Welding quality detection method and system for electro-tricycle

Abstract

The invention discloses a welding quality detection method and system for an electro-tricycle, and relates to the related field of nondestructive testing, and the method comprises the steps: determining the welding seam structure characteristics of the electro-tricycle, excavating the defect type, and constructing a welding seam detection architecture; configuring a one-step array element focusing parameter, controlling the phased array probe to perform one-step ultrasonic detection, and determining a first detection signal; the positioning module is used for receiving the first detection signal, performing single-scale feature recognition and judgment based on the quality detection module, and positioning a directional detection area of the welding seam detection framework; configuring a two-step array element focusing parameter, performing two-step ultrasonic detection under full array element echo receiving, and determining a second detection signal; and the second detection signal is received, multi-scale feature recognition and judgment are carried out based on the quality detection module, and the welding quality detection result of the electric tricycle is determined. The technical problem that existing electric tricycle welding quality detection is insufficient in detection precision and efficiency is solved, and the technical effect of improving the detection precision and efficiency is achieved.

Description

Technical Field

[0001] The present application relates to the field of non-destructive testing, and in particular to a welding quality testing method and system for an electric tricycle. Background Art

[0002] In the manufacturing process of electric tricycles, welding is a crucial link, and its quality directly affects the safety and service life of the vehicle. However, due to the complexity and diversity of weld structures, traditional welding quality inspection methods, such as visual inspection and magnetic particle inspection, can meet basic quality control needs to a certain extent, but they still have many limitations in terms of complex weld structures, small defect detection, and detection efficiency and accuracy. In particular, in the case of electric tricycles with complex weld structures and diverse defect types, it is difficult to meet the detection needs of high precision and high efficiency at the same time.

[0003] In the current related technologies, the welding quality inspection of electric tricycles has technical problems of insufficient inspection accuracy and efficiency. Summary of the invention

[0004] The present application provides a welding quality inspection method and system for an electric tricycle, which adopts the methods of determining the structural characteristics of the weld, exploring the defect types, constructing a weld inspection architecture, using phased array technology for multi-step ultrasonic detection, preliminarily locating the directional detection area through single-scale feature recognition and judgment, and then combining multi-scale feature recognition and judgment for precise detection. Through the combination of phased array ultrasonic technology and multi-scale feature recognition technology, defects in the weld, including tiny defects and deep defects, are accurately identified, the risk of missed detection and misjudgment is reduced, and the technical effect of improving detection accuracy and efficiency is achieved.

[0005] The present application provides a welding quality detection method for an electric tricycle, comprising: Determine the weld structural features of the electric tricycle, explore the defect types and construct a weld detection architecture, wherein the weld structural features are determined according to the weld material, thickness and welding method; for the weld detection architecture, configure a one-step array element focusing parameter, control the phased array probe to perform a one-step ultrasonic detection, and determine a first detection signal, wherein the phased array probe has multiple array elements built in; receive the first detection signal, perform single-scale feature recognition and judgment based on a quality detection module, and locate the directional detection area of ​​the weld detection architecture, wherein the quality detection module is a multi-scale pyramid structure; for the directional detection area, configure a two-step array element focusing parameter, perform a two-step ultrasonic detection under full-array element echo reception, and determine a second detection signal; receive the second detection signal, perform multi-scale feature recognition and judgment based on the quality detection module, and determine the welding quality detection result of the electric tricycle.

[0006] In a possible implementation, the step of configuring the focusing parameters of the array element is to perform the following processing: Traverse the weld detection architecture to determine the first structural partition; for the first structural partition, configure the first focusing parameter of the first array element, wherein the first focusing parameter defines the directional ultrasonic beam and the scanning mode, the directional ultrasonic beam defines the excitation time and phase, and the scanning mode is linear scanning; determine the Nth focusing parameter of the Nth structural partition in the weld detection architecture; integrate the first focusing parameter until the Nth focusing parameter, establish array element mapping, and determine the one-step array element focusing parameter.

[0007] In a possible implementation, the configuration of the two-step array element focusing parameters performs the following processing: The directional detection area is traversed to determine the azimuth constraint parameters, wherein the azimuth constraint parameters are determined based on the area size, shape and depth; the one-step array element focusing parameters are traversed to determine the matching focusing parameters through structural partition matching; the azimuth constraint parameters and the matching focusing parameters are integrated to determine the two-step array element focusing parameters, wherein the two-step array element focusing parameters correspond one-to-one to the directional detection area.

[0008] In a possible implementation, the following processing is performed: The one-step ultrasonic detection is a one-to-one reception of array elements and echo signals, and the two-step ultrasonic detection is a full-array element reception under a one-to-many relationship between array elements and echo signals.

[0009] In a possible implementation, the method of mining defect types and building a weld detection architecture performs the following processing: The weld structural features are traversed, similarity partitioning is performed, and structural partitions are identified; for the structural partitions, defect type mining is performed under the partitioned weld structural features to determine the defect type, wherein the defect type is a partition existence defect; a mapping between the defect type and the structural partition is established as the weld detection architecture.

[0010] In a possible implementation, the multi-scale feature recognition and determination is performed by performing the following processing: Identify the detection signal of the first directional detection area, identify the signal characteristics and verify them with each other, and determine the first characteristic part and the second characteristic part, wherein the first characteristic part is the consistent part under the reception of all array elements, and the second characteristic part is the different part under the reception of all array elements; perform multi-scale recognition and judgment on the first characteristic part and the second characteristic part to determine the welding quality inspection result of the first directional detection area.

[0011] In a possible implementation, a multi-scale recognition determination is performed on the first characteristic portion and the second characteristic portion, and the following processing is performed: For the first characteristic part, scale magnification and identification are performed based on the characteristic resolution to determine one detection result; for the second characteristic part, proportion screening and scale magnification fusion are performed to determine two detection results, wherein the screening is performed based on a preset proportion based on the full array element receiving signal; the one detection result and the two detection results are integrated as the welding quality detection result of the first directional detection area.

[0012] The present application also provides a welding quality detection system for an electric tricycle, comprising: A weld detection architecture building module, the weld detection architecture building module is used to determine the weld structural characteristics of the electric tricycle, discover defect types and build a weld detection architecture, wherein the weld structural characteristics are determined according to the weld material, thickness and welding method; a one-step ultrasonic detection module, the one-step ultrasonic detection module is used to configure the one-step array element focusing parameters for the weld detection architecture, control the phased array probe to perform one-step ultrasonic detection, and determine the first detection signal, the phased array probe has multiple array elements built in; a directional detection area positioning module, the directional detection area positioning module is used to receive the first detection signal based on quality A detection module is provided for performing single-scale feature recognition and determination, and locating the directional detection area of ​​the weld detection architecture, wherein the quality detection module is a multi-scale pyramid structure; a two-step ultrasonic detection module is provided for configuring the two-step array element focusing parameters for the directional detection area, performing two-step ultrasonic detection under full-array element echo reception, and determining a second detection signal; a welding quality detection result determination module is provided for receiving the second detection signal, performing multi-scale feature recognition and determination based on the quality detection module, and determining the welding quality detection result of the electric tricycle.

[0013] A welding quality inspection method and system for an electric tricycle proposed in this application first determine the weld structural features of the electric tricycle, explore the defect types and construct a weld inspection architecture, wherein the weld structural features are determined according to the weld material, thickness and welding method, and then configure a one-step array element focusing parameter for the weld inspection architecture, control the phased array probe to perform a one-step ultrasonic detection, determine a first detection signal, the phased array probe has multiple array elements built in, and then receive the first detection signal, perform single-scale feature recognition and judgment based on the quality inspection module, and locate the directional detection area of ​​the weld inspection architecture, wherein the quality inspection module is a multi-scale pyramid structure, and then configure a two-step array element focusing parameter for the directional detection area, perform two-step ultrasonic detection under full-array element echo reception, determine the second detection signal, and finally receive the second detection signal, perform multi-scale feature recognition and judgment based on the quality inspection module, and determine the welding quality inspection result of the electric tricycle, thereby achieving the technical effect of improving detection accuracy and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic flow chart of a welding quality inspection method for an electric tricycle provided in an embodiment of the present application.

[0016] Figure 2 A schematic structural diagram of a welding quality inspection system for an electric tricycle provided in an embodiment of the present application.

[0017] Explanation of the reference numerals: weld detection architecture building module 10 , one-step ultrasonic detection module 20 , directional detection area positioning module 30 , two-step ultrasonic detection module 40 , welding quality detection result determination module 50 . DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The present application embodiment provides a welding quality detection method for an electric tricycle, such as Figure 1 As shown, the method includes: Step S100, determine the structural characteristics of the weld of the electric tricycle, mine the defect types and build a weld detection framework, wherein the weld structural characteristics are determined according to the weld material, thickness and welding method. Specifically, collect basic information such as the material, thickness and welding method of the weld of the electric tricycle, analyze the geometric shape, material properties and welding process of the weld based on the collected information, and determine the structural characteristics of the weld. Based on the structural characteristics of the weld and combined with historical data, mine possible defect types, that is, possible quality problems in the weld, such as cracks, slag inclusions, and unfusion. According to the mined defect types and weld structural characteristics, build a non-destructive testing framework suitable for the weld, including detection paths, detection parameters, defect identification methods, etc.

[0022] In a possible implementation, the mining of defect types and construction of a weld detection architecture, step S100 further includes step S110, traversing the weld structural features, performing similarity partitioning, and identifying structural partitions. Specifically, the detailed structural information of the electric tricycle weld is comprehensively collected, including the material, thickness, shape, welding method, etc. of the weld. The collected weld structural information is analyzed to identify different structural features in the weld, such as the starting and ending positions of the weld, the bending degree of the weld, the intersection of the weld, etc. According to the structural characteristics of the weld, the weld is divided into a plurality of similar regions, namely structural partitions, which have certain similarities in weld structure, material or welding method. A unique identifier is assigned to each structural partition so that it can be accurately identified in the subsequent defect mining and detection process. Step S120, for the structural partition, the defect type mining under the partition weld structural characteristics is performed to determine the defect type, wherein the defect type is a partition existence defect. Specifically, each structural partition is analyzed to identify the structural features of the weld in the partition, such as the geometric shape of the weld, material composition, welding process, etc. Based on the structural features of the weld in the partition, combined with historical data, possible defect types are mined, and these defect types are existential defects closely related to the structural features of the partition, such as cracks, slag inclusions, and lack of fusion. The mined defect types are sorted and summarized to ensure that each structural partition has a clear list of defect types. Step S130, establish a mapping between the defect type and the structural partition as the weld detection architecture. Specifically, a mapping relationship is established for each structural partition and its corresponding defect type, that is, the possible defect type in each structural partition is determined. Based on the mapping relationship between the defect type and the structural partition, a non-destructive testing architecture suitable for the weld is constructed. This architecture is used to guide the subsequent ultrasonic detection and defect identification process to ensure that defects in the weld can be accurately and efficiently detected. This implementation method greatly reduces unnecessary detection work and improves detection efficiency by dividing the weld into structural partitions with similarities and mining and identifying defect types for each partition. At the same time, establishing a special defect type list for each structural partition can more accurately identify defects in welds, reduce the possibility of misjudgment and missed judgment, and enhance the accuracy of detection.

[0023] Step S200, for the weld detection architecture, configure a step of array element focusing parameters, control the phased array probe to perform a step of ultrasonic detection, and determine the first detection signal, wherein the phased array probe has multiple array elements built in. Specifically, according to the weld detection architecture, focus parameters are configured for each array element, including excitation time, phase limitation, etc., to form a directional ultrasonic beam. Using the configured focusing parameters, the phased array probe is controlled to perform ultrasonic detection, and the ultrasonic echo signal received by the phased array probe is the first detection signal. Among them, the phased array probe is a device that can dynamically control the direction and shape of the ultrasonic beam, has multiple array elements built in, and realizes the focusing and scanning of the ultrasonic beam by adjusting the excitation time and phase of each array element.

[0024] In a possible implementation, the step of configuring the focusing parameters of the array element, step S200 further includes step S210, traversing the weld detection architecture to determine the first structural partition. Specifically, the constructed weld detection architecture is traversed, and the weld detection architecture is constructed based on the structural features of the weld and the defect types mined. Each part of the architecture is accessed one by one in a certain logical order (such as from front to back, from top to bottom, etc.), and the first structural partition is first determined and selected. This structural partition is a basic unit in the weld detection architecture, representing a specific area or feature of the weld. Step S220, for the first structural partition, configure the first focusing parameters of the first array element, wherein the first focusing parameters define the directional ultrasonic beam and the scanning mode, the directional ultrasonic beam defines the excitation time and phase, and the scanning mode is linear scanning. Specifically, for the first structural partition, focus parameters are configured for the corresponding first array element in the phased array probe, and these focus parameters include the excitation time, phase limit and scanning mode (linear scanning, the ultrasonic beam moves in a straight line direction in the weld) of the directional ultrasonic beam (an ultrasonic beam with a specific direction and shape formed by focusing and scanning the phased array probe). Among them, the excitation time and phase limit of the directional ultrasonic beam determine the direction and shape of the ultrasonic beam, and the scanning mode determines the moving path of the ultrasonic beam in the weld. Step S230, determine the Nth focusing parameter of the Nth structural partition in the weld detection architecture. Specifically, continue to traverse the weld detection architecture and configure corresponding focusing parameters for each structural partition. This step is a repetition of step 220, but for different structural partitions and array elements. When traversing to the Nth structural partition, configure the focus parameters for the Nth array element of the partition. Step S240, integrate the first focusing parameters until the Nth focusing parameters, establish array element mapping, and determine the step array element focusing parameters. Specifically, the focusing parameters configured for each structural partition and array element are integrated, and these parameters together constitute the one-step array element focusing parameters, which are used to control the phased array probe to perform one-step ultrasonic detection. At the same time, an array element mapping is established, that is, the relationship between each array element and its corresponding focusing parameter is recorded. This implementation method can more effectively detect defects in welds by configuring specific focusing parameters for each structural partition and array element, thereby improving the targeted detection. At the same time, by integrating these parameters and establishing array element mapping, the phased array probe can be more efficiently controlled for ultrasonic detection, thereby improving detection efficiency and accuracy.

[0025] Step S300, receiving the first detection signal, performing single-scale feature recognition and determination based on the quality detection module, and locating the directional detection area of ​​the weld detection architecture, wherein the quality detection module is a multi-scale pyramid structure. Specifically, the initial ultrasonic echo signal is received from the phased array probe, and the first detection signal is feature extracted and identified using the quality detection module (a module for feature extraction, identification and determination of the detection signal), wherein a single scale (i.e., a fixed scale) is used for feature identification. Based on the identified features, the possible defects in the weld are determined, and the directional detection area where these defects are located is located, that is, the directional detection area is the weld area that needs further detection determined based on the preliminary determination results.

[0026] Step S400, for the directional detection area, configure the two-step array element focusing parameters, perform two-step ultrasonic detection under full array element echo reception, and determine the second detection signal. Specifically, according to the characteristics of the directional detection area, reconfigure the focusing parameters of the array element to improve the detection accuracy. In the two-step ultrasonic detection, all array elements participate in the reception of the echo signal, and the ultrasonic echo signal received by all array elements is the second detection signal, which is used for further feature recognition and determination.

[0027] In a possible implementation, the configuration of the two-step array element focusing parameters, step S400 further includes step S410, traversing the directional detection area, determining the azimuth constraint parameters, wherein the azimuth constraint parameters are determined based on the area size, shape and depth. Specifically, the directional detection areas determined in step S300 are traversed, and each directional detection area represents a specific position or range where defects may exist in the weld. Each area is analyzed, and the azimuth constraint parameters are determined based on its size, shape and depth. These parameters are used to limit the direction and range of the ultrasonic beam in the subsequent two-step ultrasonic detection to ensure that the ultrasonic wave can accurately cover and detect potential defects. Step S420, traverse the one-step array element focusing parameters, and determine the matching focusing parameters through structural partition matching. Specifically, traverse the one-step array element focusing parameters, which have configured specific focusing parameters for each structural partition in the weld detection architecture. By comparing the directional detection area with the structural partition in the weld detection architecture, the structural partition that best matches the directional detection area is found. The focusing parameters corresponding to these structural partitions are extracted from the one-step array element focusing parameters as matching focusing parameters. Step S430, the azimuth constraint parameters and the matching focusing parameters are fused to determine the two-step array element focusing parameters, wherein the two-step array element focusing parameters correspond one-to-one to the directional detection area. Specifically, the azimuth constraint parameters determined in step 410 are fused with the matching focusing parameters determined in step 420, and the fusion is the adjustment and optimization of the two parameter sets to ensure that they can work together to provide accurate focusing parameters for two-step ultrasonic detection. Finally, two-step array element focusing parameters corresponding one-to-one to each directional detection area are generated to control the phased array probe for more accurate ultrasonic detection. This implementation method accurately controls the direction and range of the ultrasonic beam by traversing the directional detection area and determining the azimuth constraint parameters, thereby reducing unnecessary detection and false alarms. At the same time, by traversing the one-step array element focusing parameters and performing structural partition matching, the focusing parameters that best match the directional detection area are found, further improving the accuracy of detection.

[0028] In a possible implementation, the one-step ultrasonic detection is a one-to-one reception of array elements and echo signals, and the two-step ultrasonic detection is a full-array element reception of a one-to-many relationship between array elements and echo signals.

[0029] Specifically, in one-step ultrasonic detection, each array element of the phased array probe acts as a transmitter to emit an ultrasonic beam in turn, and the reception is completed by a single array element corresponding to the transmitting array element (usually the transmitting array element itself, but in some configurations it can be adjacent or specific other array elements). This one-to-one transmission-reception mode is used to preliminarily locate abnormal areas in the weld. The transmitting array element emits an ultrasonic beam according to preset focusing parameters (such as excitation time, phase limitation and scanning mode), and the received echo signal (i.e., the signal reflected back after the ultrasonic beam encounters the weld structure or defect) is used to generate the first detection signal.

[0030] After the directional detection area is determined, the second-step ultrasonic detection is carried out. In this step, all array elements of the phased array probe participate in the transmission, but the reception is completed by all array elements at the same time, that is, full-element reception. When the transmitting array element transmits the ultrasonic beam, all array elements act as receivers to receive echo signals from the weld structure and defects. This one-to-many transmission-reception mode provides rich data for accurately quantifying the characteristics of the weld, such as the size, shape and location of the defect. The multiple echo signals received are used to generate the second detection signal to obtain the final welding quality inspection results. This implementation method quickly reduces the area where defects may exist in the weld through the preliminary positioning of the first-step ultrasonic detection, reducing unnecessary detection work, and the full-element reception mode of the second-step ultrasonic detection is used to provide rich data, so that the characteristics and defects of the weld can be analyzed more accurately, thereby improving the accuracy of detection.

[0031] Step S500, receiving the second detection signal, performing multi-scale feature recognition and judgment based on the quality detection module, and determining the welding quality detection result of the electric tricycle. Specifically, the ultrasonic echo signal is received from all array elements, and the quality detection module is used to extract and identify the features of the second detection signal at multiple scales, that is, the detection signal is extracted and identified at different scales to improve the accuracy and comprehensiveness of the recognition. According to the identified features, combined with the preset judgment criteria, the welding quality detection result of the electric tricycle is determined. The embodiment of the present application adopts the technical means of determining the structural features of the weld, mining the defect types, constructing the weld detection architecture, using phased array technology for multi-step ultrasonic detection, preliminarily locating the directional detection area through single-scale feature recognition and judgment, and then combining multi-scale feature recognition and judgment for precise detection. Through the combination of phased array ultrasonic technology and multi-scale feature recognition technology, the defects in the weld are accurately identified, including small defects and deep defects, reducing the risk of missed detection and misjudgment, and achieving the technical effect of improving detection accuracy and efficiency.

[0032] In a possible implementation, the multi-scale feature recognition and determination is performed, and step S500 further includes step S510, identifying the detection signal of the first directional detection area, identifying the signal features and mutually verifying, and determining the first feature part and the second feature part, wherein the first feature part is the consistent part under the reception of all array elements, and the second feature part is the part with differences under the reception of all array elements. Specifically, in the two-step ultrasonic detection, all array elements receive echo signals from the weld, and these signals contain information about the weld structure and potential defects. For the first directional detection area, features are extracted from the received detection signals, including the amplitude, phase, frequency, arrival time, etc. of the signal, which reflect the physical properties of the weld. The extracted features are mutually verified in the data received by all array elements. Since all array elements receive, each array element will receive the echo signal from the same weld area, so the consistency and difference can be verified by comparing the signal features received by different array elements. According to the verification results, the features are divided into two categories: the first feature part (the consistent part under the reception of all array elements) and the second feature part (the part with differences under the reception of all array elements). The first characteristic portion represents a uniform, defect-free portion of the weld, and the second characteristic portion indicates an anomaly or defect in the weld.

[0033] Step S520, for the first characteristic part and the second characteristic part, multi-scale identification and determination are performed to determine the welding quality detection result of the first directional detection area. Specifically, for the first characteristic part and the second characteristic part, a quality detection module with a multi-scale pyramid structure is used for further analysis. The multi-scale analysis can capture the characteristic information at different scales, so as to more comprehensively evaluate the quality of the weld. Based on the result of the multi-scale analysis, the first characteristic part and the second characteristic part are identified and determined. For the first characteristic part, if its characteristics remain highly consistent under the reception of the full array element, it indicates that the weld quality in this area is good. For the second characteristic part, further analyze whether its characteristic difference is caused by defects, and determine the type, size and location of the defects. The welding quality detection results of the first directional detection area are integrated to form a final inspection report, including information such as the quality status of the weld, the type, size and location of the defect. This implementation method captures rich information in the weld, including uniform parts and potential defect parts, through full array element reception and two-step ultrasonic detection. Through mutual verification and multi-scale analysis, this information is further verified and refined, thereby more accurately evaluating the quality of the weld and improving the comprehensiveness and accuracy of the detection.

[0034] In a possible implementation, multi-scale identification and determination are performed for the first feature part and the second feature part, and step S520 further includes step S521, for the first feature part, scale magnification and identification are performed based on the feature resolution, and a detection result is determined. Specifically, the feature resolution of the first feature part is determined, and the feature resolution refers to the smallest unit or scale that can distinguish different features. In the first feature part, since it is a consistent part under full array element reception, the feature resolution is high, that is, the difference between the features is small. In order to more accurately identify and analyze these subtle feature differences, the first feature part is scaled up by increasing the sampling rate, improving the signal processing accuracy, etc. After the scale is magnified, the first feature part is feature recognized by using the multi-scale pyramid structure in the quality detection module, including the shape, size, position and relationship of the feature with other features. Based on the result of feature recognition, the welding quality detection result of the first feature part is determined. If the difference between the features is within an acceptable range, it indicates that the weld quality in the area is good; if the difference exceeds the acceptable range, it indicates that there is a potential quality problem. Step S522, for the second characteristic part, perform ratio screening and scale amplification fusion to determine two detection results, wherein the screening is performed based on a preset ratio of the full array element receiving signal. Specifically, in the second characteristic part, since it is a part with differences under full array element reception, there may be multiple echo signals. In order to reduce noise and interference, firstly, based on the full array element receiving signal, a preset ratio is used for screening to screen out the discrete part with low probability. For the remaining signal part after screening, signal amplification is performed to highlight its characteristics, and feature fusion is performed to integrate the information between different signals. Feature fusion can be achieved by weighted averaging, maximum / minimum value selection, feature vector splicing, etc. Based on the results of signal amplification and fusion, the welding quality detection result of the second characteristic part is determined. If the fused features indicate obvious abnormalities or defects, it indicates that the weld quality in this area is poor. Step S523, integrate the one detection result and the two detection results as the welding quality detection result of the first directional detection area. Specifically, the two test results obtained in step 521 and step 522 are integrated, and based on the integrated results, the welding quality test results of the first directional detection area are formed, and this result is used for subsequent weld quality evaluation and improvement. This implementation method reduces misjudgment and missed detection due to noise or interference by screening signals at a preset ratio, ensuring that only the signals that are most likely to represent the true features are retained for subsequent analysis. Amplifying and fusing the screened signals enhances the visibility and recognizability of the features, which helps to discover potential defects that may be overlooked due to weak signals, and improves the accuracy and reliability of detection.

[0035] In the above, refer to Figure 1A welding quality detection method for an electric tricycle according to an embodiment of the present invention is described in detail. Figure 2 A welding quality inspection system for an electric tricycle according to an embodiment of the present invention is described.

[0036] A welding quality inspection system for an electric tricycle according to an embodiment of the present invention is used to solve the technical problems of insufficient detection accuracy and efficiency in the existing welding quality inspection of electric tricycles, thereby achieving the technical effect of improving detection accuracy and efficiency. A welding quality inspection system for an electric tricycle includes: a weld detection architecture construction module 10, a one-step ultrasonic detection module 20, a directional detection area positioning module 30, a two-step ultrasonic detection module 40, and a welding quality inspection result determination module 50.

[0037] The weld detection architecture construction module 10 is used to determine the weld structural characteristics of the electric tricycle, explore the defect type and construct the weld detection architecture, wherein the weld structural characteristics are determined according to the weld material, thickness and welding method; the one-step ultrasonic detection module 20 is used to configure the one-step array element focusing parameters for the weld detection architecture, control the phased array probe to perform one-step ultrasonic detection, and determine the first detection signal, wherein the phased array probe has multiple array elements built in; the directional detection area positioning module 30 is used to receive the first detection signal, perform single-scale feature recognition and judgment based on the quality detection module, and locate the directional detection area of ​​the weld detection architecture, wherein the quality detection module is a multi-scale pyramid structure; the two-step ultrasonic detection module 40 is used to configure the two-step array element focusing parameters for the directional detection area, perform two-step ultrasonic detection under full-array element echo reception, and determine the second detection signal; the welding quality detection result determination module 50 is used to receive the second detection signal, perform multi-scale feature recognition and judgment based on the quality detection module, and determine the welding quality detection result of the electric tricycle.

[0038] The specific configuration of the one-step ultrasonic detection module 20 will be described in detail below. As described above, the one-step array element focusing parameters are configured, and the one-step ultrasonic detection module 20 may further include: a first structural partition determination unit is used to traverse the weld detection architecture and determine the first structural partition; a first focusing parameter configuration unit is used to configure the first focusing parameter of the first array element for the first structural partition, wherein the first focusing parameter defines the directional ultrasonic beam and the scanning mode, the directional ultrasonic beam defines the excitation time and phase, and the scanning mode is linear scanning; an Nth focusing parameter determination unit is used to determine the Nth focusing parameter of the Nth structural partition in the weld detection architecture; a one-step array element focusing parameter determination unit is used to integrate the first focusing parameter until the Nth focusing parameter, and establish an array element mapping to determine the one-step array element focusing parameter.

[0039] The specific configuration of the two-step ultrasonic detection module 40 will be described in detail below. As described above, the two-step array element focusing parameters are configured, and the two-step ultrasonic detection module 40 may further include: an azimuth constraint parameter determination unit for traversing the directional detection area and determining the azimuth constraint parameters, wherein the azimuth constraint parameters are determined based on the area size, shape and depth; a structure partition matching unit for traversing the one-step array element focusing parameters and determining the matching focusing parameters through structure partition matching; a two-step array element focusing parameter determination unit for fusing the azimuth constraint parameters with the matching focusing parameters to determine the two-step array element focusing parameters, wherein the two-step array element focusing parameters correspond one-to-one to the directional detection area.

[0040] The system may further include: the one-step ultrasonic detection is a one-to-one reception of array elements and echo signals, and the two-step ultrasonic detection is a full-array element reception under a one-to-many relationship between array elements and echo signals.

[0041] The specific configuration of the weld detection architecture building module 10 will be described in detail below. As described above, the defect type is mined and the weld detection architecture is built. The weld detection architecture building module 10 may further include: a similarity partitioning unit for traversing the weld structural features, performing similarity partitioning, and identifying structural partitions; a defect type mining unit for mining defect types under the partitioned weld structural features for the structural partitions, and determining the defect type, wherein the defect type is a partition existence defect; and a mapping establishment unit for establishing a mapping between the defect type and the structural partition as the weld detection architecture.

[0042] The specific configuration of the welding quality detection result determination module 50 will be described in detail below. As described above, multi-scale feature recognition and determination are performed, and the welding quality detection result determination module 50 may further include: a feature part determination unit is used to identify the detection signal of the first directional detection area, identify the signal characteristics and verify each other, and determine the first feature part and the second feature part, wherein the first feature part is the consistent part under the reception of all array elements, and the second feature part is the part with differences under the reception of all array elements; a multi-scale recognition and determination unit is used to perform multi-scale recognition and determination on the first feature part and the second feature part, and determine the welding quality detection result of the first directional detection area.

[0043] Among them, multi-scale recognition and judgment are performed on the first characteristic part and the second characteristic part, and the multi-scale recognition and judgment unit may further include: a scale magnification and recognition subunit is used to perform scale magnification and recognition on the first characteristic part based on the characteristic resolution, and determine a detection result; a ratio screening and scale magnification fusion subunit is used to perform ratio screening and scale magnification fusion on the second characteristic part, and determine two detection results, wherein the screening is performed at a preset ratio based on the full array element receiving signal; a detection result integration subunit is used to integrate the one detection result and the two detection results as the welding quality detection result of the first directional detection area.

[0044] A welding quality detection system for an electric tricycle provided in an embodiment of the present invention can execute a welding quality detection method for an electric tricycle provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0045] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0046] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, 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 the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A welding quality detection method for an electric tricycle, characterized in that: The method comprises: Determine the weld structural features of the electric tricycle, explore the defect types and build a weld detection framework, wherein the weld structural features are determined according to the weld material, thickness and welding method; According to the weld detection architecture, a step-array element focusing parameter is configured to control a phased array probe to perform a step of ultrasonic detection to determine a first detection signal, wherein the phased array probe has a plurality of array elements built therein; receiving the first detection signal, performing single-scale feature recognition and determination based on a quality detection module, and locating a directional detection area of ​​the weld detection architecture, wherein the quality detection module is a multi-scale pyramid structure; For the directional detection area, configure two-step array element focusing parameters, perform two-step ultrasonic detection under full array element echo reception, and determine a second detection signal; The second detection signal is received, and multi-scale feature recognition and judgment are performed based on the quality detection module to determine the welding quality detection result of the electric tricycle.

2. A welding quality detection method for an electric tricycle as claimed in claim 1, characterized in that: The step of configuring the focusing parameters of the one-step array element includes: Traversing the weld detection framework to determine a first structural partition; For the first structural partition, configure a first focusing parameter of the first array element, wherein the first focusing parameter defines a directional ultrasonic beam and a scanning mode, the directional ultrasonic beam defines an excitation time and a phase, and the scanning mode is a linear scan; Determining an Nth focusing parameter of an Nth structural partition within the weld inspection architecture; The first focusing parameter is integrated until the Nth focusing parameter, and an array element mapping is established to determine the one-step array element focusing parameter.

3. The welding quality detection method of an electric tricycle as claimed in claim 1, characterized in that: The configuring of two-step array element focusing parameters includes: Traversing the directional detection area to determine an orientation constraint parameter, wherein the orientation constraint parameter is determined based on the size, shape and depth of the area; Traversing the one-step array element focusing parameters, and determining matching focusing parameters through structural partition matching; The azimuth constraint parameter and the matching focus parameter are integrated to determine the two-step array element focus parameter, wherein the two-step array element focus parameter corresponds to the directional detection area one by one.

4. The welding quality detection method of an electric tricycle as claimed in claim 1, characterized in that: The one-step ultrasonic detection is a one-to-one reception of array elements and echo signals, and the two-step ultrasonic detection is a full-array element reception under a one-to-many relationship between array elements and echo signals.

5. The welding quality detection method of an electric tricycle as claimed in claim 1, characterized in that: The method of mining defect types and building a weld inspection architecture includes: Traversing the weld structural features, performing similarity partitioning, and identifying structural partitions; For the structural partition, the defect type mining under the structural characteristics of the partition weld is performed to determine the defect type, wherein the defect type is a partition existence defect; A mapping between the defect type and the structural partition is established as the weld detection framework.

6. The welding quality detection method of an electric tricycle as claimed in claim 1, characterized in that: The multi-scale feature recognition and determination includes: Identify the detection signal of the first directional detection area, identify the signal characteristics and verify them with each other, and determine the first characteristic part and the second characteristic part, wherein the first characteristic part is the consistent part under the reception of all array elements, and the second characteristic part is the different part under the reception of all array elements; A multi-scale recognition and determination is performed on the first characteristic portion and the second characteristic portion to determine a welding quality detection result of a first directional detection area.

7. A welding quality inspection method for an electric tricycle as claimed in claim 6, characterized in that: Performing multi-scale recognition and determination on the first characteristic portion and the second characteristic portion includes: For the first characteristic part, scale magnification and identification are performed based on the characteristic resolution to determine a detection result; For the second characteristic part, ratio screening and scale amplification fusion are performed to determine two detection results, wherein the screening is performed based on a preset ratio based on the received signals of all array elements; The one detection result and the two detection results are integrated as the welding quality detection result of the first directional detection area.

8. A welding quality inspection system for an electric tricycle, characterized in that: The system is used to implement the welding quality detection method of an electric tricycle according to any one of claims 1 to 7, and the system comprises: A weld detection framework building module, wherein the weld detection framework building module is used to determine the weld structural characteristics of the electric tricycle, mine defect types and build a weld detection framework, wherein the weld structural characteristics are determined according to the weld material, thickness and welding method; A one-step ultrasonic detection module, wherein the one-step ultrasonic detection module is used to configure a one-step array element focusing parameter for the weld detection architecture, control a phased array probe to perform one-step ultrasonic detection, and determine a first detection signal, wherein the phased array probe has a plurality of array elements built therein; A directional detection area positioning module, the directional detection area positioning module is used to receive the first detection signal, perform single-scale feature recognition and determination based on the quality detection module, and locate the directional detection area of ​​the weld detection architecture, wherein the quality detection module is a multi-scale pyramid structure; A two-step ultrasonic detection module, the two-step ultrasonic detection module is used to configure the two-step array element focusing parameters for the directional detection area, perform two-step ultrasonic detection under full array element echo reception, and determine a second detection signal; A welding quality detection result determination module is used to receive the second detection signal, perform multi-scale feature recognition and judgment based on the quality detection module, and determine the welding quality detection result of the electric tricycle.

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