Assembly data quality evaluation method, device, equipment, medium and program

Abstract

The invention discloses an assembly data quality evaluation method, device and equipment, a medium and a program. The method comprises the following steps: acquiring measurement data obtained by measuring a preset standard component by a measurement device, and coordinate data obtained by measuring a preset to-be-measured point in a measurement space; determining an equipment precision evaluation result corresponding to the measurement equipment according to the measurement data; determining a registration error evaluation result corresponding to the measurement space according to the coordinate data; and according to the equipment precision evaluation result and the registration error evaluation result, determining a quality evaluation result of assembly data generated based on the measurement equipment and the measurement space. According to the technical scheme of the invention, the problem that the evaluation of the assembly data depends on artificial experience in the prior art is solved, the poor reliability of the assembly data caused by unreasonable data evaluation can be avoided, and the reliability of the assembly data evaluation is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, equipment, medium, and program for quality assessment of assembly data. Background Technology

[0002] In the manufacturing process of large, high-end equipment such as aircraft, it is typically necessary to determine the coordinate data of key points based on measuring equipment and convert it into assembly data that can be used in the component assembly process, thereby realizing data-driven machining and assembly. Therefore, the quality of assembly data determines the assembly accuracy of components and the performance of the final product, making effective evaluation of assembly data crucial.

[0003] Currently, existing methods for evaluating assembly data typically rely on staff's experience to assess data quality, which can easily lead to problems such as unreasonable data quality assessment processes, high subjectivity, and low reliability.

[0004] Therefore, there is an urgent need for a method that can systematically evaluate the quality of assembly data to ensure the reliability of data quality assessment. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, medium, and program for quality assessment of assembly data, which can avoid the problem of poor reliability caused by unreasonable data assessment and improve the reliability of assembly data assessment.

[0006] According to a first aspect of the present invention, a method for quality assessment of assembly data is provided, comprising: Acquire measurement data obtained by measuring a preset standard part with a measuring device, and coordinate data obtained by measuring a preset point to be measured in the measurement space; Based on the measurement data, determine the equipment accuracy assessment result corresponding to the measurement equipment; Based on the coordinate data, determine the registration error evaluation result corresponding to the measurement space; Based on the equipment accuracy assessment results and the registration error assessment results, the quality assessment results of the assembly data generated based on the measuring equipment and the measuring space are determined.

[0007] According to a second aspect of the present invention, an assembly data quality assessment apparatus is provided, comprising: The acquisition module is used to acquire measurement data obtained by the measuring device from measuring a preset standard part, and coordinate data obtained from measuring a preset point to be measured in the measurement space. The first determining module is used to determine the equipment accuracy evaluation result corresponding to the measuring device based on the measurement data; The second determining module is used to determine the registration error evaluation result corresponding to the measurement space based on the coordinate data; The evaluation module is used to determine the quality evaluation result based on the assembly data generated by the measuring equipment and the measuring space, according to the equipment accuracy evaluation result and the registration error evaluation result.

[0008] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the assembly data quality assessment method according to any embodiment of the present invention.

[0009] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the assembly data quality assessment method according to any embodiment of the present invention.

[0010] According to a fifth aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the assembly data quality assessment method according to any embodiment of the present invention.

[0011] The technical solution of this invention obtains measurement data from measuring a preset standard part using a measuring device, and coordinate data from measuring a preset test point in a measurement space. Then, based on the measurement data, it determines the device accuracy assessment result corresponding to the measuring device, and based on the coordinate data, it determines the registration error assessment result corresponding to the measurement space. Finally, based on the device accuracy assessment result and the registration error assessment result, it determines the quality assessment result of the assembly data generated based on the measuring device and the measurement space. This solves the problem that the assessment of assembly data in the prior art relies on human experience, avoids the problem of poor reliability caused by unreasonable data assessment, and improves the reliability of assembly data assessment.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of a method for quality assessment of assembly data provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a method for quality assessment of assembly data provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of an assembly data quality assessment device provided in Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the assembly data quality assessment method of the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0016] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 the invention 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 a 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.

[0017] Example 1 Figure 1This is a flowchart illustrating a quality assessment method for assembly data provided in Embodiment 1 of the present invention. This embodiment is applicable to the systematic quality assessment of assembly data generated during the manufacturing process of aircraft component assembly, etc. The method can be executed by an assembly data quality assessment device, which can be implemented in hardware and / or software and can be configured in on-site data management equipment, data servers, or cloud computing platforms. Figure 1 As shown, the method includes: S101. Obtain measurement data obtained by measuring the preset standard part by the measuring equipment, and coordinate data obtained by measuring the preset test point in the measurement space.

[0018] The measuring equipment can be an instrument capable of measuring the three-dimensional coordinates of a target point, such as a laser tracker, total station, or photogrammetric system. Preferably, in high-precision aircraft assembly scenarios, laser trackers are commonly used as the measuring equipment. Utilizing laser interferometric ranging and angle encoders, they can achieve micrometer-level measurement accuracy over large spatial ranges (e.g., tens of meters). The preset standard part can be a known physical reference part with stable and precise geometry and dimensions. For example, it can be a standard length rod or standard ball that has undergone metrological verification.

[0019] The measurement space can be a physical area encompassing the parts to be assembled and the required measurement points. It should be noted that different measurement spaces, due to environmental factors, will introduce different errors into the final assembly data. Preset measurement points can be target spheres or markers with theoretical three-dimensional coordinates pre-set within the corresponding measurement space; the number of these preset measurement points can be one or more. It should be noted that preset measurement points are typically arranged according to certain spatial distribution principles (such as encompassing the entire measurement volume or covering key working areas) based on the volume of the measurement space and the layout of the parts to be measured, to ensure spatial representativeness for subsequent coordinate transformation evaluation.

[0020] For example, this embodiment can obtain the three-dimensional coordinates generated by the measuring device after observing a preset standard sphere, or the length data determined by observing a preset standard length rod, and the coordinate data generated by the measuring device after observing each preset test point distributed in the measuring space from different positions within the measuring space, by receiving the output from the measuring device. This coordinate data includes the corresponding three-dimensional coordinates of the same preset test point observed by the measuring device at different positions.

[0021] Optionally, acquiring measurement data obtained by the measuring device from measuring a preset standard part may include: Obtain the coordinates of the corresponding endpoints obtained by the measuring device measuring each endpoint of the preset standard part; The measurement length of the preset standard part is calculated based on the coordinates of each endpoint, and is used as the measurement data.

[0022] For example, a pre-set standard rod calibrated by the National Institute of Metrology can be fixed on a stable frame, and two reflectors can be placed at both ends of the pre-set standard rod as target endpoints. Then, at different measurement distances, the reflectors at both ends of the standard rod are measured using a measuring device to obtain the coordinate values ​​of the target endpoints at each station, thereby calculating the length of the pre-set standard rod, which serves as the measurement data obtained by the measuring device when measuring the pre-set standard part.

[0023] In practice, in order to comprehensively evaluate the spatial measurement accuracy of the measuring equipment under the current environment and settings, rather than just the single-point accuracy, multiple standard rods can usually be placed at different distances and in different orientations within the measurement space.

[0024] For example, standard rods can be placed at the center, edge, and four corners of the space. During measurement, the laser tracker can sequentially aim at the reflectors at both ends of each standard rod, record its three-dimensional coordinates, and then automatically calculate the distance between the two ends of each standard rod through a preset program or algorithm. This yields multiple sets of measurement data, enabling the simulation of different measurement ranges and angles that may be encountered in actual measurements, and more comprehensively reflecting the overall performance of the measuring equipment.

[0025] S102. Based on the measurement data, determine the equipment accuracy evaluation result corresponding to the measurement equipment.

[0026] The equipment accuracy assessment results can be used to quantify whether the actual measurement capability of the measuring equipment meets the requirements and to evaluate the measurement accuracy of the measuring equipment. It is understood that determining the equipment accuracy assessment results of the measuring equipment can ensure the reliability of the assembly data at the data source.

[0027] For example, the measurement error of the measuring equipment can be obtained by comparing the measured values ​​of one or more key geometric quantities (such as length, three-dimensional coordinates, etc.) of a predetermined standard part with the known reference values ​​of the standard part. Then, the accuracy assessment result of the equipment can be determined by judging whether the measurement error is within the allowable range. If the error is within the allowable range, the measurement equipment accuracy can be considered good as the accuracy assessment result. Otherwise, it may indicate that the measuring equipment has a problem, and the reliability of the data it generates is low.

[0028] Optionally, determining the equipment accuracy assessment result corresponding to the measuring device based on the measurement data may include: Determine whether the measurement length in the measurement data corresponding to the preset standard part meets the preset conditions; If the conditions are met, the measuring device is considered to be in a state of usable accuracy, and this is taken as the accuracy evaluation result of the device.

[0029] The preset condition can be that the absolute error between the measured length of the preset standard part and its reference length is less than or equal to the maximum permissible error or process tolerance of the measuring equipment.

[0030] For example, the measurement length corresponding to the preset standard part in the measurement data is compared with the reference length of the preset standard part to determine the absolute error between the measurement length of the preset standard part and its reference length. Then, the absolute error between the measurement length of the preset standard part and its reference length is compared with the maximum permissible error of the measuring equipment. If the absolute error is less than the maximum permissible error, the measuring equipment can be in a usable accuracy state, which is taken as the equipment accuracy evaluation result.

[0031] S103. Based on the coordinate data, determine the registration error evaluation result corresponding to the measurement space.

[0032] The registration error evaluation result can be used to quantitatively characterize the error generated when transforming coordinate data from one coordinate system to another within the measurement space.

[0033] It should be noted that measuring assembly components with high-precision measuring equipment can determine the three-dimensional coordinates of key points on the component. However, due to the large size of the assembly components, it is difficult for the measuring equipment to measure all key points at the same location. Therefore, it is necessary to obtain the coordinates of each key point of the assembly component in different coordinate systems at different locations within the measurement space, and then transform them into a unified reference coordinate system to generate assembly data that can be used to drive the assembly process. For example, in the assembly of an aircraft wing, it is necessary to measure key points on the front spar at one station, key points on the rear spar at another station, and key points on the skin at yet another station. Since the coordinates of the key points measured at different stations are in different coordinate systems, it is necessary to transform the coordinates of all key points into the same theoretical coordinate system in order to analyze whether their relative positional relationships meet the design requirements.

[0034] This embodiment can improve the reliability of the quality assessment of assembly data by determining the registration error evaluation result when performing coordinate transformation in the measurement space.

[0035] For example, after determining the coordinate data of the target ball or marker point obtained by measuring the different positions of the measuring device in the corresponding measuring space, the transformation relationship between different coordinate systems can be calculated using the coordinate values ​​of the same preset point to be measured in different coordinate systems. Then, the coordinate data of the target ball or marker point in a certain coordinate system is transformed using the transformation relationship, and the deviation between the transformed coordinates and its theoretical coordinates in the transformed coordinate system is calculated, thereby determining the registration error evaluation result corresponding to the measuring space.

[0036] S104. Based on the equipment accuracy assessment result and the registration error assessment result, determine the quality assessment result of the assembly data generated based on the measuring equipment and the measuring space.

[0037] The quality assessment result of the assembly data can be a final comprehensive assessment conclusion. This assessment conclusion can be obtained by combining an assessment of the reliability of the assembly data source (assessment of the measurement accuracy of the measuring equipment) and an assessment of the registration process that generates the assembly data.

[0038] For example, the quality assessment result of the assembly data can be determined according to preset evaluation rules. For instance, the quality assessment result of the assembly data generated based on the measuring equipment and measurement space is determined to be of high quality only if the equipment accuracy assessment result confirms that the equipment accuracy is high and the registration error assessment result is less than a set threshold. If any assessment result fails to meet the standard, the quality assessment result can be determined to be of low quality, triggering an alarm or a re-measurement process.

[0039] The technical solution of this invention obtains measurement data from measuring a preset standard part using a measuring device, and coordinate data from measuring a preset test point in a measurement space. Then, based on the measurement data, it determines the device accuracy assessment result corresponding to the measuring device, and based on the coordinate data, it determines the registration error assessment result corresponding to the measurement space. Finally, based on the device accuracy assessment result and the registration error assessment result, it determines the quality assessment result of the assembly data generated based on the measuring device and the measurement space. This solves the problem that the assessment of assembly data in the prior art relies on human experience, avoids the problem of poor reliability caused by unreasonable data assessment, and improves the reliability of assembly data assessment.

[0040] Based on the above embodiments, the present invention also provides an optional embodiment, which can further illustrate the process of determining the registration error evaluation result in the above embodiments, specifically as follows: The points to be measured may include common points and target points; Accordingly, determining the registration error evaluation result corresponding to the measurement space based on the coordinate data may include: Determine the coordinate transformation parameters based on the first coordinate of the common point in the first coordinate system and the second coordinate of the common point in the second coordinate system; Based on the coordinate transformation parameters, by performing coordinate transformation on the second coordinate and the third coordinate of the target point in the second coordinate system respectively, a first transformation error corresponding to the common point and a second transformation error corresponding to the target point are determined; The registration error evaluation result is determined based on the first conversion error and the second conversion error.

[0041] Common points can be physical points pre-set in the measurement space that can be observed by the measuring equipment from different positions or coordinate systems. It should be noted that at least three non-collinear common points are usually required, and the layout of each common point should be evenly distributed in space, surrounding the entire workpiece measurement area.

[0042] Target points can be physical points set based on specific process features on the assembled parts (such as hole centers, edge points, or surface control points). Due to factors such as part size, obstruction, or measurement efficiency, target points may not be able to be directly measured in a single coordinate system. Therefore, coordinate transformation is required to convert them from a measurable coordinate system to a unified coordinate system.

[0043] The first coordinate system can be a coordinate system constructed with the measuring device at a certain fixed position as a reference, while the second coordinate system can be a coordinate system constructed with the measuring device at another fixed position as a reference. For example, the first coordinate system can be a global coordinate system, and the second coordinate system can be a local coordinate system.

[0044] Coordinate transformation parameters can represent the spatial transformation relationship between two coordinate systems. The first transformation error can be used to characterize the error in the coordinate transformation process, while the second transformation error can be used to characterize the error in the assembly data obtained after the transformation of the coordinate data of key points.

[0045] For example, this optional embodiment can receive the first coordinates and second coordinates corresponding to each common point obtained by the laser tracker measuring the coordinates of the same set of common points at a global station (corresponding to the first coordinate system) and another local station (corresponding to the second coordinate system).

[0046] Then, a least squares algorithm based on singular value decomposition can be used to transform and solve the first and second coordinates corresponding to each common point, and the optimal rotation matrix R and translation matrix T can be obtained as coordinate transformation parameters.

[0047] Next, the second coordinates of the common points measured at the local station can be transformed to the global coordinate system using R and T. The Euclidean distance deviation between the second coordinates and the measured first coordinates can be calculated, and the statistical measure of the deviation of each common point can be determined, thereby determining the first transformation error.

[0048] Meanwhile, the third coordinates of the target point measured at the local station can be transformed to the global coordinate system using R and T, and the transformed target point coordinates can be compared with the theoretical coordinates of the target point to determine the second transformation error.

[0049] The advantage of this setup is that it not only allows us to assess the coordinate transformation error within the measurement space by determining the first transformation error of the common point, but also allows us to assess the accuracy error of the final generated assembly data by determining the second transformation error of the target point, thereby improving the reliability of the assembly data assessment.

[0050] Example 2 Figure 2 This is a flowchart of a method for quality assessment of assembly data provided in Embodiment 2 of the present invention. This embodiment can be further optimized based on the above embodiments, and may include a quantitative assessment of the uncertainty of assembly data, thereby making the quality assessment more comprehensive and able to reflect the impact of random errors in measurement, further improving the reliability of the quality assessment of assembly data. Figure 2 As shown, the method includes: S201. Obtain measurement data obtained by measuring the preset standard part by the measuring equipment, and coordinate data obtained by measuring the preset test point in the measurement space.

[0051] S202. Based on the measurement data, determine the equipment accuracy evaluation result corresponding to the measurement equipment.

[0052] S203. Based on the coordinate data, determine the registration error evaluation result corresponding to the measurement space.

[0053] S204. Based on the pre-built error measurement model, simulate assembly data of the assembled component is generated, and the uncertainty assessment result of the assembly data is determined according to the simulated assembly data.

[0054] The error measurement model can include environmental sensitivity error parameters and geometric error parameters, which can reflect the main sources of error.

[0055] It should be noted that the equipment accuracy assessment and registration error assessment determined in steps S203 and S204 mainly focus on controlling the errors in the system generation process of assembly data. However, in actual measurements, there are also a large number of random errors (such as environmental fluctuations), leading to uncertainty in the measurement results. Therefore, in order to assess the impact of random errors on the final assembly data, this embodiment can quantify the error range of the assembly data caused by uncertain factors such as random errors of the measuring equipment and environmental fluctuations by determining the uncertainty assessment results. The uncertainty assessment results can usually be expressed as uncertainty.

[0056] For example, by adjusting the environmental sensitivity error parameters and geometric error parameters of the error measurement model, multiple sets of simulated coordinates of key points on the assembly parts can be generated. Then, by performing statistical analysis on all the sets of coordinate data obtained from the simulation, the standard deviation of their distribution can be calculated. This standard deviation is the standard uncertainty of the target point coordinates, which serves as the uncertainty assessment result.

[0057] Optionally, the step of generating simulation assembly data for the assembled component based on a pre-built error measurement model may include: Based on the uncertainty parameters of the measuring device, the probability distribution of the error measurement model parameters is determined; Based on the set number of simulations, the parameters of the error measurement model are randomly sampled according to the probability distribution to generate at least two sets of sampled parameters. Based on the error measurement model configured with each set of sampling parameters, multiple sets of simulated assembly data corresponding to the target points in the assembled components are generated.

[0058] For example, firstly, an error measurement model that reflects the main sources of error can be pre-established. Then, based on the uncertainty parameters of the measuring equipment (such as the measurement error range specified by the manufacturer), the probability distribution characteristics of each error parameter in the model are determined. Next, according to a set number of simulations (such as using the Monte Carlo method), a large number of random samples can be taken according to the probability distribution of each parameter, thereby generating multiple sets of different sampled values ​​of model parameters. Substituting each set of sampled parameters into the error measurement model, the coordinate data of the corresponding set of key points on the assembled component (i.e., simulation assembly data) can be obtained through simulation calculation.

[0059] Specifically, determining the uncertainty assessment results of assembly data can be achieved through the following steps: (1) Considering the uncertainties such as the geometric misalignment of the laser tracker and the interference of environmental fluctuations, a measurement model including environmental sensitivity error parameters and geometric error parameters is established.

[0060] (2) Based on the nominal measurement uncertainty of the laser tracker (length direction: ±0.5μm / m, angle direction: 1arc sec), add random measurement error to the measurement data used for model parameter calibration, and then recalculate the model parameter values ​​using the measurement data with random measurement error.

[0061] (3) By repeating this step N times, the calibration values ​​of multiple sets of model parameters can be calculated, and the specific distribution of each error parameter can be obtained.

[0062] (4) Based on the set number of Monte Carlo experiments M, each measurement model parameter is randomly sampled M times according to its own determined probability distribution, thus obtaining the sampled values ​​of M sets of model parameters.

[0063] (5) Substitute the sampled values ​​of the model parameters into the measurement model to calculate the corresponding model values ​​of the M measured Y (three-dimensional coordinates).

[0064] (6) Perform statistical analysis on the M model values ​​of the measured quantity Y, and take the average value of the model values ​​as the estimated value of the measured quantity Y. The standard deviation of the estimated value is the standard uncertainty.

[0065] S205. Based on the equipment accuracy assessment result, the registration error assessment result, and the uncertainty assessment result, determine the quality assessment result of the assembly data generated based on the measuring equipment and the measuring space.

[0066] It should be noted that after determining the uncertainty assessment results, the determination of the quality assessment results of the assembly data should also comprehensively consider the three assessment results: the equipment accuracy assessment results, the registration error assessment results, and the uncertainty assessment results.

[0067] For example, this embodiment can determine the quality assessment result of the assembly data by using preset rules and comprehensively considering the assessment results from three dimensions: equipment accuracy assessment result, registration error assessment result, and uncertainty assessment result.

[0068] For example, this embodiment can set multiple quality levels: when the equipment accuracy is qualified, the registration error is minimal, and the uncertainty is below the high standard threshold, the assembly data quality can be judged as Grade A (Excellent); if the equipment accuracy is qualified, the registration error meets the standard, but the uncertainty is slightly large, it is judged as Grade B (Good, random errors need attention); if any evaluation result (equipment accuracy or registration error) fails to meet the standard, it is judged as Grade C (requires processing). It is understood that this embodiment, through multi-level and multi-dimensional comprehensive evaluation, can more accurately reflect the true quality status and potential risks of the assembly data.

[0069] The technical solution of this embodiment can obtain measurement data from measuring a preset standard part by a measuring device, and coordinate data from measuring a preset test point in the measurement space. Then, based on the measurement data, the device accuracy evaluation result corresponding to the measuring device is determined, and based on the coordinate data, the registration error evaluation result corresponding to the measurement space is determined. Based on a pre-built error measurement model, simulated assembly data of the assembly component is generated, and based on the simulated assembly data, the uncertainty evaluation result of the assembly data is determined. Finally, based on the device accuracy evaluation result, the registration error evaluation result, and the uncertainty evaluation result, the quality evaluation result of the assembly data generated based on the measuring device and the measurement space is determined. Uncertainty evaluation based on error measurement model and simulation method can be further introduced to construct a three-in-one complete quality evaluation system, which significantly improves the comprehensiveness and reliability of the evaluation.

[0070] Example 3 Figure 3 This is a schematic diagram of the structure of an assembly data quality assessment device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: The acquisition module 31 can be used to acquire measurement data obtained by the measuring device measuring a preset standard part, and coordinate data obtained by measuring a preset point to be measured in the measurement space. The first determining module 32 can be used to determine the equipment accuracy evaluation result corresponding to the measuring device based on the measurement data; The second determining module 33 can be used to determine the registration error evaluation result corresponding to the measurement space based on the coordinate data; The evaluation module 34 can be used to determine the quality evaluation result based on the assembly data generated by the measuring equipment and the measuring space, according to the equipment accuracy evaluation result and the registration error evaluation result.

[0071] The technical solution of this invention obtains measurement data from measuring a preset standard part using a measuring device, and coordinate data from measuring a preset test point in a measurement space. Then, based on the measurement data, it determines the device accuracy assessment result corresponding to the measuring device, and based on the coordinate data, it determines the registration error assessment result corresponding to the measurement space. Finally, based on the device accuracy assessment result and the registration error assessment result, it determines the quality assessment result of the assembly data generated based on the measuring device and the measurement space. This solves the problem that the assessment of assembly data in the prior art relies on human experience, avoids the problem of poor reliability caused by unreasonable data assessment, and improves the reliability of assembly data assessment.

[0072] Optionally, the device further includes: a third determining module; The third determining module can be used to generate simulated assembly data of the assembly component based on a pre-built error measurement model before determining the quality assessment result of the assembly data, and determine the uncertainty assessment result of the assembly data based on the simulated assembly data.

[0073] Optionally, the third determining module can be specifically used to determine the probability distribution of the error measurement model parameters based on the uncertainty parameters of the measuring device; Based on the set number of simulations, the parameters of the error measurement model are randomly sampled according to the probability distribution to generate at least two sets of sampled parameters. Based on the error measurement model configured with each set of sampling parameters, multiple sets of simulated assembly data corresponding to the target points in the assembled components are generated.

[0074] Optionally, the acquisition module 31 can be specifically used to acquire the corresponding endpoint coordinates obtained by the measuring device measuring each endpoint of the preset standard part. The measurement length of the preset standard part is calculated based on the coordinates of each endpoint, and is used as the measurement data.

[0075] Optionally, the first determining module 32 can be used to determine whether the measurement length corresponding to the preset standard part in the measurement data meets the preset conditions; If the conditions are met, the measuring device is considered to be in a state of usable accuracy, and this is taken as the accuracy evaluation result of the device.

[0076] Optionally, the points to be measured may include common points and target points; Accordingly, the second determining module 33 can be specifically used to determine coordinate transformation parameters based on the first coordinates of the common point in the first coordinate system and the second coordinates of the common point in the second coordinate system; Based on the coordinate transformation parameters, by performing coordinate transformation on the second coordinate and the third coordinate of the target point in the second coordinate system respectively, a first transformation error corresponding to the common point and a second transformation error corresponding to the target point are determined; The registration error evaluation result is determined based on the first conversion error and the second conversion error.

[0077] The assembly data quality assessment device provided in the embodiments of the present invention can execute the assembly data quality assessment method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0078] Example 4 Figure 4A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0079] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0080] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0081] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as methods for quality assessment of assembly data.

[0082] In some embodiments, the assembly data quality assessment method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the assembly data quality assessment method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the assembly data quality assessment method by any other suitable means (e.g., by means of firmware).

[0083] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0084] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0085] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0086] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0087] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0088] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0089] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0090] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for quality assessment of assembly data, characterized in that, include: Acquire measurement data obtained by measuring a preset standard part with a measuring device, and coordinate data obtained by measuring a preset point to be measured in the measurement space; Based on the measurement data, determine the equipment accuracy assessment result corresponding to the measurement equipment; Based on the coordinate data, determine the registration error evaluation result corresponding to the measurement space; Based on the equipment accuracy assessment results and the registration error assessment results, the quality assessment results of the assembly data generated based on the measuring equipment and the measuring space are determined.

2. The method according to claim 1, characterized in that, Before determining the quality assessment results of the assembly data, the following is also included: Based on a pre-built error measurement model, simulated assembly data of the assembled parts is generated, and the uncertainty assessment result of the assembly data is determined according to the simulated assembly data.

3. The method according to claim 2, characterized in that, The simulation assembly data of the assembled component, generated based on a pre-built error measurement model, includes: Based on the uncertainty parameters of the measuring device, the probability distribution of the error measurement model parameters is determined; Based on the set number of simulations, the parameters of the error measurement model are randomly sampled according to the probability distribution to generate at least two sets of sampled parameters. Based on the error measurement model configured with each set of sampling parameters, multiple sets of simulated assembly data corresponding to the target points in the assembled components are generated.

4. The method according to claim 1, characterized in that, The measurement data obtained by the measuring device from measuring the preset standard part includes: Obtain the coordinates of the corresponding endpoints obtained by the measuring device measuring each endpoint of the preset standard part; The measurement length of the preset standard part is calculated based on the coordinates of each endpoint, and is used as the measurement data.

5. The method according to claim 1, characterized in that, The step of determining the equipment accuracy assessment result corresponding to the measuring device based on the measurement data includes: Determine whether the measurement length in the measurement data corresponding to the preset standard part meets the preset conditions; If the conditions are met, the measuring device is considered to be in a state of usable accuracy, and this is taken as the accuracy evaluation result of the device.

6. The method according to claim 1, characterized in that, The points to be measured include common points and target points; The step of determining the registration error evaluation result corresponding to the measurement space based on the coordinate data includes: Determine the coordinate transformation parameters based on the first coordinate of the common point in the first coordinate system and the second coordinate of the common point in the second coordinate system; Based on the coordinate transformation parameters, by performing coordinate transformation on the second coordinate and the third coordinate of the target point in the second coordinate system respectively, a first transformation error corresponding to the common point and a second transformation error corresponding to the target point are determined; The registration error evaluation result is determined based on the first conversion error and the second conversion error.

7. A quality assessment device for assembly data, characterized in that, include: The acquisition module is used to acquire measurement data obtained by the measuring device from measuring a preset standard part, and coordinate data obtained from measuring a preset point to be measured in the measurement space. The first determining module is used to determine the equipment accuracy evaluation result corresponding to the measuring device based on the measurement data; The second determining module is used to determine the registration error evaluation result corresponding to the measurement space based on the coordinate data; The evaluation module is used to determine the quality evaluation result based on the assembly data generated by the measuring equipment and the measuring space, according to the equipment accuracy evaluation result and the registration error evaluation result.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the quality assessment method for assembly data according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for quality assessment of assembly data as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the quality assessment method for assembly data as described in any one of claims 1-6.

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