An online assembly testing method and system for a fixing process

Abstract

The present invention discloses an online assembly test method and system for a fixing process, relating to the technical field of assembly testing. The method includes: obtaining a set of locking points of a target glass and a set of historical assembly logs; obtaining a set of historical assembly logs with unbalanced locking torques; obtaining a locking point associated topology network; respectively arranging torque sensors at the locking points in the set of locking points, and using the arranged set of torque sensors to monitor the torque of the set of locking points in a preset monitoring window to obtain a set of monitored torque sequences; combining the set of monitored torque sequences and the locking point associated topology network to perform assembly collaborative test analysis to obtain an assembly test result. The present invention solves the technical problem in the prior art that the associated influence situation between locking points during the assembly process cannot be accurately identified, resulting in potential torque imbalance not being recognized, and achieves the technical effect of improving the reliability of glass locking assembly and the accuracy of assembly test analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of assembly testing, and particularly to an online assembly testing method and system for the fixing process. Background Art

[0002] During the glass assembly process, the locking of the glass is usually carried out by means of screws, bonding, snap - fits, etc. To ensure the accurate positioning and uniform stress of the glass, an appropriate torque needs to be applied to the locking points. However, traditional assembly methods often rely on manual operations and cannot effectively monitor the torque distribution of each locking point and their mutual relationships, resulting in torque imbalance or uneven locking. Such imbalance not only affects the assembly quality but may also cause glass breakage or failure to achieve the expected assembly effect. Therefore, how to efficiently and accurately monitor and optimize the torque application during the assembly process, especially the interaction between the locking points, has become a major challenge in the current technology. Summary of the Invention

[0003] This application provides an online assembly testing method and system for the fixing process, aiming to solve the technical problem in the prior art that the associated influence situation between the locking points during the assembly process cannot be accurately identified, resulting in potential torque imbalance not being recognized.

[0004] In view of the above problems, this application provides an online assembly testing method and system for the fixing process.

[0005] In the first aspect of this application, an online assembly testing method for the fixing process is provided. The method includes:

[0006] Obtain the set of locking points of the target glass and the set of historical assembly logs;

[0007] Conduct a search for torque imbalance in the set of historical assembly logs to obtain a set of historical assembly logs with torque imbalance;

[0008] Traverse the set of historical assembly logs with torque imbalance to construct an association network for the set of locking points, and obtain a locking - point association topology network;

[0009] Arrange torque sensors at the locking points of the set of locking points respectively, and use the arranged set of torque sensors to monitor the torque of the set of locking points in a preset monitoring window to obtain a set of monitored torque sequences;

[0010] Combine the set of monitored torque sequences and the locking - point association topology network to conduct an assembly collaborative test analysis, and obtain an assembly test result.

[0011] In the second aspect of this application, an online assembly testing system for the fixing process is provided. The system includes:

[0012] A historical assembly log set acquisition module for acquiring a set of locking points and a set of historical assembly logs of a target glass;

[0013] An unbalanced assembly log set acquisition module for performing a locking torque imbalance retrieval on the set of historical assembly logs to obtain a set of historical locking torque imbalance assembly logs;

[0014] A locking point associated topology network acquisition module for traversing the set of historical locking torque imbalance assembly logs to construct an associated network for the set of locking points and obtain a locking point associated topology network;

[0015] A monitored torque sequence set acquisition module for respectively arranging torque sensors at the locking points of the set of locking points, and using the arranged set of torque sensors to perform torque monitoring on the set of locking points in a preset monitoring window to obtain a set of monitored torque sequences;

[0016] An assembly test result acquisition module for performing an assembly collaborative test analysis by combining the set of monitored torque sequences and the locking point associated topology network to obtain an assembly test result.

[0017] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0018] In the present application, by acquiring a set of locking points and a set of historical assembly logs of a target glass, then performing a locking torque imbalance retrieval on the set of historical assembly logs to obtain a set of historical locking torque imbalance assembly logs, and further traversing the set of historical locking torque imbalance assembly logs to construct an associated network for the set of locking points to obtain a locking point associated topology network, then respectively arranging torque sensors at the locking points of the set of locking points, and using the arranged set of torque sensors to perform torque monitoring on the set of locking points in a preset monitoring window to obtain a set of monitored torque sequences, and further performing an assembly collaborative test analysis by combining the set of monitored torque sequences and the locking point associated topology network to obtain an assembly test result. The technical effect of performing assembly test analysis from both the overall and local dimensions, identifying abnormal torque situations, and improving assembly reliability is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0020] Figure 1 It is a schematic flowchart of an online assembly test method for a fixing process provided by an embodiment of the present application;

[0021] Figure 2 This is a schematic structural diagram of an online assembly test system for a fixing process provided by an embodiment of the present application.

[0022] Explanation of reference numerals: Historical assembly log set acquisition module 11, unbalanced assembly log set acquisition module 12, locking point associated topology network acquisition module 13, monitored torque sequence set acquisition module 14, assembly test result acquisition module 15. Detailed implementation manners

[0023] The present application provides an online assembly test method and system for a fixing process, which are used to solve the technical problem in the prior art that the associated influence situation between locking points during the assembly process cannot be accurately identified, resulting in potential torque imbalance not being recognized.

[0024] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0025] Embodiment 1, as Figure 1 shown, the present application provides an online assembly test method for a fixing process, and the method includes:

[0026] S1: Obtain the locking point set and the historical assembly log set of the target glass;

[0027] S2: Perform a search for locking torque imbalance on the historical assembly log set to obtain a historical locking torque imbalance assembly log set;

[0028] In a possible embodiment, the target glass refers to a glass part being assembled or tested, and locking point analysis needs to be performed on it in this step. The locking point refers to each point where torque needs to be applied during the glass assembly process, and these points are usually the places where the glass contacts or connects with other components (such as a frame). The locking point set refers to the set of these key points. The historical assembly log set refers to the relevant data logs recorded during the historical assembly process, including information such as the applied torque, assembly sequence, and torque change situation of each locking point. This log provides basic data for analyzing the torque application situation during the assembly process.

[0029] First, according to the assembly plan of the target glass, determine the set of locking points to ensure that all the points that need to be locked can be identified. Subsequently, obtain the set of historical assembly logs. By referring to these logs, extract the previous assembly data. These historical data can provide important basis for analyzing possible torque imbalance problems during the assembly process. The function of this step is to provide necessary data support and basic information for subsequent imbalance retrieval and assembly testing.

[0030] The retrieval of locking torque imbalance refers to screening out the assembly data with uneven or imbalanced torque application in the historical assembly logs. Torque imbalance may manifest as too large or too small torque at a certain locking point, or inconsistent torque at multiple locking points, thus affecting the assembly effect. The set of historical locking torque imbalance assembly logs refers to the set of relevant logs screened out from the set of historical assembly logs that have torque imbalance problems during the historical assembly process. Through this set, it can be analyzed which torque distributions in the assembly process have problems, providing reference for subsequent improvement and optimization.

[0031] Preferably, first, it is necessary to scan and analyze the set of historical assembly logs. Through preset criteria (such as maximum torque value, minimum torque value, standard deviation, etc.), each log record is retrieved and screened. For example, if the torque value of a certain locking point exceeds the preset standard range, or there is an obvious imbalance in the torque values of multiple locking points, the system will mark it as unbalanced data. During the retrieval process, according to the marks, automatically identify which logs have uneven torque or other abnormal conditions. Finally, the system sorts out these identified unbalanced assembly records into a set of historical locking torque imbalance assembly logs for subsequent analysis.

[0032] S3: Traverse the set of historical locking torque imbalance assembly logs to construct an association network for the set of locking points, and obtain a locking point association topology network;

[0033] Furthermore, traverse the set of historical locking torque imbalance assembly logs to construct an association network for the set of locking points, and obtain a locking point association topology network. Step S3 of the embodiment of the present application further includes:

[0034] Traverse the set of historical locking torque imbalance assembly logs to extract unbalanced locking points, and obtain a set of unbalanced locking point combinations;

[0035] Extract the M locking points with the top M occurrence frequencies in the set of unbalanced locking point combinations as M central locking points, where M is a positive integer;

[0036] Based on the set of unbalanced locking point combinations, count the association frequencies of the M sets of associated locking points associated with the M central locking points, and obtain a set of M associated locking point association frequencies;

[0037] Determine M sets of association weights based on the set of association frequencies of the M associated locking points, and construct the locking point association topology network by combining the M central locking points, the M sets of associated locking points and the set of locking points.

[0038] In a possible embodiment, the locking point association topology network is constructed by analyzing the mutual relationship between locking points. In the locking point association topology network, locking points are connected to other locking points through an "association" relationship. This network can reflect which locking points have mutual influence or relationship in the applied torque. The set of unbalanced locking point combinations refers to the combinations containing multiple unbalanced locking points screened from the historical assembly logs, and these combinations reflect which locking points often have torque imbalance together during the assembly process.

[0039] Preferably, extract the locking point information where torque imbalance has occurred from the set of historical locking torque imbalance assembly logs. An unbalanced locking point refers to a locking point where the torque application is significantly uneven during the historical assembly process. By extracting these unbalanced points, the system can identify which locking points frequently exhibit anomalies during the historical assembly process. Then, extract the top M locking points with higher occurrence frequencies from these unbalanced locking points as the "central locking points". M is a positive integer representing the number of selected locking points.

[0040] Next, based on the set of unbalanced locking point combinations, count the other locking points associated with each central locking point, that is, the locking points where torque imbalance occurs simultaneously, and calculate the frequencies of these associations. For example, if a certain central locking point frequently co-occurs with multiple other locking points in multiple unbalanced assemblies, then the association frequency between them is higher. Through these frequency data, an "association weight" can be assigned to each associated locking point, and the magnitude of the weight reflects the degree of influence between the associated locking point and its corresponding central locking point.

[0041] Finally, construct the locking point association topology network by combining these association weights and the relationships between the central locking points and the associated locking points. In this topology network, nodes represent locking points, and connecting edges represent the association relationships between these points. The constructed topology network can not only reveal the association situation between each locking point, but also provide data support for torque adjustment and assembly strategy optimization in the subsequent assembly process.

[0042] The locking point association topology network constructed through this step can help identify which locking points are closely related and the torque application may have mutual influence, thereby providing a strong basis for subsequent assembly testing and optimization.

[0043] Further, based on the set of M associated locking point association frequencies, determine M sets of association weights, and construct the locking point association topology network in combination with the M central locking points, the M sets of associated locking points, and the set of locking points. Step S3 of the embodiment of the present application further includes:

[0044] Take the M central locking points as M first topological nodes;

[0045] Obtain a second set of topological nodes, and connect them to the M first topological nodes respectively to obtain M sets of connection edges;

[0046] Use the M sets of association weights to identify the M connection edges to obtain M sets of identified connection edges;

[0047] Based on the M central locking points and the M sets of associated locking points, identify non-associated locking points in the set of locking points to obtain a third set of topological nodes;

[0048] Connect the third set of topological nodes to the first topological node closest to them among the M first topological nodes, and identify the connection edges according to the preset position association weights to obtain a third set of topological node identified connection edges;

[0049] Based on the M first topological nodes, the second set of topological nodes, the M sets of identified connection edges, the third set of topological nodes, and the third set of topological node identified connection edges, construct a topological network to obtain the locking point association topology network.

[0050] Further, take the union of the M sets of associated locking points to obtain the second set of topological nodes.

[0051] Further, determine whether there are locking points in the set of locking points that do not belong to the M central locking points and the M sets of associated locking points. If so, add them to the third set of topological nodes.

[0052] Further, based on the set of M associated locking point association frequencies, determine M sets of association weights. Step S3 of the embodiment of the present application further includes:

[0053] Respectively divide the association frequency of an associated locking point in the set of M associated locking point association frequencies by the total frequency of the corresponding set of associated locking point association frequencies to obtain the M sets of association weights.

[0054] In a possible embodiment, the first topological node refers to the locking point that serves as the starting node in the construction of the topological network and is the central locking point where imbalances frequently occur. The second topological node set refers to other locking points that have a strong association with the central locking point, and they constitute the nodes at the second level in the topological network. The relationship between the second topological node set and the central locking point, that is, the corresponding first topological node, is determined by the association weight. Connection edge set: In the topological network, the connection between nodes is represented by edges, and the connection edge set reflects the connection relationship between the first topological node and the second topological node.

[0055] The marked connection edge set is the connection edge obtained after marking the connection edges using the association weight. The marking is used to help distinguish the different strengths of the relationship between the first topological node and the second topological node, facilitating subsequent analysis. The unassociated locking points refer to those locking points that have no direct relationship with the selected central locking point and the associated locking point set. These locking points are relatively independent in the assembly and may not be affected by the central locking point. The unassociated locking points are summarized to generate the third topological node set. That is to say, the third topological node set is the locking points selected from the locking point set that are not directly associated with any central locking point or associated locking point, and are usually located at the periphery of the topological network.

[0056] The third topological node marked connection edge set refers to the edges connecting the third topological nodes in the third topological node set to the corresponding first topological nodes, and is obtained after marking according to the position association weight preset by those skilled in the art.

[0057] In one embodiment, first, M central locking points are used as the first topological nodes of the topological network. Then, the union of the M associated locking point sets is obtained to get the second topological node set, and the second topological node set is connected to the corresponding M first topological nodes according to the association relationship with the first topological nodes, obtaining M connection edge sets. Next, these connection edges are marked using M association weight sets. The higher the weight, the stronger the association between the two. The marked connection edges help to clarify the influence magnitude between different locking points and optimize the construction of the topological network.

[0058] Preferably, the association frequency of one associated locking point in the M associated locking point association frequency sets is respectively divided by the total frequency of the corresponding associated locking point association frequency set to obtain the M association weight sets.

[0059] Furthermore, the next step is to identify the locking points that are not associated with any central locking points or associated locking points. These points are classified as "unassociated locking points" and form the third set of topological nodes. By comparing the distances between these unassociated locking points and the first topological nodes, these nodes are connected to the first topological node with the closest distance. Then, these connecting edges are identified according to the preset position association weight to form the third set of topological node identification connecting edges.

[0060] Finally, based on all topological nodes (including the first topological nodes, the second topological nodes, and the third topological nodes) and the connecting edges between them (including the identification connecting edges and the third set of topological node identification connecting edges), the construction of the entire locking point association topological network is completed.

[0061] By hierarchically constructing the locking point association topological network, the relationships among the central locking points, the associated locking points, and the unassociated locking points are clearly shown, thus providing a clear structural basis for the associated influence of the torque during the assembly process. This helps to identify potential torque imbalance risks and achieve more precise assembly control.

[0062] S4: Torque sensors are respectively arranged at the locking points of the locking point set, and the torque of the locking point set is monitored by using the arranged torque sensor set in a preset monitoring window to obtain a set of monitored torque sequences;

[0063] In a possible embodiment, the torque sensor is a sensor used to measure the rotational force (torque) applied to an object. It can monitor and record the torque changes at each locking point to ensure that the torque applied to each locking point is within an appropriate range. The arranged torque sensor set refers to the total set of torque sensors installed and configured at each locking point. These sensors are responsible for monitoring the torque state of each locking point. The preset monitoring window refers to the monitoring time range preset by those skilled in the art. During this time period, the torque sensors will continuously record and monitor the torque data of the locking points. The set of monitored torque sequences refers to the set of torque data collected from all the locking point sensors within the preset monitoring window. This data set will reflect the torque changes of each locking point during the entire monitoring process.

[0064] By installing torque sensors at each locking point, it is ensured that each locking point can monitor its torque changes in real time. Then, during the assembly process, the torque sensors will continuously record the magnitude and change trend of the applied torque. In the preset monitoring window, the torque of all locking points is continuously monitored by using the torque sensor set, and the monitored torque data is recorded as the set of monitored torque sequences. This achieves the technical effect of providing data support for subsequent assembly collaborative test analysis.

[0065] S5: Perform assembly collaborative test analysis by combining the monitored torque sequence set and the locking point associated topology network to obtain the assembly test results.

[0066] Further, when performing assembly collaborative test analysis by combining the monitored torque sequence set and the locking point associated topology network to obtain the assembly test results, step S5 of the embodiment of the present application further includes:

[0067] Traverse the monitored torque sequence set to identify torque abnormal deviations, and perform abnormal marking on the locking point set according to the identification results to obtain Q abnormal locking points, where Q is a positive integer;

[0068] Using the Q abnormal locking points as indexes, perform associated retrieval on the locking point associated topology network to determine a set of Q abnormally associated locking points and a set of Q abnormally marked connection edges;

[0069] Combine the monitored torque sequence set to perform assembly collaborative test analysis on the set of Q abnormally associated locking points and the set of Q abnormally marked connection edges to obtain the assembly test results.

[0070] In a possible embodiment, the monitored torque sequence set reflects the torque change situation of the locking point set within a preset monitoring window. The locking point associated topology network reflects the association situation between the locking point sets, providing a basis for subsequent collaborative analysis. Therefore, by discovering abnormal locking points and then performing associated abnormal analysis in combination with the locking point associated topology network, potential torque imbalance situations during the assembly process can be discovered in a timely manner.

[0071] Preferably, perform torque abnormal deviation identification on the monitored torque sequence set according to a preset locking torque sequence and a preset tolerance threshold to obtain abnormal locking points. Among them, an abnormal locking point refers to a locking point whose difference between the monitored torque and the preset locking torque exceeds the preset tolerance threshold range during torque monitoring. The torque states of these points do not meet the standards and may lead to a decline in assembly quality or damage to the glass. The preset locking torque sequence is the change situation that the locking point torque should meet preset by those skilled in the art. The preset tolerance threshold is the torque deviation range that meets the requirements preset by those skilled in the art.

[0072] Furthermore, using the Q abnormal locking points as indexes, perform associated retrieval on the locking point associated topology network to obtain a set of abnormally associated locking points related to the abnormal locking points and a set of abnormally marked connection edges between them, so as to obtain a set of Q abnormally associated locking points and a set of Q abnormally marked connection edges. The locking points in these sets of abnormally associated locking points, although not deviating from the normal range at present, may be affected by the abnormal locking points, resulting in torque imbalance or assembly problems. Therefore, the monitoring and analysis of these points are crucial.

[0073] Combined with the monitored torque sequence data, assembly collaborative test analysis is carried out on the abnormally associated locking points and their abnormally marked connecting edges. Through this collaborative analysis, potential problems in the entire assembly process can be comprehensively evaluated, especially the impact of abnormally locking points on other locking points. In this way, torque imbalance or other abnormalities occurring in the assembly process can be detected and corrected in a timely manner, thereby improving the assembly quality and efficiency. By evaluating the assembly quality from both the overall and local aspects, potential assembly risks can be detected in advance, avoiding glass damage or assembly failure caused by torque imbalance or unevenness, and thus improving the overall reliability and precision of the assembly.

[0074] Furthermore, combined with the set of monitored torque sequences, assembly collaborative test analysis is carried out on the set of Q abnormally associated locking points and the set of Q abnormally marked connecting edges to obtain the assembly test results. Step S5 of the embodiment of the present application further includes:

[0075] Extract Q abnormally associated monitored torque sequence sets of the set of Q abnormally associated locking points from the set of monitored torque sequences;

[0076] Based on a preset locking torque sequence, deviation identification is carried out on the Q abnormally associated monitored torque sequence sets to obtain a set of Q abnormally associated monitored torque deviation degrees;

[0077] Combined with the weights of the set of Q abnormally marked connecting edges, weighted calculation is carried out on the set of Q abnormally associated monitored torque deviation degrees to obtain Q abnormally associated deviation degrees;

[0078] Judge whether the Q abnormally associated deviation degrees are greater than or equal to a preset abnormally associated deviation degree threshold. If so, add the corresponding abnormally locking points and the set of abnormally associated locking points to the assembly test results.

[0079] Furthermore, based on a preset locking torque sequence, deviation identification is carried out on the Q abnormally associated monitored torque sequence sets to obtain a set of Q abnormally associated monitored torque deviation degrees. Step S5 of the embodiment of the present application further includes:

[0080] Randomly extract a first abnormally associated monitored torque sequence from the set of Q abnormally associated monitored torque sequences;

[0081] Calculate the deviation degree between the first abnormally associated monitored torque sequence and the preset locking torque sequence to obtain a first deviation degree sequence;

[0082] Calculate the mean value of the first deviation degree sequence to obtain a first abnormally associated monitored torque deviation degree;

[0083] According to the preset locking torque sequence, deviation identification is performed on the remaining abnormal correlation monitoring torque sequences in the Q abnormal correlation monitoring torque sequence sets, and the Q abnormal correlation monitoring torque deviation degree sets are obtained.

[0084] In a possible embodiment, first, torque sequences associated with abnormal locking points are extracted from the monitoring torque sequence set to form an abnormal correlation monitoring torque sequence set. These monitoring sequences reflect the possible influences on other locking points associated with the abnormal locking points during the assembly process.

[0085] Furthermore, a first abnormal correlation monitoring torque sequence is randomly extracted from the Q abnormal correlation monitoring torque sequence sets. Calculate the deviation degree between the first abnormal correlation monitoring torque sequence and the preset locking torque sequence, that is, calculate the difference between the first abnormal correlation monitoring torque sequence and the preset locking torque sequence to obtain a torque deviation value sequence, and then divide the torque deviation value sequence by the preset locking torque sequence to obtain the first deviation degree sequence. Among them, the first deviation degree sequence reflects the deviation degree of the abnormal correlation locking point corresponding to the first abnormal correlation monitoring torque sequence from the preset torque on the premise of meeting the torque requirement.

[0086] Then calculate the mean value of the first deviation degree sequence to obtain the first abnormal correlation monitoring torque deviation degree. Among them, the first abnormal correlation monitoring torque deviation degree reflects the general deviation degree of the abnormal correlation locking point corresponding to the first abnormal correlation monitoring torque sequence from the preset torque on the premise of meeting the torque requirement.

[0087] Based on the same principle as obtaining the first abnormal correlation monitoring torque deviation degree, according to the preset locking torque sequence, deviation identification is performed on the remaining abnormal correlation monitoring torque sequences in the Q abnormal correlation monitoring torque sequence sets, and the Q abnormal correlation monitoring torque deviation degree sets are obtained.

[0088] Combined with the weights in the abnormal identification connection edge set (i.e., the connection edges representing the relative importance between locking points), weighted calculation is performed on each abnormal correlation monitoring torque deviation degree to obtain the abnormal correlation deviation degree. This calculation takes into account the relative importance between different locking points, making important locking points (such as key load-bearing points) have a greater influence on the final assembly result.

[0089] Finally, by determining whether the abnormal correlation deviation degree exceeds the preset abnormal correlation deviation degree threshold, it can be decided whether to include the abnormal locking point and its associated points in the assembly test result. If the deviation degree exceeds the threshold, it indicates that there may be problems with the assembly of these points and further correction is required. The preset abnormal correlation deviation degree threshold is the maximum deviation degree allowed set by those skilled in the art themselves.

[0090] Through precise torque monitoring and deviation analysis, potential anomalies and unevenness issues during the assembly process are identified, and the assembly process is optimized based on the impact degree of each locking point. Through weighted calculation and deviation analysis, the key factors affecting the assembly quality can be more effectively identified, thereby improving the accuracy and reliability of the assembly.

[0091] In summary, an online assembly test method for the fixing process provided by the present invention has the following technical effects:

[0092] In this application, by obtaining the set of locking points of the target glass and the set of historical assembly logs, then performing a search for locking torque imbalance on the set of historical assembly logs to obtain a set of historical assembly logs with locking torque imbalance, and then traversing the set of historical assembly logs with locking torque imbalance to construct an association network for the set of locking points to obtain a locking point association topology network. Then, torque sensors are arranged at the locking points of the set of locking points respectively, and the set of arranged torque sensors is used to monitor the torque of the set of locking points in a preset monitoring window to obtain a set of monitored torque sequences. Furthermore, combined with the set of monitored torque sequences and the locking point association topology network, an assembly collaborative test analysis is performed to obtain an assembly test result. It achieves the technical effect of performing assembly test analysis from both the overall and local dimensions, identifying torque anomalies, and improving the reliability of the assembly.

[0093] Embodiment 2, based on the same inventive concept as the online assembly test method for the fixing process in the foregoing embodiment, as Figure 2 shown, this application provides an online assembly test system for the fixing process. The system in the embodiment of this application and the method embodiment are based on the same inventive concept. Among them, the system includes:

[0094] A historical assembly log set acquisition module 11, configured to acquire a set of locking points of the target glass and a set of historical assembly logs;

[0095] An unbalanced assembly log set acquisition module 12, configured to perform a search for locking torque imbalance on the set of historical assembly logs to obtain a set of historical assembly logs with locking torque imbalance;

[0096] A locking point association topology network acquisition module 13, configured to traverse the set of historical assembly logs with locking torque imbalance to construct an association network for the set of locking points to obtain a locking point association topology network;

[0097] A monitored torque sequence set acquisition module 14, configured to arrange torque sensors at the locking points of the set of locking points respectively, and use the set of arranged torque sensors to monitor the torque of the set of locking points in a preset monitoring window to obtain a set of monitored torque sequences;

[0098] An assembly test result acquisition module 15 is used to perform assembly collaborative test analysis by combining the set of monitored torque sequences and the locking point associated topology network to obtain an assembly test result.

[0099] Furthermore, the locking point associated topology network acquisition module 13 is used to perform the following steps:

[0100] Traverse the set of historical locking torque imbalance assembly logs to extract imbalance locking points, and obtain a set of imbalance locking point combinations;

[0101] Extract the top M locking points with the highest occurrence frequencies in the set of imbalance locking point combinations as M central locking points, where M is a positive integer;

[0102] Based on the set of imbalance locking point combinations, count the association frequencies of M sets of associated locking points associated with the M central locking points to obtain a set of M associated locking point association frequencies;

[0103] Based on the set of M associated locking point association frequencies, determine M sets of association weights, and combine the M central locking points, M sets of associated locking points, and the set of locking points to construct the locking point associated topology network.

[0104] Furthermore, the locking point associated topology network acquisition module 13 is used to perform the following steps:

[0105] Take the M central locking points as M first topology nodes;

[0106] Obtain a set of second topology nodes and connect them to the M first topology nodes respectively to obtain M sets of connection edges;

[0107] Use the M sets of association weights to identify the M connection edges to obtain M sets of identified connection edges;

[0108] Based on the M central locking points and the M sets of associated locking points, identify unassociated locking points in the set of locking points to obtain a set of third topology nodes;

[0109] Connect the set of third topology nodes to the first topology node closest to them among the M first topology nodes, and identify the connection edges according to the preset position association weights to obtain a set of third topology node identified connection edges;

[0110] Based on the M first topology nodes, the set of second topology nodes, the M sets of identified connection edges, the set of third topology nodes, and the set of third topology node identified connection edges, construct a topology network to obtain the locking point associated topology network.

[0111] Further, perform a union operation on the M sets of associated locking points to obtain the second set of topological nodes.

[0112] Further, determine whether there are locking points in the set of locking points that do not belong to the M central locking points and the M sets of associated locking points. If so, add them to the third set of topological nodes.

[0113] Further, the locking point associated topological network obtaining module 13 is used to execute the following steps:

[0114] Divide the associated frequency of each associated locking point in the M sets of associated locking point associated frequencies by the total frequency of the corresponding set of associated locking point associated frequencies to obtain the M sets of associated weights.

[0115] Further, the assembly test result obtaining module 15 is used to execute the following steps:

[0116] Traverse the set of monitored torque sequences to identify abnormal torque deviations, and perform abnormal identification on the set of locking points according to the identification results to obtain Q abnormal locking points, where Q is a positive integer;

[0117] Using the Q abnormal locking points as indexes, perform associated retrieval on the locking point associated topological network to determine Q sets of abnormal associated locking points and Q sets of abnormal identification connection edges;

[0118] Combined with the set of monitored torque sequences, perform assembly collaborative test analysis on the Q sets of abnormal associated locking points and the Q sets of abnormal identification connection edges to obtain the assembly test result.

[0119] Further, the assembly test result obtaining module 15 is used to execute the following steps:

[0120] Extract Q sets of abnormal associated monitored torque sequences of the Q sets of abnormal associated locking points from the set of monitored torque sequences;

[0121] Based on a preset locking torque sequence, perform deviation identification on the Q sets of abnormal associated monitored torque sequences to obtain Q sets of abnormal associated monitored torque deviation degrees;

[0122] Combine the weights of the Q sets of abnormal identification connection edges to perform weighted calculation on the Q sets of abnormal associated monitored torque deviation degrees to obtain Q abnormal associated deviation degrees;

[0123] Judge whether the Q abnormal associated deviation degrees are greater than or equal to a preset abnormal associated deviation degree threshold. If so, add the corresponding abnormal locking points and sets of abnormal associated locking points to the assembly test result.

[0124] Further, the assembly test result obtaining module 15 is configured to perform the following steps:

[0125] Randomly extract a first abnormal correlation monitoring torque sequence from the Q abnormal correlation monitoring torque sequence sets;

[0126] Calculate the deviation degree between the first abnormal correlation monitoring torque sequence and the preset locking torque sequence to obtain a first deviation degree sequence;

[0127] Calculate the mean value of the first deviation degree sequence to obtain the first abnormal correlation monitoring torque deviation degree;

[0128] According to the preset locking torque sequence, perform deviation identification on the remaining abnormal correlation monitoring torque sequences in the Q abnormal correlation monitoring torque sequence sets to obtain the Q abnormal correlation monitoring torque deviation degree sets.

[0129] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0130] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0131] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications 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 equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. An online assembly test method for a fixed process, characterized in that: The method comprises: Get the lock point set and historical assembly log set of the target glass; Performing a locking torque imbalance search on the historical assembly log set to obtain a historical locking torque imbalance assembly log set; Traversing the historical locking torque imbalance assembly log set, constructing an associated network for the locking point set, and obtaining a locking point associated topological network; Deploy torque sensors at the locking points of the locking point set respectively, and use the deployed torque sensor set to monitor the torque of the locking point set in a preset monitoring window to obtain a monitoring torque sequence set; Combining the monitoring torque sequence set and the locking point associated topological network to perform assembly collaborative test analysis to obtain assembly test results; Traversing the historical locking torque imbalance assembly log set, constructing an associated network for the locking point set, and obtaining a locking point associated topological network, including: Traversing the historical locking torque unbalanced assembly log set to extract unbalanced locking points and obtain an unbalanced locking point combination set; Extracting M locking points with the highest frequencies in the unbalanced locking point combination set as M central locking points, where M is a positive integer; Based on the unbalanced locking point combination set, counting the association frequencies of the M associated locking point sets associated with the M central locking points, to obtain the M associated locking point association frequency sets; Determine M association weight sets based on the M associated lock-payment point association frequency sets, and construct the lock-payment point association topology network in combination with the M central lock-payment points, the M associated lock-payment point sets and the lock-payment point sets; Combining the monitoring torque sequence set and the locking point associated topological network to perform assembly collaborative test analysis, obtaining assembly test results, including: Traversing the monitoring torque sequence set to identify abnormal torque deviation, marking the locking point set as abnormal according to the identification result, and obtaining Q abnormal locking points, where Q is a positive integer; Using the Q abnormal lock-payment points as indexes, performing an associated search on the lock-payment point associated topological network to determine Q abnormal associated lock-payment point sets and Q abnormal identification connection edge sets; In combination with the monitoring torque sequence set, assembly collaborative test analysis is performed on the Q abnormal associated locking point sets and the Q abnormal identification connection edge sets to obtain the assembly test result.

2. The online assembly test method of a fixing process as claimed in claim 1, characterized in that: Based on the M associated locking point association frequency sets, M associated weight sets are determined, and the locking point association topology network is constructed by combining the M central locking points, the M associated locking point sets and the locking point sets, including: The M central locking points are used as M first topological nodes; Obtain a second topological node set, and connect them to the M first topological nodes respectively to obtain M connection edge sets; Using the M association weight sets to identify the M connection edges, to obtain M identified connection edge sets; Based on the M central locking points and the M associated locking point sets, the locking point set is identified as having no associated locking points, so as to obtain a third topological node set; Connecting the third topological node set to the first topological node closest to the M first topological nodes, and marking the connection edges according to the preset position association weights to obtain a third topological node marked connection edge set; A topological network is constructed based on the M first topological nodes, the second topological node set, the M identification connection edge sets, the third topological node set and the third topological node identification connection edge set to obtain the lock point associated topological network.

3. The online assembly test method of a fixing process as claimed in claim 2, characterized in that: The M associated locking point sets are unioned to obtain the second topological node set.

4. The online assembly test method of a fixing process as claimed in claim 2, characterized in that: Determine whether there are any locking points in the locking point set that do not belong to the M central locking points and the M associated locking point sets. If so, add them to the third topological node set.

5. The online assembly test method of a fixing process as claimed in claim 1, characterized in that: Determining M association weight sets based on the M association lock point association frequency sets includes: The M association weight sets are obtained by respectively dividing the association frequency of one of the M association lock payment point association frequency sets by the total frequency of the corresponding association lock payment point association frequency sets.

6. The online assembly test method of a fixing process as claimed in claim 1, characterized in that: In combination with the monitoring torque sequence set, the assembly collaborative test analysis is performed on the Q abnormal associated locking point sets and the Q abnormal identification connection edge sets to obtain the assembly test result, including: Extracting Q abnormal associated monitoring moment sequence sets of the Q abnormal associated locking point sets from the monitoring moment sequence set; Based on the preset locking torque sequence, deviation identification is performed on the Q abnormal associated monitoring torque sequence sets to obtain Q abnormal associated monitoring torque deviation degree sets; Combining the weights of the Q abnormal identification connection edge sets, weighted calculation is performed on the Q abnormal association monitoring moment deviation sets to obtain Q abnormal association deviations; It is determined whether the Q abnormal correlation deviations are greater than or equal to a preset abnormal correlation deviation threshold. If so, the corresponding abnormal locking point and the abnormal correlation locking point set are added to the assembly test result.

7. The online assembly test method of a fixing process as claimed in claim 6, characterized in that: Based on the preset locking torque sequence, deviation identification is performed on the Q abnormal associated monitoring torque sequence sets to obtain Q abnormal associated monitoring torque deviation degree sets, including: Randomly extracting a first abnormality-associated monitoring moment sequence from the Q abnormality-associated monitoring moment sequence sets; Calculating the deviation between the first abnormality-related monitoring torque sequence and the preset locking torque sequence to obtain a first deviation sequence; Calculating the mean of the first deviation sequence to obtain a first abnormal associated monitoring torque deviation; According to the preset locking torque sequence, deviation identification is performed on the remaining abnormal associated monitoring torque sequences in the Q abnormal associated monitoring torque sequence sets to obtain the Q abnormal associated monitoring torque deviation degree sets.

8. An online assembly test system for a fixed process, characterized in that: include: A historical assembly log set acquisition module is used to acquire a lock point set and a historical assembly log set of a target glass; An unbalanced assembly log set acquisition module is used to perform a locking torque imbalance search on the historical assembly log set to obtain a historical locking torque imbalance assembly log set; A locking point associated topological network acquisition module is used to traverse the historical locking torque imbalance assembly log set to construct an associated network for the locking point set, and obtain a locking point associated topological network; A monitoring torque sequence set acquisition module is used to respectively deploy torque sensors at the locking points of the locking point set, and use the deployed torque sensor set to perform torque monitoring on the locking point set in a preset monitoring window to obtain a monitoring torque sequence set; The assembly test result acquisition module is used to perform assembly collaborative test analysis in combination with the monitoring torque sequence set and the locking point associated topological network to obtain the assembly test result.

Images