Copper busbar processing system and copper busbar processing method

Abstract

The present invention relates to the technical field of copper bar processing, specifically a copper bar processing system and a copper bar processing method. The present invention comprises: first, obtaining target information of the copper bar; second, processing the raw material according to the target information, using a copper bar processing abnormality recognition model to recognize the images processed in each step during the processing, and introducing a matching layer and an abnormality recognition layer; the matching layer matches the corresponding abnormality recognition sublayer according to the processing step to improve the accuracy of abnormality recognition; using a processing parameter analysis method to slice the processing steps with abnormalities according to time, and using a processing parameter analysis model to analyze, to obtain abnormal processing parameters, which is conducive to locating processing problem points; finally, using a processing parameter optimization method to calculate the deviation between the abnormal processing parameters and the template processing parameters, and optimizing the copper bar processing parameters according to the basic statistics and correlation obtained from the deviation; the above methods are combined to improve the quality of copper bar processing.

Description

Technical Field

[0001] The invention relates to the technical field of copper bar processing, in particular to a copper bar processing system and a copper bar processing method. Background Art

[0002] Copper busbar, also known as busbar, is a processing material widely used in electrical engineering; it is a metal strip or metal plate made of copper, which is usually used as a conductor of current in electrical distribution systems because of its good conductivity and corrosion resistance.

[0003] Copper busbars are an integral part of electrical distribution systems. For example, in power systems, copper busbars are used to distribute current from the power source to various electrical devices or circuits. They can be found in distribution boards, substations, and high-voltage power systems. In electrical equipment such as distribution boards, switchgear, and transformers, copper busbars are used to connect different electrical components to ensure the smooth flow of current. In some electrical systems, copper busbars are also used as part of the electrical protection and grounding system to provide a safe current path and prevent electrical accidents.

[0004] Copper busbars play a very important role in the electrical distribution system. Therefore, multiple links need to be strictly controlled during their processing and manufacturing to ensure their performance and reliability. The processing of copper busbars includes: processing material selection, surface treatment, and size and precision control. At present, errors in any of these links will affect the performance of the final product and cannot be put into normal use. Therefore, strict control and inspection of each processing link of the copper busbar can greatly reduce the impact of errors on the performance of the copper busbar, thereby ensuring the stable operation of the electrical distribution system.

[0005] The prior art provides many excellent methods for copper busbar processing, but most of these methods only perform quality inspection on the copper busbar after processing, but lack quality control of each processing step, which makes it impossible to guarantee the final processing quality of the copper busbar.

[0006] To this end, the present invention provides a copper busbar processing system and a copper busbar processing method. Summary of the invention

[0007] The purpose of the present invention is to propose a copper busbar processing system and a copper busbar processing method for improving the quality of copper busbar processing; the present invention mainly starts from the following aspects. First, the present invention uses monitoring equipment to monitor each processing step of the copper busbar in real time, and adopts a copper busbar processing abnormality recognition model to perform image abnormality recognition; the model extracts image features of the processed material for analysis. The weighted and fusion strategies are used to deepen the learning of abnormal features, and a matching layer is added to the model to match the abnormal identification sublayer of the corresponding processing step, so that accurate abnormal identification can be achieved according to the processing state of the processing material; secondly, the processing parameters are analyzed according to the abnormal identification results, and time slices are performed according to the substeps of the current processing step during the analysis process, and the change data of the processing materials and the working parameters of the equipment in the processing substeps are collected; the collected data are input into the parameter analysis model, and the model can analyze the data in the processing process in combination with the abnormal position and the abnormal type, so as to find specific errors in the processing process; finally, the present invention proposes a processing parameter optimization method, which uses target information to match the processing data of the same type of copper busbar template from the database, calculates the data deviation of the corresponding copper busbar template processing step according to the current processing step, and optimizes the current processing parameters through the analysis of basic statistics and parameter correlation, so that the processing data meets the standard. The above methods are combined to achieve the improvement of copper busbar processing quality.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] In the first aspect, the present invention provides a copper busbar processing system, the system comprising:

[0010] The template storage unit is used to store information of the copper busbar template; wherein the template storage unit establishes a copper busbar template database through a local server; wherein the copper busbar template database stores the copper busbar type, size information, processing flow, processing parameters and quality standards.

[0011] The planning unit is used to generate target information, including: copper busbar type, copper busbar specifications and process flow.

[0012] Information collection unit, used to collect various data during copper busbar processing;

[0013] A storage unit, used for temporarily storing the various data;

[0014] Processing unit, used for copper busbar processing;

[0015] Recording unit, used to record copper busbar processing information;

[0016] The monitoring unit is used to monitor the copper busbar processing in real time; wherein, the monitoring unit uses the copper busbar processing abnormality recognition model to identify abnormalities of the material during the processing, and the specific process includes:

[0017] The information acquisition unit acquires an image of the processed material in the current processing step;

[0018] Further, the processed material image is preprocessed to obtain a standard processed material image; wherein the preprocessing includes: removing noise from the processed material image to obtain a denoised processed material image; performing image enhancement on the denoised processed material image to obtain an enhanced processed material image; performing geometric correction on the enhanced processed material image to obtain a corrected processed material image; performing edge detection on the corrected processed material image to obtain an edge processed material image; performing alignment operation on the edge processed material image to obtain an aligned processed material image; and cropping the aligned processed material image to obtain the standard processed material image.

[0019] Furthermore, after the standard processing material image is inputted by the input layer, the feature extraction layer performs feature extraction to obtain a processing material feature map;

[0020] Further, the processing material feature map is input into the feature processing layer for weighting and fusion operations to obtain a processing material processing feature map;

[0021] Further, matching a corresponding abnormality identification sublayer according to the current processing step through a matching layer;

[0022] Further, the processing material processing characteristic map is input into the corresponding abnormality identification sublayer in the abnormality identification layer for identification according to the selection result of the matching layer;

[0023] Furthermore, the output layer outputs the recognition result of the abnormality recognition sublayer to obtain a monitoring result, including: an abnormality location and an abnormality type.

[0024] The processing parameter analysis unit is used to analyze the processing parameters during copper busbar processing, including:

[0025] When the monitoring unit finds an abnormality, the total processing time of the current processing step is obtained;

[0026] Further, the total processing time is sliced ​​to obtain a plurality of processing sub-times;

[0027] Further, the processing data in the plurality of processing sub-periods are collected; wherein the processing data includes: processing material change data and equipment parameters;

[0028] Further, the processing sub-durations are aligned with the processing data in time series, and a processing parameter data set is constructed;

[0029] Further, the processing parameter data set is processed to obtain a standard processing parameter data set;

[0030] Furthermore, the standard processing parameter data set and the monitoring results are input into a processing parameter analysis model; wherein the processing parameter analysis model represents:

[0031] ;

[0032] in, represents the model function; Represents the input feature vector, including: processing sub-time after data processing, processing material change data and equipment parameters; A feature vector representing the monitoring result; Representing parameters of the processing parameter analysis model, including weights and biases; Indicates that the processing data features and monitoring results characteristics Concatenate to form a new input vector; Indicates The weight matrix of the layer; Indicates The bias vector of the layer; represents the activation function; Indicates the number of model layers;

[0033] An optimization unit is used to optimize the parameters of the copper busbar processing equipment; wherein the optimization unit comprises:

[0034] According to the target information, the copper busbar template in the database is matched to obtain information of the copper busbar with the same template, including: size information, processing parameters and quality standards;

[0035] Further, obtaining template processing data of the same processing step as the current processing step from the information;

[0036] Further, a processing deviation matrix is ​​constructed according to the processing data of the current processing step and the template processing data;

[0037] Further, calculating the specified statistics of the deviation matrix and displaying the significant deviation areas graphically;

[0038] Further, the correlation between the parameters of the processing deviation matrix is ​​calculated to obtain a processing parameter correlation matrix;

[0039] Furthermore, the current processing parameters are optimized according to the designated statistics and the processing parameter correlation matrix to obtain the current optimized processing parameters.

[0040] Feedback unit, used to generate copper busbar processing feedback report.

[0041] A second aspect: The present invention provides a copper busbar processing method, comprising:

[0042] Obtain target information for copper busbar production; wherein the target information includes: copper busbar type, copper busbar specifications, and process flow;

[0043] Further, determining the processing procedure information of the copper busbar according to the target information;

[0044] Further, obtaining processing equipment information according to the processing procedure information;

[0045] Further, processing the processing material according to the processing procedure information;

[0046] Among them, the copper busbar processing also includes abnormal monitoring of the copper busbar processing; the specific process includes:

[0047] Using a high-resolution camera to obtain an image of the processed material at the current processing step;

[0048] Furthermore, the processed material image is preprocessed to obtain a standard processed material image; the preprocessing includes: removing noise in the processed material image to obtain a denoised processed material image; performing image enhancement on the denoised processed material image to obtain an enhanced processed material image; performing geometric correction on the enhanced processed material image to obtain a corrected processed material image; performing edge detection on the corrected processed material image to obtain an edge processed material image; performing alignment operation on the edge processed material image to obtain an aligned processed material image; and cropping the aligned processed material image to obtain the standard processed material image.

[0049] Furthermore, the standard processing material image is input into the copper busbar processing abnormality recognition model to obtain the monitoring result; wherein the copper busbar processing abnormality recognition model includes:

[0050] An input layer, used for receiving the standard processing material image;

[0051] A feature extraction layer, which extracts features from the image data received by the input layer through a convolutional neural network model;

[0052] A feature processing layer, used for performing weighting and fusion operations on the image feature map obtained by the feature extraction layer;

[0053] A matching layer, used to match the corresponding abnormality identification sublayer according to the current processing step;

[0054] An abnormality recognition layer, used for performing abnormality recognition on the processed material image; wherein the abnormality recognition layer includes a plurality of abnormality sub-layers; the structure of the plurality of abnormality sub-layers includes: a convolution sub-layer, a pooling sub-layer, a fully connected sub-layer, an activation sub-layer, a normalization sub-layer and an abnormality scoring sub-layer;

[0055] The output layer is used to output monitoring results, including: abnormal location and abnormal type;

[0056] Furthermore, when the monitoring result is abnormal, the processing parameters of the current processing step are analyzed to obtain the current processing parameter analysis result; the specific process includes: obtaining the total processing time of the current processing step; slicing the total processing time according to the processing sub-steps in the previous processing step to obtain multiple processing sub-times; collecting multiple processing data in the multiple processing sub-times; wherein the processing data includes: processing material change data and equipment parameters;

[0057] Aligning the plurality of processing sub-durations with the plurality of processing data in time series, and constructing a processing parameter data set;

[0058] The processing parameter data set is processed to obtain a standard processing parameter data set. The standard processing parameter data set and the monitoring result are input into a processing parameter analysis model; wherein the processing parameter analysis model represents:

[0059] ;

[0060] in, represents the model function; Represents the input feature vector, including: processing sub-time after data processing, processing material change data and equipment parameters; A feature vector representing the monitoring result; Representing parameters of the processing parameter analysis model, including weights and biases; Indicates that the processing data features and monitoring results characteristics Concatenate to form a new input vector; Indicates The weight matrix of the layer; Indicates The bias vector of the layer; represents the activation function; Indicates the number of model layers.

[0061] Further, the current processing parameters are optimized according to the current processing parameter analysis result to obtain the current optimized processing parameters; the specific process includes:

[0062] According to the target information, the copper busbar template in the database is matched to obtain the information of the copper busbar of the same type of template, including: size information, processing parameters and quality standards; the template processing data of the same processing step as the current processing step is obtained from the information; a processing deviation matrix is ​​constructed according to the processing data of the current processing step and the template processing data; the specified statistics of the deviation matrix are calculated, and the significant deviation area is displayed graphically; the correlation between the parameters of the processing deviation matrix is ​​calculated to obtain the processing parameter correlation matrix; the current processing parameters are optimized according to the specified statistics and the processing parameter correlation matrix to obtain the current optimized processing parameters.

[0063] Furthermore, various data of the current processing step are recorded.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1. The present invention proposes a copper busbar processing anomaly recognition model for identifying anomalies of processed materials during the copper busbar processing process; the model extracts the features of the processed material image and uses weighted and fusion strategies to strengthen the learning of abnormal features; a matching layer is added to the model to match the anomaly recognition sublayer of the corresponding processing steps, so that accurate anomaly recognition can be achieved according to the processing state of the processed material, which is beneficial to improving the quality of copper busbar processing.

[0066] 2. The present invention proposes a processing parameter analysis method for abnormal processing conditions. During the analysis process, the method performs time slicing according to the sub-steps of the current processing step, and collects the change data of the processing materials and the working parameters of the equipment in the sub-steps; the collected data are input into the processing parameter analysis model, and the model can analyze the data in the processing process in combination with the abnormal position and the abnormal type, so as to find the processing problem points and locate them quickly and accurately, providing a sufficient data basis for improving the processing quality of copper busbars.

[0067] 3. The present invention proposes a processing parameter optimization method, which uses target information to match processing data of the same type of copper busbar template from a database, calculates the data deviation of the corresponding copper busbar template processing step according to the current processing step, and optimizes the processing parameters by obtaining the basic statistics and correlation of the parameters through the data deviation, so that the processing data meets the standard and the copper busbar processing quality is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A structural diagram of a copper busbar processing system provided by an embodiment of the present invention;

[0069] Figure 2 A flow chart of a copper busbar processing system provided by an embodiment of the present invention;

[0070] Figure 3 A schematic diagram of a Class B copper busbar provided in an embodiment of the present invention;

[0071] Figure 4 A schematic diagram of a Class C copper busbar provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] Copper busbar is a kind of processing material widely used in electrical engineering. It is a kind of copper metal strip or metal plate. The processing technology of copper busbar is not unique and needs to be made according to specific needs. In the electrical distribution system, copper busbar is used in many aspects, such as distribution board, switchgear and transformer, etc. The specifications of copper busbar can be changed according to the specific electrical equipment.

[0074] Copper busbars play a very important role in the electrical distribution system. Therefore, during the copper busbar processing, multiple processing links need to be strictly controlled to ensure that the specifications and performance of the processed copper busbars meet the standards. The processing of copper busbars includes a series of steps, such as the selection of raw materials, surface treatment, and deformation shaping. When errors occur in any of these links, the use of the copper busbars will be affected. Therefore, strict control and inspection of each link in the copper busbar processing process can greatly reduce the impact of errors on the performance of the copper busbars, thereby ensuring the stable operation of the electrical distribution system. In the existing copper busbar processing methods, most methods only detect the copper busbars after processing, and rarely detect each step in the copper busbar processing process, which cannot well guarantee the processing quality of the copper busbars. To this end, the present invention proposes a copper busbar processing system and a copper busbar processing method. The present invention realizes the detection of each processing step in the copper busbar processing process, and optimizes the abnormal parts, thereby improving the quality of copper busbar processing. The following will describe the implementation process of the present invention in detail from two embodiments: Embodiment 1:

[0075] In the embodiment of the present application, the copper bar processing process of the production line A is described; in order to specifically illustrate the copper bar processing process, the embodiment of the present application will be combined with Figure 1 and Figure 2 Explain the content in Figure 1 The schematic diagram of the structure of the copper busbar processing system proposed by the present invention includes: a template storage unit, a planning unit, an information collection unit, a storage unit, a processing unit, a recording unit, a monitoring unit, a processing parameter analysis unit, an optimization unit and a feedback unit; Figure 2 The present invention proposes a flow chart of a copper busbar processing method, including: S10. Obtaining target information for copper busbar processing; S20. Obtaining a processing procedure based on the target information; S30. Obtaining processing equipment information based on the processing procedure; S40. Monitoring the processing of the original processing material; S50. Determining whether there is a processing anomaly; S60. Recording processing data. Figure 1 The units in Figure 2 The corresponding steps in the same way achieve the same function. Figure 1 and Figure 2 The combination method is explained.

[0076] The planning unit of the system obtains the copper bar production data of production line A, and extracts target information from the copper bar production data; wherein the target information includes: copper bar type, copper bar specification and process flow; corresponding Figure 1 S10 step; wherein, according to the copper busbar, it can be known that the copper busbar being processed by the production line A is type B; refer to Figure 3 , Class B copper busbar is a grounding copper busbar for power transformers, which is used to ensure the safe operation of the transformer. It grounds the metal parts of the transformer to prevent electrical hazards caused by insulation failure or short circuit. The grounding copper busbar can effectively guide the current to the ground, reduce the risk of electric shock to equipment and personnel, and protect the transformer from lightning strikes and other overvoltages; Figure 3 It can be seen that the two ends of the Class B copper busbar are bent, and the middle is a long and flat metal plate with several holes on its surface for fixation during installation.

[0077] Further, a processing procedure of type B can be obtained from the target information, corresponding to step S20; wherein the processing procedure of type B copper busbar includes: processing material selection, cutting, punching, bending, grinding, cleaning and nickel plating; for the specifications of type B copper busbar, refer to Table 1;

[0078] Table 1 Class B copper busbar specifications

[0079]

[0080] Further, according to the processing procedure of the Class B copper busbar, processing equipment information of each processing step is obtained, corresponding to S30; wherein the processing equipment includes: material selection equipment, laser cutting machine, punching machine, bending machine, angle grinder, sonic cleaning machine and electroplating equipment;

[0081] Further, the raw material is processed by the processing unit of the system according to the target information;

[0082] Furthermore, the processing process of the Class B copper busbar is monitored by the monitoring unit of the system, corresponding to step S40; the specific process includes:

[0083] The image of the processed material after the current processing step is completed is obtained by information collection of the system;

[0084] Further, the processed material image is preprocessed to obtain a standard processed material image; wherein the preprocessing mainly includes: removing noise in the processed material image to obtain a denoised processed material image; performing image enhancement on the denoised processed material image to obtain an enhanced processed material image; performing geometric correction on the enhanced processed material image to obtain a corrected processed material image; performing edge detection on the corrected processed material image to obtain an edge processed material image; performing alignment operation on the edge processed material image to obtain an aligned processed material image; and cropping the aligned processed material image to obtain the standard processed material image.

[0085] In the embodiments of the present application, a series of preprocessing methods are used to preprocess the collected processed material images. These preprocessing methods can significantly improve the accuracy and efficiency of abnormality recognition by improving image quality, standardizing size, improving feature extraction efficiency, reducing computational complexity, enhancing data consistency, and improving model training effects. This can not only help identify potential processing problems, but also improve the reliability and practicality of the entire monitoring system.

[0086] Furthermore, a copper busbar processing anomaly recognition model is used to perform anomaly recognition on the standard processing material image; wherein the copper busbar processing anomaly recognition model includes:

[0087] After the input layer receives the standard processing material image, it is input into the feature extraction layer to obtain the processing material feature map; wherein the feature extraction layer uses the CNN model as a feature extractor to obtain the edge features, texture features, shape features and color features of the standard processing material image; the processing material feature map is input into the feature processing layer to obtain the processing material feature map; the feature processing layer uses the attention mechanism to perform weighted fusion on the features that need to be paid attention to after the corresponding processing steps; for example, in the cutting step, the flatness and specifications of the material cutting should be paid attention to; in the bending step, the bending angle and material deformation should be paid attention to;

[0088] Furthermore, the matching layer performs matching according to the current processing steps; the corresponding abnormal identification sublayer in the abnormal identification layer is selected according to the matching results; wherein the abnormal identification layer includes multiple identification sublayers, such as the monitoring of the Class B copper busbar processing process in the embodiment of the present application includes: raw material selection abnormal identification sublayer, cutting abnormal identification sublayer, punching abnormal identification sublayer, bending abnormal identification sublayer, polishing abnormal identification sublayer, cleaning abnormal identification sublayer and nickel plating abnormal identification; when different types of copper busbar processing contain the same processing steps, the corresponding abnormal identification sublayers can be shared. The structure of the abnormal identification sublayer is mainly composed of a convolution sublayer, a pooling sublayer, a fully connected sublayer, an activation sublayer, a normalization sublayer and an abnormal scoring sublayer; wherein the abnormal scoring sublayer is a feature learning by the first few sublayers, and after normalization operation, the feature abnormality score in the feature map is calculated; the score can reflect the abnormal situation and abnormal state of the pixel points corresponding to the feature map;

[0089] Furthermore, the output layer outputs the monitoring result according to the recognition result of the abnormal recognition layer, including the location and type of the abnormal point in the monitoring image;

[0090] In the example of the present application, a copper busbar processing anomaly recognition model is used to identify anomalies in the copper busbar processing monitoring process; the model uses image recognition technology to extract features from the image of the processed material, and weights and fuses the extracted features through an attention mechanism to enhance the abnormal features; in addition, in order to address the abnormal situations that may occur in different processing steps, a matching layer is added to the model to match the abnormal recognition sublayer corresponding to the processing step, which can greatly improve the accuracy of abnormal recognition generated in different processing steps.

[0091] Further, when no abnormality is found in the monitoring result, the next processing step is performed, and the monitoring unit continues to monitor; when an abnormality is found in the monitoring result, the processing parameter analysis is performed on the current processing step, corresponding to step S50;

[0092] The processing parameter analysis unit of the system can realize the analysis function of the processing parameter analysis of the current processing step, and the specific process includes:

[0093] During the processing of the Class B copper busbar, the recording unit of the system records each processing step in time sequence to form a log file, and caches the data of the log file to the storage unit of the system; wherein the data recorded by the recording unit includes: processing steps, processing time points and equipment parameters; refer to Table 2, which gives the data recorded for each processing step after the Class B copper busbar is processed;

[0094] Table 2 Processing data of Class B copper busbar

[0095]

[0096] Further, according to the abnormal result identified in the current processing step; the total processing time of the current processing step is obtained;

[0097] Furthermore, the total processing time is sliced ​​according to the sub-steps in the current processing step to obtain multiple processing sub-times; for example, the punching step in Table 2 includes multiple sub-steps such as setting punching machine parameters, clamping materials, aligning the punching die, and punching, and the sub-time spent on each sub-step is also inconsistent;

[0098] Further, the processing data during the processing sub-period is obtained; wherein the processing data includes: processing material change data and equipment parameters;

[0099] Furthermore, the processing data are aligned according to the recorded time series, and a processing parameter data set is constructed;

[0100] Further, the processing parameter data set is processed to obtain a standard processing parameter data set;

[0101] Furthermore, the standard processing parameter data set and the monitoring results are input into a processing parameter analysis model; wherein the processing parameter analysis model represents:

[0102] ;

[0103] in, represents the model function; Represents the input feature vector, including: processing sub-time after data processing, processing material change data and equipment parameters; A feature vector representing the monitoring result; Representing parameters of the processing parameter analysis model, including weights and biases; Indicates that the processing data features and monitoring results characteristics Concatenate to form a new input vector; Indicates The weight matrix of the layer; Indicates The bias vector of the layer; represents the activation function; Indicates the number of model layers;

[0104] Furthermore, according to the analysis result of the processing parameter analysis unit, the processing parameters are optimized, which is implemented by the optimization unit of the system. The specific process includes:

[0105] According to the target information, the copper busbar template in the database is matched to obtain the information of the copper busbar with the same template, including: size information, processing parameters and quality standards; wherein the information of the copper busbar template is stored in the template storage unit of the system; the unit is connected to the copper busbar template database stored in the local server;

[0106] In the embodiment of the present application, a template storage unit is established for the system. The unit establishes a database through a local server to save standard data of different types of copper busbars processed by manufacturers. Not only can the standardized template ensure the consistency of processing of each copper busbar and reduce errors, but it also makes it easier to perform quality inspection and control to ensure that the product meets the specification requirements.

[0107] Further, obtaining template processing data of the same processing step as the current processing step from the information;

[0108] Further, a processing deviation matrix is ​​constructed according to the processing data of the current processing step and the template processing data;

[0109] Further, the specified statistics of the deviation matrix are calculated, and the significant deviation areas are displayed graphically; wherein the specified statistics include: mean, standard deviation, variance, maximum value and minimum value, etc.;

[0110] Furthermore, the correlation between the parameters of the processing deviation matrix is ​​calculated to obtain a processing parameter correlation matrix; wherein the correlation coefficient in the processing parameter correlation matrix is ​​calculated using the Pearson correlation coefficient calculation method, and the formula is:

[0111] ;

[0112] in, Representation parameters ; Representation parameters ; represents the covariance function; represents standard deviation;

[0113] Further, the current processing parameters are optimized according to the specified statistics and the processing parameter correlation matrix to obtain the current optimized processing parameters; wherein an objective function may be set for optimizing the current processing parameters, and the objective function reflects the cost of correcting the processing parameters to qualified processing;

[0114] The objective function is defined as: ;in, Represents the current processing parameter vector;

[0115] Represents the current processing parameter vector Related processing deviations; represents the penalty for correlation between parameters in the machining parameter correlation matrix; Represents the coefficient for adjusting the weight of the penalty term.

[0116] In the application example, a processing parameter optimization method is used to optimize the processing parameters of the processing steps with abnormalities; this method matches the processing information of the current copper busbar with the copper busbar template in the database, so that reliable standard data can be obtained; and the errors between the processing parameters of the processing steps with abnormalities and the standard parameters are calculated to obtain the correlation between the specified statistics and the parameters, which is conducive to the accurate optimization of the processing parameters and provides reference value for future copper busbar processing.

[0117] Further, the feedback unit of the system generates a copper busbar processing feedback report, corresponding to step S60;

[0118] In the embodiment of the present application, the system and method proposed in the present invention are combined to realize the processing of Class B copper busbars. The above description process mainly appears from the following aspects: first, the type, specification and production process of the copper busbar to be processed are understood through production data; second, the copper busbar is processed according to the target information; during the processing, the present invention monitors the entire processing process, and while monitoring, performs abnormality recognition on the processing results after each processing step. The abnormality recognition adopts a copper busbar processing abnormality recognition model to obtain image information of the processing material and perform feature learning; and a matching layer and an abnormality recognition layer are added to the model, and the corresponding abnormality recognition sublayer can be selected by matching the processing steps, so that The accuracy of abnormality identification can be greatly improved; for abnormal processing steps, the present invention divides the abnormal processing steps into multiple processing sub-steps by time slicing, and collects processing parameters in the processing sub-steps for analysis; the processing parameters with abnormalities are analyzed using a processing parameter analysis model, which is conducive to clearly locating the processing problem points; in order to improve the copper busbar processing quality, the present invention calculates the deviation between the abnormal processing parameters and the template processing parameters, and obtains the correlation between the basic statistics and the processing parameters according to the deviation value, so as to optimize the abnormal processing parameters; finally, various data in the copper busbar processing process are fed back to provide valuable data for future copper busbar processing, which is conducive to copper busbar processing.

[0119] Embodiment 2:

[0120] In Example 1, the system and method of the present invention are combined to realize the processing process of Class B copper busbars on production line A; in order to illustrate that the present invention has good performance in processing different types of copper busbars, the processing of Class C copper busbars will be described in the embodiment of the present application, and the process is basically the same as that of Example 1, specifically:

[0121] Get the target information of copper busbar production; it is known that type C copper busbar is a high-voltage frequency conversion copper busbar; refer to Figure 4 , copper busbars are usually used in high-voltage power systems to connect inverters and electrical equipment to achieve efficient power transmission and control. They are mainly used to transmit the output current of the inverter to the motor or other loads; Figure 3 It can be seen that the B-type copper busbar is an irregularly shaped copper busbar with several holes of different diameters on the surface and the bending parts are not Figure 3 The copper bar is also 90°, and the curved part maintains a certain curvature;

[0122] Further, determining the processing procedure information of the Class C copper busbar according to the target information;

[0123] Further, obtaining processing equipment information according to the processing procedure information;

[0124] Further, processing the processing material according to the processing procedure information;

[0125] Abnormal monitoring of Class C copper busbar processing, the specific process includes:

[0126] Using a high-resolution camera to obtain an image of the processed material at the current processing step;

[0127] Further, the processed material image is preprocessed to obtain a standard processed material image;

[0128] Further, the standard processing material image is input into the copper busbar processing abnormality recognition model to obtain the monitoring result;

[0129] Furthermore, when the monitoring result is abnormal, the processing parameters of the current processing step are analyzed to obtain the current processing parameter analysis result; the specific process includes: obtaining the total processing time of the current processing step; slicing the total processing time according to the processing sub-steps in the previous processing step to obtain multiple processing sub-times; collecting multiple processing data in the multiple processing sub-times; wherein the processing data includes: processing material change data and equipment parameters;

[0130] Aligning the plurality of processing sub-durations with the plurality of processing data in time series, and constructing a processing parameter data set;

[0131] The processing parameter data set is processed to obtain a standard processing parameter data set. The standard processing parameter data set and the monitoring result are input into a processing parameter analysis model; wherein the processing parameter analysis model represents:

[0132] ;

[0133] in, represents the model function; Represents the input feature vector, including: processing sub-time after data processing, processing material change data and equipment parameters; A feature vector representing the monitoring result; Representing parameters of the processing parameter analysis model, including weights and biases; Indicates that the processing data features and monitoring results characteristics Concatenate to form a new input vector; Indicates The weight matrix of the layer; Indicates The bias vector of the layer; represents the activation function; Indicates the number of model layers.

[0134] Further, the current processing parameters are optimized according to the current processing parameter analysis result to obtain the current optimized processing parameters; the specific process includes:

[0135] According to the target information, the copper busbar template in the database is matched to obtain the information of the copper busbar of the same type of template, including: size information, processing parameters and quality standards; the template processing data of the same processing step as the current processing step is obtained from the information; a processing deviation matrix is ​​constructed according to the processing data of the current processing step and the template processing data; the specified statistics of the deviation matrix are calculated, and the significant deviation area is displayed graphically; the correlation between the parameters of the processing deviation matrix is ​​calculated to obtain the processing parameter correlation matrix; the current processing parameters are optimized according to the specified statistics and the processing parameter correlation matrix to obtain the current optimized processing parameters.

[0136] Furthermore, various data of the current processing step are recorded.

[0137] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A copper busbar processing system, characterized in that: The system comprises: a template storage unit for storing information of copper bar templates; a planning unit for generating target information, wherein the target information comprises: copper bar type, copper bar specification and process flow; an information collection unit for collecting various data during copper bar processing; a storage unit for temporarily storing the data; a processing unit for copper bar processing; a recording unit for recording copper bar processing information, including processing steps, processing time points and equipment parameters; a monitoring unit for real-time monitoring of copper bar processing; a processing parameter analysis unit for analyzing abnormal processing parameters during copper bar processing when the monitoring result of the monitoring unit is abnormal, comprising: obtaining the total processing time of the current processing step; slicing the total processing time to obtain multiple processing sub-times; collecting processing parameters in multiple processing sub-times; the processing parameters comprise: processing material change data and the equipment parameters; aligning the processing sub-times with the processing parameters in time series and constructing a processing parameter data set; performing data processing on the processing parameter data set to obtain a standard processing parameter data set; inputting the standard processing parameter data set and the monitoring results into a processing parameter analysis model; wherein the processing parameter analysis model represents: ； in, represents the model function; Represents the input feature vector, including the processing sub-time, processing material change data and equipment parameters after data processing; A feature vector representing the monitoring result; Representing parameters of the processing parameter analysis model, including weights and biases; Indicates that and Concatenate to form a new input vector; Indicates The weight matrix of the layer; Indicates The bias vector of the layer; represents the activation function; Indicates the number of model layers; The optimization unit is used to optimize the processing parameters of the copper busbar; the feedback unit is used to generate the copper busbar processing feedback report; Among them, the monitoring unit adopts the copper busbar processing abnormality recognition model to identify the abnormality of the processed material, and the specific process includes: the information acquisition unit obtains the processed material image under the current processing step; the processed material image is preprocessed to obtain the standard processed material image; after the standard processed material image is input through the input layer, the feature extraction layer performs feature extraction to obtain the processed material feature map; the processed material feature map is input to the feature processing layer for weighting and fusion operations to obtain the processed material processing feature map; the matching layer matches the corresponding abnormality recognition sublayer according to the current processing step; according to the selection result of the matching layer, the processed material processing feature map is input to the corresponding abnormality recognition sublayer in the abnormality recognition layer for recognition; according to the recognition result of the abnormality recognition sublayer, the output layer outputs the monitoring result, including: abnormal position and abnormality type; The preprocessing includes: removing noise from the processed material image to obtain a denoised processed material image; performing image enhancement on the denoised processed material image to obtain an enhanced processed material image; performing geometric correction on the enhanced processed material image to obtain a corrected processed material image; performing edge detection on the corrected processed material image to obtain an edge processed material image; performing alignment operation on the edge processed material image to obtain an aligned processed material image; and cropping the aligned processed material image to obtain the standard processed material image.

2. A copper busbar processing system according to claim 1, characterized in that: The template storage unit establishes a copper busbar template database through a local server; wherein the copper busbar template database stores the copper busbar type, size information, processing flow, processing parameters and quality standards.

3. A copper busbar processing system according to claim 1, characterized in that: The optimization unit includes: matching the target information with the copper busbar template in the database to obtain information of the copper busbar of the same type of template, including: size information, processing parameters and quality standards; obtaining the template processing parameters of the same processing step as the current processing step from the information; constructing a processing deviation matrix according to the processing parameters of the current processing step and the template processing parameters; calculating the specified statistics of the deviation matrix, and using graphical display of significant deviation areas; calculating the correlation between the parameters of the processing deviation matrix to obtain the processing parameter correlation matrix; optimizing the current processing parameters according to the specified statistics and the processing parameter correlation matrix to obtain the current optimized processing parameters.

4. A copper busbar processing method, characterized in that: include: Obtain target information for copper busbar production; wherein the target information includes: copper busbar type, copper busbar specification and process flow; determine the processing procedure information of the copper busbar according to the target information; obtain processing equipment information according to the processing procedure information; process the processing material according to the processing procedure information; wherein the processing material is monitored during the copper busbar processing; when the monitoring result is abnormal, analyze the processing parameters of the current processing step to obtain the abnormal processing parameters, and the processing parameters include processing material change data and equipment parameters; optimize the current processing parameters according to the current processing parameter analysis result to obtain the current optimized processing parameters; record various data of the current processing step, and the recorded data includes the processing step, processing time point and equipment parameters; Wherein, monitoring the processing process of the copper busbar includes: using a high-resolution camera to obtain a processing material image under the current processing step; preprocessing the processing material image to obtain a standard processing material image; inputting the standard processing material image into a copper busbar processing anomaly recognition model to obtain the monitoring result; wherein, the copper busbar processing anomaly recognition model includes: An input layer, used for receiving the standard processing material image; A feature extraction layer, which extracts features from the image data received by the input layer through a convolutional neural network model; A feature processing layer, used for performing weighting and fusion operations on the image feature map obtained by the feature extraction layer; A matching layer, used to match the corresponding abnormality identification sublayer according to the current processing step; An abnormality recognition layer, used for performing abnormality recognition on the processed material image; wherein the abnormality recognition layer includes a plurality of abnormality sublayers; the structure of the plurality of abnormality sublayers includes: a convolution sublayer, a pooling sublayer, a fully connected sublayer, an activation sublayer, a normalization sublayer and an abnormality scoring sublayer; The output layer is used to output monitoring results, including: abnormal location and abnormal type; The preprocessing includes: removing noise from the processed material image to obtain a denoised processed material image; performing image enhancement on the denoised processed material image to obtain an enhanced processed material image; performing geometric correction on the enhanced processed material image to obtain a corrected processed material image; performing edge detection on the corrected processed material image to obtain an edge processed material image; performing alignment operation on the edge processed material image to obtain an aligned processed material image; and cropping the aligned processed material image to obtain the standard processed material image.

5. A copper busbar processing method according to claim 4, characterized in that: When the monitoring result is abnormal, the process of analyzing the processing parameters of the current processing step includes: obtaining the total processing time of the current processing step; slicing the total processing time according to the processing sub-steps in the previous processing step to obtain multiple processing sub-times; collecting multiple processing parameters in the multiple processing sub-times; wherein the processing parameters include: the processing material change data and the equipment parameters; aligning the multiple processing sub-times with the multiple processing parameters in time series, and constructing a processing parameter data set; performing data processing on the processing parameter data set to obtain a standard processing parameter data set; inputting the standard processing parameter data set and the monitoring result into a processing parameter analysis model; wherein the processing parameter analysis model represents: ； in, represents the model function; Represents the input feature vector, including the processing sub-time, processing material change data and equipment parameters after data processing; A feature vector representing the monitoring result; Representing parameters of the processing parameter analysis model, including weights and biases; Indicates that and Concatenate to form a new input vector; Indicates The weight matrix of the layer; Indicates The bias vector of the layer; represents the activation function; Indicates the number of model layers.

6. A copper busbar processing method according to claim 4, characterized in that: The process of optimizing the current processing parameters includes: matching the target information with the copper busbar template in the database to obtain information of the copper busbar of the same type of template, including: size information, processing parameters and quality standards; obtaining the template processing parameters of the same processing step as the current processing step from the information; constructing a processing deviation matrix according to the processing parameters of the current processing step and the template processing parameters; calculating the specified statistics of the deviation matrix, and using graphical display of significant deviation areas; calculating the correlation between the parameters of the processing deviation matrix to obtain the processing parameter correlation matrix; optimizing the current processing parameters according to the specified statistics and the processing parameter correlation matrix to obtain the current optimized processing parameters.

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