107] For example, the performance testing system can verify whether the components of the intelligent building 3D model are missing, whether the rendering resolution / viewpoint parameters are valid, and whether the interaction interface is normal. If the model is complete, the parameters are compliant, and the interface is available, it is judged to pass, and then the corresponding test operation sequence is matched.

[0108] In one possible implementation, the test operation sequence can adopt a layered generation logic, which specifically includes: first selecting common basic test actions, and then combining the scene structure, functional modules and other content of the 3D view instance to generate subsequent test actions in a targeted manner, thereby forming a complete test operation sequence.

[0109] For example, basic test actions include view rotation and view zoom; combined with specific scene content such as the floor layout of smart buildings, smart devices, and home models, corresponding targeted actions such as floor switching and clicking to view smart devices / home models can be added, and the two can be combined to obtain a complete test operation sequence.

[0110] In another possible implementation, the performance testing system can also adjust the test operation sequence (such as adding, deleting, sorting, modifying parameters, etc.) to adapt to the testing requirements under different testing scenarios and different testing standards.

[0111] Understandably, by using scripted operation sequences, that is, by arranging various test actions in an orderly manner according to execution logic and automatically replaying and executing them by the performance testing system, the construction of test scenarios and test actions can be standardized at the same time. This ensures that the configuration parameters of the tested object and the test scenario are consistent, and that the test process has a fixed order and fixed conditions. As a result, the collected performance data can be accurately associated with specific actions, making it easier to locate the source of performance fluctuations in the future.

[0112] In one possible implementation, the generation of 3D view instances can also be achieved by passing the structured data (including apartment structure, model layout and equipment binding relationship) in the standardized test scenario description file as parameters to the backend interface of the terminal under test, and automatically generating a loadable 3D test scenario through interface calls, thereby realizing the automated conversion from abstract description to specific visualization instance.

[0113] In this embodiment of the application, the processing flow of the 3D view instance includes an instance construction stage and a rendering and display stage. The instance construction stage is to complete the assembly of 3D objects and the configuration of display parameters by interpreting the scene description file, and generate a set of scene objects that can be called by the rendering engine. This stage only performs data processing and does not generate a visual screen. The rendering and display stage is a post-triggered screen output stage. When the test operation sequence is started and executed, the 3D view instance is rendered in real time and the corresponding 3D view is displayed to the test user.

[0114] S203: Perform operation tests on the 3D view instance according to the test operation sequence, and record the operation test data of the 3D view instance.

[0115] Understandably, the operational test data is used to record the performance of the 3D view instance during the operational test and serves as the basis for generating the test report.

[0116] The performance testing system is equipped with an automation control module and a performance acquisition module. When executing the test operation sequence, the automation control module is used to parse the test operation sequence and automatically issue test instructions to drive the terminal under test to execute each test action in sequence. At the same time, the performance acquisition module captures and summarizes various types of running data generated during the test in real time.

[0117] To ensure the correspondence between test operations and test data, this performance testing system can record corresponding time stamps and sampling information during the execution of test actions, and generate corresponding operation test data.

[0118] In one possible implementation, the performance testing system executes test actions one by one in the order of the test operation sequence, and records the corresponding performance data during the execution of each test action; that is, the performance testing system establishes a correlation record between test operations and test data for subsequent data processing and analysis.

[0119] Understandably, by using action-driven and interval-based data collection to drive the execution of test operations, performance data can be correlated with actual triggered actions, thus solving the problem that the collected results are difficult to accurately correspond to specific test actions.

[0120] S204: Based on the operation test data, generate a test report for the 3D view instance and send the test report to the test user.

[0121] The test report is used to summarize operational test data and output performance test results.

[0122] After the operation test is completed, the performance testing system organizes, statistically analyzes, and generates results from the recorded operation test data to form a corresponding test report. The generated test report can be saved as an electronic document or a report object within the platform and sent to the test user via methods such as message notification, email, API feedback, or download link.

[0123] In one possible implementation, when generating a test report, the performance testing system can first read the scenario information, test operation sequence information, and operation test data corresponding to the test task to form a complete test context; then, the performance testing system outputs the performance results corresponding to each test action based on the operation test data and presents the relevant test conclusions in the test report.

[0124] In another possible implementation, the performance testing system can store the test report before sending it to the test user for later review and comparison.

[0125] It is understood that the embodiments of this application unify scene definition with scene description files, unify test actions with test operation sequences, establish action-level performance correlations with operation test data, and summarize and output results with test reports, thereby transforming the performance testing of intelligent building 3D views from manual construction, manual operation, and global coarse-grained statistics into a standardized process of scene generation, standardized operation execution, and result output.

[0126] Therefore, under the same test scenario configuration parameters and the same test operation sequence, consistent 3D view instances can be repeatedly constructed and comparable performance results can be obtained. At the same time, when performance degradation occurs, anomalies can be associated with specific test actions, thereby improving the standardization, repeatability, and accuracy of problem localization in the performance testing of intelligent building 3D views.

[0127] The performance testing and control method for intelligent building 3D views provided in this embodiment determines the test scenario configuration parameters of the intelligent building and generates a corresponding scenario description file based on these parameters. Based on the scenario description file, a 3D view instance corresponding to the intelligent building is constructed, and a test operation sequence for performance testing is determined. Operation tests are performed on the 3D view instance according to the test operation sequence, and the operation test data of the 3D view instance is recorded. Based on the operation test data, a test report for the 3D view instance is generated and sent to the test user. This method generates structured data through parameterized configuration, solving the problem of incomparable test results caused by the reliance on manual construction of test scenarios and the lack of unified standards in existing technologies. It achieves the unification and automation of test scenarios and test processes, improves test consistency, reproducibility, and coverage efficiency, enhances the ability to locate performance anomalies, and improves test efficiency.

[0128] Figure 3 A flowchart illustrating the performance testing method for the intelligent building 3D view provided in the embodiments of this application. Figure 2 .like Figure 3 As shown, in this embodiment... Figure 2 Based on the embodiments, the performance testing method for intelligent building 3D views is described in detail. The performance testing method for intelligent building 3D views shown in this embodiment includes:

[0129] S301: Obtain the scenario configuration information input by the test user.

[0130] S302: Based on the scenario configuration information, determine the test scenario configuration parameters for the intelligent building.

[0131] The scenario configuration information is the overall configuration content set by toilet users for the three-dimensional test scenario of the intelligent building, and is the original input data for generating the test scenario configuration parameters; the scenario configuration information includes at least one of the preset complexity level and custom scenario data.

[0132] Understandably, the preset complexity level is a pre-set feature of the performance testing system, used to classify and solidify the scene complexity levels, such as L1, L2, and L3. Specifically, when the preset complexity level is L1, it corresponds to the simple level. At this time, the configuration parameters of the 3D test scene include: number of rooms ≤ 3, number of models ≤ 5, number of devices ≤ 3, number of lights (light fixtures) ≤ 2, and number of floors = 1. When the preset complexity level is L2, it corresponds to the medium level. At this time, the configuration parameters of the 3D test scene include: number of rooms 6~10, number of models 15~20, number of devices 10~15, number of lights (light fixtures) 5~10, and number of floors ≤ 2. When the preset complexity level is L3, it corresponds to the complex level. At this time, the configuration parameters of the 3D test scene include: number of rooms 12~20, number of models 30~50, number of devices 20~40, number of lights (light fixtures) 15~25, and number of floors ≤ 3.

[0133] Custom scenario data refers to non-standardized test scenarios that are set by the user, such as the number of floors, rooms, furniture models, and smart devices, when the test user manually configures the parameters.

[0134] The system obtains the scenario configuration information input by the test user, parses and processes the obtained scenario configuration information, and obtains the test scenario configuration parameters for the intelligent building.

[0135] In one possible implementation, if the scenario configuration information is at a preset complexity level, the configuration parameter library is searched according to the preset complexity level to obtain the test scenario configuration parameters for the intelligent building; if the scenario configuration information is custom scenario data, the custom scenario data is parsed to obtain the test scenario configuration parameters for the intelligent building; if the scenario configuration information includes both a preset complexity level and custom scenario data, the configuration parameter library is searched according to the preset complexity level to obtain the initial configuration parameters for the intelligent building, and the initial configuration parameters are corrected based on the custom scenario data to obtain the test scenario configuration parameters for the intelligent building.

[0136] The configuration parameter library is used to store different complexity levels of 3D test scenarios for intelligent buildings and the corresponding test scenario configuration parameters for each complexity level.

[0137] Understandably, the configuration parameter library is a built-in data storage unit of the performance testing system, used to store various complexity levels of intelligent building 3D test scenarios, as well as the test scenario configuration parameters corresponding to each complexity level. After the test user inputs any complexity level, the performance testing system calls the matching parameters in the configuration parameter library to quickly generate a standard test scenario.

[0138] The initial configuration parameters are a set of basic parameters retrieved from the configuration parameter library when both preset complexity levels and custom scene data are included.

[0139] After receiving the scenario configuration information input by the test user, if the scenario configuration information only contains a preset complexity level, the system reads the configuration parameter template corresponding to that complexity level from the configuration parameter library and outputs the parameters in the template as the initial test scenario configuration parameters. If the scenario configuration information only contains custom scenario data, the system performs field parsing, format validation, and parameter mapping on the custom scenario data, converting it into configuration parameters that can be used to construct a 3D test scenario. If both exist, the system first determines the initial test scenario configuration parameters based on the preset complexity level, and then corrects the number of floors, number of objects, spatial distribution, or interaction constraints based on the custom scenario data to obtain the final test scenario configuration parameters.

[0140] In this embodiment, parameter mapping can be accomplished through a preset field table, which is used to establish a one-to-one correspondence between user input fields and scene construction fields. In practical applications, other mapping rules can also be selected for the field table, but this embodiment does not limit this.

[0141] For example, when only a preset complexity level is input, the system reads the number of floors, rooms, furniture models, smart devices, and lighting configuration parameters corresponding to that level from the configuration parameter library and uses them as test scene configuration parameters; when only custom scene data is input, the system parses the key-value items, object items, or parameter items in it and converts the parsing results into configuration parameters that can be used to construct a 3D test scene; when both are input simultaneously, the system first extracts the initial configuration parameters according to the preset complexity level, and then adds the new, replaced, or modified items in the custom scene data to the initial configuration parameters to form the final test scene configuration parameters.

[0142] In one possible implementation, the test scenario configuration parameters may include the number of floors, room layout, furniture model density, number of smart devices, loading range, and object interactivity attributes.

[0143] In one possible implementation, the configuration parameter library can be implemented as a database table, key-value store, or structured file to stably store and quickly match different complexity levels with corresponding parameter combinations. This application does not impose any special restrictions on the configuration parameter library.

[0144] Understandably, by transforming user input into unified test scenario configuration parameters, test scenarios can be generated quickly according to preset complexity and adapted to personalized inputs, thereby improving the consistency and reproducibility of test scenario construction and providing a definite input basis for subsequent scenario description file generation, 3D view instance construction, and performance test data collection.

[0145] Since the test scenario configuration parameters directly correspond to the scenario building elements, it can reduce the differences in manual configuration and make the scenario building complexity and object distribution under different test tasks repeatable.

[0146] S303: In response to the configuration operation of the test user, determine the room type, home model type, and smart device type of the smart building.

[0147] S304: Generate scene description files for smart buildings based on the number of floors, number of rooms, room type, furniture model type, number of furniture models, smart device type, number of smart devices, and lighting configuration.

[0148] The test scenario configuration parameters include: number of floors, number of rooms, number of home models, number of smart devices, and lighting configuration.

[0149] Understandably, room type refers to the classification and definition of functional spaces within a smart building. For example, based on usage, they can be divided into bedrooms, living rooms, kitchens, bathrooms, corridors, offices, etc. Furniture model type refers to the classification of various furniture and soft furnishing models that can be placed within a smart building, such as sofas, beds, tables and chairs, cabinets, etc. Smart device type refers to the categories of smart hardware with interactive and control functions that can be configured within a smart building, such as smart TVs, smart air conditioners, smart refrigerators, etc.

[0150] After the test user inputs the number of floors, rooms, furniture models, smart devices, and lighting configurations through the configuration interface, and the performance testing system receives the configuration operation, the performance testing system interacts with the test user to further determine the room type, furniture model type, and smart device type, and writes all kinds of parameters into the scene description file.

[0151] Understandably, the scene description file can be saved in a structured text format or a hierarchical data format. The number of floors and rooms is used to determine the floor plan framework, the room type is used to limit the room distribution, the furniture model type and number of furniture models are used to determine the furniture model selection results, the smart device type and number of smart devices are used to determine the device list, and the lighting configuration is used to determine the lighting status of each room. The scene description file generated by the performance testing system can be directly read by the subsequent 3D view instance construction module and used to reconstruct the corresponding smart building test scene.

[0152] In one possible implementation, after determining the test scenario configuration parameters, the performance testing system actively interacts with the test user to obtain the room type, home model type, and smart device type corresponding to the smart building.

[0153] The performance testing system displays a list of preset room types, home model types, and smart device types on the credit configuration interface. Test users can independently select the corresponding categories according to their testing needs. The performance testing system collects the test users' selection results to determine the final room type, home model type, and smart device type.

[0154] For example, the interaction methods between this performance testing system and the test user include conventional human-computer interaction methods such as drop-down selection, single / multiple selection, and manual input. This application does not impose any special restrictions on the interaction form.

[0155] Understandably, by writing the number of floors, rooms, and type information into the scene description file, the scene structure, furniture model layout, and device binding relationships can be kept consistent. This ensures that the same configuration corresponds to the same test scene definition, making the generated test scene results reproducible and resolvable. It also enables different test tasks to build 3D view instances based on a unified structured file, thereby improving the accuracy of the correspondence between scene configuration and test objects.

[0156] In one possible implementation, based on the number of floors, the number of rooms, and the room type corresponding to each room, a preset layout template for the smart building is determined, and the corresponding apartment structure data is generated. According to the number and type of home furnishing models, preset home furnishing models of the corresponding type and matching quantity are selected from the preset model library, and a corresponding home furnishing model list is generated. The home furnishing model list includes the home furnishing model identifier and preset placement location of each preset home furnishing model. Based on the number and type of smart devices, the device identifier, device binding relationship, and initial state value of each smart device are determined, and a corresponding smart device list is generated. The device binding relationship is used to characterize the association state between each smart device and the corresponding physical home furnishing model. Based on the lighting configuration, the number of lights in each room and the initial working state of each light are determined, and a corresponding lighting configuration list is generated. Based on the apartment structure data, the home furnishing model list, the smart device list, and the lighting configuration list, a scene description file corresponding to the smart building is determined.

[0157] Understandably, device binding relationships are used to characterize the association status between each smart device and its corresponding physical model; preset layout templates are standardized spatial layout schemes pre-stored in the performance testing system. Specifically, based on the number of floors, the number of rooms, and the room type, fixed layout rules are specified for room arrangement, space partitioning, wall and passageway positions, which can quickly match and generate the overall building unit frame.

[0158] Apartment layout data refers to structured data obtained by parsing a preset layout template, given the current number of floors, number of rooms, and room type. It is used to record spatial geometry and layout information such as floor distribution, room outlines, spatial coordinates, and area division of smart buildings, and is the basic spatial data for building three-dimensional scenes.

[0159] The furniture model list refers to a structured list that summarizes all the furniture models required for this test scenario. Each entry contains a unique furniture model identifier, the model's preset layout coordinates and placement position in the scene, which guides the 3D engine to load and place the corresponding furniture model.

[0160] The smart device list refers to a list of information about all smart devices in the test scenario. This list includes the unique identifier of the device, the binding relationship between the device and the home model, and the initial operating status value after the device is powered on, which is used to complete the device mounting and status initialization.

[0161] The lighting configuration list refers to the configuration list generated for lighting fixtures in the test scenario. It counts the number of lights in each room and defines the initial on / off status parameters, brightness, color temperature, etc. of each light fixture, and manages the lighting configuration of the entire test scenario in a unified manner.

[0162] In its implementation, the performance testing system receives the number of floors, the number of rooms, and the room type corresponding to each room. It then compares these data with the template parameters in the preset layout template library, selects templates that meet the requirements for floor level, total number of rooms, and room functional zoning, and generates the apartment structure data accordingly.

[0163] For home furnishing models, the performance testing system accesses the preset model library based on the number and type of home furnishing models, matches the categories and quantities of home furnishing models, extracts the corresponding preset home furnishing models, and writes a home furnishing model identifier and preset placement position for each home furnishing model to form a home furnishing model list.

[0164] For smart devices, the performance testing system determines the device identifier based on the number and type of smart devices, whether there is a directly usable physical model for the corresponding smart device, and determines the room to which each smart device belongs, the corresponding home model or the spatial unit where the device is located. At the same time, it assigns an initial state value to each smart device to form a smart device list.

[0165] Regarding lighting configuration, the performance testing system determines the number of light fixtures based on the lighting parameters of each room, sets the initial working state for each light fixture, and generates a lighting configuration list.

[0166] Finally, the performance testing system writes the apartment layout data, home model list, smart device list, and lighting configuration list into the same scene description file for subsequent loading of 3D view instances.

[0167] In one possible implementation, for any smart device, if a directly usable physical model exists, the smart device is bound to the corresponding physical model; during subsequent 3D view instance construction, the physical model is directly called to construct the 3D model of the corresponding smart device. If no directly usable physical model exists for the smart device, a device card is constructed, and this device card is unique; during subsequent 3D view instance construction, this device card is called to replace the 3D construction of the smart device, and this device card can meet the operational requirements of all control functions of the corresponding smart device.

[0168] Understandably, since the identifiers, locations, and binding relationships of each object are pre-fixed in the file, the corresponding building scene can be directly restored according to the scene description file after the 3D view instance is loaded, thereby improving the standardization of scene construction and the consistency of repeated execution.

[0169] Using apartment layout data as the framework, home models, smart devices, and lighting configurations are written into the scene description file according to unified rules, enabling the spatial structure, object distribution, and initial state of the 3D scene to be reconstructed consistently. At the same time, the scene description file can simultaneously reflect the building layout, home model placement, device association, and lighting status, reducing the differences caused by manual construction and ensuring that the test scene remains consistent across different terminals and test rounds. This helps improve the comparability of performance test results and facilitates subsequent location analysis of specific objects and state changes.

[0170] S305: Based on the scene description file, construct a 3D view instance corresponding to the intelligent building and determine the test operation sequence for performance testing.

[0171] Step S305 is similar to step S202 above, and will not be described again here.

[0172] S306: Execute the preset test rounds in a loop according to the test operation sequence, perform repeated operation tests on the 3D view instance, and record the corresponding operation test data according to the preset data dimensions.

[0173] S307: Based on the operation test data, generate a test report of the 3D view instance and send the test report to the test user.

[0174] The preset data dimensions include at least one of view state, device response, and lighting effects; the operation test data includes operation time, minimum frame rate during operation, and memory increment during operation.

[0175] Understandably, the minimum frame rate refers to the minimum frame rate value collected within the time interval of the test operation, and the memory increment refers to the difference in memory usage of the tested terminal before and after the test operation.

[0176] The test operation sequence can consist of actions such as floor switching, view rotation, object selection, equipment control, and lighting adjustment. The performance test system executes the sequence cyclically according to the preset test rounds. Each round starts from the same initial view state to ensure the consistency of repeated operation tests. After the cyclic execution is completed, a test report of the 3D view instance is generated based on the operation test data collected in that round, and the test report is sent to the test user.

[0177] In this embodiment, the preset data dimensions can be specified in advance by the test configuration file. When the test focuses on interface rendering, the view state dimension is used; when the test focuses on device linkage, the device response dimension is used; and when the test focuses on lighting rendering, the lighting effect dimension is used. Multiple dimensions can also be enabled to record in parallel.

[0178] To achieve the recording function, the system initializes the collector at the beginning of each test round, and simultaneously starts timing and performance sampling at the operation trigger node. After the operation is completed, the collected time, minimum frame rate and memory increment are bound to the corresponding dimensions and stored to form a traceable test data set.

[0179] Understandably, by fixing the test operation sequence and repeatedly executing the preset test rounds, the same 3D view instance can obtain multiple rounds of comparable data under the same conditions. Then, by combining the view status, device response, and lighting effects for classification and recording, the time consumption, frame rate, and memory indicators can be mapped to specific test focus points, thereby providing a structured data foundation for subsequent test report generation and performance positioning.

[0180] In one possible implementation, the loop execution strategy includes a complete sequence loop strategy and a split action loop strategy.

[0181] The complete sequence loop refers to the execution of all floor switching, view rotation, equipment control and other actions in a predetermined order in each round. After one round is completed, the view is reset to the initial view before starting the next round. This complete sequence loop strategy is suitable for simulating continuous operation scenarios of the entire user process.

[0182] Split action loop refers to selecting a portion of the operation sequence in the test operation sequence and looping it separately, while the remaining actions are not repeated. It is often used for stress testing and stability verification of high-frequency interactive actions and core functional actions.

[0183] Understandably, both loop strategies can be switched in the configuration interface to adapt to diverse testing scenarios.

[0184] In one possible implementation, performance index thresholds for different performance indicators corresponding to each test operation in the test operation sequence are determined; for any test operation, the measured values ​​of each performance indicator are determined, and the measured values ​​of each performance indicator are compared with the corresponding performance index thresholds; based on the comparison results, a visualization chart corresponding to the operation test data is generated; based on the operation test data and the visualization chart, a test report of a 3D view instance is generated.

[0185] Among them, the performance index threshold refers to the pre-set performance judgment standard value, which is the critical value for judging whether the performance of the 3D view meets the standard; the actual index value refers to the real running data obtained and statistically analyzed in real time by the performance acquisition module during the performance test, which is used to reflect the actual performance of the corresponding performance indicators such as time consumption, frame rate, and memory when each test operation is executed.

[0186] The visualization charts include a time-consuming bar chart, a frame rate curve chart, and a memory curve chart. The time-consuming bar chart is used to represent the operation time of each test operation; the frame rate curve chart is used to represent the frame rate timing changes corresponding to the test operation sequence; and the memory curve chart is used to represent the memory timing changes corresponding to the test operation sequence.

[0187] For example, performance metric thresholds include the first loading time of the 3D test scene (6s), subsequent loading time (4s), floor switching (1s), editing, saving and returning (3s), device control response (500ms), sliding average frame rate (50fps), and memory leak detection (memory growth greater than 100MB after 10 loops).

[0188] After receiving the operation test data, the performance testing system first establishes a mapping relationship between operations and performance indicators based on the test operation sequence, and obtains the performance indicator thresholds corresponding to each test operation. The performance testing system then parses the measured value of the indicator for any test operation within the execution interval from the operation test data, compares it with the corresponding threshold, and marks the performance status of the operation based on the comparison results of exceeding the threshold, approaching the threshold, or meeting the threshold. Next, the system organizes the measured data of each operation according to the time sequence of the test operation sequence, and generates corresponding time-consuming bar charts, frame rate curves, and memory curves. Among them, the time-consuming bar chart uses the height of the bars to represent the operation time of each test operation, the frame rate curve uses the curve change to represent the frame rate time sequence change corresponding to the test operation sequence, and the memory curve uses the curve change to represent the memory time sequence change corresponding to the test operation sequence. Abnormal fluctuation positions can be marked in the corresponding intervals.

[0189] Based on the above charts and operational test data, the system automatically generates a 3D view instance test report containing test conclusions, threshold comparison results, and performance change illustrations, and sends it to the test user.

[0190] In one possible implementation, for memory leak detection, the system automatically calculates the memory value M0 before the first entry and the memory value M10 after the tenth exit. If the difference between the two is greater than 100MB, a red warning is output in the test report, such as "Memory leak risk: Abnormal memory growth after 10 repeated operations".

[0191] For example, the time-consuming bar chart graphically represents the execution time of each test operation, enabling a direct comparison of the time consumption indicators for each single toilet operation, and marks the threshold reference line; the frame rate curve chart uses time as the horizontal axis and frame rate as the vertical axis, dividing the time periods corresponding to different test operations and highlighting the areas with frame rates below the threshold, thereby representing the continuous change trend of the screen frame rate over time during the operation of the complete test operation sequence, reflecting the real-time smoothness of view rendering; the memory curve chart divides the time period regions corresponding to each test operation and marks the memory fallback status after a single loop execution, thereby representing the continuous change trend of device memory usage over time during the operation of the complete test operation sequence, monitoring memory usage and release.

[0192] Understandably, test reports can correlate the measured values ​​of different test operations with preset thresholds and present the temporal changes of operation time, frame rate, and memory usage in a graphical way, so that the operation test data and specific test actions form a one-to-one correspondence, thereby improving the readability of the report and the accuracy of problem location.

[0193] In one possible implementation, when generating the scene description file, the specific parameters in the scene description file can be dynamically adjusted according to the hardware configuration and network environment of the terminal device under test; specifically including at least one of the following: dynamically adjusting the number of rooms based on the GPU (Graphics Processing Unit) performance of the terminal device; dynamically adjusting the number of models based on the memory capacity of the terminal device; and dynamically adjusting the lighting configuration based on the network bandwidth of the terminal device.

[0194] Understandably, the GPU is the core hardware for rendering 3D scene space and drawing images. The number of rooms in the scene directly determines the overall space rendering volume and GPU computing load. Therefore, the number of rooms is adaptively adjusted according to the GPU performance. Device memory is mainly used for loading and caching various 3D model resources. The number of furniture models and smart devices directly occupies device memory space. Therefore, the number of furniture and device models in the scene is adaptively adjusted according to the memory capacity of the tested terminal. Dynamic lighting effects and light and shadow loading and rendering rely on network transmission resources to complete resource synchronization and loading. Network bandwidth directly affects the loading stability and rendering effect of lighting configuration. Therefore, the complexity of scene lighting configuration is adaptively adjusted according to the real-time network bandwidth status.

[0195] In other words, by collecting the GPU performance, memory capacity, and network bandwidth parameters of the terminal device in real time, and using a preset algorithm to dynamically adjust the number of rooms, models, and lighting configuration parameters in the structured data, the complexity of the test scenario is matched with the terminal performance, avoiding the problem of wasting resources on high-configuration devices or failing tests on low-configuration devices.

[0196] For example, the preset algorithm can be used to map the maximum number of rooms to be adapted based on GPU performance level; to map the maximum number of models to be adapted based on remaining memory capacity level; and to map the number of lights and the complexity of lighting effects to be adapted based on real-time network bandwidth level. Specifically, for low-configuration devices, the number of rooms and models is automatically reduced to avoid resource overload; for high-configuration devices, the number of models and lighting complexity are increased to fully expose performance bottlenecks. This application does not impose any special restrictions on the preset algorithm.

[0197] This implementation provides a performance testing method for 3D views of intelligent buildings. The method acquires scene configuration information input by the test user; determines the test scene configuration parameters based on this information; responds to the test user's configuration operations, determines the room type, furniture model type, and intelligent device type of the intelligent building; generates a scene description file corresponding to the intelligent building based on the number of floors, rooms, room type, furniture model type, number of furniture models, intelligent device type, number of intelligent devices, and lighting configuration; constructs a 3D view instance corresponding to the intelligent building based on the scene description file, and determines the test operation sequence for performance testing; repeatedly executes preset test rounds according to the test operation sequence, performing repeated operation tests on the 3D view instance, and records the corresponding operation test data according to preset data dimensions; generates a test report for the 3D view instance based on the operation test data, and sends the test report to the test user. This method generates structured data through parameterized configuration, solving the problem of incomparable test results caused by the reliance on manual construction of test scenarios and the lack of unified standards in existing technologies. Automated interface calls improve construction efficiency, achieving unification and automation of test scenarios and test processes, improving test consistency, reproducibility, and coverage efficiency, enhancing the ability to locate performance anomalies, and improving testing efficiency.

[0198] Figure 4 A schematic diagram of the performance testing device for the intelligent building 3D view provided in this application. Figure 4 As shown, this application provides a performance testing device for a three-dimensional view of an intelligent building. The performance testing device 400 for a three-dimensional view of an intelligent building includes:

[0199] The processing module 401 is used to determine the test scenario configuration parameters of the intelligent building and generate a scene description file corresponding to the intelligent building based on the test scenario configuration parameters; the test scenario configuration parameters are used to reflect the construction complexity of the three-dimensional test scenario of the intelligent building; the scene description file is used to construct the three-dimensional test scenario of the intelligent building.

[0200] The processing module 401 is also used to construct a 3D view instance corresponding to the intelligent building based on the scene description file, and determine the test operation sequence for performance testing; the test operation sequence is a set of test actions to be executed on the 3D view instance.

[0201] The processing module 401 is also used to perform operation tests on the 3D view instance according to the test operation sequence and record the operation test data of the 3D view instance.

[0202] The processing module 401 is also used to generate a test report of the 3D view instance based on the operation test data, and send the test report to the test user.

[0203] In one possible implementation, the performance testing device for the three-dimensional view of the intelligent building further includes: an acquisition module 402.

[0204] The acquisition module 402 is used to acquire scene configuration information input by the test user. The scene configuration information includes at least one of the preset complexity level and custom scene data.

[0205] The processing module 401 is also used to determine the test scenario configuration parameters of the intelligent building based on the scenario configuration information.

[0206] In one possible implementation, the processing module 401 is further configured to, when the scenario configuration information is at a preset complexity level, search the configuration parameter library according to the preset complexity level to obtain the test scenario configuration parameters of the intelligent building.

[0207] The processing module 401 is also used to parse the custom scene data when the scene configuration information is custom scene data, so as to obtain the test scene configuration parameters of the intelligent building.

[0208] The processing module 401 is also used to, when the scenario configuration information includes a preset complexity level and custom scenario data, search the configuration parameter library according to the preset complexity level, match and obtain the initial configuration parameters of the intelligent building, and correct the initial configuration parameters based on the custom scenario data to obtain the test scenario configuration parameters of the intelligent building.

[0209] The configuration parameter library is used to store different complexity levels of 3D test scenarios for intelligent buildings and the corresponding test scenario configuration parameters for each complexity level.

[0210] In one possible implementation, the processing module 401 is further configured to determine the room type, home model type, and smart device type of the smart building in response to the configuration operation of the test user.

[0211] The processing module 401 is also used to generate scene description files corresponding to the smart building based on the number of floors, number of rooms, room type, furniture model type, number of furniture models, smart device type, number of smart devices and lighting configuration.

[0212] In one possible implementation, the processing module 401 is further configured to determine the preset layout template corresponding to the smart building based on the number of floors, the number of rooms, and the room type corresponding to each room, and generate the unit structure data corresponding to the preset layout template.

[0213] The processing module 401 is also used to filter out the corresponding types and quantities of preset home models from the preset model library according to the number and type of home models, and generate the corresponding home model list; the home model list includes the home model identifier and preset placement position of each preset home model.

[0214] The processing module 401 is also used to determine the device identifier, device binding relationship and initial state value of each smart device based on the number and type of smart devices, and generate a corresponding smart device list; the device binding relationship is used to characterize the association state between each smart device and the corresponding physical model.

[0215] The processing module 401 is also used to determine the number of lights in each room and the initial working state of each light based on the lighting configuration, and to generate a corresponding lighting configuration list.

[0216] The processing module 401 is also used to determine the scene description file corresponding to the smart building based on the apartment structure data, the home model list, the smart device list and the lighting configuration list.

[0217] In one possible implementation, the processing module 401 is further configured to cyclically execute a preset test round according to the test operation sequence, perform repeated operation tests on the three-dimensional view instance, and classify and record the corresponding operation test data according to the preset data dimensions.

[0218] The preset data dimensions include at least one of view state, device response, and lighting effects; the operation test data includes operation time, minimum frame rate during operation, and memory increment during operation.

[0219] In one possible implementation, the processing module 401 is further configured to determine the performance index thresholds for different performance indicators corresponding to each test operation in the test operation sequence.

[0220] The processing module 401 is also used to determine the measured values ​​of each performance index for any test operation, and to compare the measured values ​​of each performance index with the corresponding performance index threshold.

[0221] The processing module 401 is also used to generate visualization charts corresponding to the operation test data based on the comparison results.

[0222] The processing module 401 is also used to generate a test report of a 3D view instance based on the operation test data and visualization charts;

[0223] The visualization charts include a time-consuming bar chart, a frame rate curve chart, and a memory curve chart. The time-consuming bar chart is used to represent the operation time of each test operation; the frame rate curve chart is used to represent the frame rate timing changes corresponding to the test operation sequence; and the memory curve chart is used to represent the memory timing changes corresponding to the test operation sequence.

[0224] The performance testing device for intelligent building 3D views provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0225] Figure 5 A schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device 500 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 500 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0226] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0227] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0228] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0229] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0230] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0231] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0232] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0233] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0234] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0235] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0236] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0237] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0238] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0239] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0240] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A performance testing method for a three-dimensional view of an intelligent building, characterized in that, The method includes: The test scenario configuration parameters for the intelligent building are determined, and a scene description file corresponding to the intelligent building is generated based on the test scenario configuration parameters; the test scenario configuration parameters are used to reflect the construction complexity of the three-dimensional test scenario of the intelligent building; the scene description file is used to construct the three-dimensional test scenario of the intelligent building. Based on the scene description file, a 3D view instance corresponding to the intelligent building is constructed, and a test operation sequence for performance testing is determined; the test operation sequence is a set of test actions to be performed on the 3D view instance. Perform operation tests on the 3D view instance according to the test operation sequence, and record the operation test data of the 3D view instance; Based on the operation test data, a test report for the 3D view instance is generated, and the test report is sent to the test user.

2. The method according to claim 1, characterized in that, The determination of the test scenario configuration parameters for intelligent buildings includes: Obtain the scenario configuration information input by the test user, wherein the scenario configuration information includes at least one of a preset complexity level and custom scenario data; Based on the scenario configuration information, the test scenario configuration parameters of the intelligent building are determined.

3. The method according to claim 2, characterized in that, The step of determining the test scenario configuration parameters of the intelligent building based on the scenario configuration information includes: When the scenario configuration information is at the preset complexity level, the configuration parameter library is searched according to the preset complexity level to obtain the test scenario configuration parameters of the smart building. When the scenario configuration information is the custom scenario data, the custom scenario data is parsed to obtain the test scenario configuration parameters of the smart building; When the scenario configuration information includes the preset complexity level and the custom scenario data, the configuration parameter library is searched according to the preset complexity level to obtain the initial configuration parameters of the smart building, and the initial configuration parameters are corrected based on the custom scenario data to obtain the test scenario configuration parameters of the smart building. The configuration parameter library is used to store different complexity levels of three-dimensional test scenarios for intelligent buildings and the corresponding test scenario configuration parameters for different complexity levels.

4. The method according to claim 3, characterized in that, The test scenario configuration parameters include: number of floors, number of rooms, number of home models, number of smart devices, and lighting configuration; before generating the scene description file corresponding to the smart building based on the test scenario configuration parameters, the method further includes: In response to the configuration operation of the test user, the room type, home model type, and smart device type of the smart building are determined; The step of generating a scene description file corresponding to the smart building based on the test scenario configuration parameters includes: Based on the number of floors, the number of rooms, the room type, the furniture model type, the number of furniture models, the type of smart devices, the number of smart devices, and the lighting configuration, a scene description file corresponding to the smart building is generated.

5. The method according to claim 4, characterized in that, The process of generating a scene description file corresponding to the smart building based on the number of floors, the number of rooms, the room type, the furniture model type, the number of furniture models, the type of smart device, the number of smart devices, and the lighting configuration includes: Based on the number of floors, the number of rooms, and the room type corresponding to each room, a preset layout template corresponding to the smart building is determined, and the unit structure data corresponding to the preset layout template is generated. According to the number and type of the home furnishing models, preset home furnishing models of the corresponding type and matching quantity are selected from the preset model library, and a corresponding home furnishing model list is generated; the home furnishing model list includes the home furnishing model identifier and preset placement position of each preset home furnishing model; Based on the number and type of smart devices, the device identifier, device binding relationship, and initial state value of each smart device are determined, and a corresponding smart device list is generated; the device binding relationship is used to characterize the association state between each smart device and the corresponding physical model; Based on the lighting configuration, the number of lights in each room and the initial working status of each light are determined, and a corresponding lighting configuration list is generated; Based on the apartment layout data, the home model list, the smart device list, and the lighting configuration list, the scene description file corresponding to the smart building is determined.

6. The method according to claim 1, characterized in that, The step of performing operation tests on the 3D view instance according to the test operation sequence and recording the operation test data of the 3D view instance includes: The preset test rounds are executed cyclically according to the test operation sequence to perform repeated operation tests on the three-dimensional view instance, and the corresponding operation test data are recorded according to the preset data dimensions. The preset data dimensions include at least one of view state, device response, and lighting effects; the operation test data includes operation time, minimum frame rate during operation, and memory increment during operation.

7. The method according to claim 6, characterized in that, The process of generating a test report for the 3D view instance based on the operational test data includes: Determine the performance index thresholds for different performance indicators corresponding to each test operation in the test operation sequence; For any of the aforementioned test operations, determine the measured values ​​of each performance indicator, and compare the measured values ​​of each performance indicator with the corresponding performance indicator thresholds. Based on the comparison results, a visualization chart corresponding to the operation test data is generated; Based on the operational test data and the visualization charts, a test report for the 3D view instance is generated; The visualization charts include a time-consuming bar chart, a frame rate curve chart, and a memory curve chart; the time-consuming bar chart is used to represent the operation time of each test operation; the frame rate curve chart is used to represent the frame rate timing changes corresponding to the test operation sequence; and the memory curve chart is used to represent the memory timing changes corresponding to the test operation sequence.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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