A detection cable connector automatic assembly production method and system

Abstract

The present invention relates to the field of automatic cable assembly technology, and in particular to a method and system for automatic assembly production of detection cable joints, the method comprising: obtaining the assembly process production line of the cable joint, and segmenting the assembly process production line to obtain a process flow segment, wherein the process flow segment includes a number of assembly production processes; constructing a number of detection process models corresponding to the number of process flow segments, and each detection process model identifies defects in defective cable joints; setting a quality control detection node for each process flow segment, and the quality control detection node determines defects in the cable joints that have completed the assembly production process in the process flow segment; if there are defects, the cable joint is removed from the assembly process production line, and the corresponding detection process model is introduced to obtain defect detection results. Through the present invention, the problems of low production efficiency, unstable quality and insufficient process control in cable assembly are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic cable assembly, and in particular to an automatic assembly production method and system for detecting cable joints. Background Art

[0002] With the continuous improvement of the level of modern industrial automation, the widespread application of cable assembly automation technology has not only improved production efficiency, but also greatly reduced errors in manual operations and improved product quality. However, despite the significant progress made in cable automatic assembly technology, it still faces many challenges and technical bottlenecks in practical applications. Traditional cable connector assembly relies on manual operation, which not only has low production efficiency, but is also prone to human errors, affecting product quality. In order to solve these problems, many companies have begun to introduce automated equipment to replace manual production and assembly of cable connectors.

[0003] However, the current automated assembly technology still has certain defects, such as insufficient monitoring of the process, inability to detect process problems in a timely manner, resulting in unqualified products entering the next process, increasing the difficulty of subsequent processing. At the same time, the existing automated inspection system often cannot effectively integrate the information collection and analysis of each process, making it difficult to achieve comprehensive control of the process flow.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art known to those skilled in the art. Summary of the invention

[0005] The present invention provides a detection cable connector automatic assembly production method and system, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for automatically assembling and producing a detection cable connector, characterized in that the method comprises:

[0008] Acquire an assembly process production line of a cable connector, and segment the assembly process production line to obtain process flow segments, wherein the process flow segments include a plurality of assembly production processes;

[0009] Constructing a plurality of detection process models corresponding to a plurality of process flow sections, each of the detection process models identifying defects of the defective cable joint;

[0010] A quality control detection node is set for each process flow section, and the quality control detection node performs defect judgment on the cable joints that have completed the assembly production process in the process flow section;

[0011] If there is a defect, the cable connector is removed from the assembly process production line and the corresponding detection process model is imported to obtain a defect detection result.

[0012] Furthermore, a quality control detection node is set for each process flow section, and the quality control detection node performs defect judgment on the cable joints that have completed the assembly production process in the process flow section, including:

[0013] Setting the quality control detection node at the end of each process flow segment;

[0014] Setting a detection area for each quality control detection node, wherein the detection area is set by analyzing the work information in the completed assembly production process to determine the area that needs to be detected;

[0015] Compare the work information obtained in the detection area with the requirements of the assembly production process and judge the problem steps to determine the defect detection area;

[0016] Defects of cable joints in the assembly production process within the process flow section are judged based on the defect detection area.

[0017] Furthermore, judging defects of cable joints in the assembly production process within the process flow section according to the defect detection area includes:

[0018] Based on the defect detection area, the cable joints that have completed the assembly production process within the process flow section are detected to obtain process detection information;

[0019] Generate a historical production sequence through the historical production information of the assembly production process;

[0020] Compare the process detection information with each item of information contained in the historical production sequence one by one, and generate a comparison information sequence according to the comparison result of each item;

[0021] Analyze and compare the information sequence, calculate the deviation coefficient of the process detection information, and judge the cable joint according to the deviation coefficient and the process detection information.

[0022] Furthermore, it is characterized in that an assembly process production line of a cable connector is obtained, and the assembly process production line is segmented to obtain a process flow segment, wherein the process flow segment includes several assembly production processes, including:

[0023] Setting key nodes according to the dependency relationship between the assembly production processes in the assembly process production line;

[0024] The assembly process production line is segmented according to the key nodes, the boundaries between each segment are based on the key nodes, and the processes between adjacent key nodes are classified into the same process to form the process flow segment.

[0025] Furthermore, a plurality of detection process models are constructed corresponding to the plurality of process flow sections, and each of the detection process models performs defect recognition on the defective cable joint, including:

[0026] Determine the assembly production process in the corresponding process flow segment, and extract defect recognition features according to each assembly production process;

[0027] Constructing a process detection component according to the extracted defect recognition feature, wherein the process detection component corresponds to the assembly production process one by one, and the process detection component performs defect recognition on the corresponding assembly production process according to the defect recognition feature;

[0028] The process detection components corresponding to the process flow segment are connected in series according to the assembly production process to generate the detection process model.

[0029] Further, importing the corresponding detection process model to obtain defect detection results includes:

[0030] Sending the defective cable connector into the inspection process model and sequentially entering the process inspection component according to the assembly production process;

[0031] Acquire the defect identification feature matched with each of the process detection components;

[0032] The detection process model collects the defect identification features obtained by each of the process detection components, and determines the process defect results based on the collected defect identification features.

[0033] Furthermore, defect identification features are extracted according to each of the assembly production processes, including:

[0034] Collecting visual data and electrical data of the cable interface and corresponding quality inspection results, and establishing a defect feature recognition database, wherein the defect feature recognition database manages the visual data, electrical data and quality inspection results with each assembly production process as a structural unit;

[0035] Performing deep learning on each of the structural units in the defect feature recognition database to obtain a mapping relationship between the visual data and electrical data and the quality inspection results;

[0036] The visual data and electrical data in each assembly production process are identified according to the mapping relationship to extract defect identification features.

[0037] Further, determining the process defect result according to the collected defect identification features includes:

[0038] Screening the quality inspection results to obtain a number of process defect results, and obtaining a corresponding relationship between the quality inspection results and the defect identification features according to the deep learning;

[0039] Arranging the process defect results, and each process defect result corresponds to a plurality of defect identification features;

[0040] The collected defect identification features are matched with the defect identification features in the arranged process defect results to determine the process defect results.

[0041] An automatic assembly production system for detecting cable connectors, the system comprising:

[0042] A process segmentation module is used to obtain an assembly process production line of a cable connector and segment the assembly process production line to obtain process flow segments, wherein the process flow segments include a plurality of assembly production processes;

[0043] A joint defect recognition module is configured to construct a plurality of detection process models corresponding to the plurality of process flow sections, and each of the detection process models is configured to perform defect recognition on the defective cable joint;

[0044] A defect detection and judgment module is configured to set a quality control detection node for each process flow segment, and the quality control detection node performs defect judgment on the cable joints that have completed the assembly production process within the process flow segment;

[0045] The defective process processing module removes the cable connector from the assembly process production line if a defect exists, and imports the corresponding detection process model to obtain a defect detection result.

[0046] Furthermore, the defect detection and judgment module includes:

[0047] A quality control detection setting unit is used to set the quality control detection node at the end of each process flow segment;

[0048] A detection area determination unit, which sets a detection area for each of the quality control detection nodes, wherein the detection area is set by analyzing work information in the completed assembly production process to determine an area that needs to be detected;

[0049] A defect detection and judgment unit compares the work information obtained in the detection area with the requirements of the assembly production process and judges the problem step to determine the defect detection area;

[0050] The process defect judgment unit judges defects of the cable joints in the assembly production process within the process flow segment according to the defect detection area.

[0051] The technical solution of the present invention can achieve the following technical effects:

[0052] It effectively solves the problems of low production efficiency, unstable quality and insufficient process control in cable assembly, reduces human errors, improves production consistency, and at the same time realizes comprehensive monitoring and information integration of each process flow, improving overall production efficiency.

[0053] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A schematic diagram of the process of automatic assembly production method for detecting cable connectors;

[0056] Figure 2 This is a workflow diagram of the quality control detection node;

[0057] Figure 3 This is a schematic diagram of the defect judgment process;

[0058] Figure 4 It is a structural schematic diagram of the detection process model;

[0059] Figure 5 Schematic diagram of the process for generating defect identification features. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0062] Embodiment 1

[0063] like Figure 1 As shown, the present application provides a method for automatically assembling and producing a detection cable connector, the method comprising:

[0064] S10: obtaining an assembly process production line of a cable connector, and segmenting the assembly process production line to obtain a process flow segment, wherein the process flow segment includes a plurality of assembly production processes;

[0065] S20: constructing a plurality of inspection process models corresponding to a plurality of process flow sections, each inspection process model identifying defects of a defective cable joint;

[0066] S30: setting a quality control detection node for each process flow section, and the quality control detection node performs defect judgment on the cable joints that have completed the assembly production process in the process flow section;

[0067] S40: If there is a defect, the cable connector is removed from the assembly process production line, and a corresponding inspection process model is imported to obtain a defect inspection result.

[0068] Specifically, the assembly process production line of the cable connector is obtained, and the production line is segmented into several process flow segments. Each process flow segment includes multiple assembly production processes, such as wire stripping, crimping, assembly, etc. For each process flow segment, a corresponding inspection process model is designed and constructed based on the specific characteristics of each process and the type of defects that occur. The inspection process model is used to identify and judge the possible defects of the cable connector in each process, such as incomplete crimping, insulation damage, etc. A quality control inspection node is set at the end of each process flow segment. The quality control inspection node can monitor and inspect the cable connector that has completed this process segment, and identify Whether there are defects. If the quality control inspection node detects that the cable connector has defects, the defective cable connector will be immediately removed from the assembly process production line. After removal, the cable connector will be imported into the corresponding inspection process model for further detailed defect detection and analysis to identify the type, location and cause of the defect. According to the analysis results of the inspection process model, the subsequent treatment measures are determined, such as rework, scrapping or correction of process parameters, etc. The defect detection results are promptly fed back to the process control system, and the process parameters are adjusted and optimized according to the results to prevent the recurrence of similar defects, thereby continuously improving and enhancing the overall quality control capability of the production line.

[0069] The technical solution of the present invention effectively solves the problems of low production efficiency, unstable quality and insufficient process control in cable assembly, reduces human errors, improves production consistency, and realizes comprehensive monitoring and information integration of each process flow, thereby improving overall production efficiency.

[0070] Further, if Figure 2 As shown, a quality control detection node is set for each process flow segment, and the quality control detection node performs defect judgment on the cable joints that have completed the assembly production process within the process flow segment, including:

[0071] Set up a quality control detection node at the end of each process flow segment;

[0072] Set up a test area for each quality control test node. The test area is set by analyzing the work information in the completed assembly production process to determine the area that needs to be tested;

[0073] Compare the work information obtained in the inspection area with the requirements of the assembly production process, identify the problem steps, and determine the defect inspection area;

[0074] Defects of cable joints in the assembly production process within the process flow section are judged based on the defect detection area.

[0075] As a preferred embodiment of the above embodiment, a quality control inspection node is set at the end of each process flow segment so that the cable joint can be inspected immediately after each process is completed. The position of the quality control inspection node is determined according to the logical sequence of the production process and the process characteristics. Each quality control inspection node is provided with a special inspection area. The setting of the inspection area is carried out by analyzing the working information of the completed assembly production process, including production parameters, equipment operating status, operation time, etc. By analyzing this information, it can be determined which parts or which operation steps are the areas that need to be inspected in key areas, so as to set a specific inspection range. In the inspection area, the obtained working information is compared with the preset assembly production process requirements. Through the comparison results, the steps and areas where defects may exist can be identified. Based on the comparison results, the defect detection areas are determined. These areas are key points where defects are prone to occur in the assembly production process, such as during compression. During the connection process, the tightness of the crimping and the connection status of the wires may be checked in particular. The determination of the defect detection area makes the detection process more targeted, reduces unnecessary comprehensive inspections, and thus improves detection efficiency. After the defect detection area is determined, the cable joints in these areas are judged for defects. The target areas are accurately inspected and analyzed through quality control inspection nodes to determine whether there are problems with the cable joints in these areas. For example, the appearance of the welding points is inspected using inspection equipment or the crimping quality is inspected using mechanical testing equipment to ensure the accuracy of the inspection. Based on the results of the inspection and judgment, if defects are found, the specific defect information is fed back to the production system so that corrective measures can be taken in a timely manner, such as reprocessing, correcting parameters, or replacing equipment. Products with no defects are allowed to enter the next process. This real-time inspection and feedback mechanism can effectively prevent unqualified products from entering subsequent processes and reduce scrap rates and rework costs.

[0076] Further, if Figure 3 As shown, the cable joints of the assembly production process in the process flow section are judged for defects according to the defect detection area, including:

[0077] Based on the defect detection area, the cable joints of the assembly production process within the process flow section are inspected to obtain process detection information;

[0078] Generate a historical production sequence by assembling historical production information of production processes;

[0079] Compare the process detection information with each item of information contained in the historical production sequence one by one, and generate a comparison information sequence based on the comparison result of each item;

[0080] Analyze and compare information sequences, calculate deviation coefficients of process detection information, and make judgments on cable joints based on the deviation coefficients and process detection information.

[0081] As a preferred embodiment of the above, after the defect detection areas are determined, the cable joints that have completed the process flow segment are inspected for these areas. The inspection can be achieved through various means such as X-ray inspection, ultrasonic inspection, infrared inspection, etc., and a suitable inspection method is selected according to the specific requirements of the process and the possible defect types. Through these inspection means, process inspection information about the cable joints is obtained, and historical production information of the assembly production process is collected and sorted, including production data of all previous process flow segments. A historical production sequence is constructed based on the data for comparison with the current process inspection information to discover potential process problems and defect trends. The currently acquired process inspection information is compared one by one with each piece of information in the historical production sequence to find the difference between the current inspection data and the historical production data, identify the deviation from the standard requirements or the optimal state, and generate a comparison information sequence based on the comparison results. The comparison information sequence records the comparison between each process detection information and historical production information, including whether it meets the standards, the specific value of the deviation, the deviation trend and other detailed data, which can intuitively reflect the gap between the current production status and the historical standards. The comparison information sequence is analyzed to calculate the deviation coefficient of the process detection information. The deviation coefficient is a quantitative indicator that reflects the degree of deviation between the detection information and the historical standard. For example, regression analysis or cluster analysis is used to determine which detection information exceeds the normal range or has abnormalities. Detection items with high deviation coefficients may mean that there are significant problems or instability in the process. The quality of the cable joint is judged based on the calculated deviation coefficient and the specific process detection information. If the deviation coefficient exceeds the set threshold, it is determined that the cable joint is defective and needs further processing; if the deviation coefficient is within the allowable range, the product is considered qualified and can enter the next process.

[0082] Specifically, the assembly process production line of the cable connector is obtained, and the assembly process production line is segmented to obtain process flow segments, which include several assembly production processes, including:

[0083] Set key nodes according to the dependency relationship between assembly production processes in the assembly process production line;

[0084] The assembly process production line is divided into sections according to key nodes. The boundaries between each section are based on the key nodes. The processes between adjacent key nodes are classified into the same process to form process flow segments.

[0085] As a preferred embodiment of the above embodiment, the assembly process production line of the cable connector is obtained, the various assembly production processes and their arrangement order in the production line are understood, and the dependencies between the processes are analyzed, that is, which processes need to be carried out after the completion of other processes, and which processes have sequence or quality interactions. According to the dependencies between the assembly production processes, key nodes are identified and set. Key nodes refer to points in the production flow that have an important impact on the processes. They are core processes that determine product quality, steps that require special control, or key links that affect subsequent processes. For example, the crimping process may be a key node because the quality of crimping directly affects the subsequent connection stability. Taking each key node as the boundary, all processes between adjacent key nodes are classified as the same section, that is, the process Process flow segment. For example, if there is a key node between wire stripping and crimping, and another key node between crimping and welding in the production line, then the process from wire stripping to crimping belongs to the first process flow segment, and the process from crimping to welding belongs to the second process flow segment. The processes between adjacent key nodes are classified into the same process to form process flow segments. Each process flow segment includes a series of logically related processes. These processes are physically or functionally related to each other and jointly complete a certain stage of assembly tasks. According to the specific process characteristics of each segment, quality control inspection nodes or other monitoring measures are set to ensure that the quality at the key nodes is guaranteed. According to the actual situation in the production process and the results of historical data analysis, the process flow segments are dynamically adjusted.

[0086] Further, if Figure 4 As shown, several inspection process models are constructed corresponding to several process flow sections, and each inspection process model identifies defects in defective cable joints, including:

[0087] Determine the assembly production process within the corresponding process flow segment, and extract defect recognition features based on each assembly production process;

[0088] A process detection component is constructed according to the extracted defect recognition features, the process detection component corresponds to the assembly production process one by one, and the process detection component performs defect recognition on the corresponding assembly production process according to the defect recognition features;

[0089] The corresponding process detection components in the process flow segment are connected in series according to the assembly production process to generate a detection process model.

[0090] As a preferred embodiment of the above embodiment, the specific assembly production process included in each process flow segment is determined. For each assembly production process, the corresponding defect identification features are extracted to identify the key indicators of defects that may occur in the process. The defect identification features of each process are different, and need to be customized according to the specific requirements of the process and the possible defect types. According to the extracted defect identification features, a corresponding process detection component is constructed for each assembly production process. The process detection component is a detection unit for a specific process, which is specifically used to identify and judge the defects that may occur in the process. Each process detection component must correspond to its corresponding assembly production process one by one, and detect and judge according to the specific defect identification features. All corresponding process detection components in the process flow segment are arranged in the order of the assembly production process. The sequence is connected in series to generate a detection process model, so that each detection component detects the cable connector in turn, and defects are gradually identified according to the actual order of the process. The serial detection process model gradually eliminates defective products through segmented detection. The model gradually identifies defects in each process through its serial process detection components. If a defect is detected, the detection process model can accurately locate the process and specific problem type where the defect is located, and eliminate the defective cable connector from the production line to avoid unqualified products from entering the next stage of production. According to the detection data continuously accumulated in the production process, the detection process model is optimized and adjusted. For example, after analyzing the detection data, it is found that the defect recognition rate of a certain process is low. The detection effect can be improved by adjusting the parameters of the detection component or adding new defect recognition features.

[0091] Furthermore, the corresponding inspection process model is imported to obtain defect inspection results, including:

[0092] Send the defective cable connectors to the inspection process model and enter the process inspection components in sequence according to the assembly production process;

[0093] Obtain defect identification features that match each process inspection component;

[0094] The inspection process model aggregates the defect identification features obtained by each process inspection component and determines the process defect results based on the aggregated defect identification features.

[0095] As a preferred embodiment of the above, when a defective cable joint is found during the assembly production process of the cable joint, the defective product is removed from the production line and sent to the corresponding inspection process model. By importing the defective cable joint into the inspection process model, a comprehensive defect identification and positioning can be performed on it. After the defective cable joint enters the inspection process model, it passes through each process inspection component in the order of the assembly production process. Each process inspection component will perform feature detection on the cable joint according to its corresponding production process using preset inspection means and techniques. In each process inspection component, a defect identification feature matching the component is obtained based on the previously extracted and set standards to detect whether there are defects that deviate from the standard. The inspection process model will detect each process inspection component. The defect recognition features obtained from the parts are collected, and a comprehensive defect feature set is formed by collecting the feature data of the detection components of each process. The features of the detection components of each process are evaluated through algorithms, and the deviation value, matching degree or other quantitative indicators are calculated to determine whether the status of each process is normal. For each process with defects, the model will record the type, location and severity of the defects in detail. Finally, the detection process model outputs a comprehensive defect detection result report, which includes the detection status of each process, defect details and recommended treatment measures. For the defects found, the model can recommend specific corrective measures, such as adjusting process parameters, reprocessing specific processes or performing local repairs. The results can be used to guide the adjustment of production lines, rework or optimization of quality control strategies.

[0096] Further, if Figure 5 As shown, defect identification features are extracted according to each assembly production process, including:

[0097] Collect the visual data and electrical data of the cable interface and the corresponding quality inspection results, and establish a defect feature recognition database, which manages the visual data, electrical data and quality inspection results with each assembly production process as a structural unit;

[0098] Perform deep learning on each structural unit in the defect feature recognition database to obtain the mapping relationship between visual data, electrical data and quality inspection results;

[0099] According to the mapping relationship, the visual data and electrical data in each assembly production process are identified and the defect recognition features are extracted.

[0100] As a preferred embodiment of the above embodiment, during the assembly production process, comprehensive data collection is performed on the cable joints, including visual data and electrical data. At the same time, the quality inspection results corresponding to these cable joints are obtained, including the judgment of qualified and unqualified and the specific defect type. The collected visual data, electrical data and their corresponding quality inspection results are integrated to establish a defect feature recognition database. The database performs structured management of data according to each assembly production process, that is, the data of each process is stored as an independent structural unit. For each structural unit in the defect feature recognition database, deep learning technology is applied to perform data training. The deep learning model learns the mapping relationship between visual data, electrical data and quality inspection results through a large amount of training data. The model will identify which visual features and electrical features are strongly correlated with defects. The trained deep learning model is used to identify the visual data and electrical data collected in real time on the production line. The model extracts defect-related features from the input data based on the learned mapping relationship. The feature extraction results output by the model are used to generate defect recognition features. These features will be further used in the defect judgment module in the detection process model to provide a basis for quality judgment of each process. The extracted defect recognition features are applied to the detection process model in real time for real-time detection and defect recognition of cable joints in the production process. If the detection results do not meet expectations, the system will automatically generate an alarm and record the defect type and location, and can provide corrective suggestions. For each detected defect instance, its visual data, electrical data and detection results are fed back to the defect feature recognition database as new training data to continuously optimize and update the accuracy and adaptability of the deep learning model.

[0101] Further, the process defect results are determined based on the collected defect identification features, including:

[0102] Filter the quality inspection results to obtain several process defect results, and obtain the corresponding relationship between the quality inspection results and defect identification features based on deep learning;

[0103] Arrange process defect results, and each process defect result corresponds to a number of defect identification features;

[0104] The collected defect identification features are matched with the defect identification features in the arranged process defect results to determine the process defect results.

[0105] As a preferred embodiment of the above, quality inspection results related to each process are screened out from the defect feature recognition database, including qualified and unqualified judgments and detailed defect types. By analyzing these quality inspection results, the most common or most critical defect types in a specific process are identified to form several process defect result sets. A deep learning model is used to analyze these quality inspection results and establish a mapping relationship with the defect recognition features. All process defect results are arranged, that is, each process defect result is associated with its corresponding defect recognition feature. The arrangement process ensures that each process defect result can clearly correspond to multiple specific defect recognition features, so that when judging defects, specific defect results can be quickly identified. The defect type and feature association are then performed, and the collected defect identification features are matched with the features in the pre-arranged process defect results to determine the most likely defect type. During the matching process, the deep learning model can provide more accurate feature association judgment and improve the matching accuracy. Based on the matching results, the defect results of the process are finally determined, which include the detected defect type, description of specific features, and possible impacts. The determined process defect results are fed back to the defect feature recognition database as new training data. These data are used to continuously update the deep learning model to improve its understanding and judgment of the correspondence between defect identification features and process defect results.

[0106] Embodiment 2

[0107] Based on the same inventive concept as the automatic assembly production method for a detection cable joint in the aforementioned embodiment, the present invention also provides an automatic assembly production system for a detection cable joint, the system comprising:

[0108] A process segmentation module obtains the assembly process production line of the cable connector and segments the assembly process production line to obtain process flow segments, each of which includes several assembly production processes;

[0109] The joint defect recognition module constructs several detection process models corresponding to several process flow sections, and each detection process model recognizes defects in defective cable joints;

[0110] The defect detection and judgment module sets a quality control detection node for each process flow segment, and the quality control detection node performs defect judgment on the cable joints that have completed the assembly production process within the process flow segment;

[0111] Defective process processing module, if there is a defect, the cable connector will be removed from the assembly process production line, and the corresponding inspection process model will be imported to obtain the defect detection result.

[0112] The above-mentioned adjustment system in the present invention can effectively realize the automatic assembly production method of the detection cable connector, and the technical effects that can be achieved are as described in the above-mentioned embodiments, which will not be repeated here.

[0113] Specifically, the defect detection and judgment module includes:

[0114] The quality control detection setting unit sets a quality control detection node at the end of each process flow section;

[0115] The detection area determination unit sets the detection area for each quality control detection node, and the detection area is set by analyzing the work information in the completed assembly production process to determine the area that needs to be detected;

[0116] The defect detection and judgment unit compares the work information obtained in the detection area with the requirements of the assembly production process and judges the problem steps to determine the defect detection area;

[0117] The process defect judgment unit judges defects of cable joints in the assembly production process within the process flow segment according to the defect detection area.

[0118] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the optimization effects corresponding to the method in Example 1, which will not be repeated here.

[0119] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined therein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for automatically assembling and producing a detection cable connector, characterized in that: The method comprises: Acquire an assembly process production line of a cable connector, and segment the assembly process production line to obtain process flow segments, wherein the process flow segments include a plurality of assembly production processes; Constructing a plurality of detection process models corresponding to a plurality of process flow sections, each of the detection process models identifying defects of the defective cable joint; A quality control detection node is set for each process flow section, and the quality control detection node performs defect judgment on the cable joints that have completed the assembly production process in the process flow section, including: Setting the quality control detection node at the end of each process flow segment; Setting a detection area for each quality control detection node, wherein the detection area is set by analyzing the work information in the completed assembly production process to determine the area that needs to be detected; Compare the work information obtained in the detection area with the requirements of the assembly production process and judge the problem steps to determine the defect detection area; Defect determination of the cable joints of the assembly production process in the process flow section according to the defect detection area includes: Based on the defect detection area, the cable joints that have completed the assembly production process within the process flow section are detected to obtain process detection information; Generate a historical production sequence through the historical production information of the assembly production process; Compare the process detection information with each item of information contained in the historical production sequence one by one, and generate a comparison information sequence according to the comparison result of each item; Analyze and compare the information sequence, calculate the deviation coefficient of the process detection information, and judge the cable joint according to the deviation coefficient and the process detection information; If there is a defect, the cable connector is removed from the assembly process production line and the corresponding detection process model is imported to obtain a defect detection result.

2. The automatic assembly production method for detection cable connectors according to claim 1, characterized in that: Obtain an assembly process production line for a cable connector, and segment the assembly process production line to obtain process flow segments, wherein the process flow segments include several assembly production processes, including: Setting key nodes according to the dependency relationship between the assembly production processes in the assembly process production line; The assembly process production line is segmented according to the key nodes, the boundaries between each segment are based on the key nodes, and the processes between adjacent key nodes are classified into the same process to form the process flow segment.

3. The automatic assembly production method for detection cable connectors according to claim 1, characterized in that: Constructing a plurality of detection process models corresponding to the plurality of process flow sections, each of the detection process models identifying defects of the defective cable joints, including: Determine the assembly production process within the corresponding process flow segment, and extract defect recognition features according to each assembly production process; Constructing a process detection component according to the extracted defect recognition feature, wherein the process detection component corresponds to the assembly production process one by one, and the process detection component performs defect recognition on the corresponding assembly production process according to the defect recognition feature; The process detection components corresponding to the process flow segment are connected in series according to the assembly production process to generate the detection process model.

4. The automatic assembly production method for detection cable connectors according to claim 3 is characterized in that: Importing the corresponding detection process model to obtain defect detection results includes: Sending the defective cable connector into the inspection process model and sequentially entering the process inspection component according to the assembly production process; Acquire the defect identification feature matched with each of the process detection components; The detection process model collects the defect identification features obtained by each of the process detection components, and determines the process defect results based on the collected defect identification features.

5. The automatic assembly production method for detection cable connectors according to claim 3, characterized in that: Extracting defect recognition features according to each of the assembly production processes includes: Collecting visual data and electrical data of the cable interface and corresponding quality inspection results, and establishing a defect feature recognition database, wherein the defect feature recognition database manages the visual data, electrical data and quality inspection results with each assembly production process as a structural unit; Performing deep learning on each of the structural units in the defect feature recognition database to obtain a mapping relationship between the visual data and electrical data and the quality inspection results; The visual data and electrical data in each assembly production process are identified according to the mapping relationship to extract defect identification features.

6. The automatic assembly production method for detection cable connectors according to claim 5, characterized in that: Determining process defect results according to the collected defect identification features includes: Screening the quality inspection results to obtain a number of process defect results, and obtaining a corresponding relationship between the quality inspection results and the defect identification features according to the deep learning; Arranging the process defect results, and each process defect result corresponds to a plurality of defect identification features; The collected defect identification features are matched with the defect identification features in the arranged process defect results to determine the process defect results.

7. Automatic assembly production system for detecting cable connectors, characterized in that: The automatic assembly production method for the detection cable connector according to claim 1 is adopted, and the system comprises: A process segmentation module is used to obtain an assembly process production line of a cable connector and segment the assembly process production line to obtain process flow segments, wherein the process flow segments include a plurality of assembly production processes; A joint defect recognition module is configured to construct a plurality of detection process models corresponding to the plurality of process flow sections, and each of the detection process models is configured to perform defect recognition on the defective cable joint; A defect detection and judgment module is configured to set a quality control detection node for each process flow segment, and the quality control detection node performs defect judgment on the cable joints that have completed the assembly production process within the process flow segment; A defective process processing module, if there is a defect, removes the cable connector from the assembly process production line, and imports the corresponding detection process model to obtain a defect detection result; The defect detection and judgment module includes: A quality control detection setting unit is used to set the quality control detection node at the end of each process flow segment; A detection area determination unit, which sets a detection area for each of the quality control detection nodes, wherein the detection area is set by analyzing work information in the completed assembly production process to determine an area that needs to be detected; A defect detection and judgment unit compares the work information obtained in the detection area with the requirements of the assembly production process and judges the problem step to determine the defect detection area; The process defect judgment unit judges defects of the cable joints in the assembly production process within the process flow segment according to the defect detection area.

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