Multi-dimensional quality inspection report generation method, device and medium

Abstract

This application relates to the field of data processing technology, specifically providing a method, device, and medium for generating multi-dimensional quality inspection reports, aiming to solve the problem of low efficiency in existing report generation. To this end, the multi-dimensional quality inspection report generation method of this application includes: obtaining a list of assets to be inspected; performing quality inspection on each asset in the list to be inspected, generating quality inspection data; determining the asset type based on the quality inspection data; determining the individual quality inspection result corresponding to each asset based on the quality inspection data and asset type; determining the asset-level overall status of each asset based on the individual quality inspection result; and generating a quality inspection report based on the asset-level overall status and the individual quality inspection results. This significantly reduces the cost of manual data processing and judgment, improves the accuracy of quality inspection results, and generates a structured and traceable quality inspection report.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically providing a method, device, and medium for generating multidimensional quality inspection reports. Background Technology

[0002] In the field of 3D simulation, such as in embodied intelligence and remote robotic manipulation, the quality of 3D assets directly affects the usability of the simulation. Current 3D asset quality inspection typically relies on manual methods, such as manually opening the model, observing collision objects, running the simulation, and recording the results. This method is inefficient and prone to errors.

[0003] Currently, while some automated tools exist, these tools typically only generate logs or data for single-item inspections. For example, collision detection scripts output collision data, joint test scripts output motion data, and physics parameter extraction scripts output parameter tables. These data and reports from different inspection items are independent of each other, lacking unified evaluation standards and status rules. This forces technicians to expend considerable effort on manual summarization and analysis, making it difficult to form verifiable, comprehensive quality conclusions for individual assets or even entire batches of assets. Furthermore, existing methods often lack effective differentiation of asset types; for instance, rigid body assets without moving joints are easily misjudged as failures in interactive testing, thus affecting the accuracy of quality inspection results. Summary of the Invention

[0004] To overcome the aforementioned deficiencies, this application is proposed to provide solutions, or at least partially solutions, to the technical problems in the prior art where quality inspection results for 3D assets are scattered, difficult to uniformly evaluate, and lack clear conclusions, resulting in low report generation efficiency. This application provides a method, device, and medium for generating multidimensional quality inspection reports.

[0005] In a first aspect, this application provides a method for generating a multi-dimensional quality inspection report, the method comprising:

[0006] Obtain the list of assets to be inspected;

[0007] Each asset in the list of assets to be inspected is subjected to quality inspection, and quality inspection data is generated.

[0008] The asset type is determined based on the quality inspection data;

[0009] Determine the individual quality inspection result for each asset based on the quality inspection data and asset type;

[0010] The overall asset status of each asset is determined based on the individual quality inspection results corresponding to each asset.

[0011] A quality inspection report is generated based on the overall asset status and the individual quality inspection results.

[0012] In one specific embodiment of the multidimensional quality inspection report generation method of this application, obtaining the list of assets to be inspected includes:

[0013] Retrieve the user-specified asset root directory;

[0014] Identify the asset directory type under the asset root directory, where the asset directory type includes single asset directory, batch asset directory, and single USD file;

[0015] Create a list of assets to be tested based on the asset catalog type.

[0016] In one embodiment of the multidimensional quality inspection report generation method of this application, the quality inspection data includes interaction detection data, collision detection data, motion detection data, and physical structure detection data;

[0017] The quality inspection of each asset in the list of assets to be inspected includes: performing interaction detection, collision detection, slope motion detection and physical structure detection on each asset, and generating interaction detection data, collision detection data, motion detection data and physical structure detection data.

[0018] In one embodiment of the multidimensional quality inspection report generation method of this application, the asset type includes joint assets and rigid assets;

[0019] The step of determining the asset type based on the quality inspection data includes:

[0020] Obtain either the motion detection data or the physical structure detection data from the quality inspection data;

[0021] Determine whether the asset contains movable joints based on either the motion detection data or the physical detection data;

[0022] If so, then the asset type is a joint asset;

[0023] If not, then the asset type is a rigid body asset.

[0024] In one embodiment of the multi-dimensional quality inspection report generation method of this application, determining the single quality inspection result corresponding to each asset based on the quality inspection data and asset type includes:

[0025] Obtain the set of judgment rules that match the asset type;

[0026] Using the aforementioned set of judgment rules, each test data item in the quality inspection data is compared with its corresponding preset threshold.

[0027] Based on the comparison results, generate individual quality inspection results corresponding to each test data;

[0028] The individual quality inspection result for each asset is determined based on the individual quality inspection results corresponding to each test data.

[0029] In one embodiment of the multi-dimensional quality inspection report generation method of this application, the overall asset-level status includes pass, fail, and warning; determining the overall asset-level status of each asset based on the individual quality inspection result corresponding to each asset includes:

[0030] Determine whether all quality inspection results for each asset are passed.

[0031] If so, the overall asset-level status of the asset is determined to be passed;

[0032] If not, the overall asset-level status of the asset will be determined as failing.

[0033] If the asset has a single quality inspection result that results in a warning, then the asset-level overall status of the asset will be determined as a warning.

[0034] In one embodiment of the multidimensional quality inspection report generation method of this application, the step of generating a quality inspection report based on the overall asset status and the single quality inspection result includes:

[0035] Based on the overall asset status, determine the number of assets that have passed, received warnings, or failed, and calculate the pass rate.

[0036] Determine the asset type distribution based on the asset types described;

[0037] Based on the single quality inspection results, determine the problem statistics of each test data;

[0038] An asset details table is generated based on the overall asset status and the individual quality inspection results of each asset.

[0039] The quality inspection report is generated based on the pass rate, asset type distribution, problem statistics, and asset details.

[0040] In one embodiment of the multidimensional quality inspection report generation method of this application, after generating the quality inspection report, the method further includes: adding the inspection timestamp of each asset, the script version used, the parameter configuration, the input asset path, the original data file name, and the key judgment criteria to the quality inspection report.

[0041] In a second aspect, a computer device is provided, comprising:

[0042] At least one processor;

[0043] And, a memory communicatively connected to the at least one processor;

[0044] The memory stores a computer program, which, when executed by the at least one processor, is the aforementioned method for generating multidimensional quality inspection reports.

[0045] In a third aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, the program codes being adapted to be loaded and run by a processor to perform the multidimensional quality inspection report generation method described in any of the preceding claims.

[0046] The above-described technical solutions of this application have at least one or more of the following beneficial effects:

[0047] The multi-dimensional quality inspection report generation method in this application includes: obtaining a list of assets to be inspected; performing quality inspection on each asset in the list to generate quality inspection data; determining the asset type based on the quality inspection data; determining the individual quality inspection result for each asset based on the quality inspection data and asset type; determining the overall asset-level status of each asset based on the individual quality inspection result; and generating a quality inspection report based on the overall asset-level status and the individual quality inspection results. This significantly reduces the cost of manual data processing and judgment, improves the accuracy of quality inspection results, and generates a structured and traceable quality inspection report. Attached Figure Description

[0048] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Furthermore, similar numbers in the drawings are used to denote similar components, wherein:

[0049] Figure 1 This is a schematic diagram of the main process of a multi-dimensional quality inspection report generation method in one embodiment of this application;

[0050] Figure 2 This is a schematic diagram of the main structure of a multi-dimensional quality inspection report generation device in one embodiment of this application;

[0051] Figure 3 This is a schematic diagram of the structure of a computer device in one embodiment of this application. Detailed Implementation

[0052] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0053] In the description of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and can also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0054] In this application, "assets" refers to digital assets in the field of physics simulation. These assets may be one or more 3D model files in USD (Universal Scene Description) format or their associated asset catalog. The assets contain at least visual mesh data, collision mesh data, and physical attribute parameters, and can be loaded into a physics engine for motion simulation and collision detection. For example, the assets may be vehicle component models in intelligent cockpit simulation, robotic arm models in robot simulation, or interactive object models in virtual reality scenes, but are not limited to these.

[0055] See appendix Figure 1 , Figure 1 This is a schematic diagram of the main flow of a multi-dimensional quality inspection report generation method according to an embodiment of this application. Figure 1 As shown, the multidimensional quality inspection report generation method in this application embodiment mainly includes the following steps S10-S60.

[0056] Step S10: Obtain the list of assets to be tested.

[0057] Step S20: Perform quality inspection on each asset in the asset list to be inspected and generate quality inspection data.

[0058] Step S30: Determine the asset type based on the quality inspection data.

[0059] Step S40: Determine the single quality inspection result for each asset based on the quality inspection data and asset type.

[0060] Step S50: Determine the overall asset status of each asset based on the single quality inspection results corresponding to each asset.

[0061] Step S60: Generate a quality inspection report based on the overall asset status and individual quality inspection results.

[0062] Based on steps S10-S60 above, the following steps are performed: First, a list of assets to be inspected is obtained; then, quality inspection is conducted on each asset in the list, generating quality inspection data; the asset type is determined based on the quality inspection data; the individual quality inspection result for each asset is determined based on the quality inspection data and asset type; the overall asset-level status of each asset is determined based on the individual quality inspection result; and a quality inspection report is generated based on the overall asset-level status and the individual quality inspection results. This significantly reduces the cost of manual data processing and judgment, improves the accuracy of quality inspection results, and generates a structured, traceable quality inspection report. It effectively solves the problems of data fragmentation, inconsistent judgment standards, low efficiency in large-scale testing, and difficulty in tracing quality issues in the digital asset quality inspection process.

[0063] The following provides further explanation of steps S10 to S60.

[0064] Regarding step S10, in one specific implementation, obtaining the list of assets to be detected includes: obtaining the asset root directory specified by the user; identifying the asset directory type under the asset root directory, where the asset directory type includes single asset directory, batch asset directory and single USD file; and establishing the list of assets to be detected based on the asset directory type.

[0065] Specifically, the system first retrieves the user-specified asset root directory. Then, it automatically identifies the asset directory type within that root directory, determining whether it contains a single asset, a batch asset directory containing multiple assets, or a single USD file. Finally, based on the identified directory type, a complete list of assets to be inspected is created. This can be achieved by retrieving a pre-defined template for the asset list and filling in the identified directory type. In this way, users no longer need to manually organize and specify each asset file; the system can adaptively process different types of asset data, solving the problem of tedious initialization work in batch quality inspection scenarios.

[0066] The above is a further explanation of step S10. Step S20 will be further explained below.

[0067] In one specific implementation, the quality inspection data includes interaction detection data, collision detection data, motion detection data, and physical structure detection data; quality inspection is performed on each asset in the asset list to be inspected, including: performing interaction detection, collision detection, slope motion detection, and physical structure detection on each asset separately, and generating interaction detection data, collision detection data, motion detection data, and physical structure detection data.

[0068] Specifically, to ensure comprehensive quality inspection, the asset inspection process can cover multiple key dimensions. For example, each asset can undergo interaction detection, collision detection, slope motion detection, and physical structure detection. The generated inspection data can be presented in the form of JSON, logs, or intermediate reports. This multi-dimensional inspection design is based on simulating the various core behaviors of assets in a real simulation environment, thereby ensuring their usability.

[0069] Interaction detection checks whether the movable parts of an asset (such as joints, hinges, sliders, etc.) move as intended by the design. During detection, these parts are driven to move, and interaction detection data such as the number of joints, joint type, target stroke, actual stroke, amount of movement, and timeout status are obtained.

[0070] Collision detection checks whether the visual model of an asset (i.e., the surface seen by the user) matches the physical collision model (i.e., the simplified shell used by the physics engine to calculate collisions), calculates the coverage of both, identifies uncovered areas, and counts the number of collision objects, obtaining collision detection data including coverage, uncovered areas, and the number of collision objects.

[0071] Ramp motion detection simulates the dynamic behavior of assets placed on a ramp within the physics engine. By observing the asset's sliding or rolling process on the ramp, its stability, maximum speed, final speed, and whether clipping events occur during the simulation are recorded. This allows for the determination of whether the asset's performance in the dynamic physical environment meets expectations, yielding motion detection data including stability, maximum speed, final speed, clipping events, and simulation duration.

[0072] Physical structure inspection directly reads the inherent physical parameters from the asset file, obtaining physical structure inspection data including the number of rigid bodies, the number of colliders, their mass, axis-aligned bounding boxes, estimated density, material friction coefficient, and coefficient of restitution. These parameters do not depend on runtime simulation but are static physical property data of the asset itself, used to evaluate the rationality of its physical configuration.

[0073] By collecting four key types of data—interaction, collision, motion, and physical structure—the simulation quality of assets can be comprehensively evaluated. This overcomes the limitation that a single detection script cannot cover all quality dimensions, providing a reliable guarantee for generating high-quality simulation assets.

[0074] The above is a further explanation of step S20. Step S30 will be further explained below.

[0075] In one specific embodiment, the asset type includes joint assets and rigid assets; determining the asset type based on quality inspection data includes: obtaining either motion detection data or physical structure detection data from the quality inspection data; determining whether the asset contains movable joints based on either motion detection data or physical detection data; if yes, the asset type is a joint asset; if no, the asset type is a rigid asset.

[0076] Movable joints include rotational joints, prismatic joints, hinge joints, and sliding joints.

[0077] Specifically, when determining asset types, to achieve accurate classification of different assets, asset types can specifically include joint assets and rigid body assets. The process of determining asset types includes: obtaining either motion detection data or physical structure detection data from the quality inspection data, and determining whether the asset contains movable joints based on these data. If the result is yes, the asset type is determined to be a joint asset; otherwise, if the result is no, it is determined to be a rigid body asset. Furthermore, if no motion detection data is generated for the asset and the physical structure does not contain movable joints, it is not considered an interaction detection failure, but rather a rigid body asset.

[0078] This classification ensures that subsequent quality inspection rules can be applied in a targeted manner. For example, the judgment criteria for interaction travel can be applied only to joint assets, thereby solving the problem of misjudgment caused by confusion of asset types and avoiding the incorrect marking of rigid body assets without joints as failures because they cannot perform interaction tests.

[0079] The above is a further explanation of step S30. Step S40 will be further explained below.

[0080] In one specific embodiment, determining the single quality inspection result corresponding to each asset based on quality inspection data and asset type includes: obtaining a set of judgment rules matching the asset type; using the set of judgment rules, comparing each test data in the quality inspection data with the corresponding preset threshold; generating the single quality inspection result corresponding to each test data based on the comparison result; and determining the single quality inspection result corresponding to each asset based on the single quality inspection result corresponding to each test data.

[0081] The judgment rule set is a set of judgment rules pre-stored in the system that corresponds to the asset type (rigid body asset or joint asset). Each judgment rule includes at least one quality inspection item identifier and its corresponding threshold range, which is used to compare each measurement data with the threshold to generate a single data item conclusion. When the system determines that the current asset belongs to rigid body asset or joint asset based on the quality inspection data, it can retrieve the rule matching the asset type from the pre-stored judgment rule set, and compare and judge each measurement data one by one according to the threshold range in the rule, and finally obtain the single data item conclusion for each measurement data item.

[0082] Specifically, the process begins by acquiring a set of judgment rules that match the asset type; for example, different rules are applied to joint assets and rigid body assets. Then, using this rule set, various inspection data points (such as collision coverage and the number of clipping events) in the quality inspection data are compared with the corresponding preset thresholds in the rule set. Finally, based on the comparison results, individual quality inspection results (such as pass, warning, or fail) are generated for each inspection data point, and these results are aggregated to determine the final individual quality inspection result for the asset. This rule- and threshold-based automated judgment method replaces subjective human judgment with objective and unified standards, solving the problems of inconsistent evaluation standards and ambiguous conclusions in traditional quality inspection, and achieving standardization and automation of quality judgment.

[0083] For example, regarding collision coverage, a coverage rate below 60% is considered a failure, 60% to 85% is considered a warning, and above 85% is considered a pass. Regarding the number of clipping incidents, more than 50 incidents are considered a failure, 10 to 50 incidents are considered a warning, and less than 10 incidents are considered a pass. For jointed assets, the interaction quality is comprehensively judged based on the ratio of actual joint travel to target travel and whether a timeout has occurred. For physical parameters, reasonableness prompts are generated based on indicators such as quality, AABB bounding box, calculated density, material friction coefficient, and number of colliders, rather than simply judging whether an action passes or fails.

[0084] The above is a further explanation of step S40. Step S50 will be further explained below.

[0085] In one specific embodiment, the overall asset-level status includes pass, fail, and warning; determining the overall asset-level status of each asset based on the individual quality inspection results corresponding to each asset includes: determining whether all individual quality inspection results corresponding to each asset are pass; if yes, then the overall asset-level status of the asset is determined to be pass; if no, then the overall asset-level status of the asset is determined to be fail; if the asset has an individual quality inspection result with a warning, then the overall asset-level status of the asset is determined to be warning.

[0086] Specifically, the overall asset status can include three categories: Pass, Fail, and Warning. Specifically, it determines whether all individual quality inspection results for an asset are Pass. If so, the overall asset status for that asset is determined to be Pass. If any individual quality inspection result for an asset is Fail, the overall asset status for that asset is determined to be Fail. If none of the individual quality inspection results for an asset are Fail, but there are warnings, the overall asset status for that asset is determined to be Warning. This hierarchical status merging logic helps ensure that no critical defect is overlooked, while also identifying assets with potential risks. It provides a clear and unambiguous final conclusion for asset acceptance, solving the problem of easily overlooking warnings during manual aggregation.

[0087] The above is a further explanation of step S50. Step S60 will be further explained below.

[0088] In one specific embodiment of this application, a quality inspection report is generated based on the overall asset status and individual quality inspection results, including: determining the number of passed, warning, and failed assets among all assets based on the overall asset status and calculating the pass rate; determining the asset type distribution based on asset type; determining the problem statistics of each inspection data based on the individual quality inspection results; generating an asset detail table based on the overall asset status and individual quality inspection results of each asset; and generating a quality inspection report based on the pass rate, asset type distribution, problem statistics, and asset detail table.

[0089] Specifically, firstly, based on the overall asset-level status of all assets, the number of assets that passed, received warnings, and failed is counted, and the overall pass rate is calculated. Secondly, based on the asset type of each asset (rigid body assets or joint assets), the quantity and proportion of each type of asset are counted to form an asset type distribution, which is used to understand the composition of assets covered by this quality inspection. Thirdly, based on the individual quality inspection results of all assets, the total number of failures or warnings for each quality inspection item (such as collision coverage, molding failure, etc.) is counted, thus forming problem statistics to help quickly identify which type of quality problem is most frequent. Then, returning to the detailed table with each asset as a row and each inspection data and its conclusion as a column, an asset detail table is generated based on the overall asset-level status and individual quality inspection results of each asset, arranged according to a preset sorting rule (such as failures first), for users to view item by item. Finally, the pass rate, asset type distribution, problem statistics, and asset detail table are integrated to generate the final quality inspection report.

[0090] For example, the system automatically generates reports in Markdown, HTML, PDF, Word, or other formats. These reports include overall conclusions, the number of passes / warnings / failures, pass rate, asset type distribution, problem classification statistics, asset details, explanations of failures, explanations of warnings, physical parameter details, original JSON summary, and quality inspection time. Asset details are sorted in the order of failure, warning, pass, and skipped, and rigid body assets and hinge assets are displayed separately.

[0091] This structured report generation method aggregates scattered information into a comprehensive report containing both macro-level statistics and micro-level details, solving the shortcomings of traditional reports that are limited in content and fragmented in information, and providing strong data support for project management and quality improvement.

[0092] In one implementation, after generating the quality inspection report, the method further includes adding the inspection timestamp for each asset, the script version used, parameter configuration, input asset path, original data file name, and key judgment criteria to the quality inspection report.

[0093] Specifically, to enhance the traceability and reproducibility of the report, additional important metadata can be added to each asset after the quality inspection report is generated. This metadata may include: the timestamp of the inspection, the version number of the quality inspection script used, the specific parameter configuration used during the inspection, the original input path of the asset, the filename of the original data, and the core basis for making key judgments. This creates a complete snapshot of each quality inspection operation, making all necessary information readily available when quality disputes arise or problems need to be reproduced.

[0094] By embedding traceable information in the report, the pain points of traditional quality inspection results being difficult to review and problems being difficult to locate are solved, ensuring the transparency and reliability of the quality inspection process.

[0095] This application significantly reduces the cost of manual processing and judgment by automatically synthesizing scattered multi-dimensional detection results such as interactions, collisions, motion, and physical parameters into unified asset-level quality conclusions. Simultaneously, the system can automatically distinguish between rigid body assets and joint assets, avoiding misjudging rigid body assets without movable joints as interaction failures, thus improving accuracy. It supports batch asset pass rate statistics, problem type classification summaries, and detailed table output, facilitating overall acceptance and quality control of large-scale asset libraries. It supports recording operational statuses such as timeout termination, skipped detection, and reuse of existing reports, facilitating resume inspection after interruptions. The final quality inspection report retains both conclusions and original judgment criteria, enabling the location, review, and reproduction of quality issues for each asset, balancing the convenience of manual acceptance with the traceability of automated re-inspection.

[0096] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of this application.

[0097] Furthermore, this application also provides a multi-dimensional quality inspection report generation device.

[0098] See appendix Figure 2 , Figure 2 This is a main structural block diagram of a multi-dimensional quality inspection report generation device according to an embodiment of this application. Figure 2 As shown, the multidimensional quality inspection report generation device in this embodiment mainly includes an acquisition module 11, a quality inspection module 12, a first determination module 13, a second determination module 14, a third determination module 15, and a report generation module 16. In some embodiments, one or more of the acquisition module 11, the quality inspection module 12, the first determination module 13, the second determination module 14, the third determination module 15, and the report generation module 16 can be combined into a single module.

[0099] In some embodiments, the acquisition module 11 can be configured to acquire a list of assets to be detected.

[0100] The quality inspection module 12 can be configured to perform quality inspection on each asset in the list of assets to be inspected and generate quality inspection data.

[0101] The first determination module 13 can be configured to determine the asset type based on quality inspection data.

[0102] The second determination module 14 can be configured to determine the single quality inspection result for each asset based on the quality inspection data and asset type.

[0103] The third determination module 15 can be configured to determine the overall asset-level status of each asset based on the single quality inspection result corresponding to each asset.

[0104] The report generation module 16 can be configured to generate quality inspection reports based on the overall asset status and individual quality inspection results.

[0105] In one implementation, a description of the specific functions can be found in steps S10-S60.

[0106] The aforementioned multi-dimensional quality inspection report generation device is used to perform Figure 1The embodiments of the multidimensional quality inspection report generation method shown are similar in technical principle, technical problem solved and technical effect produced. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the multidimensional quality inspection report generation device can be referred to the contents described in the embodiments of the multidimensional quality inspection report generation method, and will not be repeated here.

[0107] Furthermore, it should be understood that since the various modules are only provided to illustrate the functional units of the device described in this application, the physical devices corresponding to these modules may be the processor itself, or a part of the processor's software, hardware, or a combination of both. Therefore, the number of modules shown in the figures is merely illustrative.

[0108] Those skilled in the art will understand that the various modules in the device can be adaptively split or combined. Such splitting or combining of specific modules will not cause the technical solution to deviate from the principles of this application; therefore, the technical solutions after splitting or combining will fall within the protection scope of this application.

[0109] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0110] Furthermore, this application also provides a computer device, which may include at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program, which, when executed by the at least one processor, implements the multi-dimensional quality inspection report generation method described in any of the above embodiments. See also Figure 3 As shown, Figure 3 The diagram illustrates the structure of a computer device, which includes a processor 100 and a memory 200.

[0111] Furthermore, this application also provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program that performs the multidimensional quality inspection report generation method of the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described multidimensional quality inspection report generation method. For ease of explanation, only the parts related to the embodiments of this application are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this application. The computer-readable storage medium can be a memory device formed by various electronic devices. Optionally, in the embodiments of this application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0112] The technical solution of this application has been described in conjunction with the specific embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A method for generating multidimensional quality inspection reports, characterized in that, The method includes: Obtain the list of assets to be inspected; Each asset in the list of assets to be inspected is subjected to quality inspection, and quality inspection data is generated. The asset type is determined based on the quality inspection data; Determine the individual quality inspection result for each asset based on the quality inspection data and asset type; The overall asset status of each asset is determined based on the individual quality inspection results corresponding to each asset. A quality inspection report is generated based on the overall asset status and the individual quality inspection results.

2. The method for generating multi-dimensional quality inspection reports according to claim 1, characterized in that, The process of obtaining the list of assets to be tested includes: Retrieve the user-specified asset root directory; Identify the asset directory type under the asset root directory, where the asset directory type includes single asset directory, batch asset directory, and single USD file; Create a list of assets to be tested based on the asset catalog type.

3. The method for generating multi-dimensional quality inspection reports according to claim 1, characterized in that, The quality inspection data includes interaction detection data, collision detection data, motion detection data, and physical structure detection data; The quality inspection of each asset in the list of assets to be inspected includes: performing interaction detection, collision detection, slope motion detection and physical structure detection on each asset, and generating interaction detection data, collision detection data, motion detection data and physical structure detection data.

4. The method for generating multi-dimensional quality inspection reports according to claim 1, characterized in that, The asset types include joint assets and rigid assets; The step of determining the asset type based on the quality inspection data includes: Obtain either the motion detection data or the physical structure detection data from the quality inspection data; Determine whether the asset contains movable joints based on either the motion detection data or the physical detection data; If so, then the asset type is a joint asset; If not, then the asset type is a rigid body asset.

5. The method for generating multidimensional quality inspection reports according to claim 1, characterized in that, The step of determining the single quality inspection result for each asset based on the quality inspection data and asset type includes: Obtain the set of judgment rules that match the asset type; Using the aforementioned set of judgment rules, each test data item in the quality inspection data is compared with its corresponding preset threshold. Based on the comparison results, generate individual quality inspection results corresponding to each test data; The individual quality inspection result for each asset is determined based on the individual quality inspection results corresponding to each test data.

6. The method for generating multi-dimensional quality inspection reports according to claim 1, characterized in that, The overall asset status includes pass, fail, and warning; The process of determining the overall asset status of each asset based on the individual quality inspection results for each asset includes: Determine whether all quality inspection results for each asset are passed. If so, the overall asset-level status of the asset is determined to be passed; If not, the overall asset-level status of the asset will be determined as failing. If the asset has a single quality inspection result that results in a warning, then the asset-level overall status of the asset will be determined as a warning.

7. The method for generating multidimensional quality inspection reports according to claim 1, characterized in that, The process of generating a quality inspection report based on the overall asset status and the individual quality inspection results includes: Based on the overall asset status, determine the number of assets that have passed, received warnings, or failed, and calculate the pass rate. Determine the asset type distribution based on the asset types described; Based on the single quality inspection results, determine the problem statistics of each test data; An asset details table is generated based on the overall asset status and the individual quality inspection results of each asset. The quality inspection report is generated based on the pass rate, asset type distribution, problem statistics, and asset details.

8. The method for generating multidimensional quality inspection reports according to claim 1, characterized in that, After generating the quality inspection report, the process also includes adding the inspection timestamp for each asset, the script version used, parameter configuration, input asset path, original data file name, and key judgment criteria to the quality inspection report.

9. A computer device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores a computer program, which, when executed by the at least one processor, implements the multidimensional quality inspection report generation method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the multidimensional quality inspection report generation method according to any one of claims 1 to 8.

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