Control system and method for welding process, and device, medium and program

Abstract

Provided in the present disclosure are a control system and method for a welding process, and a device, a medium and a program. The control system comprises: a sensor module, which is configured to collect data during a welding process; a data analysis module, which is configured to preprocess the data during the welding process, extract key features from the preprocessed data, perform weighted fusion processing on the key features to obtain comprehensive features, and perform defect identification on the basis of the comprehensive features by means of a machine learning model to identify and obtain defect features; a knowledge graph module, which is configured to use a knowledge graph to obtain, on the basis of the defect features, a repair policy corresponding to the defect features; a control module, which is configured to transmit to the data analysis module the data in the welding process that is received from the sensor module, receive the repair policy, and generate a repair instruction on the basis of the repair policy; and a repair module, which is configured to automatically adjust welding parameters or control an actuation mechanism to perform a repair operation on the basis of the repair instruction.

Description

Control systems, methods, equipment, media and procedures for welding processes

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on the application with CN application number 202411750614.1 and application date December 2, 2024, and claims its priority. The disclosed content of the CN application is hereby introduced as a whole into this application. Technical Field

[0003] The present disclosure relates to the field of intelligent manufacturing, and in particular to a control system, method, equipment, medium and program for a welding process. Background Art

[0004] With the rapid development of intelligent manufacturing, welding, as one of the key processes in the manufacturing industry, its quality directly affects the reliability and service life of the product. Summary of the Invention

[0005] According to one aspect of the present disclosure, a control system for a welding process is provided, comprising: a sensor module configured to collect data during a welding process; a data analysis module configured to preprocess the data during the welding process, extract key features from the preprocessed data, perform weighted fusion processing on the key features to obtain comprehensive features, and perform defect identification based on the comprehensive features and through a machine learning model to obtain defect features; a knowledge graph module configured to obtain a repair strategy corresponding to the defect feature based on the defect feature and using a knowledge graph, wherein the knowledge graph is a knowledge graph including welding parameters, defect features and repair strategies; a control module configured to transmit the data during the welding process received from the sensor module to the data analysis module, receive the repair strategy, and generate a repair instruction based on the repair strategy; and a repair module configured to automatically adjust the welding parameters or control the actuator to perform a repair operation based on the repair instruction.

[0006] In some embodiments, the knowledge graph is stored in the form of a graph structure, the nodes of the knowledge graph represent the welding parameters, the defect characteristics or the repair strategies, and the edges of the knowledge graph represent the association relationships between different nodes.

[0007] In some embodiments, the sensor module includes: a visual sensor configured to collect image data during the welding process; an acoustic sensor configured to collect acoustic wave signal data during the welding process; and an infrared sensor configured to collect temperature distribution characteristic data during the welding process; wherein the data during the welding process includes the image data, the acoustic wave signal data and the temperature distribution characteristic data.

[0008] In some embodiments, the data analysis module is configured to perform denoising, normalization, and time alignment processing on the image data, the acoustic wave signal data, and the temperature distribution characteristic data.

[0009] In some embodiments, the key features include: the image key features F in the image data vision , the acoustic spectrum feature F in the acoustic wave signal data acoustic and the temperature gradient characteristic F in the temperature distribution characteristic data infrared ; Wherein, the comprehensive feature F combined F combined =ω1*F vision +ω2*F acoustic +ω3*F infrared ,

[0010] Wherein, ω1 is the weight of the key image feature, ω2 is the weight of the acoustic spectrum feature, and ω3 is the weight of the temperature gradient feature.

[0011] In some embodiments, the data analysis module is configured to perform defect identification based on the comprehensive features, combined with the knowledge graph, and through the machine learning model.

[0012] In some embodiments, the control module is further configured to dynamically schedule the data acquisition frequency and data transmission rate of the sensor module.

[0013] In some embodiments, the sensor module is also configured to collect data during the repair process; the data analysis module is also configured to determine whether the repair result meets the expected result based on the data during the repair process; if the repair result meets the expected result, the data during the repair process is transmitted to the knowledge graph module; if the repair result does not meet the expected result, a re-repair information is sent to the knowledge graph module; the knowledge graph module is also configured to record the data during the repair process and update the knowledge graph when the repair result meets the expected result; if the repair result does not meet the expected result, a re-repair strategy different from the current repair strategy is sent to the control module based on the re-repair information; the control module is also configured to send a re-repair instruction to the repair module based on the re-repair strategy, so that the repair module performs the repair operation again.

[0014] In some embodiments, the control module is further configured to detect a current working state of the sensor module, and adjust working parameters of the sensor module according to historical data of the working state of the sensor module and the current working state.

[0015] According to another aspect of the present disclosure, a control method for a welding process is provided, comprising: receiving data in a welding process; preprocessing the data in the welding process, extracting key features from the preprocessed data, performing weighted fusion processing on the key features to obtain comprehensive features, performing defect identification based on the comprehensive features and through a machine learning model to obtain defect features; obtaining a repair strategy corresponding to the defect feature based on the defect feature and using a knowledge graph, wherein the knowledge graph is a knowledge graph including welding parameters, defect features and repair strategies; generating repair instructions based on the repair strategy; and automatically adjusting welding parameters or controlling an actuator to perform a repair operation based on the repair instructions.

[0016] In some embodiments, the knowledge graph is stored in the form of a graph structure, the nodes of the knowledge graph represent the welding parameters, the defect characteristics or the repair strategies, and the edges of the knowledge graph represent the association relationships between different nodes.

[0017] In some embodiments, the data during the welding process includes image data, acoustic wave signal data, and temperature distribution characteristic data.

[0018] In some embodiments, preprocessing the data in the welding process includes: performing denoising, normalization, and time alignment on the image data, the acoustic wave signal data, and the temperature distribution characteristic data.

[0019] In some embodiments, the key features include: the image key features F in the image data vision , the acoustic spectrum feature F in the acoustic wave signal data acoustic and the temperature gradient characteristic F in the temperature distribution characteristic data infrared ; Wherein, the comprehensive feature F combined F combined =ω1*F vision +ω2*F acoustic +ω3*F infrared ,

[0020] Wherein, ω1 is the weight of the key image feature, ω2 is the weight of the acoustic spectrum feature, and ω3 is the weight of the temperature gradient feature.

[0021] In some embodiments, defect identification is performed based on the comprehensive features and through a machine learning model, including: based on the comprehensive features, combined with the knowledge graph, and defect identification is performed through the machine learning model.

[0022] In some embodiments, the control method further includes: dynamically scheduling the data acquisition frequency and data transmission rate of the sensor module.

[0023] In some embodiments, the control method also includes: receiving data from a repair process; judging whether a repair result meets an expected result based on the data from the repair process; recording the data from the repair process and updating the knowledge graph if the repair result meets the expected result; obtaining a re-repair strategy that is different from the current repair strategy if the repair result does not meet the expected result; generating a re-repair instruction based on the re-repair strategy; and re-performing the repair operation based on the re-repair instruction.

[0024] In some embodiments, the control method further includes: detecting a current working state of the sensor module; and adjusting working parameters of the sensor module according to historical data of the working state of the sensor module and the current working state.

[0025] According to another aspect of the present disclosure, an electronic device is provided, including: a memory; and a processor coupled to the memory, wherein the processor is configured to execute the aforementioned control method based on instructions stored in the memory.

[0026] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which computer instructions are stored. When the computer instructions are executed by a processor, the control method as described above is implemented.

[0027] According to another aspect of the present disclosure, a computer program is provided, comprising: instructions, which, when executed by a processor, enable the processor to perform the control method as described above.

[0028] Other features and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0030] The present disclosure can be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0031] FIG1 is a block diagram schematically illustrating a control system for a welding process according to some embodiments of the present disclosure;

[0032] FIG2 is a block diagram schematically illustrating a structure of a sensor module according to some embodiments of the present disclosure;

[0033] FIG3 is a block diagram schematically illustrating a structure of a data analysis module according to some embodiments of the present disclosure;

[0034] FIG4 is a block diagram schematically illustrating a structure of a knowledge graph module according to some embodiments of the present disclosure;

[0035] FIG5 is a block diagram schematically illustrating a structure of a control module according to some embodiments of the present disclosure;

[0036] FIG6 is a block diagram schematically illustrating a structure of a repair module according to some embodiments of the present disclosure;

[0037] 7 is a flow chart illustrating a control method for a welding process according to some embodiments of the present disclosure;

[0038] FIG8 is a flow chart illustrating a control method for a welding process according to other embodiments of the present disclosure;

[0039] FIG9 is a block diagram schematically illustrating a structure of an electronic device according to some embodiments of the present disclosure;

[0040] FIG10 is a block diagram schematically illustrating a structure of an electronic device according to some other embodiments of the present disclosure. DETAILED DESCRIPTION

[0041] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0042] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0043] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0044] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0045] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0046] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0047] The inventors of the present disclosure found that in related technologies, welding defect repair mainly relies on manual intervention. Especially in complex welding scenarios, manual repair often requires a lot of time and manpower, and is prone to secondary defects, affecting the overall welding quality and production efficiency.

[0048] In view of this, embodiments of the present disclosure provide a control system for a welding process to improve welding quality and production efficiency.

[0049] FIG1 is a block diagram schematically illustrating a control system for a welding process according to some embodiments of the present disclosure. As shown in FIG1 , the control system includes a sensor module 110 , a data analysis module 120 , a knowledge graph module 130 , a control module 140 , and a repair module 150 .

[0050] The sensor module 110 is configured to collect data during the welding process.

[0051] The data analysis module 120 is configured to preprocess data from the welding process, extract key features from the preprocessed data, perform weighted fusion processing on the key features to obtain comprehensive features, and perform defect identification based on the comprehensive features using a machine learning model to obtain defect features. Here, the machine learning model is a machine learning algorithm, and a known machine learning algorithm can be used.

[0052] The knowledge graph module 130 is configured to obtain a repair strategy corresponding to the defect characteristics based on the defect characteristics and using the knowledge graph. The knowledge graph is a knowledge graph that includes welding parameters, defect characteristics, and repair strategies.

[0053] For example, the knowledge graph is stored in the form of a graph structure. The nodes of the knowledge graph represent welding parameters, defect characteristics or repair strategies, and the edges of the knowledge graph represent the association relationships between different nodes.

[0054] The control module 140 is configured to transmit the welding process data received from the sensor module 110 to the data analysis module 120 , receive a repair strategy, generate a repair instruction based on the repair strategy, and send the repair instruction to the repair module 150 .

[0055] The repair module 150 is configured to automatically adjust welding parameters or control actuators to perform repair operations based on the repair instructions.

[0056] Here, welding parameters are relevant parameters in the welding process, such as welding current, welding voltage, welding speed, etc. The repair module 150 can automatically adjust the welding parameters based on the repair instruction, so that the adjusted welding parameters meet the desired welding effect.

[0057] For example, the actuator may be a welding robot. That is, the repair module 150 may control the welding robot to perform a repair operation based on the repair instruction.

[0058] Thus, a control system for a welding process according to some embodiments of the present disclosure is provided. The control system includes: a sensor module configured to collect data during the welding process; a data analysis module configured to preprocess the data during the welding process, extract key features from the preprocessed data, perform weighted fusion processing on the key features to obtain comprehensive features, and perform defect identification based on the comprehensive features and using a machine learning model to identify defect features; a knowledge graph module configured to obtain a repair strategy corresponding to the defect features based on the defect features and using a knowledge graph, wherein the knowledge graph is a knowledge graph containing welding parameters, defect features, and repair strategies; a control module configured to transmit the data during the welding process received from the sensor module to the data analysis module, receive the repair strategy, and generate repair instructions based on the repair strategy; and a repair module configured to automatically adjust the welding parameters or control the actuator to perform the repair operation based on the repair instructions. In this control system, data from the welding process is collected and defects are identified after relevant processing of these data. The repair strategy corresponding to the defect is obtained through the knowledge graph, and the corresponding repair instructions are generated. Based on the repair instructions, the welding parameters are automatically adjusted or the actuator is controlled to perform the repair operation. In this way, the automation and intelligence of the defect repair in the welding process are realized, and the welding quality and production efficiency are improved.

[0059] Moreover, in the above system, the association relationships in the knowledge graph can be used to quickly infer the best repair plan, realizing the combination of static knowledge and real-time data, which makes it easier for the system to respond and make decisions quickly after detecting defects in real time, and then repair welding defects in time during the welding process, thereby improving the product yield.

[0060] The structure or function of each module will be described in detail below with reference to FIG. 2 to FIG. 6 .

[0061] FIG2 is a block diagram schematically illustrating a structure of a sensor module according to some embodiments of the present disclosure.

[0062] As shown in FIG. 2 , the sensor module 110 may include a visual sensor 111 , an acoustic sensor 112 , and an infrared sensor 113 .

[0063] The vision sensor 111 is configured to collect image data during the welding process.

[0064] The acoustic sensor 112 is configured to collect acoustic wave signal data during the welding process.

[0065] The infrared sensor 113 is configured to collect temperature distribution characteristic data during the welding process.

[0066] In this way, the data during the welding process may include image data, acoustic wave signal data, and temperature distribution characteristic data.

[0067] In related technologies, welding quality inspection techniques typically rely on a single type of sensor. Due to the single data source, single-sensor inspection methods are limited in the comprehensiveness and accuracy of information obtained. In the above-mentioned embodiments of the present disclosure, however, a visual sensor, an acoustic sensor, and an infrared sensor are provided as a sensor module (which can be referred to as a multimodal sensor module). This facilitates the subsequent use of data from multiple sensors, such as visual, acoustic, and infrared, to achieve more comprehensive and accurate welding quality inspection.

[0068] In other embodiments, in some scenarios, one or two of a visual sensor, an acoustic sensor, and an infrared sensor may be used as a sensor module, and detection of welding defects in these specific scenarios may also be achieved.

[0069] In other embodiments, the sensor module may include other sensors in addition to the visual sensor, the acoustic sensor, and the infrared sensor, which may further improve the accuracy of defect detection.

[0070] In some embodiments, as shown in FIG2 , a synchronous clock system 114 may be provided in the sensor module so that the visual sensor, acoustic sensor, and infrared sensor can realize real-time data collection and synchronous transmission through the synchronous clock system, and the time consistency and spatial alignment of the data of various types of sensors can be maintained.

[0071] In this embodiment, all sensors are connected to a unified synchronized clock system 114. This synchronized clock system provides a precise time reference for each sensor, ensuring synchronized data acquisition at the same time. The synchronized clock system can be implemented in hardware (e.g., using GPS (Global Positioning System) time synchronization or Network Time Protocol (NTP)), or it can be based on a software algorithm to calibrate and adjust time differences between different sensors.

[0072] For example, during the data collection process, the data collected by each sensor will be accompanied by a precise timestamp, which is generated by a synchronized clock system to ensure that data from different sensors can be compared and aligned under the same time base.

[0073] For example, during the data fusion phase, the data streams are corrected and aligned using a data alignment algorithm, utilizing time-stamped data. This algorithm takes into account possible time deviations between sensors and achieves precise alignment through methods such as interpolation and delay correction.

[0074] In other embodiments, various types of data in the welding process are spatially aligned, that is, data from different sensors correspond to the same position or area in space, so that the system can fuse and analyze multimodal data in a unified spatial coordinate system, thereby improving the accuracy and reliability of detection.

[0075] In the above embodiment, the sensor module includes a visual sensor, an acoustic sensor, and an infrared sensor. These sensors are used to collect image data, acoustic signals, and temperature distribution characteristics during the welding process, respectively. These sensors utilize a synchronized clock system to achieve real-time data acquisition and synchronous transmission, ensuring maximum temporal consistency and spatial alignment between the various sensor data types.

[0076] Figure 3 is a block diagram schematically illustrating a data analysis module according to some embodiments of the present disclosure. The data analysis module is primarily responsible for fusing and analyzing data collected by multimodal sensors.

[0077] As shown in FIG3 , the data analysis module 120 can pre-process the data during the welding process. For example, the data analysis module 120 is configured to perform denoising (i.e., noise reduction), normalization, and time alignment on the image data, acoustic signal data, and temperature distribution feature data.

[0078] Here, a denoising method known to those skilled in the art may be used to perform denoising processing.

[0079] For example, in the normalization process, the normalized data X′ is

[0080] Where X is the original data, μ is the mean of the data, and σ is the standard deviation of the data.

[0081] By normalizing the image data, acoustic signal data and temperature distribution feature data respectively, it is convenient to perform weighted fusion of these different types of data.

[0082] Time alignment aligns data at the same instant. For example, if 10 images and 20 acoustic signals are collected within the same time period, the images and acoustic signals at the same instant can be aligned, and the other acoustic signals can be removed. Time alignment facilitates weighted data fusion.

[0083] In other embodiments, the data analysis module 120 may also be configured to perform spatial alignment processing on the image data, acoustic signal data, and temperature distribution feature data. Specifically, the image data, acoustic signal data, and temperature distribution feature data collected from the same location are aligned. This spatial alignment process facilitates weighted data fusion.

[0084] In the above embodiment, by performing pre-processing such as denoising, normalization, time alignment and space alignment on the collected visual data, acoustic data and infrared data, a unified standard format is provided for subsequent data processing, thereby facilitating the processing of information redundancy.

[0085] As shown in FIG3 , the data analysis module 120 can also extract key features from the pre-processed data. For example, the key features include: image key features F in the image data vision , acoustic spectrum features F in acoustic signal data acoustic And the temperature gradient feature F in the temperature distribution characteristic data infrared For example, the key image features may include weld width and / or weld pool shape parameters.

[0086] As shown in FIG3 , the data analysis module 120 may also perform weighted fusion processing on the key features to obtain comprehensive features, that is, the features are fused using a weighted fusion algorithm to generate a comprehensive feature vector.

[0087] For example, the comprehensive feature F combined F combined =ω1*F vision +ω2*F acoustic +ω3*F infrared , (2)

[0088] Here, ω1 is the weight of the key image features (i.e., visual data features), ω2 is the weight of the acoustic spectrum features, and ω3 is the weight of the temperature gradient features. These weights can be dynamically adjusted based on the reliability of various types of data in the actual production environment.

[0089] During the weighted fusion process, the weights of key image features, acoustic spectrum features, and temperature gradient features (i.e., infrared temperature features) can be dynamically adjusted based on the specific application scenario and reliability in the production environment. In other words, the specific values ​​of these weights can be adjusted and optimized based on actual usage.

[0090] In some embodiments, the weight ω1 of a key image feature (i.e., a visual data feature) may range from 0.3 to 0.5. For example, in an environment with good lighting conditions, the visual sensor can provide high-quality image data, so a higher weight may be assigned (e.g., ω1 may be 0.45). For another example, in an environment with heavy smoke or adverse lighting, the accuracy of the visual data may be affected, and the weight may be reduced (e.g., ω1 may be 0.35).

[0091] In some embodiments, the weight ω2 of the acoustic spectral feature can range from 0.2 to 0.4. For example, if the acoustic signal generated by the welding process is very obvious and has a significant characteristic, the weight of the acoustic feature can be increased (for example, ω2 can be 0.35). For another example, if there is background noise or the acoustic feature is not obvious, the weight can be reduced (for example, ω2 can be 0.25).

[0092] In some embodiments, the weight ω3 of the temperature gradient feature can range from 0.3 to 0.5. For example, in a scenario where welding temperature is a key indicator for defect detection, the temperature gradient feature is more important and the weight may be set to 0.45. For another example, if the ambient temperature fluctuates significantly, making the infrared data unstable, the weight may be reduced to 0.35.

[0093] The sum of the weights ω1, ω2, and ω3 is 1, and these weights can be optimized through experimental data analysis to find the optimal combination for various production environments. Dynamic weight adjustment can flexibly respond to changes in the quality of various data sources, thereby improving the accuracy and reliability of defect detection.

[0094] In the above examples, key features such as weld width, weld pool shape parameters, spectral characteristics, and temperature gradient are extracted from various data types. These features are then fused using a weighted fusion algorithm to generate a comprehensive feature vector. The weights in this weighted fusion are dynamically adjusted based on the reliability of the data, which improves the accuracy of feature extraction and information fusion analysis.

[0095] As shown in Figure 3, the data analysis module 120 can also identify defects based on comprehensive features and using a machine learning model to obtain defect features. For example, the data analysis module can be configured to identify defects based on comprehensive features, combined with a knowledge graph, and using a machine learning model. This can improve the accuracy of defect identification.

[0096] The fused comprehensive feature vector is combined with the defect model in the knowledge graph and a machine learning algorithm (for example, support vector machine (SVM) or convolutional neural network (CNN)) is used to perform defect recognition.

[0097] For example, the process of defect identification includes the following steps.

[0098] In step 1, feature fusion is performed. That is, the visual, acoustic, and infrared sensor data are integrated into a comprehensive feature vector.

[0099] In step 2, the knowledge graph is used to provide context. That is, the knowledge graph is used to provide semantic and prior information related to the defect.

[0100] In step 3, the model is trained. That is, a machine learning model (e.g., SVM or CNN) is trained using the labeled data to identify the relationship between features and defect types.

[0101] In step 4, online recognition is performed. In practical applications, the feature vector of new data is input into the model and defect recognition is performed in combination with the knowledge graph.

[0102] By combining data from multiple sensors, such as visual, acoustic, and infrared, a more comprehensive and accurate welding quality inspection can be achieved. Multimodal data fusion technology, through collaborative analysis of different types of sensor data, can effectively compensate for the shortcomings of a single data source and enhance the robustness and detection accuracy of the system. The above process enhances the accuracy and efficiency of defect detection by integrating different technologies. For example, the accuracy of the above-mentioned detection algorithm in the embodiment of the present disclosure can reach over 95% in an experimental environment, which is significantly better than the 85% of single-sensor data detection methods in related technologies.

[0103] In addition, by structuring and associating information such as parameters, processes, and defect types in the welding process through knowledge graphs, data analysis and decision support can be effectively performed.

[0104] FIG4 is a block diagram schematically illustrating the structure of a knowledge graph module according to some embodiments of the present disclosure.

[0105] The knowledge graph module 130 manages knowledge related to welding processes, defect types, and repair strategies, and uses reasoning mechanisms to analyze and make decisions based on real-time data. Based on welding process standards, historical data, and expert knowledge, a knowledge base containing welding parameters, defect characteristics, and repair strategies can be established. This knowledge base is stored in a graph structure (i.e., a knowledge graph).

[0106] In a knowledge base, nodes represent parameters (e.g., welding parameters or defect characteristics) or knowledge points (e.g., repair strategies). For example, a knowledge base may include parameter nodes (representing welding parameters), defect nodes (representing defect characteristics), and repair nodes (representing repair strategies). For another example, a knowledge base may also include standard documents and / or experience documents. Standard documents may include process selection, defect identification, and repair methods.

[0107] In a knowledge base, an edge represents the relationship between one node and another. For example, an edge can represent the correspondence between a defect and a repair strategy.

[0108] By leveraging the relationships within the knowledge graph, the system can quickly infer the optimal repair solution after detecting a defect using a reasoning mechanism. For example, if a weld temperature is detected to be too high, the system can infer that the repair solution requires reducing the welding current or increasing the cooling time. This reasoning mechanism can utilize a logistic regression algorithm, improving the efficiency and accuracy of reasoning.

[0109] In the above example, the knowledge graph module manages knowledge related to welding processes, defect types, and repair strategies, and uses reasoning to analyze and make decisions based on real-time data. By establishing a knowledge base encompassing welding parameters, defect characteristics, and repair strategies, and leveraging the relationships within the knowledge graph to rapidly infer optimal repair solutions, the system achieves the integration of static knowledge and real-time data.

[0110] Furthermore, the subsequent control module converts the repair plan into specific adjusted welding parameters or operating instructions, which enables the system to respond and make decisions quickly after detecting defects in real time.

[0111] FIG5 is a block diagram schematically illustrating a structure of a control module according to some embodiments of the present disclosure.

[0112] The control module 140 is the core coordination unit of the system, responsible for managing the communication and coordination between the sensor module, data analysis module, knowledge graph module and repair module.

[0113] In some embodiments, the control module 140 is further configured to dynamically schedule the data acquisition frequency and data transmission rate of the sensor module. Here, the control module can dynamically schedule the data acquisition frequency and transmission rate of each sensor of the sensor module according to system requirements to improve the real-time performance of the system.

[0114] After detecting a welding defect, the control module sends repair instructions to the repair module based on the reasoning results of the knowledge graph module. In some cases, the control module can also monitor the execution of the repair process.

[0115] In some embodiments, the control module 140 is further configured to detect the current operating state of the sensor module and adjust the operating parameters of the sensor module based on the historical data of the sensor module's operating state and the current operating state. For example, the historical data may include the operating state of the sensor module and the operating parameters of the sensor module corresponding to the operating state. Therefore, during system operation, the control module can self-check the operating state of each module and automatically adjust or optimize the parameters in certain circumstances to improve system stability and efficiency.

[0116] FIG6 is a block diagram schematically illustrating a structure of a repair module according to some embodiments of the present disclosure.

[0117] The repair module 150 is used to execute the repair solution inferred by the system. The repair module 150 can automatically adjust the parameters of the welding equipment (i.e., welding parameters), such as welding current and welding speed, to address the detected welding defects. In the case of complex defects (e.g., cracks, pores), the repair module 150 can control the welding robot to perform repair operations, such as repair welding and grinding. For example, the welding robot can be controlled with an accuracy of within ±0.1 mm (millimeter) to improve the accuracy of the repair.

[0118] After the repair is completed, the system can perform defect detection again to test the repair effect. If the repair effect does not meet the expected effect, the system will further adjust the parameters or perform a second repair.

[0119] So far, the structure or function of each of the above modules has been described in detail.

[0120] In some embodiments, the sensor module is further configured to collect data from the repair process. The data analysis module is further configured to determine whether the repair result meets the expected result based on the data from the repair process. If the repair result meets the expected result, the data from the repair process is transmitted to the knowledge graph module. If the repair result does not meet the expected result, the re-repair information is sent to the knowledge graph module. The knowledge graph module is further configured to record the data from the repair process and update the knowledge graph if the repair result meets the expected result. If the repair result does not meet the expected result, the control module is sent a re-repair strategy different from the current repair strategy based on the re-repair information. The control module is further configured to send a re-repair instruction to the repair module based on the re-repair strategy, causing the repair module to perform the repair operation again. In this way, after the repair is completed, the system can re-test the weld quality to determine whether the repair result meets the expected result. If the repair result meets the expected result, the system records the repair process and updates the knowledge graph. If the repair result does not meet the expected result, the system will perform a second repair. This update of the knowledge graph facilitates the provision of a more accurate repair solution later, and the re-repair can improve the repair effect.

[0121] Therefore, after each repair operation, the system will record the data of the detection and repair process in the knowledge graph module for analysis and updating of the knowledge base, so that the knowledge graph can be dynamically updated and self-learned, thereby improving the level of integration between the knowledge graph and real-time data.

[0122] In the prior art, weld defect detection and repair are typically performed separately: welding is completed first, followed by detection and repair. This approach introduces significant time lags, preventing defects from being repaired in a timely manner during the welding process. This delay can even lead to product scrapping.

[0123] In the embodiments of the present disclosure, through real-time data acquisition by the sensor module, rapid defect identification by the data analysis module, and real-time decision generation by the knowledge graph module, the system can continuously monitor quality during the welding process and immediately trigger the repair process once a defect is found; the control module, as the coordination unit of the system, can immediately issue repair instructions based on the reasoning results of the knowledge graph after detecting a defect, and monitor the execution of the repair process in real time, thereby eliminating the time interval between detection and repair; the repair module can automatically adjust the parameters of the welding equipment or control the welding robot to perform repair operations, such as repair welding and grinding, after the system detects a welding defect. Through a real-time feedback mechanism, the system will re-check the weld quality after the repair is completed to ensure the repair result. Therefore, the control system of the embodiments of the present disclosure can achieve real-time monitoring, defect detection, intelligent analysis and automatic repair of the welding process through the collaborative work of the above modules. This can solve the problem that welding defect detection and repair are usually performed separately in related technologies, enabling the system to detect and repair defects in a timely manner during the welding process.

[0124] Thus, the embodiments of the present disclosure provide a real-time detection and automatic repair system for welding defects based on multimodal data fusion and knowledge graphs. The system integrates multiple sensor data, knowledge graph reasoning and automated control technology to achieve high-precision defect detection and automatic repair during the welding process, thereby improving the overall welding quality, reducing production costs and enhancing production efficiency.

[0125] FIG7 is a flow chart illustrating a control method for a welding process according to some embodiments of the present disclosure. For example, the control method can be executed in an electronic device. As shown in FIG7 , the control method includes steps S702 to S710.

[0126] In step S702, data during the welding process is received.

[0127] For example, data during the welding process includes image data, acoustic wave signal data, and temperature distribution characteristic data.

[0128] In step S704, the data of the welding process is preprocessed, key features are extracted from the preprocessed data, and the key features are weighted fused to obtain comprehensive features. Based on the comprehensive features, defect identification is performed through a machine learning model to identify defect features.

[0129] In some embodiments, data in the welding process is preprocessed, including: performing denoising, normalization, and time alignment on image data, acoustic wave signal data, and temperature distribution characteristic data.

[0130] In some embodiments, the key features include: image key features F in the image data vision, acoustic spectrum features F in acoustic signal data acoustic And the temperature gradient feature F in the temperature distribution characteristic data infrared .

[0131] In some embodiments, the comprehensive feature F combined F combined =ω1*F vision +ω2*F acoustic +ω3*F infrared ,

[0132] Among them, ω1 is the weight of the key features of the image, ω2 is the weight of the acoustic spectrum feature, and ω3 is the weight of the temperature gradient feature.

[0133] In some embodiments, defect identification is performed based on comprehensive features and through a machine learning model, including: based on comprehensive features, combined with a knowledge graph, and through a machine learning model.

[0134] In step S706, a repair strategy corresponding to the defect feature is obtained based on the defect feature and using the knowledge graph, which includes welding parameters, defect features, and repair strategies.

[0135] The knowledge graph is stored in the form of a graph structure. The nodes of the knowledge graph represent welding parameters, defect characteristics or repair strategies, and the edges of the knowledge graph represent the association relationships between different nodes.

[0136] In step S708 , a repair instruction is generated based on the repair strategy.

[0137] In step S710, based on the repair instruction, the welding parameters are automatically adjusted or the actuator is controlled to perform the repair operation.

[0138] Thus, a control method for a welding process according to some embodiments of the present disclosure is provided. The control method includes: receiving data from a welding process; preprocessing the data from the welding process, extracting key features from the preprocessed data, performing weighted fusion processing on the key features to obtain comprehensive features, performing defect identification based on the comprehensive features and using a machine learning model to identify defect features; obtaining a repair strategy corresponding to the defect features based on the defect features and using a knowledge graph, wherein the knowledge graph is a knowledge graph containing welding parameters, defect features, and repair strategies; generating repair instructions based on the repair strategy; and automatically adjusting welding parameters or controlling an actuator to perform a repair operation based on the repair instructions. In this control method, by collecting data from the welding process and identifying defects after relevant processing of the data, obtaining a repair strategy corresponding to the defect through the knowledge graph, and generating corresponding repair instructions, and then automatically adjusting welding parameters or controlling an actuator to perform a repair operation based on the repair instructions, the control method achieves automation and intelligence in repairing defects in the welding process, thereby improving welding quality and production efficiency.

[0139] Moreover, in the above method, the association relationship in the knowledge graph can be used to quickly infer the best repair plan, realizing the combination of static knowledge and real-time data, so that the system can quickly respond and make decisions after detecting defects in real time, and then repair welding defects in time during the welding process, thereby improving the product yield.

[0140] In some embodiments, the control method further includes: dynamically scheduling the data acquisition frequency and data transmission rate of the sensor module.

[0141] In some embodiments, the above-mentioned control method also includes: receiving data during the repair process; judging whether the repair result meets the expected result based on the data during the repair process; if the repair result meets the expected result, recording the data during the repair process and updating the knowledge graph; if the repair result does not meet the expected result, obtaining a re-repair strategy different from the current repair strategy; generating a re-repair instruction based on the re-repair strategy; and re-performing the repair operation based on the re-repair instruction.

[0142] In some embodiments, the control method further includes: detecting a current working state of the sensor module; and adjusting working parameters of the sensor module according to historical data of the working state of the sensor module and the current working state.

[0143] Figure 8 is a flow chart illustrating a control method for a welding process according to other embodiments of the present disclosure. This embodiment is applicable to real-time detection and automatic repair of robot welding quality. This method can be executed by a control system provided by an embodiment of the present disclosure. This control system can be implemented using software and / or hardware and can be configured in an electronic device. As shown in Figure 8, the control method includes steps S801 to S807.

[0144] In step S801, the device is initialized.

[0145] Start the system, load the welding process parameters and repair strategies from the knowledge graph through the control module, and set the acquisition frequency, accuracy requirements, and data preprocessing parameters of each sensor in the sensor module. The operator can view the system's initialization status through the user interface and make corresponding adjustments.

[0146] In step S802, data collection is performed.

[0147] The sensor module begins collecting real-time visual data (i.e., image data), acoustic data, and infrared data from the welding process. A synchronized clock system ensures the time consistency of these data types. The control module transmits this collected data to the data analysis module.

[0148] In step S803, data fusion and analysis are performed.

[0149] The data analysis module preprocesses the collected data, extracts features, and performs weighted fusion to generate a comprehensive feature vector. The system then uses machine learning algorithms (e.g., defect recognition algorithms) to identify defects during the welding process and transmits the identification results to the knowledge graph module. For example, after identifying a defect, the data analysis module transmits the comprehensive feature vector to the knowledge graph module.

[0150] In step S804, a repair strategy is obtained.

[0151] The knowledge graph module uses reasoning to generate the optimal repair plan based on the detected defect type and location. This repair plan can include welding parameter adjustments or robot operation instructions. Once the repair plan is generated, it is executed through the control module.

[0152] In step S805, automatic repair is performed.

[0153] The repair module automatically adjusts welding parameters or controls the welding robot to perform repair operations based on the instructions of the control module. In addition, the repair results can be monitored in real time through sensors.

[0154] In step S806 , feedback and adjustment are performed.

[0155] After the repair is complete, the system retests the weld quality to determine whether the repair results (i.e., repair effects) meet the expected results (i.e., expected effects). If the repair results meet the expected results, the system records the parameter data during the repair process and updates the knowledge graph; if the repair results do not meet the expected results, the system will continue to adjust and perform a second repair.

[0156] For example, for incomplete penetration defects, the system automatically provides a new process parameter for repair. If the provided process parameter can solve the defect problem, it is considered that the provided repair solution meets the expected results. Otherwise, it is considered that the provided repair solution does not meet the expected results.

[0157] If the repair results obtained from the inspection meet the expected results, the system can record the parameter data during the repair process and use this data to update the knowledge graph. The updated data may include at least one of: welding process parameters, defect types and characteristics, repair strategies, and process monitoring data.

[0158] For example, welding process parameters may include welding current, welding speed, temperature and other parameters.

[0159] For example, the defect types and characteristics may include: defect types (eg, cracks, pores) and their characteristics detected and repaired during the welding process.

[0160] For example, a remediation strategy may include documenting and optimizing the remediation steps and actions for a specific defect.

[0161] For example, process monitoring data can include collecting real-time monitoring data related to the repair process. This process monitoring data can help improve the accuracy of future inspections and repairs.

[0162] In step S807, system maintenance and optimization are performed.

[0163] Every repair operation and test result is recorded and transmitted to the knowledge graph module for analysis. This data is used to update the knowledge base and optimize repair strategies. Furthermore, the system adjusts the operating parameters of each module based on historical data and current status, further improving system efficiency.

[0164] In some embodiments, the system (eg, a control module) may adjust sensor parameters.

[0165] For example, if historical data indicates that specific welding process parameters (e.g., temperature or current) are repeatedly detected as problematic, the system can automatically adjust the sensitivity of the infrared sensor or the resolution of the vision sensor to more accurately monitor those parameters.

[0166] For example, in the current state, if the system detects a large change in ambient light, the exposure time of the visual sensor can be adjusted to improve image quality.

[0167] In some embodiments, the system can optimize data fusion weights. For example, if historical data shows that acoustic data contributes less to defect detection under certain conditions, the system can reduce its weight and increase the weight of visual and infrared data to improve the detection accuracy of the fused data.

[0168] In some embodiments, the system can automatically adjust repair parameters. For example, based on historical successful repair case data, the speed and path of the welding robot can be adjusted to perform the repair work more efficiently.

[0169] In some embodiments, the system can perform strategy optimization. For example, if historical data shows that a certain defect often takes a long time to repair, the system can adjust the repair strategy and steps in advance to save time.

[0170] In some embodiments, the system can update the knowledge graph. For example, the system can update the knowledge graph based on information collected from historical data on common defect types and their effective repair strategies to speed up reasoning and provide more accurate repair solutions.

[0171] In the above steps, the system can perform self-inspection and optimization regularly, continuously improve the knowledge graph by analyzing historical data and repair records, and improve the operating efficiency of each module.

[0172] Thus, a control method for a welding process according to further embodiments of the present disclosure has been provided. This method can address the limitations of related art methods in complex welding environments and high-precision requirements. By introducing multimodal data fusion and knowledge graph technology, this method can achieve higher detection accuracy, real-time performance, and automated repair capabilities during the welding process, thereby improving overall welding quality, reducing production costs, and enhancing production efficiency.

[0173] Additionally, while the system is operating automatically, the operator can monitor the welding process and system status in real time through the user interface. In some cases, the operator can manually adjust some system parameters or intervene in the repair process to address special circumstances or improve the repair effect.

[0174] In the system and method of the embodiments of the present disclosure, by integrating data from multiple sensors such as vision, acoustics, and infrared, and adopting a weighted fusion algorithm, deep fusion of multi-dimensional information and high-precision defect detection in the welding process are achieved, surpassing the limitations of the single sensor method in related technologies.

[0175] Furthermore, the system and method of the disclosed embodiments construct a knowledge graph encompassing welding parameters, defect types, and repair strategies. This utilizes a reasoning mechanism to generate optimal repair solutions after real-time defect detection. The knowledge graph's dynamic updates and self-learning capabilities enhance the system's adaptability and intelligence.

[0176] In addition, in the system and method of the embodiments of the present disclosure, a repair module has been developed that can automatically adjust welding parameters or control the robot to perform repair operations based on the detection results and the inferred repair plan, and ensure continuous optimization of welding quality through a closed-loop control mechanism, reducing manual intervention.

[0177] Furthermore, in the system and method of the embodiments of the present disclosure, through the synchronous clock system and real-time processing algorithm, combined with machine learning technology, real-time monitoring of the welding process and high-precision defect identification are achieved, thereby improving the detection efficiency and accuracy of the system.

[0178] FIG9 is a block diagram schematically illustrating a structure of an electronic device according to some embodiments of the present disclosure. The electronic device includes a memory 910 and a processor 920. In particular:

[0179] The memory 910 may be a disk, a flash memory, or any other non-volatile storage medium. The memory is used to store the instructions in the embodiments corresponding to FIG. 7 and / or FIG. 8 .

[0180] The processor 920 is coupled to the memory 910 and can be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 920 is used to execute instructions stored in the memory, thereby realizing automated and intelligent defect repair in the welding process, thereby improving welding quality and production efficiency.

[0181] In some embodiments, as shown in FIG10 , the electronic device 1000 includes a memory 1010 and a processor 1020. The processor 1020 is coupled to the memory 1010 via a BUS 1030. The electronic device 1000 may also be connected to an external storage device 1050 via a storage interface 1040 to access external data, and may also be connected to a network or another computer system (not shown) via a network interface 1060, which will not be described in detail here.

[0182] In this embodiment, data instructions are stored in a memory and then processed by a processor, thereby achieving automation and intelligence in repairing defects in the welding process, thereby improving welding quality and production efficiency.

[0183] In another embodiment, the present disclosure further provides a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the method in the corresponding embodiment of Figure 7 and / or Figure 8. It should be understood by those skilled in the art that the embodiments of the present disclosure may be provided as methods, devices, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transitory storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0184] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram and the combination of the processes and / or boxes in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0185] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0186] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0187] In some embodiments of the present disclosure, a computer program product is further provided. The computer program product includes a computer program or instructions. When the computer program or instructions are executed by a processor, the control method as described above is implemented.

[0188] In some embodiments of the present disclosure, a computer program is further provided, comprising: instructions, which, when executed by a processor, enable the processor to perform the control method as described above.

[0189] The present disclosure has been described in detail so far. To avoid obscuring the concept of the present disclosure, some details known in the art have not been described. Based on the above description, those skilled in the art can fully understand how to implement the technical solutions disclosed herein.

[0190] Although some specific embodiments of the present disclosure have been described in detail by way of examples, those skilled in the art will appreciate that the above examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Those skilled in the art will appreciate that modifications may be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A control system for a welding process, comprising: a sensor module configured to collect data during the welding process; a data analysis module configured to preprocess data from the welding process, extract key features from the preprocessed data, perform weighted fusion processing on the key features to obtain comprehensive features, and perform defect identification based on the comprehensive features using a machine learning model to identify defect features; a knowledge graph module configured to obtain a repair strategy corresponding to the defect feature based on the defect feature and using a knowledge graph, wherein the knowledge graph is a knowledge graph including welding parameters, defect features, and repair strategies; a control module configured to transmit the data during the welding process received from the sensor module to the data analysis module, receive the repair strategy, and generate a repair instruction based on the repair strategy; and The repair module is configured to automatically adjust welding parameters or control the actuator to perform repair operations based on the repair instruction.

2. The control system according to claim 1, wherein: The knowledge graph is stored in a graph structure form, the nodes of the knowledge graph represent the welding parameters, the defect characteristics or the repair strategy, and the edges of the knowledge graph represent the association relationship between different nodes.

3. The control system according to claim 1 or 2, wherein: The sensor module includes: a vision sensor configured to collect image data during the welding process; an acoustic sensor configured to collect acoustic wave signal data during the welding process; and an infrared sensor configured to collect temperature distribution characteristic data during the welding process; The data during the welding process includes the image data, the acoustic wave signal data and the temperature distribution characteristic data.

4. The control system according to claim 3, wherein: The data analysis module is configured to perform denoising, normalization and time alignment processing on the image data, the acoustic wave signal data and the temperature distribution characteristic data.

5. The control system according to claim 4, wherein: The key features include: the image key features F in the image data vision , the acoustic spectrum feature F in the acoustic wave signal data acoustic and the temperature gradient characteristic F in the temperature distribution characteristic data infrared ; Among them, the comprehensive feature F combined for F combined =ω1*F vision +ω2*F acoustic +ω3*F infrared , Wherein, ω1 is the weight of the key image feature, ω2 is the weight of the acoustic spectrum feature, and ω3 is the weight of the temperature gradient feature.

6. The control system according to any one of claims 1 to 5, wherein: The data analysis module is configured to perform defect identification based on the comprehensive features, combined with the knowledge graph, and through the machine learning model.

7. The control system according to any one of claims 1 to 6, wherein: The control module is further configured to dynamically schedule the data acquisition frequency and data transmission rate of the sensor module.

8. The control system according to any one of claims 1 to 7, wherein: The sensor module is further configured to collect data during the repair process; The data analysis module is further configured to determine whether a repair result meets an expected result based on the data in the repair process, and if so, transmit the data in the repair process to the knowledge graph module; if not, send re-repair information to the knowledge graph module; The knowledge graph module is further configured to, if the repair result meets the expected result, record the data in the repair process and update the knowledge graph; if the repair result does not meet the expected result, send a second repair strategy different from the current repair strategy to the control module based on the second repair information; The control module is further configured to send a re-repair instruction to the repair module based on the re-repair strategy, so that the repair module performs the repair operation again.

9. The control system according to any one of claims 1 to 8, wherein: The control module is further configured to detect a current working state of the sensor module and adjust working parameters of the sensor module according to historical data of the working state of the sensor module and the current working state.

10. A control method for a welding process, comprising: Receive data during welding; Preprocessing data from the welding process, extracting key features from the preprocessed data, performing weighted fusion processing on the key features to obtain comprehensive features, and performing defect recognition based on the comprehensive features and using a machine learning model to identify defect features; Obtaining a repair strategy corresponding to the defect feature using a knowledge graph based on the defect feature, wherein the knowledge graph is a knowledge graph including welding parameters, defect features, and repair strategies; generating a repair instruction based on the repair strategy; and Based on the repair instruction, the welding parameters are automatically adjusted or the actuator is controlled to perform the repair operation.

11. The control method according to claim 10, wherein: The knowledge graph is stored in a graph structure form, the nodes of the knowledge graph represent the welding parameters, the defect characteristics or the repair strategy, and the edges of the knowledge graph represent the association relationship between different nodes.

12. The control method according to claim 10 or 11, wherein: The data in the welding process includes image data, acoustic wave signal data and temperature distribution characteristic data.

13. The control method according to claim 12, wherein: Preprocessing the data in the welding process includes: The image data, the acoustic wave signal data and the temperature distribution characteristic data are subjected to denoising, normalization and time alignment processing.

14. The control method according to claim 13, wherein: The key features include: the image key features F in the image data vision , the acoustic spectrum feature F in the acoustic wave signal data acoustic and the temperature gradient characteristic F in the temperature distribution characteristic data infrared ; Among them, the comprehensive feature F combined for F combined =ω1*F vision +ω2*F acoustic +ω3*F infrared , Wherein, ω1 is the weight of the key image feature, ω2 is the weight of the acoustic spectrum feature, and ω3 is the weight of the temperature gradient feature.

15. The control method according to any one of claims 10 to 14, wherein: Defect identification is performed based on the comprehensive features and through a machine learning model, including: Based on the comprehensive features, combined with the knowledge graph, and through the machine learning model, defect identification is performed.

16. The control method according to any one of claims 10 to 15, further comprising: Dynamically schedule the data acquisition frequency and data transmission rate of the sensor module.

17. The control method according to any one of claims 10 to 16, further comprising: Receive data from the repair process; Based on the data from the repair process, determining whether the repair result meets the expected result; When the repair result meets the expected result, record the data of the repair process and update the knowledge graph; When the repair result does not meet the expected result, obtaining a second repair strategy different from the current repair strategy; generating a re-repair instruction based on the re-repair strategy; and The repair operation is performed again based on the re-repair instruction.

18. The control method according to any one of claims 10 to 17, further comprising: Detect the current working status of the sensor module; and Adjusting the operating parameters of the sensor module according to the historical data of the operating status of the sensor module and the current operating status.

19. An electronic device comprising: Memory; as well as A processor coupled to the memory, wherein the processor is configured to execute the control method according to any one of claims 10 to 18 based on instructions stored in the memory.

20. A computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are executed by a processor to implement the control method according to any one of claims 10 to 18.

21. A computer program comprising: Instructions, when executed by a processor, cause the processor to perform the control method according to any one of claims 10 to 18.

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