Online screw machine torsion detection method and system

Abstract

The invention provides an online screw machine torsion detection method and system. The method comprises the following steps: monitoring torsion data of a screw machine in a screw tightening process in real time; performing spatial conversion on the torsion data according to preset spatial parameters to obtain a screw torsion sequence, and performing neighborhood disturbance evaluation on the screw torsion sequence to obtain a plurality of inter-window disturbance factors; a torsion characteristic curve in the screw tightening process is generated through the torsion data, a predicted torsion sequence in the screw tightening process is determined according to the torsion characteristic curve, and the data heterotaxis degree of each torsion data point in the torsion data is determined based on the predicted torsion sequence; and determining all abnormal torsion data points by using all the inter-window disturbance factors and the data heterotaxis of each torsion data point, and generating an abnormal torsion detection report according to all the abnormal torsion data points. By the adoption of the scheme, abnormity in torsion data can be monitored in real time in the screw tightening process, timely and reliable screw torsion detection is achieved, and the production quality of products is improved.

Description

Technical Field

[0001] The present application relates to the technical field of torque monitoring, and more specifically, to an online screw machine torque detection method and system. Background Art

[0002] In modern industrial production, screw machines are widely used in various automated assembly lines to fasten parts together to ensure the structural stability and reliability of the product. Torque is one of the most important parameters in the screw tightening process, which is directly related to the assembly quality, service life and safety of the product. Therefore, online screw machine torque detection has become an important part of quality control. The core goal of this system is to monitor the tightening torque data in real time during the operation of the screw machine to ensure that it meets the preset process standards, and to promptly alarm or adjust when abnormalities occur, thereby improving production efficiency and reducing defective products.

[0003] However, in practical applications, torque sensors must be able to measure tightening torque in real time and accurately while avoiding environmental interference such as temperature changes, vibrations, and electromagnetic interference. In addition, during the operation of the screw machine, torque data often changes dynamically, and high-speed rotation and impact force may cause data fluctuations, so high-frequency data acquisition and intelligent algorithms are required to filter out noise and ensure data stability and reliability. Therefore, how to monitor anomalies in torque data in real time during the screw tightening process and achieve timely and reliable screw torque detection to improve product production quality is a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides an online screw machine torque detection method and system, which can monitor anomalies in torque data in real time during the screw tightening process, realize timely and reliable screw torque detection, and improve product production quality.

[0005] In a first aspect, the present application provides an online screw machine torque detection method, the detection method comprising the following steps: Real-time monitoring of the torque data of the screw machine during the screw tightening process; Performing spatial transformation on the torque data according to preset spatial parameters to obtain a screw torque sequence, performing neighborhood disturbance evaluation on the screw torque sequence, and then obtaining a plurality of inter-window disturbance factors during the screw tightening process; Generate a torque characteristic curve during the screw tightening process through the torque data, determine a predicted torque sequence during the screw tightening process according to the torque characteristic curve, and determine the data anomaly of each torque data point in the torque data based on the predicted torque sequence; All the inter-window disturbance factors and the data anomalies of each torque data point are used to determine all the abnormal torque data points in the screw tightening process, and an abnormal torque detection report is generated based on all the abnormal torque data points.

[0006] Preferably, the torque data of the screw machine during the screw tightening process is monitored in real time by a torque sensor.

[0007] Preferably, performing spatial transformation on the torque data according to preset spatial parameters to obtain the screw torque sequence specifically includes: Determining the torque level and torque fluctuation during the screw tightening process according to the torque data; The torque level, the torque fluctuation and the preset spatial parameters are used to perform spatial mapping on the torque data, thereby obtaining a screw torque sequence.

[0008] Preferably, performing neighborhood disturbance evaluation on the screw torque sequence to obtain a plurality of inter-window disturbance factors during the screw tightening process specifically includes: Get the set data sliding window; determining a torque dependency according to the screw torque sequence; Determining the screw torque data points contained in each data sliding window in the screw torque sequence; A neighborhood disturbance evaluation is performed on the data sliding window according to the torque dependence, the screw torque data points contained in the data sliding window, and the screw torque data points contained in the adjacent data sliding window to obtain the inter-window disturbance factor of the data sliding window during the screw tightening process, and then obtain multiple inter-window disturbance factors during the screw tightening process.

[0009] Preferably, generating a torque characteristic curve during the screw tightening process through the torque data is to fit the torque data using an exponential curve fitting algorithm, thereby generating the torque characteristic curve during the screw tightening process.

[0010] Preferably, determining the data anomaly of each torque data point in the torque data based on the predicted torque sequence specifically includes: For each torque data point in the torque data, obtaining a predicted torque corresponding to the torque data point in the predicted torque sequence; The data anomaly of the torque data point is determined by the predicted torque, and then the data anomaly of each torque data point in the torque data is obtained.

[0011] Preferably, using all the inter-window disturbance factors and the data anomalies of each torque data point to determine all abnormal torque data points during the screw tightening process specifically includes: For each torque data point in the torque data, determining an inter-window disturbance factor corresponding to the torque data point; Determining the data anomaly coefficient of the torque data point by the inter-window disturbance factor and the data anomaly degree of the torque data point, and then obtaining the data anomaly coefficient of each torque data point; The torque data points whose data abnormality coefficient is greater than the set threshold are taken as abnormal torque data points, and then all abnormal torque data points in the screw tightening process are obtained.

[0012] In a second aspect, the present application provides an online screw machine torque detection system for performing an online screw machine torque detection method, wherein the online screw machine torque detection system includes an abnormality detection unit, and the abnormality detection unit includes: Torque monitoring module, used to monitor the torque data of the screw machine in real time during the screw tightening process; A disturbance evaluation module, used to perform spatial transformation on the torque data according to preset spatial parameters to obtain a screw torque sequence, perform neighborhood disturbance evaluation on the screw torque sequence, and further obtain multiple inter-window disturbance factors during the screw tightening process; an abnormality determination module, used to generate a torque characteristic curve in the screw tightening process through the torque data, determine a predicted torque sequence in the screw tightening process according to the torque characteristic curve, and determine the data anomaly of each torque data point in the torque data based on the predicted torque sequence; The report generation module is used to determine all abnormal torque data points in the screw tightening process using all inter-window disturbance factors and data anomalies of each torque data point, and generate an abnormal torque detection report based on all abnormal torque data points.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned online screw machine torque detection method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned online screw machine torque detection method when executed.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: The present application provides an online screw machine torque detection method and system, which monitors the torque data of the screw machine during the screw tightening process in real time; performs spatial transformation on the torque data according to preset spatial parameters to obtain a screw torque sequence, performs neighborhood disturbance evaluation on the screw torque sequence, and then obtains multiple inter-window disturbance factors during the screw tightening process; generates a torque characteristic curve during the screw tightening process through the torque data, determines a predicted torque sequence during the screw tightening process based on the torque characteristic curve, and determines the data anomaly of each torque data point in the torque data based on the predicted torque sequence; uses all the inter-window disturbance factors and the data anomaly of each torque data point to determine all abnormal torque data points during the screw tightening process, and generates an abnormal torque detection report based on all the abnormal torque data points.

[0016] It can be seen that in the present application, first, the screw torque data is spatially transformed according to the preset spatial parameters to obtain the screw torque sequence, and the inter-window disturbance factor is calculated through the neighborhood disturbance evaluation, which can help to more accurately capture the abnormal fluctuations in the tightening process, and by quantifying the local fluctuations of the torque data, the occurrence of torque anomalies can be identified in time; then, by generating the torque characteristic curve in the screw tightening process and predicting the torque sequence based on this, the torque change trend in the normal tightening process can be accurately captured, and the data anomaly of each torque data point is determined by the torque data and the predicted torque sequence, so that abnormal fluctuations can be discovered in time; finally, by combining the inter-window disturbance factor and the data anomaly, the abnormal torque data points that appear in the screw tightening process can be more accurately detected, and then a detailed abnormal torque detection report can be generated, which provides effective support for quality control in the production process, can identify and correct anomalies in time, and avoid the expansion of quality problems, thereby improving the overall production efficiency, product quality and equipment operation stability.

[0017] In summary, the technical solution adopted in the present application can monitor the anomalies in the torque data in real time during the screw tightening process, realize timely and reliable screw torque detection, and improve the production quality of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 is an exemplary flow chart of an online screw machine torque detection method according to some embodiments of the present application; Figure 2is an exemplary flow chart of determining the data anomaly of each torque data point in torque data according to some embodiments of the present application; Figure 3 is an exemplary flow chart for determining all abnormal torque data points during a screw tightening process according to some embodiments of the present application; Figure 4 is a schematic diagram of exemplary hardware and / or software of an abnormality detection unit according to some embodiments of the present application; Figure 5 It is a structural schematic diagram of a computer device for implementing an online screw machine torque detection method according to some embodiments of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] The embodiment of the present application provides an online screw machine torque detection method and system, the core of which is to monitor the torque data of the screw machine in the process of screw tightening in real time; perform spatial transformation on the torque data according to preset spatial parameters to obtain a screw torque sequence, perform neighborhood disturbance evaluation on the screw torque sequence, and then obtain multiple inter-window disturbance factors in the process of screw tightening; generate a torque characteristic curve in the process of screw tightening through the torque data, determine the predicted torque sequence in the process of screw tightening according to the torque characteristic curve, and determine the data anomaly of each torque data point in the torque data based on the predicted torque sequence; use all the inter-window disturbance factors and the data anomaly of each torque data point to determine all abnormal torque data points in the process of screw tightening, and generate an abnormal torque detection report according to all the abnormal torque data points. The above scheme can monitor the anomalies in the torque data in real time during the process of screw tightening, realize timely and reliable screw torque detection, and improve the production quality of the product.

[0022] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of an online screw machine torque detection method according to some embodiments of the present application. The online screw machine torque detection method 100 mainly includes the following steps: In step 101, the torque data of the screw machine during the screw tightening process is monitored in real time.

[0023] In specific implementation, the torque data of the screw machine during the screw tightening process can be monitored in real time through the torque sensor; in this application, the automatic screw machine is installed with a radio frequency identification (RFID) tag or unit to uniquely identify each screw machine. The RFID reading device installed in the detection system can scan the ID information of each screw machine to ensure that the detected data matches the corresponding screw machine. The torque sensor is installed on the screw hole to measure the torque during the screw tightening process in real time. When the screw machine is working, the RFID reader will identify the ID information of the screw machine, and the torque sensor will monitor the tightening force of the screw in real time, that is, obtain the torque data during the screw tightening process.

[0024] In step 102, the torque data is spatially transformed according to preset spatial parameters to obtain a screw torque sequence, and a neighborhood disturbance evaluation is performed on the screw torque sequence to obtain a plurality of inter-window disturbance factors during the screw tightening process.

[0025] In some embodiments, the torque data is spatially transformed according to preset spatial parameters to obtain the screw torque sequence in the following manner, namely: Determining the torque level and torque fluctuation during the screw tightening process according to the torque data; The torque level, the torque fluctuation and the preset spatial parameters are used to perform spatial mapping on the torque data, thereby obtaining a screw torque sequence.

[0026] In specific implementation, first, the torque level and torque fluctuation during the screw tightening process can be determined according to the torque data, wherein the torque level represents the average torque during the screw tightening process, and the torque fluctuation is an indicator of the degree of fluctuation of the torque data during the screw tightening process. In actual implementation, the average value of all torque data points in the torque data can be used as the torque level during the screw tightening process, and the standard deviation of all torque data points in the torque data can be used as the torque fluctuation during the screw tightening process; then, the torque level, torque fluctuation and preset spatial parameters can be used to perform spatial mapping on the torque data to obtain a screw torque sequence, wherein the screw torque sequence is a representation of the torque data in a low-dimensional space, and the spatial parameter is a parameter used to control the degree of spatial mapping. In actual implementation, the spatial mapping can be represented in the following way: ; in, Indicates the screw torque sequence, Indicates torque data, Indicates the torque level, represents the torque fluctuation, k represents the preset spatial parameter, It means taking a specific value in the low-dimensional space. The space mapping can be completed by the above method to obtain the screw torque sequence.

[0027] In some embodiments, the neighborhood disturbance evaluation is performed on the screw torque sequence to obtain multiple inter-window disturbance factors during the screw tightening process in the following manner, namely: Get the set data sliding window; determining a torque dependency according to the screw torque sequence; Determining the screw torque data points contained in each data sliding window in the screw torque sequence; A neighborhood disturbance evaluation is performed on the data sliding window according to the torque dependence, the screw torque data points contained in the data sliding window, and the screw torque data points contained in the adjacent data sliding window to obtain the inter-window disturbance factor of the data sliding window during the screw tightening process, and then obtain multiple inter-window disturbance factors during the screw tightening process.

[0028] In the specific implementation, first, it is necessary to define a data sliding window. The data sliding window is a time window. In the present application, the data sliding window contains n screw torque data points, and the sliding step of the data sliding window is 1. In the actual implementation, the sliding step of the data sliding window can also be set to other values, which are not limited here; then, the torque dependence can be determined according to the screw torque sequence, wherein the torque dependence represents the degree of mutual dependence between the screw torque data points in the screw torque sequence, and the covariance of all the screw torque data points in the screw torque sequence can be used as the torque dependence; then, it can be determined that each data in the screw torque sequence The screw torque data points contained in the sliding window, that is, the screw torque data points contained in the corresponding data sliding window in the screw torque sequence can be determined according to the position of each data sliding window; finally, the data sliding window can be evaluated for neighborhood disturbance according to the torque dependence, the screw torque data points contained in the data sliding window and the screw torque data points contained in the adjacent data sliding window, so as to obtain the inter-window disturbance factor of the data sliding window during the screw tightening process, wherein the inter-window disturbance factor represents the degree of data disturbance between adjacent data sliding windows during the screw tightening process. In actual implementation, the inter-window disturbance factor can be determined by the following formula: ; in, Represents a data sliding window The inter-window disturbance factor during the screw tightening process, n represents the data sliding window The total number of screw torque data points contained in, Represents a data sliding window The j-th screw torque data point contained in Represents a data sliding window The j-th screw torque data point contained in , v represents the torque level of the screw torque sequence, It represents the torque dependence. Through the above method, the inter-window disturbance factor corresponding to each data sliding window can be obtained, that is, multiple inter-window disturbance factors in the screw tightening process can be obtained.

[0029] It should be noted that the screw torque data is spatially transformed according to the preset spatial parameters to obtain the screw torque sequence, and the inter-window disturbance factor is calculated through the neighborhood disturbance evaluation. This can help to more accurately capture abnormal fluctuations in the tightening process. By quantifying the local fluctuations of the torque data, the occurrence of torque anomalies, such as excessive or low torque, can be identified in a timely manner, thereby providing real-time feedback.

[0030] In step 103, a torque characteristic curve in the screw tightening process is generated by the torque data, a predicted torque sequence in the screw tightening process is determined according to the torque characteristic curve, and a data anomaly of each torque data point in the torque data is determined based on the predicted torque sequence.

[0031] In some embodiments, the torque characteristic curve generated during the screw tightening process by the torque data is to fit the torque data using an exponential curve fitting algorithm, thereby generating the torque characteristic curve during the screw tightening process. In specific implementation, the torque characteristic curve during the screw tightening process is generated by fitting, and the torque characteristic curve can reflect the trend of torque change during the screw tightening process, especially the torque increase in the initial tightening stage, the stable stage in the middle of tightening, and the torque peak in the locking stage, etc., which can not only provide accurate predictions for the tightening process, but also help to detect abnormalities in the process in a timely manner.

[0032] In some embodiments, the predicted torque sequence in the screw tightening process is determined according to the torque characteristic curve in the following manner, namely: Inputting each torque data point in the torque data into the torque characteristic curve for prediction, thereby obtaining a predicted torque corresponding to each torque data point; The predicted torque sequence in the screw tightening process is constructed by the predicted torque corresponding to all torque data points.

[0033] In specific implementation, first, each torque data point in the torque data can be input into the torque characteristic curve for prediction, so as to obtain the predicted torque corresponding to each torque data point. In actual implementation, the predicted torque can be determined by the following method: in, Represents torque data points The corresponding predicted torque, represents the i-th torque data point in the torque data, A, B and C represent the exponential fitting parameters of the torque characteristic curve, and the predicted torque corresponding to each torque data point can be obtained in the above manner. In actual implementation, the exponential fitting parameters can be set through historical experiments, which will not be repeated here; then, the predicted torque sequence in the screw tightening process can be constructed through the predicted torques corresponding to all torque data points, that is, the predicted torques corresponding to all torque data points are arranged according to timestamps, thereby obtaining the predicted torque sequence in the screw tightening process.

[0034] Preferably, in some embodiments, reference Figure 2 As shown in FIG. 1 , this figure is an exemplary flow chart of determining the data anomaly of each torque data point in the torque data in some embodiments of the present application. In this embodiment, determining the data anomaly of each torque data point in the torque data based on the predicted torque sequence can be implemented by the following steps: In step 1031, for each torque data point in the torque data, a predicted torque corresponding to the torque data point is obtained in the predicted torque sequence; In step 1032, the data anomaly of the torque data point is determined by the predicted torque, and then the data anomaly of each torque data point in the torque data is obtained.

[0035] In the specific implementation, first, for each torque data point in the torque data, the predicted torque corresponding to the torque data point can be obtained in the predicted torque sequence by traversal; then, the data anomaly of the torque data point can be determined by the predicted torque, wherein the data anomaly indicates the degree of abnormality of the torque data point. In actual implementation, the absolute value of the difference between the torque data point and its corresponding predicted torque can be calculated, and the result can be used as the data anomaly of the torque data point. The data anomaly of each torque data point in the torque data can be obtained in the above manner.

[0036] It should be noted that by generating a torque characteristic curve during the screw tightening process and predicting the torque sequence based on it, the torque change trend during the normal tightening process can be accurately captured. By determining the data anomaly of each torque data point through the torque data and the predicted torque sequence, abnormal fluctuations can be discovered in time, and problems such as excessive or low torque can be identified.

[0037] In step 104, all the inter-window disturbance factors and the data anomalies of each torque data point are used to determine all the abnormal torque data points in the screw tightening process, and an abnormal torque detection report is generated according to all the abnormal torque data points.

[0038] Preferably, in some embodiments, reference Figure 3As shown in FIG. 1 , this figure is an exemplary flow chart for determining all abnormal torque data points in the screw tightening process in some embodiments of the present application. In this embodiment, all the inter-window disturbance factors and the data anomalies of each torque data point are used to determine all the abnormal torque data points in the screw tightening process, which can be achieved by the following steps: In step 1041, for each torque data point in the torque data, determine the inter-window disturbance factor corresponding to the torque data point; In step 1042, the data anomaly coefficient of the torque data point is determined by the inter-window disturbance factor and the data anomaly degree of the torque data point, thereby obtaining the data anomaly coefficient of each torque data point; In step 1043, the torque data points whose data abnormality coefficients are greater than the set threshold are taken as abnormal torque data points, and then all abnormal torque data points in the screw tightening process are obtained.

[0039] In specific implementation, first, for each torque data point in the torque data, the inter-window disturbance factor corresponding to the torque data point can be determined, that is, a data sliding window with the torque data point as the window center is determined, and the inter-window disturbance factor corresponding to the data sliding window is used as the inter-window disturbance factor corresponding to the torque data point; then, the data anomaly coefficient of the torque data point can be determined by the inter-window disturbance factor and the data anomaly of the torque data point, wherein the data anomaly coefficient represents the possibility that the torque data point is abnormal data during the screw tightening process, and the product of the data anomaly of the torque data point and the corresponding inter-window disturbance factor can be used as the data anomaly coefficient of the torque data point, and the data anomaly coefficient of each torque data point can be obtained in the above manner; finally, the torque data point whose data anomaly coefficient is greater than the set threshold can be used as an abnormal torque data point, wherein the threshold can be set according to historical experience and data analysis, and will not be repeated here, and all abnormal torque data points in the screw tightening process can be obtained in the above manner.

[0040] In some embodiments, an abnormal torque detection report is generated based on all abnormal torque data points; in specific implementation, an abnormal torque detection report can be generated based on all torque data points marked as abnormal, wherein the abnormal torque detection report includes: Abnormal data point list: Lists all torque data points marked as abnormal, as well as their corresponding time, torque value, inter-window disturbance factor and data anomaly.

[0041] Abnormal cause analysis: Based on the analysis of the anomaly trend and disturbance factors, possible abnormal causes are given, such as equipment failure, improper process, operational error, etc.

[0042] Impact assessment: Analyze the impact of abnormal data points on the overall production process or product quality to assess whether they will lead to substandard products.

[0043] Recommended actions: Provide suggestions for detected abnormalities, such as adjusting the tightening process, checking equipment, optimizing production parameters, etc.

[0044] Trend Analysis: Analyze abnormal trends in the tightening process to help identify potential systemic problems.

[0045] It should be noted that by combining the inter-window disturbance factor and the data anomaly, the abnormal torque data points that appear during the screw tightening process can be detected more accurately, and then a detailed abnormal torque detection report can be generated, which provides effective support for quality control in the production process, can identify and correct anomalies in a timely manner, and avoid the expansion of quality problems, thereby improving overall production efficiency, product quality and equipment operation stability.

[0046] It can be seen that in the present application, first, the screw torque data is spatially transformed according to the preset spatial parameters to obtain the screw torque sequence, and the inter-window disturbance factor is calculated through the neighborhood disturbance evaluation, which can help to more accurately capture the abnormal fluctuations in the tightening process, and by quantifying the local fluctuations of the torque data, the occurrence of torque anomalies can be identified in time; then, by generating the torque characteristic curve in the screw tightening process and predicting the torque sequence based on this, the torque change trend in the normal tightening process can be accurately captured, and the data anomaly of each torque data point is determined by the torque data and the predicted torque sequence, so that abnormal fluctuations can be discovered in time; finally, by combining the inter-window disturbance factor and the data anomaly, the abnormal torque data points that appear in the screw tightening process can be more accurately detected, and then a detailed abnormal torque detection report can be generated, which provides effective support for quality control in the production process, can identify and correct anomalies in time, and avoid the expansion of quality problems, thereby improving the overall production efficiency, product quality and equipment operation stability.

[0047] In summary, the technical solution adopted in the present application can monitor the anomalies in the torque data in real time during the screw tightening process, realize timely and reliable screw torque detection, and improve the production quality of the product.

[0048] In addition, in another aspect of the present application, in some embodiments, the present application provides an online screw machine torque detection system, the online screw machine torque detection system includes an abnormality detection unit, reference Figure 4 , which is a schematic diagram of exemplary hardware and / or software of an abnormality detection unit according to some embodiments of the present application, the abnormality detection unit 400 includes: a torque monitoring module 401, a disturbance assessment module 402, an abnormality determination module 403 and a report generation module 404, which are respectively described as follows: Torque monitoring module 401, in this application, the torque monitoring module 401 is mainly used to monitor the torque data of the screw machine during the screw tightening process in real time; The disturbance evaluation module 402 in the present application is mainly used to perform spatial transformation on the torque data according to preset spatial parameters to obtain a screw torque sequence, perform neighborhood disturbance evaluation on the screw torque sequence, and then obtain multiple inter-window disturbance factors during the screw tightening process; The abnormality determination module 403 in the present application is mainly used to generate a torque characteristic curve in the screw tightening process through the torque data, determine a predicted torque sequence in the screw tightening process according to the torque characteristic curve, and determine the data anomaly of each torque data point in the torque data based on the predicted torque sequence; The report generation module 404 in the present application is mainly used to determine all abnormal torque data points in the screw tightening process using all inter-window disturbance factors and data anomalies of each torque data point, and generate an abnormal torque detection report based on all abnormal torque data points.

[0049] The above describes in detail the examples of the online screw machine torque detection method and system provided by the embodiments of the present application. It can be understood that in order to realize the above functions, the corresponding device includes hardware structures and / or software modules corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0050] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned online screw machine torque detection method.

[0051] In some embodiments, reference Figure 5 , the dotted line in the figure indicates that the unit or the module is optional, and the figure is a structural schematic diagram of a computer device according to an online screw machine torque detection method provided in an embodiment of the present application. The online screw machine torque detection method described in the above embodiment can be Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device 500 can be a terminal device, a server or a chip.

[0052] The processor 501 may be a general-purpose processor or a special-purpose processor. For example, the processor 501 may be a central processing unit (CPU), which may be used to control the computer device 500, execute software programs, and process data of the software programs. The computer device 500 may also include a communication unit 505 to implement input (reception) and output (transmission) of signals.

[0053] For example, the computer device 500 may be a chip, the communication unit 505 may be an input and / or output circuit of the chip, or the communication unit 505 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other device.

[0054] For another example, the computer device 500 may be a terminal device or a server, and the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0055] The computer device 500 may include one or more memories 502, on which a program 504 is stored. The program 504 can be executed by the processor 501 to generate instructions 503, so that the processor 501 performs the method described in the above method embodiment according to the instructions 503. Optionally, data (such as a target audit model) may also be stored in the memory 502. Optionally, the processor 501 may also read the data stored in the memory 502, and the data may be stored at the same storage address as the program 504, or the data may be stored at a different storage address from the program 504.

[0056] The processor 501 and the memory 502 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of the terminal device.

[0057] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or a software-based instruction in the processor 501. The processor 501 can be a central processing unit, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, such as discrete gates, transistor logic devices or discrete hardware components.

[0058] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0059] For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned online screw machine torque detection method when executing.

[0060] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0061] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An online screw machine torque detection method, characterized in that: The detection method comprises the following steps: Real-time monitoring of the torque data of the screw machine during the screw tightening process; Performing spatial transformation on the torque data according to preset spatial parameters to obtain a screw torque sequence, performing neighborhood disturbance evaluation on the screw torque sequence, and then obtaining a plurality of inter-window disturbance factors during the screw tightening process; Generate a torque characteristic curve during the screw tightening process through the torque data, determine a predicted torque sequence during the screw tightening process according to the torque characteristic curve, and determine the data anomaly of each torque data point in the torque data based on the predicted torque sequence; All the inter-window disturbance factors and the data anomalies of each torque data point are used to determine all the abnormal torque data points in the screw tightening process, and an abnormal torque detection report is generated based on all the abnormal torque data points.

2. An online screw machine torque detection method as claimed in claim 1, characterized in that: The torque data of the screw machine during the screw tightening process is monitored in real time through the torque sensor.

3. The method for detecting torque of an online screw machine according to claim 1, characterized in that: The torque data is spatially transformed according to preset spatial parameters to obtain the screw torque sequence, which specifically includes: Determining the torque level and torque fluctuation during the screw tightening process according to the torque data; The torque level, the torque fluctuation and the preset spatial parameters are used to perform spatial mapping on the torque data, thereby obtaining a screw torque sequence.

4. The method for detecting torque of an online screw machine according to claim 1, characterized in that: The neighborhood disturbance evaluation is performed on the screw torque sequence to obtain multiple inter-window disturbance factors during the screw tightening process, specifically including: Get the set data sliding window; determining a torque dependency according to the screw torque sequence; Determining the screw torque data points contained in each data sliding window in the screw torque sequence; A neighborhood disturbance evaluation is performed on the data sliding window according to the torque dependence, the screw torque data points contained in the data sliding window, and the screw torque data points contained in the adjacent data sliding window to obtain the inter-window disturbance factor of the data sliding window during the screw tightening process, and then obtain multiple inter-window disturbance factors during the screw tightening process.

5. The method for detecting torque of an online screw machine according to claim 1, characterized in that: Generating a torque characteristic curve during the screw tightening process through the torque data is to fit the torque data using an exponential curve fitting algorithm, thereby generating the torque characteristic curve during the screw tightening process.

6. The method for detecting torque of an online screw machine according to claim 1, characterized in that: Determining the data anomaly of each torque data point in the torque data based on the predicted torque sequence specifically includes: For each torque data point in the torque data, obtaining a predicted torque corresponding to the torque data point in the predicted torque sequence; The data anomaly of the torque data point is determined by the predicted torque, and then the data anomaly of each torque data point in the torque data is obtained.

7. The method for detecting torque of an online screw machine according to claim 1, characterized in that: All the abnormal torque data points during the screw tightening process are determined using all the inter-window disturbance factors and the data anomalies of each torque data point, including: For each torque data point in the torque data, determining an inter-window disturbance factor corresponding to the torque data point; Determining the data anomaly coefficient of the torque data point by the inter-window disturbance factor and the data anomaly degree of the torque data point, and then obtaining the data anomaly coefficient of each torque data point; The torque data points whose data abnormality coefficient is greater than the set threshold are taken as abnormal torque data points, and then all abnormal torque data points in the screw tightening process are obtained.

8. An online screw machine torque detection system, used to execute an online screw machine torque detection method according to any one of claims 1 to 7, the online screw machine torque detection system comprising an abnormality detection unit, characterized in that: The abnormality detection unit comprises: Torque monitoring module, used to monitor the torque data of the screw machine in real time during the screw tightening process; A disturbance evaluation module, used to perform spatial transformation on the torque data according to preset spatial parameters to obtain a screw torque sequence, perform neighborhood disturbance evaluation on the screw torque sequence, and further obtain multiple inter-window disturbance factors during the screw tightening process; an abnormality determination module, used to generate a torque characteristic curve in the screw tightening process through the torque data, determine a predicted torque sequence in the screw tightening process according to the torque characteristic curve, and determine the data anomaly of each torque data point in the torque data based on the predicted torque sequence; The report generation module is used to determine all abnormal torque data points in the screw tightening process using all inter-window disturbance factors and data anomalies of each torque data point, and generate an abnormal torque detection report based on all abnormal torque data points.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the online screw machine torque detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the online screw machine torque detection method as described in any one of claims 1 to 7.

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