CN113076350B - Welding abnormality detection method, welding abnormality detection device, computer device, and storage medium - Google Patents
Welding abnormality detection method, welding abnormality detection device, computer device, and storage medium Download PDFInfo
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Abstract
The application relates to a welding abnormality detection method, a welding abnormality detection device, computer equipment and a storage medium. The application receives a welding abnormality detection request; feeding back various preset welding abnormality detection models; obtaining corresponding model combination information and weight setting information; inputting welding data into a preset welding abnormality detection model, and obtaining detection results of each model corresponding to the welding data; and receiving a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the detection results of the models and the weight setting information. According to the application, after the welding abnormality detection request is received, the operation of a proper combination of preset welding abnormality detection models is selected according to various feedback preset welding abnormality detection models by responding to the field detection personnel, and the weight proportion of the models is set by responding to the field detection personnel, so that the welding abnormality detection recognition rate for various welding data is improved under the condition of different requirements on the basis of complex and changeable working conditions.
Description
Technical Field
The present application relates to the field of welding technologies, and in particular, to a method and apparatus for detecting welding anomalies, a computer device, and a storage medium.
Background
Welding is one of the most important processes in the field of mechanical manufacturing, and according to the welding property, mode, application occasion and other aspects, welding can be classified into manual welding, semi-automatic welding, automatic welding and other forms. Due to abnormal welding in the welding process, the phenomena of welding leakage, welding penetration, uneven welding seam forming and the like of the welded product are often caused. Therefore, it is necessary to perform welding abnormality detection after the completion of welding, thereby ensuring the quality of the welded product.
At present, welding abnormality detection can be generally performed through an abnormality detection model, however, when welding detection is performed, the requirements of customers on welding products and the welding working conditions often change along with the requirements of orders, and at this time, the single abnormality detection model cannot generally meet all conditions of welding abnormality, so that the recognition rate of welding abnormality detection is affected.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a welding abnormality detection method, apparatus, computer device, and storage medium that can improve the welding abnormality detection recognition rate in the case of different demands.
A method of detecting welding anomalies, the method comprising:
Receiving a welding abnormality detection request, and determining welding data corresponding to the welding abnormality detection request;
feeding back various preset welding abnormality detection models according to the welding abnormality detection request;
Obtaining model combination information and weight setting information corresponding to various preset welding abnormality detection models, wherein the model combination information is corresponding selection information of the preset welding abnormality detection models used in a welding abnormality detection process;
searching a preset welding abnormality detection model appointed by the model combination information from the various preset welding abnormality detection models, inputting the welding data into the preset welding abnormality detection model corresponding to the model combination information, and obtaining each model detection result corresponding to the welding data;
and acquiring a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the model detection results and the weight setting information.
In one embodiment, the obtaining the model combination information and the weight setting information corresponding to the various preset welding anomaly detection models includes:
obtaining model combination information corresponding to the various preset welding anomaly detection models;
Searching historical weight setting information ranking of the model combination corresponding to the model combination information in the historical data;
Ranking a feedback weight setting recommendation table according to the historical weight setting information;
And acquiring weight setting information fed back according to the weight setting recommendation table.
In one embodiment, the obtaining the model combination information and the weight setting information corresponding to the various preset welding anomaly detection models includes:
Obtaining model combination information corresponding to the various preset welding abnormality detection models, and feeding back corresponding test welding data with result labels according to the model combination information;
acquiring test weight setting data corresponding to the test welding data;
acquiring the test welding data and a multi-model welding abnormality test result corresponding to the test weight setting data through a preset welding abnormality detection model corresponding to the model combination information;
feeding back the abnormal test result of the multi-model welding;
and acquiring weight setting information fed back according to the multi-model welding abnormal test result.
In one embodiment, the feeding back various preset welding anomaly detection models according to the welding anomaly detection request includes:
Acquiring historical response time and historical recognition rate corresponding to various preset welding anomaly detection models;
Information labeling is carried out on the various preset welding anomaly detection models according to the historical response time and the historical recognition rate;
And feeding back various preset welding abnormality detection models marked by information according to the welding abnormality detection request.
In one embodiment, the obtaining the multi-model welding anomaly detection result corresponding to the welding anomaly detection request according to the model detection results and the weight setting information includes:
Obtaining an anomaly detection weighted fusion result according to the detection results of the models and the weight setting information;
And when the abnormality detection weighted fusion result is smaller than a preset abnormality detection threshold, judging that the multi-model welding abnormality detection result corresponding to the welding abnormality detection request is abnormal.
In one embodiment, after obtaining the multi-model welding anomaly detection result corresponding to the welding anomaly detection request according to the model detection results and the weight setting information, the method further includes:
When the abnormal detection result of the multi-model welding is abnormal welding, generating a welding detection report of the abnormal detection result of the multi-model welding;
And feeding back the welding detection report.
A welding anomaly detection device, the device comprising:
The request acquisition module is used for receiving a welding abnormality detection request and determining welding data corresponding to the welding abnormality detection request;
The model feedback module is used for feeding back various preset welding abnormality detection models according to the welding abnormality detection request;
The information setting module is used for acquiring model combination information and weight setting information corresponding to the various preset welding abnormality detection models, wherein the model combination information is corresponding selection information of the preset welding abnormality detection models used in the welding abnormality detection process;
The model detection module is used for searching the preset welding abnormality detection model specified by the model combination information from the various preset welding abnormality detection models, inputting the welding data into the preset welding abnormality detection model corresponding to the model combination information, and obtaining each model detection result corresponding to the welding data;
And the abnormality detection module is used for acquiring a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the detection results of the models and the weight setting information.
In one embodiment, the information setting module is specifically configured to:
obtaining model combination information corresponding to the various preset welding anomaly detection models;
Searching historical weight setting information ranking of the model combination corresponding to the model combination information in the historical data;
Ranking a feedback weight setting recommendation table according to the historical weight setting information;
And acquiring weight setting information fed back according to the weight setting recommendation table.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
Receiving a welding abnormality detection request, and determining welding data corresponding to the welding abnormality detection request;
feeding back various preset welding abnormality detection models according to the welding abnormality detection request;
Obtaining model combination information and weight setting information corresponding to various preset welding abnormality detection models, wherein the model combination information is corresponding selection information of the preset welding abnormality detection models used in a welding abnormality detection process;
searching a preset welding abnormality detection model appointed by the model combination information from the various preset welding abnormality detection models, inputting the welding data into the preset welding abnormality detection model corresponding to the model combination information, and obtaining each model detection result corresponding to the welding data;
and acquiring a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the model detection results and the weight setting information.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Receiving a welding abnormality detection request, and determining welding data corresponding to the welding abnormality detection request;
feeding back various preset welding abnormality detection models according to the welding abnormality detection request;
Obtaining model combination information and weight setting information corresponding to various preset welding abnormality detection models, wherein the model combination information is corresponding selection information of the preset welding abnormality detection models used in a welding abnormality detection process;
searching a preset welding abnormality detection model appointed by the model combination information from the various preset welding abnormality detection models, inputting the welding data into the preset welding abnormality detection model corresponding to the model combination information, and obtaining each model detection result corresponding to the welding data;
and acquiring a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the model detection results and the weight setting information.
The welding abnormality detection method, the welding abnormality detection device, the computer equipment and the storage medium determine welding data corresponding to the welding abnormality detection request by receiving the welding abnormality detection request; feeding back various preset welding abnormality detection models according to the welding abnormality detection request; obtaining model combination information and weight setting information corresponding to various preset welding abnormality detection models; searching a preset welding abnormality detection model designated by model combination information from various preset welding abnormality detection models, inputting welding data into the preset welding abnormality detection model corresponding to the model combination information, and obtaining detection results of each model corresponding to the welding data; and receiving a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the detection results of the models and the weight setting information. According to the application, after the welding abnormality detection request is received, various preset welding abnormality detection models are fed back, and then the abnormality detection of the welding process is carried out according to model combination information and weight setting information fed back by field detection personnel, namely, the welding abnormality detection is carried out through the flexible matching of the detection models and weights provided by the field detection personnel, so that the welding abnormality detection recognition rate of various welding data can be improved under the condition of different requirements of complex and changeable working conditions.
Drawings
FIG. 1 is an application environment diagram of a welding anomaly detection method in one embodiment;
FIG. 2 is a flow chart of a method of detecting welding anomalies in one embodiment;
FIG. 3 is a schematic diagram illustrating performance of various types of welding anomaly detection models in one embodiment;
FIG. 4 is a performance schematic of various types of welding anomaly detection models in one embodiment;
FIG. 5 is a schematic flow chart illustrating a sub-process of step 205 in FIG. 2 according to one embodiment;
FIG. 6 is a schematic flow chart illustrating a sub-process of step 205 in FIG. 2 according to another embodiment;
FIG. 7 is a schematic flow chart illustrating a sub-process of step 203 in FIG. 2 according to one embodiment;
FIG. 8 is a block diagram of a welding anomaly detection device in one embodiment;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The welding abnormality detection method provided by the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. When a welding detection worker on the terminal 102 side needs to detect a welding result and determine whether the welding is abnormal. The corresponding welding data of the welding anomaly detection request may be submitted simultaneously by sending the corresponding welding anomaly detection request to the server 104. These welding data are detected by the server 104 to determine whether there is a welding anomaly in the welding process. The server 104 receives the welding abnormality detection request and determines welding data corresponding to the welding abnormality detection request; feeding back various preset welding abnormality detection models according to the welding abnormality detection request; obtaining model combination information and weight setting information corresponding to various preset welding abnormality detection models; searching a preset welding abnormality detection model designated by model combination information from various preset welding abnormality detection models, inputting welding data into the preset welding abnormality detection model corresponding to the model combination information, and obtaining detection results of each model corresponding to the welding data; and receiving a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the detection results of the models and the weight setting information. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the server 104 may be implemented by a stand-alone server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, a welding anomaly detection method is provided, which is described by taking the application of the method to the server 104 in fig. 1 as an example, and includes the following steps:
Step 201, a welding abnormality detection request is received, and welding data corresponding to the welding abnormality detection request is determined.
The welding anomaly detection request is used for requesting the server 104 to perform anomaly detection on specified welding data to determine whether an anomaly exists in a welding process corresponding to the welding data. The welding data specifically refers to various data generated in the welding process, including data such as welding variables, welding curves, welding information and the like in the welding process. For ultrasonic welding, the welding data may include welding current, instrument Ready signal, welding voltage, welding ultrasonic output control signal, cylinder lifting and dropping control signal, welding air pressure, probe position detected by the sensor, voltage phase difference, current phase difference, phase discrimination result, welding frequency, active power, reactive power and the like of the welding process.
Specifically, when the welding inspection staff on the terminal 102 side needs to verify the welding process after the welding is completed, and when it is ensured that the welding result is not abnormal, a welding abnormality detection request may be sent to the server 104 to request the server 104 to perform corresponding welding abnormality detection. In one embodiment, the welding anomaly detection request may be automatically sent to the server 104 after the welding is completed, without manual sending by a worker, and by automatically sending the welding anomaly detection request, the detection efficiency may be effectively improved. The server 104 further determines the welding data corresponding to the welding abnormality detection request after receiving the welding abnormality detection request. In one embodiment, the welding anomaly detection request may contain corresponding welding data. In another embodiment, the welding anomaly detection request may include information such as a data tag or a storage address of the corresponding welding data, and the server 104 may determine the welding data corresponding to the welding anomaly detection request according to the data tag or the storage address after the welding anomaly detection request.
And 203, feeding back various preset welding abnormality detection models according to the welding abnormality detection request.
The various preset welding abnormality detection models are specifically models for detecting welding abnormality by a pointer, can be divided into two types of mechanism models and data models in principle, and can be divided into four types according to modeling complexity. Various welding anomaly detection models have advantages and disadvantages in terms of response speed, accuracy, interpretability and maintenance difficulty, and particularly reference can be made to fig. 3. In an actual field, after a welding expert defines a plurality of data models or mechanism models with high recognition rate for deployment, the requirements of clients often change along with the demands of orders, and at this time, a single type of abnormality detection model cannot generally meet all conditions, so that the application considers a multi-model fusion system to detect welding abnormalities. In the application, the types specifically included in various preset welding abnormality detection models include a welding curve similarity judgment model, a welding variable expert system judgment model, a welding curve deep learning judgment model, a welding energy abnormality judgment model and the like. Meanwhile, in a model library of the server, each model has different parameter configurations, and the module maintains default parameters of all models, such as a threshold value of a welding energy abnormality judgment model, a network structure in a welding curve deep learning judgment model, the number of layers, the iteration number and the like.
Specifically, a plurality of preset welding anomaly detection models are pre-stored in the server, each model is stored locally in a file (for example, a pkl format file), and after a welding anomaly detection request is received, the server feeds back various preset welding anomaly detection models to a welding detection worker according to the request of the terminal 102 so as to perform multi-model fusion welding anomaly detection setting.
Step 205, obtaining model combination information and weight setting information corresponding to various preset welding abnormality detection models, wherein the model combination information is corresponding selection information of the preset welding abnormality detection models used in the welding abnormality detection process.
The model combination information refers to information fed back to the server 104 by determining which models the welding data corresponding to the welding abnormality detection request needs to pass through to perform abnormality detection after the welding detection staff selects among various feedback preset welding abnormality detection models. The weight setting information refers to data corresponding to weight proportion of each model in the model combination information in the final welding abnormality detection process, and different models have different contributions to the final detection result, so that the recognition rate of abnormality detection can be improved through weight setting.
Specifically, the server 104 may specifically feed back various preset welding anomaly detection models in a list form, and the welding inspection staff may select the models in the welding anomaly detection process by checking the models in the list. And simultaneously filling the weight duty ratio corresponding to the model to feed back corresponding weight setting information. According to the model information and the client requirements, welding detection staff can perform corresponding model selection and weight setting to ensure the recognition rate of welding abnormality detection.
Step 207, searching for a preset welding abnormality detection model designated by the model combination information from various preset welding abnormality detection models, inputting the welding data into the preset welding abnormality detection model corresponding to the model combination information, and obtaining each model detection result corresponding to the welding data.
Step 209, receiving a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the detection result of each model and the weight setting information.
The model detection results refer to the obtained data after the calculation of the welding data by each preset welding abnormality detection model corresponding to the model combination information, and the obtained data comprise a plurality of same or different abnormality detection results. The multi-model welding abnormality detection result corresponding to the welding abnormality detection request refers to a final detection result obtained by performing multi-model fusion calculation according to the detection results of the models and the weight setting information.
Specifically, when the weights of the models are configured, the welding data can be input into the models to be calculated, in the process, the models can perform anomaly detection on the welding data, a final model detection result is output to the server 104, and the server 104 performs weighted fusion on the model monitoring results of the models and outputs the model monitoring results, so that the final multi-model welding anomaly detection result is obtained. In one embodiment, each type of preset welding anomaly detection model only outputs two results, namely "OK (welding no anomaly)" and "NG (welding anomaly)", so that the weighted output logic only needs to determine which result weight is above 0.5 to be considered as the final output. As shown in fig. 4, the result of anomaly recognition of the welding data input by the models 1 and 3 is OK, the result of recognition of the model 2 is NG, the weight is combined again, the OK duty ratio is 0.7, and the NG duty ratio is 0.3, so the final output after weighted fusion is OK, that is, the result of detection of the multi-model welding anomaly corresponding to the welding data is that no anomaly exists.
According to the welding abnormality detection method, the welding data corresponding to the welding abnormality detection request is determined by receiving the welding abnormality detection request; feeding back various preset welding abnormality detection models according to the welding abnormality detection request; obtaining model combination information and weight setting information corresponding to various preset welding abnormality detection models; searching a preset welding abnormality detection model designated by model combination information from various preset welding abnormality detection models, inputting welding data into the preset welding abnormality detection model corresponding to the model combination information, and obtaining detection results of each model corresponding to the welding data; and receiving a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the detection results of the models and the weight setting information. According to the application, after the welding abnormality detection request is received, various preset welding abnormality detection models are fed back, and then abnormality detection of the welding process is carried out according to model combination information and weight setting information fed back by field detection personnel, namely, the flexible matching of the detection models and weights provided by the field detection personnel is adopted, so that the welding abnormality detection recognition rate for various welding data can be improved under the condition of different requirements of complex and changeable working conditions.
In one embodiment, as shown in FIG. 5, step 205 includes:
Step 502, obtaining model combination information corresponding to various preset welding abnormality detection models.
Step 504, searching the historical data for the historical weight setting information ranking of the model combination corresponding to the model combination information.
Step 506, ranking the feedback weight setting recommendation table according to the historical weight setting information.
Step 508, obtaining weight setting information fed back according to the weight setting recommendation table.
The historical weight setting information refers to weight size configuration information corresponding to each preset welding abnormality detection model in the historical welding abnormality detection for the current model combination information. The ranking of the historical weight setting information is information obtained by ranking weight settings in the historical data according to the set times.
Specifically, the welding inspection staff of the terminal 102 may first select which models need to be used and submit corresponding model combination information to the server. The server 104 then extracts and summarizes weight setting schemes corresponding to combinations of these models in the historical data based on the historical data and the models selected by the current welding inspection worker, and determines a ranking of the historical weight setting information according to the frequency of use of the welding inspection worker. Ranking according to the historical weight setting information, feeding back a weight setting recommendation table to the welding detection staff, and selecting corresponding weight setting by the welding detection staff according to the recommendation table; the server 104 acquires weight setting information fed back by the welding inspection staff according to the weight setting recommendation table. In this embodiment, the server 104 may recommend that the welding detection staff select the most multimode weight setting coefficients according to the model selected by the welding detection staff, so as to save the trial-and-error time of the welding detection staff and improve the working efficiency.
In one embodiment, as shown in FIG. 6, step 205 includes:
And 601, obtaining model combination information corresponding to various preset welding abnormality detection models, and feeding back corresponding test welding data with result labels according to the model combination information.
Step 603, obtaining test weight setting data corresponding to the test welding data.
Step 605, obtaining test welding data and a multi-model welding abnormality test result corresponding to the test weight setting data through a preset welding abnormality detection model corresponding to the model combination information.
In step 607, the multi-model welding anomaly test result is fed back.
And step 609, obtaining weight setting information fed back according to the multi-model welding anomaly test result.
The test welding data with the result label are used for providing model debugging for a model corresponding to the model combination information of the current welding detection staff, so that optimal weight setting is determined. The test weight setting data and the multi-model welding abnormality test result are respectively intermediate data in the model debugging process and correspond to the weight setting information and the multi-model welding abnormality detection result.
Specifically, after the welding detection staff feeds back the model combination information through the terminal 102, it may also determine whether to debug the selected model combination, and when it is determined that the model is debugged, the server 104 feeds back corresponding test welding data with a result label according to the model combination information, and the welding detection staff may select the test welding data to use, and then set weights for the test welding data, and feed back corresponding test weight setting data to the server 104. With reference to the above test procedure, the server 104 may obtain test welding data and a multi-model welding anomaly test result corresponding to the test weight setting data through a preset welding anomaly detection model corresponding to the model combination information. The steps 603 to 607 may be iterated for several times, then the welding detection staff may select different weights to test the welding data for model debugging, then the selected weights are determined according to the test welding data and the multi-model abnormal welding test result in the iterative calculation process, and finally the server 104 obtains the weight setting information fed back by the welding detection staff according to the multi-model abnormal welding test result. In this embodiment, the model result may be manually verified and the weight manually changed through the model debugging process, and all records of the model debugging may be saved and used as sample data for subsequent optimization.
In one embodiment, as shown in fig. 7, step 203 includes:
step 702, obtaining historical response time and historical recognition rate corresponding to various preset welding anomaly detection models.
And step 704, labeling information of various preset welding abnormality detection models according to the historical response time and the historical recognition rate.
Step 706, feeding back various preset welding abnormality detection models marked by information according to the welding abnormality detection request.
The historical response time and the historical recognition rate are obtained based on historical data in the model operation process. Specifically, when model information is fed back to the welding inspection staff on the terminal 102 side, some algorithm models can be selected from a built-in model library of the server, and after the algorithm models are selected, the interface displays the historical response time and the historical recognition rate of each model in the past welding anomaly detection. And then, a welding detection staff on the terminal 102 side checks a required model based on the auxiliary information such as the historical response time, the historical recognition rate and the like according to the model information and the client requirements, and then, a step of setting subsequent weights is carried out. In this embodiment, by labeling information on various preset welding anomaly detection models, the welding detection staff on the terminal 102 side can be helped to understand the running condition of the model, so that the processing efficiency of the model selection process and the accuracy of model selection are improved.
In one embodiment, step 209 includes: obtaining an abnormal detection weighted fusion result according to the detection result of each model and the weight setting information; and when the abnormality detection weighted fusion result is smaller than the preset abnormality detection threshold, judging that the multimode welding abnormality detection result corresponding to the welding abnormality detection request is welding abnormality.
Specifically, when the weighted fusion is performed, an anomaly detection weighted fusion result can be obtained according to each model detection result and weight setting information. In one embodiment, each type of preset welding anomaly detection model only outputs two results, namely "OK (welding no anomaly)" and "NG (welding anomaly)", so that the weighted output logic only needs to determine which result weight is above 0.5 to be considered as the final output. In another embodiment, each type of preset welding anomaly detection model outputs an anomaly score corresponding to the welding data, and the calculation process of the anomaly detection weighted fusion result of the final welding data includes: and carrying out product operation on the abnormal scores of the welding data and the corresponding weights of the models to obtain the weighted scores corresponding to the models, and adding all the weighted scores to obtain the final fusion abnormal score. And judging whether the multi-model welding abnormality detection result corresponding to the welding abnormality detection request is abnormal welding or normal welding by fusing whether the abnormality score is larger than or equal to a preset abnormality detection threshold value. In this embodiment, the model detection result and the weight setting information are weighted and calculated, and then the weighted calculation result is compared with the preset abnormal detection threshold value to obtain the final welding detection result, so that the final welding detection result can be effectively calculated, and the accuracy of welding result calculation is ensured.
In one embodiment, after step 209, further comprises: when the abnormal detection result of the multi-model welding is abnormal welding, generating a welding detection report of the abnormal detection result of the multi-model welding; and feeding back a welding detection report.
Specifically, when the multi-model welding abnormality detection result corresponding to the welding abnormality detection request is determined to be abnormal welding, corresponding abnormal alarm is triggered, an alarm signal is transmitted to a response interface of the server, and when the multi-model welding abnormality detection result corresponding to the welding abnormality detection request is determined to be normal welding, no operation is performed, and the next group of welding signals is continuously detected. Meanwhile, when an anomaly alarm is triggered, the server 104 automatically generates a welding detection report corresponding to the multi-model welding anomaly detection result, so that fault recording and tracing are facilitated, and the content of the welding detection report includes, but is not limited to, welding time, a battery core code, a tab code, configuration information of each model, judgment results of each model, output information of each model (a data model can output similarity, anomaly probability and the like, a mechanism model can output information of a variable triggering anomaly rule and the like), equipment suppliers, contact ways of field equipment responsible persons and the like. In the embodiment, the abnormal welding can be displayed more intuitively by generating the welding detection report, so that the tracking of the abnormal welding is facilitated, and the processing efficiency of the subsequent processing process is improved.
It should be understood that, although the steps in the flowcharts of fig. 2-7 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in FIGS. 2-7 may include multiple steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the steps or stages in other steps or other steps.
In one embodiment, as shown in fig. 8, there is provided a welding abnormality detection apparatus including:
the request acquisition module 801 is configured to receive a welding anomaly detection request, and determine welding data corresponding to the welding anomaly detection request.
The model feedback module 803 is configured to feed back various preset welding anomaly detection models according to the welding anomaly detection request.
The information setting module 805 is configured to obtain model combination information and weight setting information corresponding to various preset welding anomaly detection models, where the model combination information is corresponding selection information of the preset welding anomaly detection model used in the welding anomaly detection process.
The model detection module 807 is configured to search a preset welding anomaly detection model specified by the model combination information from various preset welding anomaly detection models, input the welding data into the preset welding anomaly detection model corresponding to the model combination information, and obtain each model detection result corresponding to the welding data.
The anomaly detection module 809 is configured to receive a multi-model welding anomaly detection result corresponding to the welding anomaly detection request according to each model detection result and the weight setting information.
In one embodiment, the information setting module 805 is specifically configured to: obtaining model combination information corresponding to various preset welding abnormality detection models; searching historical weight setting information ranking of model combination corresponding to the model combination in the historical data; ranking feedback weights according to historical weight setting information to set a recommendation table; and acquiring weight setting information fed back according to the weight setting recommendation table.
In one embodiment, the information setting module 805 is further configured to: obtaining model combination information corresponding to various preset welding abnormality detection models, and feeding back corresponding test welding data with result labels according to the model combination information; acquiring test weight setting data corresponding to the test welding data; obtaining test welding data and a multi-model welding abnormality test result corresponding to the test weight setting data through a preset welding abnormality detection model corresponding to the model combination information; feeding back a multi-model welding abnormal test result; and acquiring weight setting information fed back according to the multi-model welding abnormal test result.
In one embodiment, the model feedback module 803 is specifically configured to: acquiring historical response time and historical recognition rate corresponding to various preset welding anomaly detection models; information labeling is carried out on various preset welding anomaly detection models according to the historical response time and the historical recognition rate; and feeding back various preset welding abnormality detection models marked by the information according to the welding abnormality detection request.
In one embodiment, the anomaly detection module 809 is specifically configured to: obtaining an abnormal detection weighted fusion result according to the detection result of each model and the weight setting information; and when the abnormality detection weighted fusion result is smaller than the preset abnormality detection threshold, judging that the multimode welding abnormality detection result corresponding to the welding abnormality detection request is welding abnormality.
In one embodiment, the system further comprises a report generating module for: when the abnormal detection result of the multi-model welding is abnormal welding, generating a welding detection report of the abnormal detection result of the multi-model welding; and feeding back a welding detection report.
The specific limitation of the welding abnormality detection device may be referred to as limitation of the welding abnormality detection method hereinabove, and will not be described herein. The above-described respective modules in the welding abnormality detection apparatus may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 9. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store traffic forwarding data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of welding anomaly detection.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
Receiving a welding abnormality detection request, and determining welding data corresponding to the welding abnormality detection request;
Feeding back various preset welding abnormality detection models according to the welding abnormality detection request;
obtaining model combination information and weight setting information corresponding to various preset welding abnormality detection models, wherein the model combination information is corresponding selection information of the preset welding abnormality detection models used in the welding abnormality detection process;
Searching a preset welding abnormality detection model designated by model combination information from various preset welding abnormality detection models, inputting welding data into the preset welding abnormality detection model corresponding to the model combination information, and obtaining detection results of each model corresponding to the welding data;
And receiving a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the detection results of the models and the weight setting information.
In one embodiment, the processor when executing the computer program further performs the steps of: obtaining model combination information corresponding to various preset welding abnormality detection models; searching historical weight setting information ranking of model combination corresponding to the model combination in the historical data; ranking feedback weights according to historical weight setting information to set a recommendation table; and acquiring weight setting information fed back according to the weight setting recommendation table.
In one embodiment, the processor when executing the computer program further performs the steps of: obtaining model combination information corresponding to various preset welding abnormality detection models, and feeding back corresponding test welding data with result labels according to the model combination information; acquiring test weight setting data corresponding to the test welding data; obtaining test welding data and a multi-model welding abnormality test result corresponding to the test weight setting data through a preset welding abnormality detection model corresponding to the model combination information; feeding back a multi-model welding abnormal test result; and acquiring weight setting information fed back according to the multi-model welding abnormal test result.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring historical response time and historical recognition rate corresponding to various preset welding anomaly detection models; information labeling is carried out on various preset welding anomaly detection models according to the historical response time and the historical recognition rate; and feeding back various preset welding abnormality detection models marked by the information according to the welding abnormality detection request.
In one embodiment, the processor when executing the computer program further performs the steps of: obtaining an abnormal detection weighted fusion result according to the detection result of each model and the weight setting information; and when the abnormality detection weighted fusion result is smaller than the preset abnormality detection threshold, judging that the multimode welding abnormality detection result corresponding to the welding abnormality detection request is welding abnormality.
In one embodiment, the processor when executing the computer program further performs the steps of: when the abnormal detection result of the multi-model welding is abnormal welding, generating a welding detection report of the abnormal detection result of the multi-model welding; and feeding back a welding detection report.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
Receiving a welding abnormality detection request, and determining welding data corresponding to the welding abnormality detection request;
Feeding back various preset welding abnormality detection models according to the welding abnormality detection request;
obtaining model combination information and weight setting information corresponding to various preset welding abnormality detection models, wherein the model combination information is corresponding selection information of the preset welding abnormality detection models used in the welding abnormality detection process;
Searching a preset welding abnormality detection model designated by model combination information from various preset welding abnormality detection models, inputting welding data into the preset welding abnormality detection model corresponding to the model combination information, and obtaining detection results of each model corresponding to the welding data;
And receiving a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the detection results of the models and the weight setting information.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining model combination information corresponding to various preset welding abnormality detection models; searching historical weight setting information ranking of model combination corresponding to the model combination in the historical data; ranking feedback weights according to historical weight setting information to set a recommendation table; and acquiring weight setting information fed back according to the weight setting recommendation table.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining model combination information corresponding to various preset welding abnormality detection models, and feeding back corresponding test welding data with result labels according to the model combination information; acquiring test weight setting data corresponding to the test welding data; obtaining test welding data and a multi-model welding abnormality test result corresponding to the test weight setting data through a preset welding abnormality detection model corresponding to the model combination information; feeding back a multi-model welding abnormal test result; and acquiring weight setting information fed back according to the multi-model welding abnormal test result.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring historical response time and historical recognition rate corresponding to various preset welding anomaly detection models; information labeling is carried out on various preset welding anomaly detection models according to the historical response time and the historical recognition rate; and feeding back various preset welding abnormality detection models marked by the information according to the welding abnormality detection request.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining an abnormal detection weighted fusion result according to the detection result of each model and the weight setting information; and when the abnormality detection weighted fusion result is smaller than the preset abnormality detection threshold, judging that the multimode welding abnormality detection result corresponding to the welding abnormality detection request is welding abnormality.
In one embodiment, the computer program when executed by the processor further performs the steps of: when the abnormal detection result of the multi-model welding is abnormal welding, generating a welding detection report of the abnormal detection result of the multi-model welding; and feeding back a welding detection report.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile memory may include Read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, or the like. Volatile memory can include random access memory (RandomAccessMemory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can take many forms, such as static random access memory (StaticRandomAccessMemory, SRAM) or dynamic random access memory (DynamicRandomAccessMemory, DRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method of detecting welding anomalies, the method comprising:
receiving a welding abnormality detection request, and determining welding data corresponding to the welding abnormality detection request, wherein the welding data comprises welding variables, welding curves and welding information in a welding process;
Feeding back various preset welding abnormality detection models according to the welding abnormality detection request, wherein the preset welding abnormality detection models comprise a welding curve similarity judgment model, a welding variable expert system judgment model, a welding curve deep learning judgment model and a welding energy abnormality judgment model;
the method comprises the steps of obtaining model combination information and weight setting information corresponding to various preset welding abnormality detection models, wherein the model combination information is corresponding selection information of the preset welding abnormality detection models used in a welding abnormality detection process, and the model combination information is determined based on the preset welding abnormality detection models required by the welding data;
Searching a preset welding abnormality detection model appointed by the model combination information from the various preset welding abnormality detection models, inputting the welding data into the preset welding abnormality detection model appointed by the model combination information, and obtaining each model detection result corresponding to the welding data;
and acquiring a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the model detection results and the weight setting information.
2. The method of claim 1, wherein the obtaining model combination information and weight setting information corresponding to the various preset welding anomaly detection models comprises:
obtaining model combination information corresponding to the various preset welding anomaly detection models;
Searching historical weight setting information ranking of the model combination corresponding to the model combination information in the historical data;
Ranking a feedback weight setting recommendation table according to the historical weight setting information;
And acquiring weight setting information fed back according to the weight setting recommendation table.
3. The method of claim 1, wherein the obtaining model combination information and weight setting information corresponding to the various preset welding anomaly detection models comprises:
obtaining model combination information corresponding to the various preset welding anomaly detection models;
feeding back corresponding test welding data with result labels according to the model combination information;
acquiring test weight setting data corresponding to the test welding data;
acquiring the test welding data and a multi-model welding abnormality test result corresponding to the test weight setting data through a preset welding abnormality detection model corresponding to the model combination information;
feeding back the abnormal test result of the multi-model welding;
and acquiring weight setting information fed back according to the multi-model welding abnormal test result.
4. The method of claim 1, wherein feeding back each type of preset welding anomaly detection model according to the welding anomaly detection request comprises:
Acquiring historical response time and historical recognition rate corresponding to various preset welding anomaly detection models;
Information labeling is carried out on the various preset welding anomaly detection models according to the historical response time and the historical recognition rate;
And feeding back various preset welding abnormality detection models marked by information according to the welding abnormality detection request.
5. The method of claim 1, wherein the obtaining the multi-model welding anomaly detection result corresponding to the welding anomaly detection request according to the model detection results and the weight setting information comprises:
Obtaining an anomaly detection weighted fusion result according to the detection results of the models and the weight setting information;
And when the abnormality detection weighted fusion result is smaller than a preset abnormality detection threshold, judging that the multi-model welding abnormality detection result corresponding to the welding abnormality detection request is abnormal.
6. The method according to claim 1, wherein after obtaining the multi-model welding anomaly detection result corresponding to the welding anomaly detection request according to the model detection results and the weight setting information, further comprises:
When the abnormal detection result of the multi-model welding is abnormal welding, generating a welding detection report of the abnormal detection result of the multi-model welding;
And feeding back the welding detection report.
7. A welding abnormality detection device, characterized by comprising:
The request acquisition module is used for receiving a welding abnormality detection request and determining welding data corresponding to the welding abnormality detection request, wherein the welding data comprises welding variables, welding curves and welding information in a welding process;
The model feedback module is used for feeding back various preset welding abnormality detection models according to the welding abnormality detection request, wherein the preset welding abnormality detection models comprise a welding curve similarity judgment model, a welding variable expert system judgment model, a welding curve deep learning judgment model and a welding energy abnormality judgment model;
The information setting module is used for acquiring model combination information and weight setting information corresponding to the various preset welding abnormality detection models, wherein the model combination information is corresponding selection information of the preset welding abnormality detection model used in the welding abnormality detection process, and the model combination information is determined based on the preset welding abnormality detection model required by the welding data;
The model detection module is used for searching the preset welding abnormality detection model specified by the model combination information from the various preset welding abnormality detection models, inputting the welding data into the preset welding abnormality detection model corresponding to the model combination information, and obtaining each model detection result corresponding to the welding data;
And the abnormality detection module is used for acquiring a multi-model welding abnormality detection result corresponding to the welding abnormality detection request according to the detection results of the models and the weight setting information.
8. The apparatus of claim 7, wherein the information setting module is specifically configured to:
obtaining model combination information corresponding to the various preset welding anomaly detection models;
Searching historical weight setting information ranking of the model combination corresponding to the model combination information in the historical data;
Ranking a feedback weight setting recommendation table according to the historical weight setting information;
And acquiring weight setting information fed back according to the weight setting recommendation table.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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