An automatic identification method for the number of times of tightening in an aircraft pipeline assembly process

Abstract

The application provides a kind of aircraft pipeline assembly process tightening frequency automatic identification method, it is related to aviation pipeline installation identification technical field.In the application, first, obtain first pipeline assembly data;Second, the first pipeline assembly data is filtered to abnormal data, and the second pipeline assembly data corresponding to the first pipeline assembly data is formed;Then, the target data extraction is carried out to the second pipeline assembly data, and the third pipeline assembly data corresponding to the second pipeline assembly data is formed;Finally, based on the third pipeline assembly data, the target tightening frequency is analyzed.Based on the above, the problem that the aircraft pipeline assembly process cannot be effectively monitored in the prior art can be improved.

Description

Technical Field

[0001] This application relates to the field of aviation pipeline installation identification technology, and more specifically, to a method for automatically identifying the number of tightening operations during aircraft pipeline assembly. Background Technology

[0002] With the rapid development of the aviation industry, the performance requirements of aircraft are becoming increasingly stringent, leading to higher demands on the assembly performance of piping systems. Traditional aircraft piping assembly is done manually, which, while offering some flexibility, has inherent limitations that become increasingly apparent when faced with complex and varied piping layouts. During the constant torque installation of conduits, the lack of effective process control means makes it difficult to ensure that operators perform the tightening operations according to the specified number of tightening cycles, even if the tightening torque can be controlled. This makes it challenging to guarantee the consistency and reliability of piping assembly. Furthermore, the inherent uncertainty of manual operation means that the same procedure performed by different operators may produce significant differences, increasing the potential risks to piping assembly quality. Therefore, how to maintain the flexibility of manual assembly while improving the controllability and traceability of the assembly process has become one of the urgent problems to be solved in achieving high-performance aircraft piping assembly. In summary, there is currently a lack of a method that can automatically identify and record the number of tightening cycles performed by operators, making it difficult to effectively monitor the assembly process, thereby standardizing the installation process and improving the assembly quality of aircraft piping. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide an automatic identification method for the number of tightening cycles in the aircraft piping assembly process, so as to improve the problem of difficulty in effectively monitoring the aircraft piping assembly process in the prior art.

[0004] To achieve the above objectives, this application adopts the following technical solution:

[0005] A method for automatically identifying the number of tightening cycles during aircraft piping assembly includes:

[0006] Acquire first pipeline assembly data, wherein the first pipeline assembly data includes torque data and outer nut rotation angle data obtained by monitoring the aircraft pipeline assembly process at multiple monitoring nodes during the tightening process;

[0007] Abnormal data filtering is performed on the first pipeline assembly data to form the second pipeline assembly data corresponding to the first pipeline assembly data;

[0008] The second pipeline assembly data is subjected to target data extraction to form the third pipeline assembly data corresponding to the second pipeline assembly data. The target data extraction is used to extract data related to the number of tightening times of the operator from the second pipeline assembly data.

[0009] Based on the assembly data of the third pipeline, the target tightening count is determined, wherein the target tightening count refers to the number of times the operator tightens the outer nut during the tightening process.

[0010] In a preferred embodiment of this application, in the above-mentioned automatic identification method for the number of tightening cycles during aircraft piping assembly, the step of obtaining the first piping assembly data includes:

[0011] Acquire raw piping assembly data, wherein the raw piping assembly data includes raw torque data and raw angle data of the outer nut rotation obtained by monitoring the aircraft piping assembly process at multiple monitoring nodes during the tightening process;

[0012] The original pipeline assembly data is filtered to form filtered pipeline assembly data, wherein the number of monitoring nodes corresponding to the filtering torque data and filtering angle data in the filtered pipeline assembly data is less than or equal to the number of multiple monitoring nodes.

[0013] The torque data of the original pipeline assembly data and the filtered pipeline assembly data are matched to form multiple matched torque data corresponding to the multiple monitoring nodes;

[0014] Based on the original angle data and matching torque data corresponding to the multiple monitoring nodes in the original pipeline assembly data, the first pipeline assembly data is formed by combining them.

[0015] In a preferred embodiment of this application, in the above-mentioned automatic identification method for the number of tightening operations during aircraft piping assembly, the step of matching the torque data of the original piping assembly data and the filtered piping assembly data to form multiple matched torque data corresponding to the multiple monitoring nodes includes:

[0016] The process begins by iterating through the original torque data corresponding to the first monitoring node in the original pipeline assembly data, and then continues by iterating through the filtered torque data corresponding to the first monitoring node in the filtered pipeline assembly data.

[0017] If the original torque data traversed at the moment is not equal to the filtered torque data traversed at the moment, then make the matching torque data corresponding to the monitoring node in the original pipeline assembly data traversed at the moment equal to the original torque data traversed at the moment, and traverse the next monitoring node in the original pipeline assembly data, and fix the monitoring node in the filtered pipeline assembly data.

[0018] If the current traversed raw torque data is equal to the current traversed filtered torque data, then set the matching torque data corresponding to the monitoring node in the current traversed raw pipeline assembly data to 0, and traverse the next monitoring node in the raw pipeline assembly data and the next monitoring node in the filtered pipeline assembly data.

[0019] In a preferred embodiment of this application, in the above-described automatic identification method for the number of tightening operations during aircraft piping assembly, the step of filtering abnormal data from the first piping assembly data to form second piping assembly data corresponding to the first piping assembly data includes:

[0020] The first pipeline assembly data is subjected to isolated data filtering to form isolated filtered pipeline assembly data.

[0021] Torque anomaly filtering is performed on the isolated filter pipeline assembly data to form torque filter pipeline assembly data;

[0022] The torque filter pipeline assembly data is subjected to extreme difference abnormality filtering to form extreme difference filter pipeline assembly data.

[0023] Based on the range filter pipeline assembly data, the second pipeline assembly data is obtained.

[0024] In a preferred embodiment of this application, in the above-mentioned automatic identification method for the number of tightening operations during aircraft piping assembly, the step of filtering the first piping assembly data into isolated filtered piping assembly data includes:

[0025] The process begins by traversing the first monitoring node in the first pipeline assembly data to form the currently traversed monitoring node;

[0026] If the currently traversed monitoring node belongs to the first monitoring node, then determine whether the torque data of the second monitoring node in the first pipeline assembly data is equal to 0. If it is equal to 0, the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined to be 0. Alternatively, if it is not equal to 0, the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined to be the torque data of the currently traversed monitoring node in the first pipeline assembly data.

[0027] If the currently traversed monitoring node does not belong to the first monitoring node and does not belong to the last monitoring node, then determine whether the torque data of the previous and next monitoring nodes of the currently traversed monitoring node in the first pipeline assembly data are both equal to 0. If they are both equal to 0, then the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined to be 0. Alternatively, if they are not both equal to 0, then the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined to be the torque data of the currently traversed monitoring node in the first pipeline assembly data.

[0028] If the currently traversed monitoring node is the last monitoring node, then determine whether the torque data of the previous monitoring node in the first pipeline assembly data is equal to 0. If it is equal to 0, then the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined to be 0. Alternatively, if it is not equal to 0, then the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined as the torque data of the currently traversed monitoring node in the first pipeline assembly data.

[0029] In a preferred embodiment of this application, in the above-mentioned automatic identification method for the number of tightening operations during aircraft piping assembly, the step of filtering the isolated filter piping assembly data for torque anomalies to form torque-filtered piping assembly data includes:

[0030] The process begins by traversing the data from the first monitoring node in the isolated filter pipeline assembly data to form the currently traversed monitoring node.

[0031] If the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is less than or equal to the predetermined critical torque data, then the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is determined to be 0.

[0032] If the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is greater than the critical torque data, then the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is determined as the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data.

[0033] In a preferred embodiment of this application, in the above-mentioned automatic identification method for the number of tightening cycles during aircraft piping assembly, the step of performing extreme difference abnormality filtering on the torque-filtered piping assembly data to form extreme difference filtered piping assembly data includes:

[0034] Starting from the first monitoring node in the torque filter pipeline assembly data, the current monitoring node is traversed, and the initial value of the target parameter is configured to 0.

[0035] If the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is equal to 0, then traverse to the next monitoring node.

[0036] If the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is not equal to 0, then the torque data of the currently traversed monitoring node in the range filter pipeline assembly data is determined based on the next node after the currently traversed monitoring node.

[0037] In a preferred embodiment of this application, in the above-mentioned automatic identification method for the number of tightening operations during aircraft piping assembly, the step of determining the torque data of the currently traversed monitoring node in the range filtering piping assembly data based on the next node after the currently traversed monitoring node includes:

[0038] If the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is not equal to 0, then the value of the target parameter is updated to form the current value, and the torque data of the currently traversed monitoring node in the range filter pipeline assembly data is assigned to the target matrix, wherein the number of torque data in the target matrix is ​​equal to the current value of the target parameter.

[0039] Determine whether the torque data of the next node after the currently traversed monitoring node is equal to 0 in the torque filter pipeline assembly data;

[0040] If the torque data of the next node in the torque filter pipeline assembly data is not equal to 0, then traverse the next monitoring node;

[0041] If the torque data of the next node in the torque filter pipeline assembly data is equal to 0, then the difference and ratio between the maximum and minimum values ​​in the torque data of the target matrix are determined. If the relationship between the difference and the ratio and the corresponding critical value does not meet the preset conditions, the target parameter is updated to the initial value and the next monitoring node is traversed. Alternatively, if the relationship between the difference and the ratio and the corresponding critical value meets the preset conditions, at least the torque data of the currently traversed monitoring node in the range filter pipeline assembly data is determined to be 0.

[0042] In a preferred embodiment of this application, in the above-described automatic identification method for the number of tightening operations during aircraft piping assembly, the step of extracting target data from the second piping assembly data to form third piping assembly data corresponding to the second piping assembly data includes:

[0043] Starting from the first monitoring node in the second pipeline assembly data, the process is traversed to form the currently traversed monitoring node, and it is determined whether the torque data of the currently traversed monitoring node in the second pipeline assembly data is equal to 0.

[0044] If the torque data of the currently traversed monitoring node in the second pipeline assembly data is equal to 0, then traverse the next monitoring node;

[0045] If the torque data of the currently traversed monitoring node in the second pipeline assembly data is not equal to 0, then the torque data and angle data of the currently traversed monitoring node in the second pipeline assembly data are determined as the torque data and angle data of a monitoring node in the third pipeline assembly data, and the next monitoring node is traversed.

[0046] In a preferred embodiment of this application, in the above-mentioned automatic identification method for the number of tightening operations during aircraft piping assembly, the step of analyzing the target number of tightening operations based on the third piping assembly data includes:

[0047] Starting from the first monitoring node in the third pipeline assembly data, the process is traversed to form the currently traversed monitoring node, and it is determined whether the torque data of the currently traversed monitoring node in the third pipeline assembly data is equal to 0, and whether the torque data of the next monitoring node after the currently traversed monitoring node in the third pipeline assembly data is equal to 0.

[0048] If the torque data of the currently traversed monitoring node in the third pipeline assembly data is equal to 0, or if the torque data of the next monitoring node after the currently traversed monitoring node in the third pipeline assembly data is not equal to 0, then traverse the next monitoring node.

[0049] If the torque data of the currently traversed monitoring node in the third pipeline assembly data is not equal to 0, and the torque data of the next monitoring node after the currently traversed monitoring node in the third pipeline assembly data is equal to 0, then the target tightening count is updated, and the next monitoring node is traversed.

[0050] This application provides an automatic identification method for the number of tightening operations during aircraft piping assembly. First, first piping assembly data is acquired. Second, abnormal data is filtered from the first piping assembly data to form second piping assembly data corresponding to the first data. Then, target data is extracted from the second piping assembly data to form third piping assembly data corresponding to the second data. Finally, based on the third data, the target number of tightening operations is analyzed. Based on the above, on the one hand, because the torque data applied by the operator and the rotation angle data of the outer nut during tightening, obtained from monitoring the aircraft piping assembly process at multiple monitoring nodes, can be analyzed, the number of tightening operations during the tightening of the outer nut can be determined. On the other hand, because the data is filtered and extracted after acquisition and before analysis, the reliability of the analyzed data is high, ensuring the reliability and accuracy of the determined target number of tightening operations. This achieves effective monitoring of the aircraft piping assembly process, thus improving the problem of difficulty in effectively monitoring the aircraft piping assembly process in existing technologies. Attached Figure Description

[0051] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings.

[0052] Figure 1 A structural block diagram of an electronic device provided in an embodiment of this application.

[0053] Figure 2 This is a flowchart illustrating the automatic identification method for the number of tightening cycles during aircraft piping assembly provided in this application embodiment.

[0054] Figure 3 A schematic diagram of the original data provided in the embodiments of this application.

[0055] Figure 4 This is a schematic diagram of the filtered data provided in an embodiment of this application.

[0056] Figure 5 This is a schematic diagram illustrating the matching of raw data and filtered data provided in the embodiments of this application.

[0057] Figure 6 This is a schematic diagram of the torque data matching process provided in the embodiments of this application.

[0058] Figure 7 This is a schematic diagram of the isolated data filtering process provided in an embodiment of this application.

[0059] Figure 8 This is a schematic diagram of isolated data filtering provided in an embodiment of this application.

[0060] Figure 9 This is a schematic diagram of the torque anomaly filtering process provided in an embodiment of this application.

[0061] Figure 10 This is a schematic diagram of torque anomaly filtering provided in an embodiment of this application.

[0062] Figure 11 This is a schematic diagram of the extreme abnormality filtering process provided in an embodiment of this application.

[0063] Figure 12 This is a schematic diagram of extremely abnormal filtering provided in an embodiment of this application.

[0064] Figure 13 This is a schematic diagram of the target data extraction process provided in an embodiment of this application.

[0065] Figure 14 This is a schematic diagram of the target data after extraction, provided in an embodiment of this application.

[0066] Figure 15 This is a flowchart illustrating the process of analyzing the target number of tightening cycles provided in an embodiment of this application.

[0067] Figure 16 This is a schematic diagram illustrating the number of tightening operations for the target analysis provided in this application embodiment.

[0068] Figure 17 Another schematic diagram of the original data provided for the embodiments of this application.

[0069] Figure 18 Another schematic diagram of filtered data provided in an embodiment of this application.

[0070] Figure 19 Another schematic diagram illustrating the matching of raw data and filtered data provided in the embodiments of this application.

[0071] Figure 20 This is another schematic diagram showing isolated data filtering provided in an embodiment of this application.

[0072] Figure 21 This is another schematic diagram showing the torque anomaly filtering provided in an embodiment of this application.

[0073] Figure 22 Another schematic diagram provided for an embodiment of this application after extreme anomaly filtering.

[0074] Figure 23 This is another schematic diagram showing the extracted target data provided in an embodiment of this application.

[0075] Figure 24 Another schematic diagram illustrating the number of tightening targets provided in this application embodiment.

[0076] Figure 25 A block diagram illustrating the automatic identification device for the number of tightening cycles during aircraft piping assembly provided in this application embodiment. Detailed Implementation

[0077] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0078] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0079] like Figure 1 As shown in the figure, this application provides an electronic device. The electronic device may include a memory, a processor, and an automatic recognition device for the number of tightening cycles during aircraft piping assembly.

[0080] In detail, the memory and the processor are electrically connected directly or indirectly to enable data transmission or interaction. For example, the memory and the processor can be electrically connected via one or more communication buses or signal lines. The automatic identification device for the number of tightening cycles in the aircraft piping assembly process includes at least one software functional module stored in the memory in the form of software or firmware. The processor is used to execute the executable computer program stored in the memory, such as the software functional module and computer program included in the automatic identification device for the number of tightening cycles in the aircraft piping assembly process, to implement the automatic identification method for the number of tightening cycles in the aircraft piping assembly process provided in this application embodiment.

[0081] Optionally, the memory may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0082] Optionally, the processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a system on chip (SoC), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0083] Understandable. Figure 1 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown may include, for example, a communication unit for exchanging information with other devices.

[0084] Combination Figure 2 This application also provides an automatic identification method for the number of tightening cycles in an aircraft piping assembly process, applicable to the aforementioned electronic device. The method steps defined in the relevant process of the automatic identification method for the number of tightening cycles in an aircraft piping assembly process can be implemented by the electronic device. The following will describe... Figure 2 The specific process shown will be explained in detail.

[0085] Step S110: Obtain the first pipeline assembly data.

[0086] In this embodiment, the electronic device can acquire first pipeline assembly data. This first pipeline assembly data includes torque data and outer nut rotation angle data applied by the operator during tightening at multiple monitoring nodes (which can be time nodes arranged according to the chronological order of operations), obtained by monitoring the aircraft pipeline assembly process. For example, the torque data and outer nut rotation angle data applied by the operator during tightening are available at the first monitoring node, at the second monitoring node, and at the third monitoring node.

[0087] Step S120: Filter abnormal data from the first pipeline assembly data to form the second pipeline assembly data corresponding to the first pipeline assembly data.

[0088] In this embodiment of the application, after obtaining the first pipeline assembly data, the electronic device can perform abnormal data filtering on the first pipeline assembly data to form the second pipeline assembly data corresponding to the first pipeline assembly data. This can also be understood as updating or correcting the abnormal data, so that in the subsequent target data extraction process, data related to the number of tightening operations by the operator can be effectively extracted, thereby improving the reliability of the number of times analysis.

[0089] Step S130: Extract target data from the second pipeline assembly data to form the third pipeline assembly data corresponding to the second pipeline assembly data.

[0090] In this embodiment, after the second pipeline assembly data is generated, the electronic device can perform target data extraction on the second pipeline assembly data to generate third pipeline assembly data corresponding to the second pipeline assembly data. Specifically, target data extraction is used to extract data related to the number of tightening operations performed by the operator from the second pipeline assembly data.

[0091] Step S140: Based on the assembly data of the third pipeline, analyze the target number of tightening cycles.

[0092] In this embodiment of the application, after the third pipeline assembly data is generated, the electronic device can analyze the target tightening count based on the third pipeline assembly data. The target tightening count refers to the number of times the operator tightens the outer nut during the tightening process.

[0093] Based on the above, on the one hand, since the torque data and outer nut rotation angle data obtained from monitoring the aircraft piping assembly process at multiple monitoring nodes can be analyzed, the number of tightening operations performed by the operator during the tightening process of the outer nut can be determined. On the other hand, since the data is filtered and extracted after acquisition and before analysis, the reliability of the data analyzed by the user is high, ensuring the reliability and accuracy of the determined target number of tightening operations. This enables effective monitoring of the aircraft piping assembly process, thus improving the problem of difficulty in effectively monitoring the aircraft piping assembly process in existing technologies.

[0094] Firstly, regarding step S110, it should be noted that the specific method for obtaining the first pipeline assembly data is not limited and can be selected according to actual needs.

[0095] For example, in an alternative implementation, the acquired raw piping assembly data can be directly used as the first piping assembly data, which can improve the efficiency of automatic identification of tightening counts to a certain extent. The raw piping assembly data includes raw torque data applied by the operator during tightening and raw angle data of the outer nut rotation obtained from monitoring the aircraft piping assembly process at multiple monitoring nodes.

[0096] For example, in another alternative implementation, in order to improve the reliability of the first pipeline assembly data obtained, so as to ensure that the basis for subsequent frequency analysis is more reliable, the above-mentioned step S110 may further include steps S111, S112, S113 and S114, the specific contents of each step are as follows.

[0097] Step S111: Obtain the original pipeline assembly data.

[0098] In this embodiment, raw piping assembly data can be obtained. This raw piping assembly data includes raw torque data applied by the operator during tightening and raw angle data of the outer nut rotation, obtained by monitoring the aircraft piping assembly process at multiple monitoring nodes. For example, the raw angle data can be represented as AP. i (i=1,2,...,m), the original torque data can be represented as TP i (i=1,2,...,m), where i represents the monitoring node and m represents the number of monitoring nodes. Additionally, in some other implementations, AP can also be used... i and TP i By combining the data, we obtain the original data DP. i ,Right now

[0099] ;

[0100] Furthermore, it can be done via AP i x-axis, TP i Using the vertical axis as the ordinate, plot a line chart to obtain the original data line chart, as shown below. Figure 3 As shown, AP1 and TP1 are data points in the original data line chart, and AP2 and TP2 are data points in the original data line chart. m TP m This refers to a data point in the original data line chart.

[0101] Step S112: Filter the original pipeline assembly data to form filtered pipeline assembly data.

[0102] In this embodiment, after obtaining the original pipeline assembly data, the original pipeline assembly data can be filtered to form filtered pipeline assembly data. The number of monitoring nodes corresponding to the filtering torque data and filtering angle data in the filtered pipeline assembly data is less than or equal to the number of multiple monitoring nodes. Furthermore, it should be noted that filtering can be implemented using various filters, such as low-pass filters, high-pass filters, Kalman filters, etc., or filters configured according to actual conditions can be used. The filtering process is not the focus of this application, and therefore will not be elaborated upon here. For example, the filtering angle data in the filtered pipeline assembly data can be represented as AF. j (j=1,2,...,n), the filtering torque data can be represented as TF. j (j=1,2,...,n), where j represents the number of monitoring nodes and n represents the number of monitoring nodes. Additionally, in some other implementations, AF can also be used... j and TF j By combining the data, the filtered data DF is obtained. j ,Right now:

[0103] ;

[0104] Furthermore, it can be done with AF i x-axis, TF i Using the vertical axis as the ordinate, plot a line graph to obtain a line graph of the filtered data, as shown below. Figure 4 As shown, AF1 and TF1 are data points in the line graph of the filtered data, and AF2 and TF2 are data points in the line graph of the filtered data. n TF n This is a data point in the line graph of the filtered data.

[0105] Step S113: Match the torque data of the original pipeline assembly data and the filtered pipeline assembly data to form multiple matched torque data corresponding to the multiple monitoring nodes.

[0106] In this embodiment, after the filter pipeline assembly data is generated, torque data can be matched between the original pipeline assembly data and the filter pipeline assembly data to generate multiple matched torque data corresponding to the multiple monitoring nodes. That is, the torque data can be matched as follows: Figure 3 The original data shown and Figure 4 Match the filtered data shown, such as Figure 5 As shown.

[0107] Step S114: Based on the multiple original angle data corresponding to the multiple monitoring nodes and the multiple matching torque data corresponding to the multiple monitoring nodes in the original pipeline assembly data, the first pipeline assembly data is formed by combining them.

[0108] In this embodiment of the application, after forming multiple matching torque data corresponding to the multiple monitoring nodes, the first pipeline assembly data can be formed by combining the multiple original angle data corresponding to the multiple monitoring nodes and the multiple matching torque data corresponding to the multiple monitoring nodes in the original pipeline assembly data.

[0109] It is understood that in step S113 above, the specific method of matching the torque data of the original pipeline assembly data and the filtered pipeline assembly data is not limited. For example, in an alternative embodiment, in order to ensure the accuracy of the matching and avoid omission of some data of the monitoring node in the matching process, step S113 above may further include steps S113a, S113b and S113c, the specific contents of each step are as follows.

[0110] Step S113a: Start traversing the original torque data corresponding to the first monitoring node in the original pipeline assembly data, and start traversing the filtered torque data corresponding to the first monitoring node in the filtered pipeline assembly data.

[0111] In this embodiment, the traversal can begin with the original torque data corresponding to the first monitoring node in the original pipeline assembly data, thus sequentially forming the first, second, and third traversed original torque data, etc. Similarly, the traversal can begin with the filtering torque data corresponding to the first monitoring node in the filtering pipeline assembly data, thus sequentially forming the first, second, and third traversed filtering torque data, etc.

[0112] Step S113b: If the original torque data traversed at the moment is not equal to the filtered torque data traversed at the moment, then make the matching torque data corresponding to the monitoring node in the original pipeline assembly data traversed at the moment equal to the original torque data traversed at the moment, and traverse the next monitoring node in the original pipeline assembly data, and fix the monitoring node in the filtered pipeline assembly data.

[0113] In this embodiment, after forming the currently traversed original torque data and the currently traversed filtered torque data, if the currently traversed original torque data is not equal to the currently traversed filtered torque data, then the matching torque data corresponding to the monitoring node in the currently traversed original pipeline assembly data is made equal to the currently traversed original torque data. Furthermore, the next monitoring node in the original pipeline assembly data is traversed, and the monitoring node in the filtered pipeline assembly data is fixed, i.e., the currently traversed filtered torque data remains unchanged.

[0114] Step S113c: If the currently traversed original torque data is equal to the currently traversed filtered torque data, then set the matching torque data corresponding to the monitoring node in the currently traversed original pipeline assembly data to 0, and traverse the next monitoring node in the original pipeline assembly data and the next monitoring node in the filtered pipeline assembly data.

[0115] In this embodiment of the application, after forming the currently traversed original torque data and the currently traversed filtered torque data, if the currently traversed original torque data is equal to the currently traversed filtered torque data, then the matching torque data corresponding to the monitoring node in the currently traversed original pipeline assembly data is set to 0, and the next monitoring node in the original pipeline assembly data and the next monitoring node in the filtered pipeline assembly data are traversed.

[0116] In other words, a matching torque array can be configured as TM. i (i=1,2,...,m), where i represents the monitoring node and m represents the number of monitoring nodes. Then, combined with... Figure 6 Starting from the first monitoring node i=1 and ending at i=m, for TP i Perform a traversal and compare TP in a loop. i and TF j The value of TP. Based on this, if TP i ≠TF j Then TM i =TP i Increment the value of i by one, leave the value of j unchanged, and proceed to the next loop. If TP i =TF j Then TM iWhen the value of i equals 0, the value of i is incremented by one, and the value of j is also incremented by one, then the next loop begins. The specific loop rules are as follows:

[0117] ;

[0118] ;

[0119] When j=n, and TP is satisfied at the same time i =TF j At that time, the value of j no longer increases. After the traversal is complete, TM i There are a total of m numbers, which are the multiple matching torque data.

[0120] Secondly, regarding step S120, it should be noted that the specific method for filtering abnormal data in the first pipeline assembly data is not limited and can be selected according to actual needs.

[0121] For example, in an alternative implementation, the first pipeline assembly data may be subjected to any one of isolated data filtering, torque anomaly filtering, and extreme anomaly filtering, i.e., only isolated data filtering, or only torque anomaly filtering, or only extreme anomaly filtering, or multiple types of isolated data filtering, torque anomaly filtering, and extreme anomaly filtering may be performed in parallel. For example, the first pipeline assembly data may be subjected to isolated data filtering, torque anomaly filtering, and extreme anomaly filtering respectively, and then the intersection of the multiple filtering results may be performed to form the second pipeline assembly data.

[0122] For example, in another alternative implementation, in order to ensure the accuracy of filtration, multiple filtrations can be performed sequentially. Based on this, the above-mentioned step S120 can further include steps S121, S122, S123 and S124, the specific contents of each step are as follows.

[0123] Step S121: Perform isolated data filtering on the first pipeline assembly data to form isolated filtered pipeline assembly data.

[0124] In this embodiment of the application, the first pipeline assembly data can be filtered for isolated data to form isolated filtered pipeline assembly data. That is, the isolated points in the first pipeline assembly data are updated and corrected, so that the accuracy of the subsequent analysis process is higher.

[0125] Step S122: Perform torque anomaly filtering on the isolated filter pipeline assembly data to form torque filter pipeline assembly data.

[0126] In this embodiment, after the isolated filter pipeline assembly data is generated, torque anomaly filtering can be performed on the isolated filter pipeline assembly data to generate torque-filtered pipeline assembly data. That is, smaller torques in the isolated filter pipeline assembly data can be updated and corrected, resulting in higher accuracy in subsequent analysis.

[0127] Step S123: Perform extreme difference abnormal filtering on the torque filter pipeline assembly data to form extreme difference filter pipeline assembly data.

[0128] In this embodiment, after the torque filtering pipeline assembly data is generated, the torque filtering pipeline assembly data can be subjected to range anomaly filtering to generate range filtering pipeline assembly data. That is, data with small ranges in the range filtering pipeline assembly data can be updated and corrected, resulting in higher accuracy in subsequent analysis processes.

[0129] Step S124: Based on the range filter pipeline assembly data, obtain the second pipeline assembly data.

[0130] In this embodiment of the application, after the range filter piping assembly data is formed, second piping assembly data can be obtained based on the range filter piping assembly data. For example, after the aforementioned three types of filtering (updating, correction), the formed range filter piping assembly data can have high accuracy, and therefore can be directly used as the second piping assembly data.

[0131] It is understood that the specific method for filtering isolated data in step S121 above is not limited. For example, in an alternative embodiment, in order to fully identify isolated data, step S121 above may include the following:

[0132] First, start traversing from the first monitoring node in the first pipeline assembly data to form the currently traversed monitoring node;

[0133] Secondly, if the currently traversed monitoring node belongs to the first monitoring node, then it is determined whether the torque data of the second monitoring node in the first pipeline assembly data is equal to 0. If it is equal to 0 (i.e., it belongs to isolated data), the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined to be 0. Alternatively, if it is not equal to 0 (i.e., it does not belong to isolated data), the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined to be the torque data of the currently traversed monitoring node in the first pipeline assembly data. That is, the torque data is maintained without updating or correcting.

[0134] Moreover, if the currently traversed monitoring node does not belong to the first monitoring node and does not belong to the last monitoring node, determine whether the torque data of the previous monitoring node and the next monitoring node of the currently traversed monitoring node in the first pipeline assembly data are both equal to 0. When both are equal to 0 (i.e., it belongs to isolated data), determine the torque data of the currently traversed monitoring node in the isolated filtered pipeline assembly data as 0. Or, when they are not both equal to 0 (i.e., it does not belong to isolated data, such as one is equal to 0 and the other is not equal to 0, or both are not equal), determine the torque data of the currently traversed monitoring node in the isolated filtered pipeline assembly data as the torque data of the currently traversed monitoring node in the first pipeline assembly data, that is, maintain the torque data without updating or correcting it.

[0135] In addition, if the currently traversed monitoring node belongs to the last monitoring node, determine whether the torque data of the previous monitoring node of the currently traversed monitoring node in the first pipeline assembly data is equal to 0. When it is equal to 0 (i.e., it belongs to isolated data), determine the torque data of the currently traversed monitoring node in the isolated filtered pipeline assembly data as 0. Or, when it is not equal to 0 (i.e., it does not belong to isolated data), determine the torque data of the currently traversed monitoring node in the isolated filtered pipeline assembly data as the torque data of the currently traversed monitoring node in the first pipeline assembly data, that is, maintain the torque data without updating or correcting it.

[0136] Specifically, in combination with Figure 7 , starting from the first monitoring node i = 1 until i = m ends, traverse TM i and calculate the values of the two before and after TM i . When i = 1, if TM2 = 0, then consider TM1 as an isolated point and set TM1 = 0; if TM2 ≠ 0, then consider TM1 as a continuous point and do not change the value of TM1. When 1 < i < m, if TM i-1 = 0 and TM i+1 = 0, then consider TM i as an isolated point and set TM i = 0; if TM i-1 ≠ 0 or TM i+1 ≠ 0, then consider TM i as a continuous point and do not change the value of TM i . When i = m, if TM m-1 = 0, then consider TM m as an isolated point and set TM m = 0; if TM m-1 ≠ 0, then consider TM m not as an isolated point and do not change the value of TM mThe value of . The specific judgment rules are as follows:

[0137] .

[0138] After the traversal is complete, TM i There are m numbers in total, and TM is at this time. i There are no data points where both the beginning and end are zero. Therefore, using AP... i x-axis, TM i Use the vertical axis to plot a scatter plot, such as... Figure 8 As shown.

[0139] It is understood that the specific method of filtering for torque anomalies in isolated filter pipeline assembly data in step S122 above is not limited. For example, in an alternative embodiment, in order to fully identify excessively small torques, step S122 above may include the following:

[0140] First, the monitoring node is traversed starting from the first monitoring node in the isolated filter pipeline assembly data to form the currently traversed monitoring node;

[0141] Secondly, if the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is less than or equal to the predetermined critical torque data (i.e. the torque data is too small), then the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is determined to be 0.

[0142] Secondly, if the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is greater than the critical torque data (i.e., the torque data is relatively large), then the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is determined as the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data, that is, the torque data is maintained without updating or correcting.

[0143] Specifically, combined Figure 9 Starting from the first monitoring node i=1 and ending at i=m, for TM i Perform a traversal and compare TM. i The magnitude between the critical torque data CT and the TM. i If the torque value is less than or equal to CT, then the torque data is considered too low, causing TM to be affected. i =0. If TM i If the torque value is greater than CT, then the torque data is considered to meet the requirements, and TM is not changed. i The value of . The specific judgment rules are as follows:

[0144] .

[0145] Based on this, after the traversal is complete, TM i There are m numbers in total. At this point, TM i There are several consecutive data segments, and the rest of the data are all 0. Thus, using AP... i x-axis, TM i Use the vertical axis to plot a scatter plot, such as... Figure 10 As shown, AP1 and TM1 are data points in the scatter plot, and AP2 and TM2 are data points in the scatter plot. m TM m is a data point in the scatter plot.

[0146] It is understood that the specific method of performing extreme difference filtering on the torque filter pipeline assembly data in step S123 above is not limited. For example, in an alternative embodiment, in order to fully identify excessively small ranges, step S123 above may include the following:

[0147] First, starting from the first monitoring node in the torque filter pipeline assembly data, the current monitoring node is traversed to form the current monitoring node, and the initial value of the target parameter is configured to 0.

[0148] Secondly, if the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is equal to 0, then traverse the next monitoring node, and directly use the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data as the torque data of the currently traversed monitoring node in the range filter pipeline assembly data.

[0149] Furthermore, if the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is not equal to 0, then based on the next node of the currently traversed monitoring node, the torque data of the currently traversed monitoring node in the range filter pipeline assembly data is determined. In the process of determining the torque data, the range of at least one relevant torque data is analyzed, and the torque data in the range filter pipeline assembly data is determined based on the analysis results.

[0150] For example, in an alternative implementation, the step of determining the torque data of the currently traversed monitoring node in the range filter pipeline assembly data based on the next node of the currently traversed monitoring node if the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is not equal to 0 may include the following:

[0151] First, if the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is not equal to 0, then the value of the target parameter is updated (for example, by incrementing by one) to form the current value, and the torque data of the currently traversed monitoring node in the range filter pipeline assembly data is assigned to the target matrix (initially an empty set), wherein the number of torque data in the target matrix is ​​equal to the current value of the target parameter;

[0152] Secondly, determine whether the torque data of the next node after the currently traversed monitoring node is equal to 0 in the torque filter pipeline assembly data;

[0153] Furthermore, if the torque data of the next node in the torque filter pipeline assembly data is not equal to 0, then the next monitoring node is traversed, and the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is directly used as the torque data of the currently traversed monitoring node in the range filter pipeline assembly data.

[0154] Additionally, if the torque data of the next node in the torque filter pipeline assembly data is equal to 0, then the difference and ratio between the maximum and minimum values ​​in the torque data of the target matrix are determined. Furthermore, if the relationship between the difference and the ratio and the corresponding critical value does not meet the preset conditions, the target parameter is updated to the initial value (e.g., 0), and the next monitoring node is traversed (and the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is directly used as the torque data of the currently traversed monitoring node in the range filter pipeline assembly data). Alternatively, if the relationship between the difference and the ratio and the corresponding critical value meets the preset conditions, at least the torque data of the currently traversed monitoring node in the range filter pipeline assembly data is determined to be 0.

[0155] Specifically, combined Figure 11 Starting from the first monitoring node i=1 and ending at i=m-1, for TM i Perform the traversal. The initial value of j is 0. Define the critical extreme value difference as ML, and the critical extreme value ratio as MR. If TM i If TMi = 0, skip this iteration. If TMi ≠ 0, increment j by 1 and set TMi = 0. i The value is assigned to the temporary matrix T j Simultaneously, the following judgments are made:

[0156] If TM i+1 If T = 0, then it is considered that the end of a continuous data segment has been reached. Calculate T. j The range d = max(T) j )-min(T j ). Calculate T jThe ratio of extreme values ​​r = min(T) j ) / max(T j After the calculation is complete, clear T. j The value of is determined, and the values ​​of d and r are compared. If d < ML and r > MR, then from k=1 to k=j, TM is set. i-k+1 All values ​​are set to 0. After evaluating d and r, the value of j is set to 0. Based on this, after the traversal is complete, AP is used as the basis for... i x-axis, TM i Use the vertical axis to plot a scatter plot, such as... Figure 12 As shown, AP1 and TM1 are data points in the scatter plot, and AP2 and TM2 are data points in the scatter plot. m TM m is a data point in the scatter plot.

[0157] Thirdly, regarding step S130, it should be noted that the specific method for extracting target data from the second pipeline assembly data is not limited and can be selected according to actual needs.

[0158] For example, in an alternative implementation, data related to the number of tightening operations performed by the operator can be extracted by extracting non-zero values ​​from the torque data. Specifically, step S130 above can further include the following:

[0159] First, start traversing from the first monitoring node in the second pipeline assembly data to form the currently traversed monitoring node, and determine whether the torque data of the currently traversed monitoring node in the second pipeline assembly data is equal to 0.

[0160] Secondly, if the torque data of the currently traversed monitoring node in the second pipeline assembly data is equal to 0, then traverse the next monitoring node;

[0161] In addition, if the torque data of the currently traversed monitoring node in the second pipeline assembly data is not equal to 0, then the torque data and angle data of the currently traversed monitoring node in the second pipeline assembly data are determined as the torque data and angle data of a monitoring node in the third pipeline assembly data, and the next monitoring node is traversed.

[0162] Specifically, combined Figure 13 You can first set up AP i and TM i By combining the data, we obtain the identification data DM. i ,Right now:

[0163] ;

[0164] Then, the retained angle data AZ can be initialized. r (r=1,2,...,s) and retaining torque data TZ r ,(r=1,2,...,s), where r represents the number of monitoring nodes and s represents the number of monitoring nodes. Starting from i=1, for TM i Perform the traversal. The initial value of s is 1. If TM i If TM = 0, then skip this iteration. i If ≠0, then s increments by 1, TZ s =TM i AZ s =AP i Thus, the extracted non-zero value data is DZ. r :

[0165] .

[0166] Based on this, AZ r x-axis, TZ r Use the vertical axis to plot a scatter plot, such as... Figure 14 As shown, AZ1 and TZ1 are data points in the scatter plot, and AZ2 and TZ2 are data points in the scatter plot. m TZ m is a data point in the scatter plot.

[0167] Fourthly, regarding step S140, it should be noted that the specific method for analyzing the target number of tightening cycles is not limited and can be selected according to actual needs.

[0168] For example, in an alternative implementation, in order to reliably determine the number of tightening operations performed by the operator, continuous non-zero segments in the third pipeline assembly data can be analyzed and identified. Based on this, step S140 above can further include the following:

[0169] First, the traversal can start from the first monitoring node in the third pipeline assembly data to form the currently traversed monitoring node, and determine whether the torque data of the currently traversed monitoring node in the third pipeline assembly data is equal to 0, and whether the torque data of the next monitoring node after the currently traversed monitoring node in the third pipeline assembly data is equal to 0.

[0170] Secondly, if the torque data of the currently traversed monitoring node in the third pipeline assembly data is equal to 0, or if the torque data of the next monitoring node after the currently traversed monitoring node in the third pipeline assembly data is not equal to 0, then traverse the next monitoring node.

[0171] Additionally, if the torque data of the currently traversed monitoring node in the third pipeline assembly data is not equal to 0, and the torque data of the next monitoring node after the currently traversed monitoring node in the third pipeline assembly data is equal to 0, then the target tightening count is updated (for example, it can be incremented by one), and the next monitoring node is traversed.

[0172] Specifically, combined Figure 15 TM in the third pipeline assembly data i The value is an alternating sequence of consecutive zero values ​​and consecutive non-zero values, and the TM is statistically analyzed. i The number of consecutive non-zero value segments in the data yields the number of tightening operations performed by the operator. The initial value of the tightening count C is 1. The calculation method is as follows:

[0173] Starting from the first monitoring node i=1 and ending at i=m-1, for TM i Perform a traversal.

[0174] If TM i =0 or TM i+1 If ≠0, skip this iteration; if TM i ≠ 0 and TM i+1 If the sum is 0, then it is considered that the end of a continuous non-zero value segment has been reached, and the number of tightening times is C = C + 1.

[0175] Based on this, AF i x-axis, TF i Plot a line graph with the vertical axis as the ordinate, and then in the same image, plot the lines using AZ... r TZ is the x-axis. r Using the y-axis as the plotting axis, a scatter plot can be created to add non-zero values ​​to the original line chart (e.g., ...). Figure 16 This process yields a composite image of the recognition results. In this composite image, each consecutive scatter plot represents one tightening operation. By plotting the image, the corresponding positions of the identified tightening counts can be visually observed.

[0176] To facilitate understanding of the above-mentioned automatic identification method for the number of tightening cycles in the aircraft piping assembly process, the following examples are provided in this application embodiment, combining two specific application scenarios.

[0177] Example 1

[0178] In this embodiment, an aircraft hydraulic line with a conduit diameter of 14 mm was selected for automatic identification of the number of tightening cycles.

[0179] Step S1: Match the original data and the filtered data

[0180] The raw data in Example 1 consisted of 264 data pairs. (Based on AP)i TP is the x-axis. i A line chart is drawn using the vertical axis to obtain the original data line chart, as shown below. Figure 3 As shown, AP1 and TP1 are data points in the original data line chart, and AP2 and TP2 are data points in the original data line chart. 264 TP 264 This refers to a data point in the original data line chart.

[0181] ;

[0182] The filtered data in Example 1 consists of 107 data pairs. Using AF... j x-axis, TF j A line graph is plotted on the ordinate to obtain the filtered data, as shown below. Figure 4 As shown, AF1 and TF1 are data points in the line graph of the filtered data, and AF2 and TF2 are data points in the line graph of the filtered data. 107 TF 107 This is a data point in the line graph of the filtered data.

[0183] ;

[0184] Starting from the first data point (i.e., the monitoring node mentioned above) i=1 and ending at i=264, for TP i Perform a traversal and compare TP in a loop. i and TF j The value of TF. j The value of changes depending on the comparison result, and its initial value is TF1. Result 1: TPi ≠ TFj, then TMi = TPi, the value of i is incremented by one, the value of j remains unchanged, and the loop continues. Result 2: TPi = TFj, then TMi = 0, the value of i is incremented by one, and the value of j is also incremented by one, and the loop continues. When j = n, and result 2 is satisfied, the value of j no longer increases.

[0185] After the traversal is complete, TM i There are 264 numbers in total. (Based on AP) i x-axis, TM i Use the vertical axis to plot a scatter plot, such as... Figure 5 As shown, AP1 and TM1 are data points in the scatter plot, and AP2 and TM2 are data points in the scatter plot. 264 TM 264 is a data point in the scatter plot.

[0186] Step S2: Filter out isolated data points

[0187] Starting from the first data point i = 1 until i = 264 ends, traverse TM i and calculate the values of TM i before and after.

[0188] When i = 1, if TM2 = 0, then consider TM1 as an isolated point and set TM1 = 0; if TM2 ≠ 0, then consider TM1 as a continuous point and do not change the value of TM2.

[0189] When 1 < i < 264, if TM i-1 = 0 and TM i+1 = 0, then consider TM i as an isolated point and set TM i = 0; if TM i-1 ≠ 0 or TM i+1 ≠ 0, then consider TM i as a continuous point and do not change the value of TM[[ID=2​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​If TMi > 2, then the torque value is considered to meet the requirements, and TM is not changed. i The value of .

[0196] After the traversal is complete, TM i There are 264 numbers in total. At this point, TM... i There are several consecutive data segments, with the rest being 0. Plot a scatter plot with APi as the x-axis and TMi as the y-axis, as shown below. Figure 9 As shown, AP1 and TM1 are data points in the scatter plot, and AP2 and TM2 are data points in the scatter plot. 264 TM 264 is a data point in the scatter plot.

[0197] Step S4: Filter data points with excessively small ranges

[0198] Starting from the first data point i=1 and ending at i=263, for TM i Perform the traversal. The initial value of j is 0. Define the critical extreme value difference as ML=3, and the critical extreme value ratio as MR=0.7.

[0199] If TM i If the value is 0, then skip this iteration.

[0200] If TM i If ≠0, then j increments by 1, and TM is set to 0. i The value is assigned to the temporary matrix T j Simultaneously, the following judgments are made:

[0201] If TM i+1 If T = 0, then it is considered that the end of a continuous data segment has been reached. Calculate T. j The range d = max(T) j )-min(T j ). Calculate T j The ratio of extreme values ​​r = min(T) j ) / max(Tj). After calculation, clear T. j The value of is determined, and the values ​​of d and r are compared. If d < ML and r > MR, then from k=1 to k=j, TM is... i-k+1 All values ​​are set to 0. After judging d and r, the value of j is changed to 0. After the traversal is complete, a scatter plot is drawn with APi as the x-axis and TMi as the y-axis, as shown below. Figure 12 As shown, AP1 and TM1 are data points in the scatter plot, and AP2 and TM2 are data points in the scatter plot. 264 TM 264 is a data point in the scatter plot.

[0202] Step S5: Extract non-zero data

[0203] AP i and TM i By combining the data, we obtain the identification data DM. i .

[0204] Initialize and retain angle data AZ r (r=1,2,...,s), retain torque data TZ r (r=1,2,...,s), where r represents the position of the data point and s represents the number of data points. Starting from i=1, for TM... i Perform the traversal. The initial value of s is 1.

[0205] If TM i If the value is 0, skip this iteration.

[0206] If TM i If ≠0, then s increments by 1, TZ s =TM i AZ s =AP i .

[0207] After the iteration is complete, the value of s is 87. The extracted non-zero values ​​are DZ. r With AZ r x-axis, TZ r Use the vertical axis to plot a scatter plot, such as... Figure 14 As shown, AZ1 and TZ1 are data points in the scatter plot, and AZ2 and TZ2 are data points in the scatter plot. 87 TZ 87 is a data point in the scatter plot.

[0208] ;

[0209] Step S6: Calculate the number of tightening cycles and display the recognition results.

[0210] TM i The value is an alternating sequence of consecutive zero values ​​and consecutive non-zero values. (Statistical TM) i The number of consecutive non-zero value segments in the data yields the number of tightening operations performed by the operator. The initial value of the tightening count C is 1. The specific calculation method is as follows:

[0211] Starting from the first data point i=1 and ending at i=263, for TM i Perform a traversal.

[0212] If TM i =0 or TM i+1 If the value is not equal to 0, then skip this iteration.

[0213] If TM i ≠ 0 and TM i+1 If the sum is 0, then it is considered that the end of a continuous non-zero value segment has been reached, and the number of tightening times is C = C + 1.

[0214] After the traversal is complete, C=6, therefore, the number of tightening times identified is 6.

[0215] Plot a line graph with AFi as the x-axis and TFi as the y-axis, and then plot the line graph with AZ in the same image. r TZ is the x-axis. r Using the vertical axis as the scatter plot, non-zero values ​​can be added to the original data line chart to obtain a combined chart of recognition results, such as... Figure 16 As shown in the diagram, each consecutive scatter symbol in the combined recognition result image represents one tightening operation. By plotting the image, users can visually see the corresponding positions of the identified tightening counts on the graph.

[0216] Example 2

[0217] In this embodiment, an aircraft hydraulic line with a conduit diameter of 8mm was selected for automatic identification of the number of tightening cycles.

[0218] Step S1: Match the original data and the filtered data

[0219] The raw data in Example 2 consists of 326 data pairs. (Based on AP) i TP is the x-axis. i A line chart is plotted on the vertical axis, resulting in the original data line chart as shown below. Figure 17 As shown, AP1 and TP1 are data points in the original data line chart, and AP2 and TP2 are data points in the original data line chart. 326 TP 326 This refers to a data point in the original data line chart.

[0220] ;

[0221] The filtered data in Example 2 includes a total of 116 data pairs. Using AF... j TF is the x-axis. j A line graph is plotted on the ordinate, resulting in the filtered data line graph as shown below. Figure 18 As shown, AF1 and TF1 are data points in the line graph of the filtered data, and AF2 and TF2 are data points in the line graph of the filtered data. 116 TF 116 This is a data point in the line graph of the filtered data.

[0222] ;

[0223] Starting from the first data point i = 1 until i = 326 ends, traverse TP i and loop to compare the values of TP i and TF j . The value of TF<00..​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​The value of .

[0231] When i=326, if TM 325 =0, then TM is considered 326 It is an isolated point, making TM 326 =0; if TM 326 If ≠0, then TM is considered to be... 326 Not an isolated point, does not change TM 326 The value of .

[0232] After the traversal is complete, TM i There are 326 numbers in total, and at this time TM i There are no data points where both the first and last two digits are zero. (Based on AP) i TM is the x-axis. i Use the vertical axis to plot a scatter plot, such as... Figure 20 As shown.

[0233] Step S3: Filter data points with insufficient torque

[0234] Starting from the first data point i=1 and ending at i=326, for TM i Perform a traversal and compare TM. i The magnitude between the critical torque CT and the critical torque CT. The value of CT is 2.

[0235] If TM i If the torque value is ≤2, then the torque value is considered too low, causing TM to... i =0.

[0236] If TM i If the torque value is greater than 2, the torque value is considered to meet the requirements, and TM is not changed. i The value of .

[0237] After the traversal is complete, TM i There are 326 numbers in total. At this point, TM... i There are several consecutive data segments, and the rest of the data are all 0. (Based on AP) i TM is the x-axis. i Use the vertical axis to plot a scatter plot, such as... Figure 21 As shown.

[0238] Step S4: Filter data points with excessively small ranges

[0239] Starting from the first data point i=1 and ending at i=325, for TM i Perform the traversal. The initial value of j is 0. Define the critical extreme value difference as ML=3, and the critical extreme value ratio as MR=0.7.

[0240] If TM i If the value is 0, then skip this iteration.

[0241] If TM i If ≠0, then j increments by 1, and TM is set to 0. i The value is assigned to the temporary matrix T j Simultaneously, the following judgments are made:

[0242] If TM i+1 If T = 0, then it is considered that the end of a continuous data segment has been reached. Calculate T. j The range d = max(T) j )-min(T j ). Calculate T j The ratio of extreme values ​​r = min(T) j ) / max(T j After the calculation is complete, clear T. j The value of is determined, and the values ​​of d and r are compared. If d < ML and r > MR, then from k=1 to k=j, TM is... i-k+1 All values ​​are set to 0. After judging d and r, the value of j is changed to 0. After the traversal is complete, a scatter plot is drawn with APi as the x-axis and TMi as the y-axis, as shown below. Figure 22 As shown.

[0243] Step S5: Extract non-zero data

[0244] AP i and TM i By combining the data, we obtain the identification data DM. i .

[0245] Initialize and retain angle data AZ r (r=1,2,...,s), retain torque data TZ r (r=1,2,...,s), where r represents the position of the data point and s represents the number of data points. Starting from i=1, for TM... i Perform the traversal. The initial value of s is 1.

[0246] If TM i If the value is 0, skip this iteration.

[0247] If TMi ≠ 0, then s increments by 1, and TZ s =TM i AZ s =AP i .

[0248] After the iteration is complete, the value of s is 103. The extracted non-zero values ​​are DZ. r With AZ r x-axis, TZ r Use the vertical axis to plot a scatter plot, such as... Figure 23As shown, AZ1 and TZ1 are data points in the scatter plot, and AZ2 and TZ2 are data points in the scatter plot. 103 TZ 103 is a data point in the scatter plot.

[0249] ;

[0250] Step S6: Calculate the number of tightening cycles and display the recognition results.

[0251] TM i The value is an alternating sequence of consecutive zero values ​​and consecutive non-zero values. (Statistical TM) i The number of consecutive non-zero value segments in the data yields the number of tightening operations performed by the operator. The initial value of the tightening count C is 1. The specific calculation method is as follows:

[0252] Starting from the first data point i=1 and ending at i=326, for TM i Perform a traversal.

[0253] If TM i =0 or TM i+1 If the value is not equal to 0, then skip this iteration.

[0254] If TM i ≠ 0 and TM i+1 If the sum is 0, then it is considered that the end of a continuous non-zero value segment has been reached, and the number of tightening times is C = C + 1.

[0255] After the traversal is complete, C=4, therefore the number of tightening times identified is 4.

[0256] With AF i x-axis, TF i Plot a line graph with the vertical axis as the ordinate, and then in the same image, plot the lines using AZ... r TZ is the x-axis. r Using the vertical axis as the scatter plot, non-zero values ​​can be added to the original data line chart to obtain a combined chart of recognition results, such as... Figure 24 As shown in the diagram, each consecutive scatter symbol in the combined recognition result image represents one tightening operation. By plotting the image, users can visually see the corresponding positions of the identified tightening counts on the graph.

[0257] Combination Figure 25 This application also provides an automatic identification device for the number of tightening cycles during aircraft piping assembly, applicable to the aforementioned electronic equipment. The automatic identification device for the number of tightening cycles during aircraft piping assembly may include a data acquisition module, a data filtering module, a data extraction module, and a data analysis module.

[0258] Specifically, the data acquisition module can be used to acquire first pipeline assembly data, wherein the first pipeline assembly data includes torque data applied by the operator during tightening and outer nut rotation angle data obtained at multiple monitoring nodes during the aircraft pipeline assembly process. In this embodiment, the data acquisition module can be used to perform... Figure 2 The relevant content regarding the data acquisition module in step S110 shown can be found in the preceding description of step S110.

[0259] Specifically, the data filtering module can be used to filter abnormal data from the first pipeline assembly data to form second pipeline assembly data corresponding to the first pipeline assembly data. In this embodiment, the data filtering module can be used to perform... Figure 2 The relevant content regarding the data filtering module in step S120 shown can be found in the preceding description of step S120.

[0260] Specifically, the data extraction module can be used to extract target data from the second pipeline assembly data to form third pipeline assembly data corresponding to the second pipeline assembly data. The target data extraction is used to extract data related to the number of tightening operations performed by the operator from the second pipeline assembly data. In this embodiment, the data extraction module can be used to perform... Figure 2 The relevant content regarding the data extraction module in step S130 shown can be found in the preceding description of step S130.

[0261] Specifically, the data analysis module can be used to analyze the target tightening count based on the third pipeline assembly data, wherein the target tightening count refers to the number of times the operator tightens the outer nut during the tightening process. In this embodiment, the data analysis module can be used to perform... Figure 2 The relevant content regarding the data analysis module in step S140 shown can be found in the preceding description of step S140.

[0262] In this embodiment of the application, corresponding to the above-described automatic identification method for the number of tightening cycles in the aircraft piping assembly process applied to the electronic device, a computer-readable storage medium is also provided. The computer-readable storage medium stores a computer program, which executes each step of the automatic identification method for the number of tightening cycles in the aircraft piping assembly process when it is run.

[0263] The steps executed by the aforementioned computer program during its operation will not be described in detail here, but can be found in the explanation of the automatic identification method for the number of tightening cycles during the aircraft piping assembly process described above.

[0264] In summary, the automatic identification method for the number of tightening operations during aircraft piping assembly provided in this application involves: first, acquiring first piping assembly data; second, filtering abnormal data from the first piping assembly data to form second piping assembly data corresponding to the first piping assembly data; then, extracting target data from the second piping assembly data to form third piping assembly data corresponding to the second piping assembly data; and finally, analyzing the target number of tightening operations based on the third piping assembly data. Based on the above, on the one hand, since the torque data applied by the operator and the rotation angle data of the outer nut during tightening obtained from monitoring the aircraft piping assembly process at multiple monitoring nodes can be analyzed, the number of tightening operations during the tightening of the outer nut can be determined. On the other hand, since the data is filtered and extracted after acquisition and before analysis, the reliability of the user-analyzed data is high, ensuring the reliability and accuracy of the determined target number of tightening operations, thereby achieving effective monitoring of the aircraft piping assembly process. Therefore, it can improve the problem of difficulty in effectively monitoring the aircraft piping assembly process in existing technologies.

[0265] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0266] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0267] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, electronic device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. In the absence of further restrictions, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0268] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for automatically identifying the number of tightening cycles during aircraft piping assembly, characterized in that, include: Acquire first pipeline assembly data, wherein the first pipeline assembly data includes torque data and outer nut rotation angle data obtained by monitoring the aircraft pipeline assembly process at multiple monitoring nodes during the tightening process; Abnormal data filtering is performed on the first pipeline assembly data to form the second pipeline assembly data corresponding to the first pipeline assembly data; The second pipeline assembly data is subjected to target data extraction to form the third pipeline assembly data corresponding to the second pipeline assembly data. The target data extraction is used to extract data related to the number of tightening times of the operator from the second pipeline assembly data. Based on the third pipeline assembly data, the target tightening count is analyzed, including: starting from the first monitoring node in the third pipeline assembly data, traversing to form the currently traversed monitoring node, and determining whether the torque data of the currently traversed monitoring node in the third pipeline assembly data is equal to 0, and whether the torque data of the next monitoring node in the third pipeline assembly data is equal to 0; if the torque data of the currently traversed monitoring node in the third pipeline assembly data is equal to 0, or if the torque data of the next monitoring node in the third pipeline assembly data is not equal to 0, then traversing to the next monitoring node; if the torque data of the currently traversed monitoring node in the third pipeline assembly data is not equal to 0, and the torque data of the next monitoring node in the third pipeline assembly data is equal to 0, then the target tightening count is updated, and the next monitoring node is traversed, wherein the target tightening count refers to the number of times the operator tightens the outer nut during the tightening process.

2. The automatic identification method for the number of tightening cycles during aircraft piping assembly according to claim 1, characterized in that, The step of obtaining the first pipeline assembly data includes: Acquire raw piping assembly data, wherein the raw piping assembly data includes raw torque data and raw angle data of the outer nut rotation obtained by monitoring the aircraft piping assembly process at multiple monitoring nodes during the tightening process; The original pipeline assembly data is filtered to form filtered pipeline assembly data, wherein the number of monitoring nodes corresponding to the filtering torque data and filtering angle data in the filtered pipeline assembly data is less than or equal to the number of multiple monitoring nodes. The torque data of the original pipeline assembly data and the filtered pipeline assembly data are matched to form multiple matched torque data corresponding to the multiple monitoring nodes; Based on the original angle data and matching torque data corresponding to the multiple monitoring nodes in the original pipeline assembly data, the first pipeline assembly data is formed by combining them.

3. The automatic identification method for the number of tightening cycles during aircraft piping assembly according to claim 2, characterized in that, The step of matching the torque data of the original pipeline assembly data and the filtered pipeline assembly data to form multiple matched torque data corresponding to the multiple monitoring nodes includes: The process begins by iterating through the original torque data corresponding to the first monitoring node in the original pipeline assembly data, and then continues by iterating through the filtered torque data corresponding to the first monitoring node in the filtered pipeline assembly data. If the original torque data traversed at the moment is not equal to the filtered torque data traversed at the moment, then make the matching torque data corresponding to the monitoring node in the original pipeline assembly data traversed at the moment equal to the original torque data traversed at the moment, and traverse the next monitoring node in the original pipeline assembly data, and fix the monitoring node in the filtered pipeline assembly data. If the current traversed raw torque data is equal to the current traversed filtered torque data, then set the matching torque data corresponding to the monitoring node in the current traversed raw pipeline assembly data to 0, and traverse the next monitoring node in the raw pipeline assembly data and the next monitoring node in the filtered pipeline assembly data.

4. The automatic identification method for the number of tightening cycles during aircraft piping assembly as described in claim 1, characterized in that, The step of filtering abnormal data from the first pipeline assembly data to form the second pipeline assembly data corresponding to the first pipeline assembly data includes: The first pipeline assembly data is subjected to isolated data filtering to form isolated filtered pipeline assembly data. Torque anomaly filtering is performed on the isolated filter pipeline assembly data to form torque filter pipeline assembly data; The torque filter pipeline assembly data is subjected to extreme difference abnormality filtering to form extreme difference filter pipeline assembly data. Based on the range filter pipeline assembly data, the second pipeline assembly data is obtained.

5. The automatic identification method for the number of tightening cycles during aircraft piping assembly according to claim 4, characterized in that, The step of filtering the first pipeline assembly data into isolated filtered pipeline assembly data includes: The process begins by traversing the first monitoring node in the first pipeline assembly data to form the currently traversed monitoring node; If the currently traversed monitoring node belongs to the first monitoring node, then determine whether the torque data of the second monitoring node in the first pipeline assembly data is equal to 0. If it is equal to 0, the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined to be 0. Alternatively, if it is not equal to 0, the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined to be the torque data of the currently traversed monitoring node in the first pipeline assembly data. If the currently traversed monitoring node does not belong to the first monitoring node and does not belong to the last monitoring node, then determine whether the torque data of the previous and next monitoring nodes of the currently traversed monitoring node in the first pipeline assembly data are both equal to 0. If they are both equal to 0, then the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined to be 0. Alternatively, if they are not both equal to 0, then the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined to be the torque data of the currently traversed monitoring node in the first pipeline assembly data. If the currently traversed monitoring node is the last monitoring node, then determine whether the torque data of the previous monitoring node in the first pipeline assembly data is equal to 0. If it is equal to 0, then the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined to be 0. Alternatively, if it is not equal to 0, then the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is determined as the torque data of the currently traversed monitoring node in the first pipeline assembly data.

6. The automatic identification method for the number of tightening cycles during aircraft piping assembly according to claim 4, characterized in that, The step of performing torque anomaly filtering on the isolated filter pipeline assembly data to form torque-filtered pipeline assembly data includes: The process begins by traversing the data from the first monitoring node in the isolated filter pipeline assembly data to form the currently traversed monitoring node. If the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is less than or equal to the predetermined critical torque data, then the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is determined to be 0. If the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data is greater than the critical torque data, then the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is determined as the torque data of the currently traversed monitoring node in the isolated filter pipeline assembly data.

7. The automatic identification method for the number of tightening cycles during aircraft piping assembly according to claim 4, characterized in that, The step of performing extreme difference filtering on the torque filter pipeline assembly data to form extreme difference filter pipeline assembly data includes: Starting from the first monitoring node in the torque filter pipeline assembly data, the current monitoring node is traversed, and the initial value of the target parameter is configured to 0. If the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is equal to 0, then traverse to the next monitoring node. If the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is not equal to 0, then the torque data of the currently traversed monitoring node in the range filter pipeline assembly data is determined based on the next node after the currently traversed monitoring node.

8. The automatic identification method for the number of tightening cycles during aircraft piping assembly according to claim 7, characterized in that, The step of determining the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data if the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is not equal to 0, based on the next node of the currently traversed monitoring node, includes: If the torque data of the currently traversed monitoring node in the torque filter pipeline assembly data is not equal to 0, then the value of the target parameter is updated to form the current value, and the torque data of the currently traversed monitoring node in the range filter pipeline assembly data is assigned to the target matrix, wherein the number of torque data in the target matrix is ​​equal to the current value of the target parameter. Determine whether the torque data of the next node after the currently traversed monitoring node is equal to 0 in the torque filter pipeline assembly data; If the torque data of the next node in the torque filter pipeline assembly data is not equal to 0, then traverse the next monitoring node; If the torque data of the next node in the torque filter pipeline assembly data is equal to 0, then the difference and ratio between the maximum and minimum values ​​in the torque data of the target matrix are determined. If the relationship between the difference and the ratio and the corresponding critical value does not meet the preset conditions, the target parameter is updated to the initial value and the next monitoring node is traversed. Alternatively, if the relationship between the difference and the ratio and the corresponding critical value meets the preset conditions, at least the torque data of the currently traversed monitoring node in the range filter pipeline assembly data is determined to be 0.

9. The automatic identification method for the number of tightening cycles during aircraft piping assembly according to claim 1, characterized in that, The step of extracting target data from the second pipeline assembly data to form the third pipeline assembly data corresponding to the second pipeline assembly data includes: Starting from the first monitoring node in the second pipeline assembly data, the process is traversed to form the currently traversed monitoring node, and it is determined whether the torque data of the currently traversed monitoring node in the second pipeline assembly data is equal to 0. If the torque data of the currently traversed monitoring node in the second pipeline assembly data is equal to 0, then traverse the next monitoring node; If the torque data of the currently traversed monitoring node in the second pipeline assembly data is not equal to 0, then the torque data and angle data of the currently traversed monitoring node in the second pipeline assembly data are determined as the torque data and angle data of a monitoring node in the third pipeline assembly data, and the next monitoring node is traversed.

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