A self-adaptive optimization method and system for connector production technology

Abstract

The present invention relates to the field of artificial intelligence technology, and is a method and system for adaptively optimizing connector production techniques, comprising: monitoring a connector production technology pipeline to obtain a connector product set and a monitoring result sequence; performing product performance testing on the connector product set to obtain product test data; identifying process anomalies on the monitoring result sequence to obtain an anomaly identification result; using a pre-trained anomaly-fault bidirectional detection model to predict the fault type of the anomaly identification result to obtain a predicted fault type; when the test fault type is different from the predicted fault type, using the anomaly-fault bidirectional detection model to identify anomalies on the faulty product monitoring result sequence to obtain a predicted anomaly identification result to assist in improving the production technology pipeline. The present invention can solve the problem that when modifying the current connector production process, a large amount of manpower and time is required for trial and error debugging.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and system for adaptively optimizing connector production techniques. Background Art

[0002] With the development and popularization of electronic devices, USB or Type-C connectors have become essential tools for transmitting data and power between electronic devices. However, these connectors are subject to production process limitations, making them prone to substandard quality. This can lead to data loss or damage when connected to electronic devices.

[0003] To improve connector production processes, product samples are regularly sampled and tested to obtain test results. Based on these test results, the CNC parameters used during manufacturing are modified, ultimately completing the production process. These process modifications rely on subjective human evaluation and are subject to lags. Adjustments to the production process are only initiated when new quality issues emerge or the number of defective products increases. These modified processes only address the immediate issues and fail to achieve optimal performance. Changes to production materials or equipment require re-planning the production process, incurring significant labor costs. Summary of the Invention

[0004] The present invention provides an adaptive optimization method and system for connector production technology, the main purpose of which is to solve the problem that the production process of connectors such as USB or Type-C is difficult to reach perfection, and when materials and equipment change, modifying the production process requires a lot of manpower and time for exploration and debugging.

[0005] To achieve the above objectives, the present invention provides an adaptive optimization method for connector production technology, comprising:

[0006] Obtain a production technology assembly line for target connector models, monitor the production technology assembly line using pre-built edge detection equipment, and obtain a set of connector products and a sequence of monitoring results corresponding to each connector product in the set within a preset time period after the production technology assembly line is started.

[0007] Performing product performance testing on the connector product set according to a preset test strategy to obtain product test data, extracting faulty products from the connector product set based on the product test data, and obtaining a test fault type of the faulty product;

[0008] Performing process anomaly identification on a monitoring result sequence of a target product in the connector product set to obtain an anomaly identification result, and marking the anomaly identification result on the target product;

[0009] Using a pre-trained anomaly-fault bidirectional detection model, predicting the fault type of the anomaly recognition result to obtain a predicted fault type;

[0010] Determining whether the test fault type corresponds to the predicted fault type;

[0011] When the test fault type corresponds to the predicted fault type, outputting the production step name corresponding to the abnormality identification result to a pre-built list of items to be improved;

[0012] When the test fault type is different from the predicted fault type, performing a monitoring data tracing operation on the faulty product to obtain a faulty product monitoring result sequence;

[0013] Using the anomaly-fault bidirectional detection model, anomaly identification is performed on the faulty product monitoring result sequence to obtain a predicted anomaly identification result;

[0014] Identify the production step name corresponding to the predicted anomaly recognition result, and transform the predicted anomaly recognition result into a prediction improvement enhancement direction according to a preset improvement strategy;

[0015] The production step name and the predicted improvement and enhancement direction are saved in the list to be improved.

[0016] Optionally, the monitoring result sequence includes the production step name, machine numerical control parameters, and product scanning images corresponding to each production step in the production technology assembly line, wherein the product scanning image includes a photographic image and an infrared scanning structure image.

[0017] Optionally, performing product performance testing on the connector product set according to a preset test strategy to obtain product test data includes:

[0018] According to a preset test strategy, using a pre-built simulation service, product simulation is performed on the monitoring result sequence to obtain a simulation model, and a test process based on a repetitive test type item is performed on the simulation model to obtain a simulation test result;

[0019] Performing a test process based on a one-time detection type item on the connector product set to obtain a test result;

[0020] The simulation test results and the experimental test results are uniformly output to obtain product test data.

[0021] Optionally, performing process anomaly identification on the monitoring result sequence of the target product in the connector product set to obtain an anomaly identification result includes:

[0022] Extracting a target product from the connector product set, and extracting machine numerical control parameters and product scan images from a monitoring result sequence of the target product;

[0023] Using the product scan image and the machine numerical control parameters, performing an abnormal operation comparison operation based on the operation purpose and operation result of the machine on the monitoring result sequence to obtain an abnormal operation identification result;

[0024] Obtaining a standard product structure diagram of the target product, using the product scan image, and performing structural distribution anomaly identification on the target product according to the standard product structure diagram to obtain a structural anomaly identification result;

[0025] The operation abnormality recognition result and the structural abnormality recognition result are output to obtain an abnormality recognition result.

[0026] Optionally, the use of a pre-trained anomaly-fault bidirectional detection model to predict the fault type of the anomaly identification result to obtain the predicted fault type includes:

[0027] Using a pre-trained anomaly-fault bidirectional detection model, a feature extraction operation is performed on the anomaly recognition result to obtain an anomaly feature vector;

[0028] Performing feature recognition on the abnormal feature vector to obtain feature recognition results, and grouping the feature recognition results into preset abnormality types to obtain feature clusters corresponding to each abnormality type;

[0029] Fully connected classification judgment is performed on each of the feature clusters to obtain a fault probability distribution value corresponding to a preset fault type set, and a predicted fault type is extracted from the fault type set based on the fault probability distribution value.

[0030] Optionally, before predicting the fault type of the anomaly identification result using a pre-trained anomaly-fault bidirectional detection model and obtaining the predicted fault type, the method further includes:

[0031] Get the pre-built production exception type set and fault type set:

[0032] F={f1,f2,…,f m}

[0033] A={a1,a2,…,a n}

[0034] Where F represents the set of production anomaly types, m represents the number of types in the set of production anomaly types, and f mrepresents the mth type of production anomaly type in the production anomaly type set, A represents the fault type set; n represents the number of types in the production anomaly type set, a n represents the nth type of fault in the set of fault types;

[0035] Obtain sample data including normal production process data and abnormal production process data, wherein each production process data in the sample data is also equipped with a corresponding fault type:

[0036] D={(x i ,y fi ,y ai )}

[0037] Where D represents the sample data, x i Represents the i-th production process data in the sample data, y fi Indicates the fault type corresponding to the i-th production process data in the sample data, y ai Indicates the production anomaly type corresponding to the i-th production process data in the sample data;

[0038] Obtain an anomaly-fault bidirectional detection model comprising two output layers and a generative adversarial network, and configure the production anomaly type set and the fault type set as standard output types of the two output layers, respectively, wherein the two output layers are a process anomaly prediction output layer and a fault judgment output layer, and the generative adversarial network includes a generator and a discriminator;

[0039] Extracting a target sample from the sample data in sequence, performing a network forward operation on the target sample using the anomaly-fault bidirectional detection model to obtain a predicted sample fault and a predicted sample production anomaly;

[0040] Using the pre-built model overall loss function, according to the actual fault type and actual production anomaly type corresponding to the target sample in the sample data, the loss value of the predicted sample fault and the predicted sample production anomaly is calculated to obtain the overall loss value. The model overall loss function is expressed as:

[0041] L 整体 =L total +αL G +βL D

[0042] Where, L 整体 Expressed as the overall loss function of the model, L total It represents the output layer loss function of the process anomaly prediction output layer and the fault judgment output layer, L G represents the loss function of the generator G, L Drepresents the loss function of the discriminator D, α and β represent weight parameters for balancing the loss function of the output layer and the loss function of the generative adversarial network;

[0043] Among them, L total Expressed as:

[0044] L total =λL cls +(1-λ)L reg

[0045]

[0046] Where, L total represents the output layer loss function, L cls represents the classification loss function of the fault judgment output layer, L reg represents the regression loss function of the process anomaly prediction output layer, λ is the weight parameter; y i represents the predicted sample fault corresponding to the i-th target sample, represents the actual fault type corresponding to the i-th target sample, and N represents the number of types in the fault type set; a i Indicates that the predicted sample corresponding to the i-th target sample produces an abnormality, represents the actual production anomaly type corresponding to the i-th target sample, and M represents the number of samples of the sample data;

[0047] Among them, L G Expressed as:

[0048]

[0049] Where, represents the generated data calculated by the generator for the predicted sample failure and the predicted sample production anomaly, P g represents the standard normal distribution of the generator, represents the discriminant result of the discriminator on the generated data, x represents the real fault type and the real production abnormality type, P r Representing the standard normal distribution of the actual fault type and the actual production abnormality type;

[0050] Minimize the overall loss value according to the gradient descent algorithm to obtain the network model parameters when the overall loss value is minimized, and perform network inverse parameter update on the anomaly-fault bidirectional detection model according to the network model parameters to obtain an updated anomaly-fault bidirectional detection model;

[0051] Determining whether the overall loss value converges;

[0052] When the overall loss value has not converged, returning to the above step of sequentially extracting a target sample from the sample data, and iteratively optimizing the updated anomaly-fault bidirectional detection model;

[0053] When the overall loss value converges, the training process is stopped to obtain a trained anomaly-fault bidirectional detection model.

[0054] Optionally, after obtaining the product test data, the method further includes:

[0055] Saving the product test data into a pre-built model feedback database;

[0056] According to a preset time interval frequency, the anomaly-fault bidirectional detection model is trained using the model feedback database to obtain an optimized anomaly-fault bidirectional detection model.

[0057] Optionally, after marking the abnormality identification result on the target product, the method further includes:

[0058] Supervising the abnormality recognition result within a preset supervision time period to obtain an abnormality rate;

[0059] Determining whether the abnormality rate is greater than a preset warning threshold;

[0060] When the abnormality rate is greater than the warning threshold, an alarm prompt message is generated.

[0061] Optionally, the step of converting the predicted anomaly identification result into a prediction improvement enhancement direction according to a preset improvement strategy includes:

[0062] According to a preset improvement strategy, the standard product structure diagram is compared with the predicted abnormality recognition result to obtain the difference steps and difference values;

[0063] The difference value is inverted to obtain the value to be improved, the difference step is converted into the step to be improved, and the predicted improvement and enhancement direction is obtained according to the value to be improved and the step to be improved.

[0064] To achieve the above objectives, the present invention further provides an adaptive optimization system for connector production technology, comprising:

[0065] A process monitoring module is used to obtain the production technology pipeline of the target model connector, monitor the production technology pipeline using pre-built edge detection equipment, and obtain a set of connector products and a sequence of monitoring results corresponding to each connector product in the set within a preset time after the production technology pipeline is started;

[0066] A product testing module is configured to perform product performance testing on the connector product set according to a preset test strategy to obtain product test data, extract faulty products from the connector product set based on the product test data, obtain the test fault type of the faulty product, perform process anomaly identification on the monitoring result sequence of the target product in the connector product set to obtain an anomaly identification result, and mark the anomaly identification result on the target product;

[0067] a fault prediction module, configured to use a pre-trained anomaly-fault bidirectional detection model to predict the fault type of the anomaly identification result to obtain a predicted fault type, determine whether the test fault type corresponds to the predicted fault type, and, when the test fault type corresponds to the predicted fault type, output the name of the production step corresponding to the anomaly identification result to a pre-built list of items to be improved;

[0068] The module for identifying points to be improved is used to perform monitoring data tracing operations on the faulty product when the test fault type is different from the predicted fault type, to obtain a sequence of monitoring results of the faulty product, and to use the anomaly-fault bidirectional detection model to perform anomaly identification on the sequence of monitoring results of the faulty product to obtain a predicted anomaly identification result, and to identify the production step name corresponding to the predicted anomaly identification result, and according to a preset improvement strategy, to convert the predicted anomaly identification result into a predicted improvement enhancement direction, and to save the production step name and the predicted improvement enhancement direction to the list to be improved.

[0069] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0070] a memory storing at least one instruction; and

[0071] The processor executes the instructions stored in the memory to implement the above-mentioned adaptive optimization method for connector production technology.

[0072] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned adaptive optimization method of connector production technology.

[0073] The present invention solves the problems described in the background technology. First, the present invention uses edge detection equipment to monitor the steps of the connector production technology pipeline through digital monitoring and image monitoring. When the connector production starts, a monitoring result sequence for each connector will be obtained. First, the present invention compares the image recognition results of the numerical control parameters with the actual production results to complete the abnormality identification and obtain the abnormality identification results. These abnormality identification results can effectively solve the problems such as uneven product packaging injection molding, incorrect or asymmetric part installation positions, etc. Then, a pre-trained abnormality-fault bidirectional detection model is used to predict the fault type of the abnormality identification results to obtain a predicted fault type. When the predicted fault type is the same as the actual detected test fault type, it indicates that only the step where the abnormality occurs needs to be improved; and when the predicted fault type is different from the test fault type, it indicates that there are deficiencies in the technical process that need to be repaired. The abnormality-fault bidirectional detection model has two output layers, which can find possible deficiencies in the technical process based on the test fault type, and can also predict the fault type of the product based on the deficiencies in the technical process. When training the model, the present invention also adds a generative adversarial network to further increase the mapping relationship between process abnormalities and fault types. Therefore, the present invention can solve the problem that the production process of connectors such as USB or type-C is difficult to achieve perfection, and when materials or equipment change, modifying the production process requires a lot of manpower and time for exploration and debugging. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A schematic flow chart of a method for adaptively optimizing connector production techniques according to an embodiment of the present invention;

[0075] Figure 2 A functional module diagram of a self-adaptive optimization system for connector production technology provided by one embodiment of the present invention;

[0076] Figure 3 A schematic structural diagram of an electronic device for implementing the adaptive optimization method for connector production technology provided by one embodiment of the present invention.

[0077] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0078] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0079] The present invention provides an adaptive optimization method for connector production technology. The method can be executed by at least one of the following electronic devices, including a server and a terminal, that can be configured to execute the method provided by the present invention. In other words, the method can be executed by software or hardware installed on a terminal or server device, where the software can be a blockchain platform. The server can include, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0080] Reference Figure 1 FIG. 1 is a flow chart of a method for adaptively optimizing connector production techniques according to an embodiment of the present invention. In this embodiment, the method for adaptively optimizing connector production techniques includes:

[0081] S1. Obtain the production technology pipeline of the target model connector, use the pre-built edge detection equipment to monitor the production technology pipeline, and obtain the connector product set and the monitoring result sequence corresponding to each connector product in the connector product set within the preset time of the production technology pipeline startup.

[0082] In an embodiment of the present invention, the target model connector is a USB or type-C type connector, and the production technology pipeline includes material selection, injection molding, stamping, electroplating, assembly and welding, etc. These steps can be replaced as technology advances.

[0083] Furthermore, the edge detection equipment includes an optical camera, an infrared camera, and a data extraction service interface. The images captured by the optical camera can be used to check for deformation, burrs, or missing material on the product's exterior, while the infrared scanning structural images captured by the infrared camera can be used to check for proper positioning of the connector's internal structure and uniform plastic wrapping. The data extraction service interface directly extracts the machine numerical control parameters for each step, facilitating comparison with the product scanned image to identify discrepancies between ideal values ​​and actual machine operation values.

[0084] Specifically, in an embodiment of the present invention, after the production technology assembly line is monitored by the edge detection device, when the assembly line is started, batch production can be carried out within a preset time period to obtain a set of connector products, and the edge detection device will obtain a monitoring result sequence for each connector product. The monitoring result sequence includes the production step name, machine CNC parameters, and product scan images corresponding to each production step in the production technology assembly line, wherein the product scan images include photographed images and infrared scanning structure images.

[0085] S2. According to a preset test strategy, perform product performance testing on the connector product set to obtain product test data, and extract faulty products from the connector product set based on the product test data, and obtain the test fault type of the faulty product.

[0086] Among them, the testing strategy refers to the planning strategy for product testing items such as stamping quality testing, electroplating layer testing, welding quality testing, usage times testing, electrical performance testing and mechanical performance testing through both actual testing and model prediction.

[0087] In detail, in an embodiment of the present invention, performing product performance testing on the connector product set according to a preset test strategy to obtain product test data includes:

[0088] According to a preset test strategy, using a pre-built simulation service, product simulation is performed on the monitoring result sequence to obtain a simulation model, and a test process based on a repetitive test type item is performed on the simulation model to obtain a simulation test result;

[0089] Performing a test process based on a one-time detection type item on the connector product set to obtain a test result;

[0090] The simulation test results and the experimental test results are uniformly output to obtain product test data.

[0091] In an embodiment of the present invention, a simulation test is performed using a monitoring result sequence as a data basis and a BIM service as a simulation service. The BIM (Building Information Modeling) technology is a revolutionary method for architectural design, construction, and management that can use digital information to design the physical and functional characteristics of each building or infrastructure project, including functions such as three-dimensional modeling, lifecycle management, and performance analysis testing.

[0092] In this embodiment of the present invention, the simulation model built through BIM services performs complex, repetitive, and time-consuming product testing. Simple, one-time tests, such as sealing and waterproofing, can be conducted using actual products. Ultimately, the results of both tests are combined and outputted to produce the final product test data.

[0093] The present invention also extracts faulty products from the connector product set based on the product test data, and marks the test fault type of each faulty product.

[0094] S3. Perform process anomaly identification on the monitoring result sequence of the target product in the connector product set to obtain an anomaly identification result, and mark the anomaly identification result on the target product.

[0095] Specifically, in an embodiment of the present invention, the process anomaly identification is performed on the monitoring result sequence of the target product in the connector product set to obtain the anomaly identification result, including:

[0096] Extracting a target product from the connector product set, and extracting machine numerical control parameters and product scan images from a monitoring result sequence of the target product;

[0097] Using the product scan image and the machine numerical control parameters, performing an abnormal operation comparison operation based on the operation purpose and operation result of the machine on the monitoring result sequence to obtain an abnormal operation identification result;

[0098] Obtaining a standard product structure diagram of the target product, using the product scan image, and performing structural distribution anomaly identification on the target product according to the standard product structure diagram to obtain a structural anomaly identification result;

[0099] The operation abnormality recognition result and the structural abnormality recognition result are output to obtain an abnormality recognition result.

[0100] The standard product structure diagram refers to a scanned image of a fully qualified product that has undergone various tests, and also includes externally photographed images and structure scanned images, which are used to provide a standardized structure of the product.

[0101] In the embodiment of the present invention, based on the above steps, anomaly detection is performed from two perspectives: first, by comparing the machine's numerical control parameters with the product scan image to check for any anomalies in the machine's performance; second, by comparing the standard product structure diagram with the product scan image to check the structural distribution of the target product, such as the uniformity of the plastic packaging, the sealing performance, and the relative position distribution of various components. The anomaly identification steps in the embodiment of the present invention perform anomaly comparisons within each step of the production technology pipeline.

[0102] The embodiment of the present invention obtains an abnormality identification result through process abnormality identification. When the abnormality identification result is a null value, a no abnormality mark is provided; and when there is content in the abnormality identification, the abnormal step name and the abnormal point in the monitoring result sequence are marked on the target product together, which can facilitate the subsequent direct extraction of defective products in the connector product set.

[0103] Furthermore, in an embodiment of the present invention, after marking the abnormality identification result on the target product, the method further includes:

[0104] Supervising the abnormality recognition result within a preset supervision time period to obtain an abnormality rate;

[0105] Determining whether the abnormality rate is greater than a preset warning threshold;

[0106] When the abnormality rate is greater than the warning threshold, an alarm prompt message is generated.

[0107] In the embodiment of the present invention, if the abnormality rate is less than the warning threshold value of 1%, it can be considered that the product quality is unqualified due to accidental factors. If the abnormality rate is greater than 1%, it can be determined that a failure has occurred in the production technology assembly line and an alarm prompt is required.

[0108] S4. Using a pre-trained anomaly-fault bidirectional detection model, predict the fault type of the anomaly recognition result to obtain a predicted fault type.

[0109] In an embodiment of the present invention, the anomaly-fault bidirectional detection model is a neural network model including two output layers and a generative adversarial network, which is used to judge the fault type based on the production anomaly type of the production technology assembly line, or to infer the production anomaly type based on the fault type detected.

[0110] In detail, in an embodiment of the present invention, the use of a pre-trained anomaly-fault bidirectional detection model to predict the fault type of the anomaly recognition result to obtain the predicted fault type includes:

[0111] Using a pre-trained anomaly-fault bidirectional detection model, a feature extraction operation is performed on the anomaly recognition result to obtain an anomaly feature vector;

[0112] Performing feature recognition on the abnormal feature vector to obtain feature recognition results, and grouping the feature recognition results into preset abnormality types to obtain feature clusters corresponding to each abnormality type;

[0113] Fully connected classification judgment is performed on each of the feature clusters to obtain a fault probability distribution value corresponding to a preset fault type set, and a predicted fault type is extracted from the fault type set based on the fault probability distribution value.

[0114] In this embodiment of the present invention, feature extraction and recognition are first used to identify various anomalies in the production process line, yielding various production anomaly types. These anomaly types are the m most important labels extracted through principal component analysis, such as "injection molding, processing, assembly, etc." Each anomaly type is then grouped into these m labels. Furthermore, feature engineering is performed on the fault types to yield n labels, such as "water wading, leakage, loss resistance, etc."

[0115] Then, in the embodiment of the present invention, the mapping relationship between the [Production Anomaly Type] label and the [Fault Type] label is used to perform a network full connection operation on the feature clusters corresponding to each production anomaly type to obtain the fault probability distribution value of the fault type. In the embodiment of the present invention, based on the detection experience in actual business scenarios, the fault type with a score greater than a certain threshold, such as 80%, is extracted as the predicted fault type.

[0116] Furthermore, in an embodiment of the present invention, before predicting the fault type of the anomaly identification result using the pre-trained anomaly-fault bidirectional detection model and obtaining the predicted fault type, the method further includes:

[0117] Get the pre-built production exception type set and fault type set:

[0118] F={f1,f2,…,f m}

[0119] A={a1,a2,…,a n}

[0120] Where F represents the set of production anomaly types, m represents the number of types in the set of production anomaly types, and f m represents the mth type of production anomaly type in the production anomaly type set, A represents the fault type set; n represents the number of types in the production anomaly type set, a n represents the nth type of fault in the set of fault types;

[0121] Obtain sample data including normal production process data and abnormal production process data, wherein each data in the sample data also includes a fault mark:

[0122] D={(x i ,y fi ,y ai )}

[0123] Where D represents the sample data, x i Represents the i-th production process data in the sample data, y fi Indicates the fault type corresponding to the i-th production process data in the sample data, y ai Indicates the production anomaly type corresponding to the i-th production process data in the sample data;

[0124] Obtain an anomaly-fault bidirectional detection model with two output layers and a generative adversarial network, and configure the production anomaly type set and the fault type set as standard output types of the two output layers, wherein the two output layers are a process anomaly prediction output layer and a fault judgment output layer, and the generative adversarial network includes a generator and a discriminator;

[0125] Extracting a target sample from the sample data in sequence, performing a network forward operation on the target sample using the anomaly-fault bidirectional detection model to obtain a predicted sample fault and a predicted sample production anomaly;

[0126] Using the pre-built model overall loss function, according to the actual fault type and actual production anomaly type corresponding to the target sample in the sample data, the loss value of the predicted sample fault and the predicted sample production anomaly is calculated to obtain the overall loss value. The model overall loss function is expressed as:

[0127] L 整体 =L total +αL G +βL D

[0128] Where, L 整体 Expressed as the overall loss function of the model, L total It represents the output layer loss function of the process anomaly prediction output layer and the fault judgment output layer, L G represents the loss function of the generator G, L D represents the loss function of the discriminator D, α and β represent weight parameters for balancing the loss function of the output layer and the loss function of the generative adversarial network;

[0129] Among them, L total Expressed as:

[0130] L total =λL cls +(1-λ)L reg

[0131]

[0132] Where, L total represents the output layer loss function, L cls represents the classification loss function of the fault judgment output layer, L reg represents the regression loss function of the process anomaly prediction output layer, λ is the weight parameter; y i represents the predicted sample fault corresponding to the i-th target sample, represents the actual fault type corresponding to the i-th target sample, and N represents the number of types in the fault type set; a i Indicates that the predicted sample corresponding to the i-th target sample produces an abnormality, represents the actual production anomaly type corresponding to the i-th target sample, and M represents the number of samples of the sample data;

[0133] Among them, L G Expressed as:

[0134]

[0135] Where, represents the generated data calculated by the generator for the predicted sample failure and the predicted sample production anomaly, P g represents the standard normal distribution of the generator, represents the discriminant result of the discriminator on the generated data, x represents the real fault type and the real production abnormality type, P r Representing the standard normal distribution of the actual fault type and the actual production abnormality type;

[0136] Minimize the overall loss value according to the gradient descent algorithm to obtain the network model parameters when the overall loss value is minimized, and update the parameters of the anomaly-fault bidirectional detection model according to the network model parameters to obtain an updated anomaly-fault bidirectional detection model;

[0137] Determining whether the overall loss value converges;

[0138] When the overall loss value has not converged, returning to the above step of sequentially extracting a target sample from the sample data, and iteratively optimizing the updated anomaly-fault bidirectional detection model;

[0139] When the overall loss value converges, the training process is stopped to obtain a trained anomaly-fault bidirectional detection model.

[0140] Specifically, in an embodiment of the present invention, the sample data includes three parameters: production process data, fault type, and production anomaly type. The model's feature extraction network can perform feature extraction on all three parameters. However, when reaching the output layer, the output layer only extracts the relevant feature vectors of two of these parameters for analysis. For example, the process anomaly prediction output layer only uses the relevant feature vectors of the production process data and fault type parameters to calculate the predicted sample production anomaly.

[0141] In the embodiment of the present invention, the two output layers are respectively a process anomaly prediction output layer and a fault judgment output layer. The process anomaly prediction output layer is a regression prediction network, while the fault judgment output layer is a classification judgment network. total =λL cls +(1-λ)L reg The loss function of is more common. total =λL cls +(1-λ)L reg L cls and L reg are also their respective commonly used loss functions.

[0142] In this embodiment of the present invention, to improve the mapping relationship between the set of production anomaly types and the set of fault types, a generative adversarial network (GAN) is used to constrain the training process. This GAN generates more diverse and possible synthetic data to train our multi-task model, thereby improving the model's generalization and robustness.

[0143] Finally, the model training progress is controlled by the convergence of the overall loss value. When the overall loss value converges, it indicates that the model has a large improvement effect and can still be trained; when the overall loss value converges, it indicates that the model has a small improvement effect. In order to avoid model robustness, the training process is stopped to obtain a trained anomaly-fault bidirectional detection model.

[0144] Furthermore, in another embodiment of the present invention, after obtaining the product test data, the method further includes:

[0145] Saving the product test data into a pre-built model feedback database;

[0146] According to a preset time interval frequency, the anomaly-fault bidirectional detection model is trained using the model feedback database to obtain an optimized anomaly-fault bidirectional detection model.

[0147] According to the above steps, the present invention can maintain continuous optimization of the abnormality-fault bidirectional detection model, avoiding the degradation of connector product quality caused by changes over time, changes in machine life, changes in materials, and other factors.

[0148] S5. Determine whether the test fault type corresponds to the predicted fault type;

[0149] When the test fault type corresponds to the predicted fault type, S6 , output the production step name corresponding to the abnormality identification result to a pre-built list to be improved.

[0150] In an embodiment of the present invention, when the test fault type corresponds to the predicted fault type, it indicates that the defective product is produced because of the abnormal step corresponding to the abnormal identification result in the monitoring result sequence, so that the production step name corresponding to the abnormal identification result can be directly added to the pre-built list for improvement, waiting for professional technicians to debug or repair.

[0151] When the test fault type is different from the predicted fault type, S7 , performing a monitoring data tracing operation on the faulty product to obtain a monitoring result sequence of the faulty product.

[0152] In the embodiment of the present invention, when the test fault type is different from the predicted fault type, it indicates that in addition to problems with observable production skills, there are also invisible technical problems that need to be improved.

[0153] S8. Using the anomaly-fault bidirectional detection model, perform anomaly identification on the faulty product monitoring result sequence to obtain a predicted anomaly identification result.

[0154] In this embodiment of the present invention, the anomaly-fault bidirectional detection model first identifies the fault type in the faulty product monitoring result sequence. This is then output through the process anomaly prediction output layer of the anomaly-fault bidirectional detection model to obtain a predicted anomaly identification result. For example, it is predicted that the package diameter of the data cable in the stamping step of the production technology assembly line needs to be increased by 0.2 mm.

[0155] S9. Identify the production step name corresponding to the predicted abnormality recognition result, and transform the predicted abnormality recognition result into a prediction improvement enhancement direction according to a preset improvement strategy.

[0156] In detail, in an embodiment of the present invention, the method of converting the predicted anomaly recognition result into a predicted improvement enhancement direction according to a preset improvement strategy includes:

[0157] According to a preset improvement strategy, the standard product structure diagram is compared with the predicted abnormality recognition result to obtain the difference steps and difference values;

[0158] The difference value is inverted to obtain the value to be improved, the difference step is converted into the step to be improved, and the predicted improvement and enhancement direction is obtained according to the value to be improved and the step to be improved.

[0159] The improvement strategy refers to a percentage improvement strategy that uses different improvement methods based on different steps. For example, the stamping step can change the hardness of the plastic material and the injection molding level, while the electroplating step can change the thickness and uniformity of the electroplated layer, which may affect the measured moisture resistance of the electroplated layer. Since each step has different operations, the improvement technology and units are also different. Therefore, a percentage improvement method is used to improve each step separately.

[0160] Specifically, in an embodiment of the present invention, if the predicted anomaly identification result indicates that the diameter of the data line package value in the stamping step needs to be increased by 0.2mm, but the anomaly identification result only shows a missing 0.1mm, then if the machine numerical control parameters for the stamping step indicate a 1mm plastic attachment, the present invention transforms the discrepancy step into a step to be improved. In actual operation, not only must the 0.1mm shown in the anomaly identification result be added, but an additional 0.1mm thickness must also be added to achieve optimal hardness performance.

[0161] Therefore, the final predicted improvement and enhancement direction is to first improve the equipment execution problem so that the machine will not lose the 0.1mm thickness, and then in the machine CNC parameters of the stamping step of the production technology assembly line, change the attached 1mm plastic to attached 1.1mm plastic, which can completely improve the hardness performance problem of the faulty product.

[0162] S10. Save the production step name and the predicted improvement and enhancement direction in the list to be improved.

[0163] In an embodiment of the present invention, the production step names and predicted improvement and enhancement directions, as well as the abnormality recognition results generated in step S2 can all be saved in the list to be improved, waiting for professional technicians to debug.

[0164] The present invention solves the problems described in the background technology. First, the present invention uses edge detection equipment to monitor the steps of the connector production technology pipeline through digital monitoring and image monitoring. When the connector production starts, a monitoring result sequence for each connector will be obtained. First, the present invention compares the image recognition results of the numerical control parameters with the actual production results to complete the abnormality identification and obtain the abnormality identification results. These abnormality identification results can effectively solve the problems such as uneven product packaging injection molding, incorrect or asymmetric part installation positions, etc. Then, a pre-trained abnormality-fault bidirectional detection model is used to predict the fault type of the abnormality identification results to obtain a predicted fault type. When the predicted fault type is the same as the actual detected test fault type, it indicates that only the step where the abnormality occurs needs to be improved; and when the predicted fault type is different from the test fault type, it indicates that there are deficiencies in the technical process that need to be repaired. The abnormality-fault bidirectional detection model has two output layers, which can find possible deficiencies in the technical process based on the test fault type, and can also predict the fault type of the product based on the deficiencies in the technical process. When training the model, the present invention also adds a generative adversarial network to further increase the mapping relationship between process abnormalities and fault types. Therefore, the present invention can solve the problem that the production process of connectors such as USB or type-C is difficult to achieve perfection, and when materials or equipment change, modifying the production process requires a lot of manpower and time for exploration and debugging.

[0165] like Figure 2 , which is a functional module diagram of an adaptive optimization system for connector production technology provided by an embodiment of the present invention.

[0166] The adaptive optimization system 100 for connector production technology described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the adaptive optimization system 100 can include a process monitoring module 101, a product testing module 102, a fault prediction module 103, and an improvement area identification module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These modules are stored in the electronic device's memory.

[0167] The process monitoring module 101 is used to obtain a production technology pipeline for a target connector model, monitor the production technology pipeline using a pre-built edge detection device, and obtain a set of connector products and a sequence of monitoring results corresponding to each connector product in the set within a preset time after the production technology pipeline is started;

[0168] The product testing module 102 is configured to perform product performance testing on the connector product set according to a preset test strategy to obtain product test data, extract faulty products from the connector product set based on the product test data, obtain the test fault type of the faulty product, perform process anomaly identification on the monitoring result sequence of the target product in the connector product set to obtain an anomaly identification result, and mark the anomaly identification result on the target product;

[0169] The fault prediction module 103 is configured to use a pre-trained anomaly-fault bidirectional detection model to predict the fault type of the anomaly identification result to obtain a predicted fault type, and to determine whether the test fault type corresponds to the predicted fault type. When the test fault type corresponds to the predicted fault type, the module outputs the name of the production step corresponding to the anomaly identification result to a pre-built list of items to be improved.

[0170] The module 104 for identifying points to be improved is used to perform monitoring data tracing operations on the faulty product when the test fault type is different from the predicted fault type, to obtain a sequence of monitoring results of the faulty product, and to use the anomaly-fault bidirectional detection model to perform anomaly identification on the sequence of monitoring results of the faulty product to obtain a predicted anomaly identification result, and to identify the production step name corresponding to the predicted anomaly identification result, and according to a preset improvement strategy, to convert the predicted anomaly identification result into a predicted improvement enhancement direction, and to save the production step name and the predicted improvement enhancement direction in the list to be improved.

[0171] In detail, the modules in the adaptive optimization system 100 for connector production technology according to the embodiment of the present invention are used in the same manner as above. Figure 1The adaptive optimization method of connector production technology described in the previous section is the same technical means and can produce the same technical effects, so I will not go into details here.

[0172] like Figure 3 , which is a structural diagram of an electronic device for implementing a method for adaptively optimizing connector production techniques provided by an embodiment of the present invention.

[0173] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a program for a self-adaptive optimization method for connector production technology.

[0174] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Furthermore, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the adaptive optimization method program of the connector production technology, but can also be used to temporarily store data that has been output or is to be output.

[0175] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits. It executes or runs programs or modules stored in the memory 11 (e.g., a program for an adaptive optimization method for connector production technology) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0176] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0177] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0178] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for powering the various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby implementing functions such as charging management, discharging management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0179] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0180] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0181] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0182] The adaptive optimization method program for connector production technology stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve the following:

[0183] Obtain a production technology assembly line for target connector models, monitor the production technology assembly line using pre-built edge detection equipment, and obtain a set of connector products and a sequence of monitoring results corresponding to each connector product in the set within a preset time period after the production technology assembly line is started.

[0184] Performing product performance testing on the connector product set according to a preset test strategy to obtain product test data, extracting faulty products from the connector product set based on the product test data, and obtaining a test fault type of the faulty product;

[0185] Performing process anomaly identification on a monitoring result sequence of a target product in the connector product set to obtain an anomaly identification result, and marking the anomaly identification result on the target product;

[0186] Using a pre-trained anomaly-fault bidirectional detection model, predicting the fault type of the anomaly recognition result to obtain a predicted fault type;

[0187] Determining whether the test fault type corresponds to the predicted fault type;

[0188] When the test fault type corresponds to the predicted fault type, outputting the production step name corresponding to the abnormality identification result to a pre-built list of items to be improved;

[0189] When the test fault type is different from the predicted fault type, performing a monitoring data tracing operation on the faulty product to obtain a faulty product monitoring result sequence;

[0190] Using the anomaly-fault bidirectional detection model, anomaly identification is performed on the faulty product monitoring result sequence to obtain a predicted anomaly identification result;

[0191] Identify the production step name corresponding to the predicted anomaly recognition result, and transform the predicted anomaly recognition result into a prediction improvement enhancement direction according to a preset improvement strategy;

[0192] The production step name and the predicted improvement and enhancement direction are saved in the list to be improved.

[0193] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0194] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0195] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0196] Obtain a production technology assembly line for target connector models, monitor the production technology assembly line using pre-built edge detection equipment, and obtain a set of connector products and a sequence of monitoring results corresponding to each connector product in the set within a preset time period after the production technology assembly line is started.

[0197] Performing product performance testing on the connector product set according to a preset test strategy to obtain product test data, extracting faulty products from the connector product set based on the product test data, and obtaining a test fault type of the faulty product;

[0198] Performing process anomaly identification on a monitoring result sequence of a target product in the connector product set to obtain an anomaly identification result, and marking the anomaly identification result on the target product;

[0199] Using a pre-trained anomaly-fault bidirectional detection model, predicting the fault type of the anomaly recognition result to obtain a predicted fault type;

[0200] Determining whether the test fault type corresponds to the predicted fault type;

[0201] When the test fault type corresponds to the predicted fault type, outputting the production step name corresponding to the abnormality identification result to a pre-built list of items to be improved;

[0202] When the test fault type is different from the predicted fault type, performing a monitoring data tracing operation on the faulty product to obtain a faulty product monitoring result sequence;

[0203] Using the anomaly-fault bidirectional detection model, anomaly identification is performed on the faulty product monitoring result sequence to obtain a predicted anomaly identification result;

[0204] Identify the production step name corresponding to the predicted anomaly recognition result, and transform the predicted anomaly recognition result into a prediction improvement enhancement direction according to a preset improvement strategy;

[0205] The production step name and the predicted improvement and enhancement direction are saved in the list to be improved.

[0206] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only exemplary, and actual implementations may have other division methods.

[0207] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0208] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0209] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0210] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Second-order terms are used to indicate names and do not imply any particular order.

[0211] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An adaptive optimization method for connector production technology, characterized in that: The method comprises: Obtain a production technology assembly line for target connector models, monitor the production technology assembly line using pre-built edge detection equipment, and obtain a set of connector products and a sequence of monitoring results corresponding to each connector product in the set within a preset time period after the production technology assembly line is started. Performing product performance testing on the connector product set according to a preset test strategy to obtain product test data, extracting faulty products from the connector product set based on the product test data, and obtaining a test fault type of the faulty product; Performing process anomaly identification on a monitoring result sequence of a target product in the connector product set to obtain an anomaly identification result, and marking the anomaly identification result on the target product; Using a pre-trained anomaly-fault bidirectional detection model, predicting the fault type of the anomaly recognition result to obtain a predicted fault type; Determining whether the test fault type corresponds to the predicted fault type; When the test fault type corresponds to the predicted fault type, outputting the production step name corresponding to the abnormality identification result to a pre-built list of items to be improved; When the test fault type is different from the predicted fault type, performing a monitoring data tracing operation on the faulty product to obtain a faulty product monitoring result sequence; Using the anomaly-fault bidirectional detection model, anomaly identification is performed on the faulty product monitoring result sequence to obtain a predicted anomaly identification result; Identify the production step name corresponding to the predicted anomaly recognition result, and transform the predicted anomaly recognition result into a prediction improvement enhancement direction according to a preset improvement strategy; The production step name and the predicted improvement and enhancement direction are saved in the list to be improved.

2. The method for adaptive optimization of connector production technology according to claim 1, characterized in that: The monitoring result sequence includes the production step name, machine numerical control parameters, and product scan images corresponding to each production step in the production technology assembly line, wherein the product scan images include photographic images and infrared scanning structure images.

3. The method for adaptive optimization of connector production technology according to claim 2, characterized in that: The step of performing product performance testing on the connector product set according to a preset test strategy to obtain product test data includes: According to a preset test strategy, using a pre-built simulation service, product simulation is performed on the monitoring result sequence to obtain a simulation model, and a test process based on a repetitive test type item is performed on the simulation model to obtain a simulation test result; Performing a test process based on a one-time detection type item on the connector product set to obtain a test result; The simulation test results and the experimental test results are uniformly output to obtain product test data.

4. The method for adaptive optimization of connector production technology according to claim 3, characterized in that: The performing process anomaly identification on the monitoring result sequence of the target product in the connector product set to obtain an anomaly identification result includes: Extracting a target product from the connector product set, and extracting machine numerical control parameters and product scan images from a monitoring result sequence of the target product; Using the product scan image and the machine numerical control parameters, performing an abnormal operation comparison operation based on the operation purpose and operation result of the machine on the monitoring result sequence to obtain an abnormal operation identification result; Obtaining a standard product structure diagram of the target product, using the product scan image, and performing structural distribution anomaly identification on the target product according to the standard product structure diagram to obtain a structural anomaly identification result; The operation abnormality recognition result and the structural abnormality recognition result are output to obtain an abnormality recognition result.

5. The method for adaptive optimization of connector production technology according to claim 4, characterized in that: The method of using the pre-trained anomaly-fault bidirectional detection model to predict the fault type of the anomaly recognition result to obtain the predicted fault type includes: Using a pre-trained anomaly-fault bidirectional detection model, a feature extraction operation is performed on the anomaly recognition result to obtain an anomaly feature vector; Performing feature recognition on the abnormal feature vector to obtain feature recognition results, and grouping the feature recognition results into preset abnormality types to obtain feature clusters corresponding to each abnormality type; Fully connected classification judgment is performed on each of the feature clusters to obtain a fault probability distribution value corresponding to a preset fault type set, and a predicted fault type is extracted from the fault type set based on the fault probability distribution value.

6. The method for adaptive optimization of connector production technology according to claim 5, characterized in that: Before predicting the fault type of the abnormality identification result using the pre-trained abnormality-fault bidirectional detection model to obtain the predicted fault type, the method further includes: Get the pre-built production exception type set and fault type set: F={f1,f2,…,f m } <h2 style=";text-align:left;direction:ltr">A = {a1,a2,…,a<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr">} Where F represents the set of production anomaly types, m represents the number of types in the set of production anomaly types, and f m represents the mth type of production anomaly type in the production anomaly type set, A represents the fault type set; n represents the number of types in the production anomaly type set, a n represents the nth type of fault in the set of fault types; Obtain sample data including normal production process data and abnormal production process data, wherein each production process data in the sample data is also equipped with a corresponding fault type: D={(x i ,and fi ,and ai )} Where D represents the sample data, x i Represents the i-th production process data in the sample data, y fi Indicates the fault type corresponding to the i-th production process data in the sample data, y ai Indicates the production anomaly type corresponding to the i-th production process data in the sample data; Obtain an anomaly-fault bidirectional detection model comprising two output layers and a generative adversarial network, and configure the production anomaly type set and the fault type set as standard output types of the two output layers, respectively, wherein the two output layers are a process anomaly prediction output layer and a fault judgment output layer, and the generative adversarial network includes a generator and a discriminator; Extracting a target sample from the sample data in sequence, performing a network forward operation on the target sample using the anomaly-fault bidirectional detection model to obtain a predicted sample fault and a predicted sample production anomaly; Using the pre-built model overall loss function, according to the actual fault type and actual production anomaly type corresponding to the target sample in the sample data, the loss value of the predicted sample fault and the predicted sample production anomaly is calculated to obtain the overall loss value. The model overall loss function is expressed as: L 整体 =L total +αL G +βL D Where, L 整体 Expressed as the overall loss function of the model, L total It represents the output layer loss function of the process anomaly prediction output layer and the fault judgment output layer, L G represents the loss function of the generator G, L D represents the loss function of the discriminator D, α and β represent the weight parameters used to balance the loss function of the output layer and the loss function of the generative adversarial network; Among them, L total Expressed as: THE total =λL cls +(1-λ)L reg Where, L total represents the output layer loss function, L cls represents the classification loss function of the fault judgment output layer, L reg represents the regression loss function of the process anomaly prediction output layer, λ is the weight parameter; y i represents the predicted sample fault corresponding to the i-th target sample, represents the actual fault type corresponding to the i-th target sample, and N represents the number of types in the fault type set; a i Indicates that the predicted sample corresponding to the i-th target sample produces an abnormality, represents the actual production anomaly type corresponding to the i-th target sample, and M represents the number of samples of the sample data; Among them, L G Expressed as: Where, represents the generated data calculated by the generator for the predicted sample failure and the predicted sample production anomaly, P g represents the standard normal distribution of the generator, represents the discriminant result of the discriminator on the generated data, x represents the real fault type and the real production abnormality type, P r Representing the standard normal distribution of the actual fault type and the actual production abnormality type; Minimize the overall loss value according to the gradient descent algorithm to obtain the network model parameters when the overall loss value is minimized, and perform network inverse parameter update on the anomaly-fault bidirectional detection model according to the network model parameters to obtain an updated anomaly-fault bidirectional detection model; Determining whether the overall loss value converges; When the overall loss value has not converged, returning to the above step of sequentially extracting a target sample from the sample data, and iteratively optimizing the updated anomaly-fault bidirectional detection model; When the overall loss value converges, the training process is stopped to obtain a trained anomaly-fault bidirectional detection model.

7. The method for adaptively optimizing connector production technology according to claim 6, wherein: After obtaining the product test data, the method further includes: Saving the product test data into a pre-built model feedback database; According to a preset time interval frequency, the anomaly-fault bidirectional detection model is trained using the model feedback database to obtain an optimized anomaly-fault bidirectional detection model.

8. The method for adaptive optimization of connector production technology according to claim 7, characterized in that: After marking the abnormality identification result on the target product, the method further includes: Supervising the abnormality recognition result within a preset supervision time period to obtain an abnormality rate; Determining whether the abnormality rate is greater than a preset warning threshold; When the abnormality rate is greater than the warning threshold, an alarm prompt message is generated.

9. The method for adaptive optimization of connector production technology according to claim 8, characterized in that: The method of converting the predicted anomaly identification result into a prediction improvement enhancement direction according to a preset improvement strategy includes: According to a preset improvement strategy, the standard product structure diagram is compared with the predicted abnormality recognition result to obtain the difference steps and difference values; The difference value is inverted to obtain the value to be improved, the difference step is converted into the step to be improved, and the predicted improvement and enhancement direction is obtained according to the value to be improved and the step to be improved.

10. An adaptive optimization system for connector production technology, characterized in that: The system comprises: A process monitoring module is used to obtain the production technology pipeline of the target model connector, monitor the production technology pipeline using pre-built edge detection equipment, and obtain a set of connector products and a sequence of monitoring results corresponding to each connector product in the set within a preset time after the production technology pipeline is started; A product testing module is configured to perform product performance testing on the connector product set according to a preset test strategy to obtain product test data, extract faulty products from the connector product set based on the product test data, obtain the test fault type of the faulty product, perform process anomaly identification on the monitoring result sequence of the target product in the connector product set to obtain an anomaly identification result, and mark the anomaly identification result on the target product; a fault prediction module, configured to use a pre-trained anomaly-fault bidirectional detection model to predict the fault type of the anomaly identification result to obtain a predicted fault type, determine whether the test fault type corresponds to the predicted fault type, and, when the test fault type corresponds to the predicted fault type, output the name of the production step corresponding to the anomaly identification result to a pre-built list of items to be improved; The module for identifying points to be improved is used to perform monitoring data tracing operations on the faulty product when the test fault type is different from the predicted fault type, to obtain a sequence of monitoring results of the faulty product, and to use the anomaly-fault bidirectional detection model to perform anomaly identification on the sequence of monitoring results of the faulty product to obtain a predicted anomaly identification result, and to identify the production step name corresponding to the predicted anomaly identification result, and according to a preset improvement strategy, to convert the predicted anomaly identification result into a predicted improvement enhancement direction, and to save the production step name and the predicted improvement enhancement direction to the list to be improved.

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