Screw feeding pressure self-adaptive adjusting method, device and equipment

Abstract

The invention relates to a screw feeding pressure self-adaptive adjusting method, device and equipment. The method comprises the following steps: constructing a Lagrange control model for screw feeding equipment to obtain a control reference parameter group; acquiring a real-time air pressure data flow and calculating an air pressure regulation and control instruction; a torque-rotating speed-temperature mapping function is constructed, and torque output parameters are obtained through calculation; torque compensation parameters are calculated, integral operation is carried out on the torque compensation parameters and locking angle sensor data, and a torque-locking angle characteristic curve is generated; carrying out locking state evaluation to obtain a state evaluation index, carrying out deviation analysis on the state evaluation index and a set locking threshold value, and carrying out multi-dimensional parameter cooperative calculation to obtain a system correction matrix; and inputting the system correction matrix into a Lagrange control model, updating a dynamic weight coefficient, and outputting an optimized control instruction set. According to the screw feeding device, the reliability and the maintenance efficiency of the device are improved, and the technical problems of dynamic control, parameter optimization and fault early warning of a traditional screw feeding device are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of screw feeding, and in particular to a method, device and equipment for self-adapting screw feeding pressure regulation. Background Art

[0002] Traditional screw feeding equipment mainly relies on fixed air pressure parameters and preset torque values ​​for control. It cannot dynamically adjust according to the characteristics of screws of different specifications, and is prone to problems such as unstable feeding, jamming, and inadequate locking.

[0003] The screw feeding equipment currently on the market generally has technical bottlenecks such as difficulty in accurately controlling feeding parameters, large fluctuations in torque output, and insufficient temperature compensation. Especially in the assembly process of high-precision electronic products, due to the diversity of screw specifications and complex assembly conditions, the traditional open-loop control method is difficult to meet the needs of efficient and stable assembly, affecting product assembly quality and production efficiency. In addition, the existing technology lacks a coordinated control mechanism for the screw feeding pressure and the torque output of the electric screwdriver, and cannot achieve dynamic optimization of the feeding process and the locking process. At the same time, due to the lack of effective status monitoring and fault warning means, abnormal conditions during equipment operation are difficult to detect and handle in a timely manner, increasing production maintenance costs and quality control difficulties. Summary of the invention

[0004] The main purpose of the present invention is to provide a method, device and equipment for adaptively adjusting the screw feeding pressure. The present invention improves the reliability and maintenance efficiency of the equipment and solves the technical problems of traditional screw feeding equipment in dynamic control, parameter optimization and fault warning.

[0005] To achieve the above object, the present invention provides a method for self-adapting screw feeding pressure, comprising the following steps: Extract screw specification parameters from screw feeding equipment, collect electric screwdriver speed signal and initial torque signal to build Lagrangian control model, and obtain control benchmark parameter group; Acquire real-time air pressure data stream, calculate the conveying cycle deviation and screw impact stress value according to the control reference parameter group, and obtain air pressure control instructions through difference compensation operation; The air pressure control command is transmitted to the electric batch driver, and the torque-speed-temperature mapping function is constructed in combination with the real-time displacement data collected by the angular displacement sensor, and the torque output parameter is calculated by the least square method; Performing adaptive compensation calculation on the torque output parameter to obtain a torque compensation parameter, and integrating the torque compensation parameter with the locking angle sensor data to generate a torque-locking angle characteristic curve; Based on the torque-lock angle characteristic curve, a locking state evaluation is performed to obtain a state evaluation index, a deviation analysis is performed between the state evaluation index and a set locking threshold, and a system correction matrix is ​​obtained through multi-dimensional parameter collaborative calculation; The system correction matrix is ​​input into the Lagrangian control model, the dynamic weight coefficient is updated, and the optimized control instruction set is output.

[0006] The present invention also provides a screw feeding pressure self-adapting regulating device, comprising: The acquisition module is used to extract screw specification parameters from the screw feeding equipment, and to collect the electric screwdriver speed signal and initial torque signal to construct the Lagrangian control model to obtain the control reference parameter group; A calculation module is used to obtain a real-time air pressure data stream, and calculate the conveying cycle deviation and the screw impact stress value according to the control reference parameter group, and obtain an air pressure control instruction through a difference compensation operation; A construction module is used to transmit the air pressure control command to the electric batch driver, construct a torque-speed-temperature mapping function in combination with the real-time displacement data collected by the angular displacement sensor, and obtain the torque output parameter by calculating the least square method; A compensation module, used for performing adaptive compensation calculation on the torque output parameter to obtain a torque compensation parameter, and performing an integration operation on the torque compensation parameter and the locking angle sensor data to generate a torque-locking angle characteristic curve; An analysis module, for evaluating the locking state based on the torque-locking angle characteristic curve, obtaining a state evaluation index, performing deviation analysis between the state evaluation index and a set locking threshold, and obtaining a system correction matrix through collaborative calculation of multi-dimensional parameters; The output module is used to input the system correction matrix into the Lagrangian control model, update the dynamic weight coefficient, and output the optimized control instruction set.

[0007] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0008] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0009] In summary, the technical solution provided by the present invention realizes the adaptive adjustment of the screw feeding pressure by establishing a Lagrangian control model, integrates multiple technical features such as air pressure control, torque compensation and temperature correction, and forms a complete closed-loop control system. The present invention adopts multi-sensor data fusion technology to collect and process parameters such as air pressure, displacement, temperature, etc. in real time, establishes an accurate torque-locking angle characteristic curve, and ensures the stability of the feeding and locking process through a dynamic weight update mechanism; at the same time, the present invention introduces a fault alarm and state evaluation mechanism, and timely discovers and handles abnormal conditions through multi-dimensional parameter collaborative calculation, significantly improving the reliability and maintenance efficiency of the equipment, and solving the technical problems of traditional screw feeding equipment in dynamic control, parameter optimization and fault warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 1 is a schematic diagram of the steps of a method for self-adapting screw feeding pressure adjustment in one embodiment of the present invention; Figure 2 It is a structural block diagram of a screw feeding pressure adaptive regulating device in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0011] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0013] Reference Figure 1 , this embodiment provides a method for self-adapting the screw feeding pressure, comprising the following steps: S1, extract screw specification parameters of the screw feeding equipment, collect the electric screwdriver speed signal and initial torque signal to build a Lagrangian control model, and obtain a control benchmark parameter group; Among them, the screw specification parameters are entered through the input interface of the LCD control system, and the specification parameters include the total length, diameter, weight and material of the screw. The entered screw parameters are classified according to the preset specification range to obtain the initial matrix of screw parameters, which contains the characteristic data of all entered screws for subsequent analysis and calculation. The ratio of the total length L of the screw and the nut diameter D in the initial matrix of screw parameters is calculated to screen out the screw specifications that meet the requirements. Only valid parameter groups with L / D greater than 1.3 are selected to ensure that the selected screws are suitable for the operation of the feeding equipment in terms of geometric characteristics. At the same time, the speed signal of the electric screwdriver in the no-load state is collected through the encoder. These analog signals are digitized and the corresponding speed digital quantity is obtained through signal sampling and conversion. In order to enhance the dynamic response capability of the control model, the angular displacement signal of the electric screwdriver during movement is collected. The angular displacement sensor monitors the displacement change of the electric screwdriver in real time, and obtains the real-time speed change rate of the electric screwdriver by differential operation of the displacement data, which reflects the speed change of the electric screwdriver in different working stages and helps to accurately describe the dynamic characteristics of the system during the screw locking process. In the process of screw locking, since the increase in temperature will affect the performance and torque output of the electric screwdriver, a temperature sensor is introduced to collect the temperature of the environment and equipment in real time. The temperature data is input into the compensation function to calculate the initial torque coefficient, and the initial torque coefficient is temperature-corrected according to the current temperature to obtain the corrected torque coefficient. Based on the effective parameter group, the digital value of the speed and the speed change rate, the motion equation of the torque output system of the electric screwdriver is established. The motion equation describes the dynamic characteristics of the electric screwdriver during the locking process and reflects the state of the screw feeding. In order to accurately adjust the relationship between the air pressure and the speed during the feeding process, the speed and pressure parameters of the feeder in the compressed air pipeline are sampled, and the functional relationship between the air pressure and the speed is obtained by the data fitting method. The speed change of the feeding system under different air pressure conditions is described, which helps to achieve accurate control of the air pressure. The motion equation of the torque output system of the electric screwdriver, the corrected torque coefficient, and the air pressure-speed function relationship are substituted into the Lagrangian control model to construct a complete dynamic model and obtain the control reference parameter group. The dynamic model can comprehensively describe the dynamic characteristics of the screw feeding and locking process, and the control benchmark parameter group provides an important basis for the adaptive adjustment of the feeding pressure.

[0014] S2, obtain the real-time air pressure data stream, and calculate the conveying cycle deviation and the screw impact stress value according to the control reference parameter group, and obtain the air pressure control instruction through the difference compensation operation; Specifically, the air pressure data is collected by distributed pressure sensors arranged in the compressed air feeding pipeline. These distributed pressure sensors can sense the air pressure changes in the pipeline in real time. The collected analog signal is converted into a digital signal by the signal conversion module to obtain the initial data of the air pressure. The initial data is processed by a digital filter to eliminate the noise interference in the environment. After filtering, a real-time air pressure data stream is obtained. Based on the feeder speed parameter in the control reference parameter group, the real-time air pressure data stream is periodically analyzed. By dynamically monitoring the feeding process, the time required for a single screw to be transported from the feeder to the electric screwdriver chuck is calculated, that is, the delivery time value of a single screw. Based on the delivery time value, the feeding cycle is calculated, and the actual delivery cycle obtained is compared with the preset standard cycle of 0.6 seconds to obtain the deviation value of the delivery cycle. If there is a significant deviation between the delivery cycle and the standard cycle, it will affect the accuracy and efficiency of screw locking, and these deviations will be corrected in real time. At the same time, the impact force when the screw reaches the electric screwdriver chuck is detected by a high-precision torque sensor to obtain the stress condition of the screw during the delivery process. Through stress conversion, the impact stress value of the screw is obtained. The impact stress value of the screw is judged by a threshold value, and the actual impact stress is compared with the preset stress threshold value to determine whether the screw is subjected to excessive impact or extrusion during the conveying process. If the impact stress exceeds the preset threshold, it means that the air pressure during the feeding process needs to be adjusted to reduce the stress of the screw during the conveying process and ensure the smoothness of the screw conveying. In order to calculate the required stress compensation amount, the pressure function in the control reference parameter group is combined with the current impact stress value to calculate and obtain an accurate stress compensation amount. The conveying cycle deviation and the stress compensation amount are input into the adaptive algorithm module, and the air pressure correction value for correcting the current air pressure is obtained by weighted calculation of these two parameters. The adaptive algorithm module dynamically adjusts the air pressure according to the weights of different deviation values ​​to ensure that the feeding process can proceed smoothly within the standard cycle. The difference between the air pressure correction value and the current air pressure data is used as input, and the air pressure control instruction is generated through compensation operation. The air pressure control instruction is used to adjust the supply of compressed air in real time to optimize the feeding state of the screw, ensuring that sufficient pressure can be provided to quickly convey the screw during the feeding process, and the screw will not be damaged due to excessive pressure.

[0015] S3, transmit the air pressure control command to the electric batch driver, build the torque-speed-temperature mapping function in combination with the real-time displacement data collected by the angular displacement sensor, and calculate the torque output parameter by the least square method; It should be noted that the air pressure control command is input into the electric screwdriver, and the air pressure data is processed in sections according to the preset pressure range to obtain the control reference value of the electric screwdriver. The control reference value is used to guide the driving operation of the electric screwdriver under different air pressure levels to ensure that the air pressure regulation during the feeding process can adapt to the different requirements of the screw locking process. Through segmented processing, the electric screwdriver can perform refined drive control for different pressure levels to improve its dynamic response capability. The rotation state of the chuck is detected in real time by the angular displacement sensor installed on the chuck of the electric screwdriver. These detection data can intuitively reflect the operation of the electric screwdriver. The original signal obtained by the angular displacement sensor is processed by the signal conditioning circuit to remove noise and interference to obtain high-quality real-time displacement data. These data represent the current displacement state of the electric screwdriver, and the real-time speed value and acceleration value of the electric screwdriver are obtained through timing analysis and differential calculation. Real-time speed and acceleration are important parameters for describing the dynamic characteristics of the electric screwdriver, which together constitute the basic dynamic characteristics of the electric screwdriver movement and help describe the dynamic response of the electric screwdriver under different pressure conditions. At the same time, the temperature in the working environment of the electric screwdriver is collected by the temperature sensor. Temperature changes will affect the motion characteristics and torque output of the electric batch. The ambient temperature data collected by the temperature sensor is input into the compensation calculation unit, and the temperature compensation factor is obtained through correction operation. The temperature compensation factor is used to correct the performance deviation caused by the change of ambient temperature to ensure that the working state of the electric batch can maintain consistency under different temperature conditions. The compensation calculation is designed based on the influence of temperature on material properties and mechanical movement. The corrected parameters more accurately reflect the actual working conditions of the electric batch. On this basis, the speed response equation is constructed based on the control reference value and real-time speed value of the electric batch. This equation can describe the response characteristics of the electric batch under different pressure and speed conditions, and combined with the temperature compensation factor, it is substituted into the speed response equation to obtain the corrected speed curve. According to the corrected speed curve and real-time acceleration value, the dynamic characteristic equation of the electric batch is established. This equation can comprehensively describe the relationship between the speed, acceleration and external conditions of the electric batch. By performing function conversion on the equation, a mapping function containing the relationship between torque, speed and temperature is obtained, that is, the torque-speed-temperature mapping function. This mapping function is used to describe the torque characteristics output by the electric batch under different temperature and speed conditions, which is the core basis of torque control in feeding pressure regulation. In order to obtain specific torque output parameters, the constructed torque-speed-temperature mapping function is calculated by the least squares method. The least squares method is an error minimization method that fits the collected data to find the function form that best fits the data characteristics. Through error iteration operation, the torque initial value sequence is obtained. This sequence represents the initial torque output of the electric batch under different working conditions. The torque initial value sequence is weighted averaged. Considering the difference in the importance of the initial torque value under different working conditions, higher weights are assigned to key data to obtain more stable and accurate torque output parameters.The torque output parameter is used for real-time control of the electric screwdriver to ensure that during the screw locking process, the electric screwdriver can provide appropriate torque to achieve stable screw locking.

[0016] S4, performing adaptive compensation calculation on the torque output parameter to obtain a torque compensation parameter, and performing integration operation on the torque compensation parameter and the locking angle sensor data to generate a torque-locking angle characteristic curve; Specifically, the torque output parameters are sampled and analyzed, and data calibration is performed based on the working range of the torque of the electric screwdriver to obtain a standardized torque reference sequence. The torque reference sequence is used as the basis for subsequent compensation calculations to ensure the accuracy and consistency of the torque output. In order to improve the compensation process and ensure that the torque output matches the actual demand, a compensation calculation model is constructed, and the speed data of the electric screwdriver in the no-load state is substituted into the compensation calculation model for comprehensive analysis and calculation to obtain the torque compensation parameter. This compensation parameter can effectively correct the torque deviation caused by system nonlinearity or external interference, and improve the torque accuracy of the entire system in the actual locking process. The torque accuracy of the electric screwdriver is dynamically compensated according to the torque compensation parameter. During the compensation process, the compensated torque value is obtained through data correction operations. The compensated torque value more accurately reflects the actual torque output of the electric screwdriver during the locking process, ensuring that the torque can adapt to the mechanical requirements of the screw locking in real time, and avoiding the problem of locking failure or damage due to insufficient or excessive torque. At the same time, the locking angle sensor collects the angle change data during the locking process and samples and processes these data. Since there are noise and invalid signals in the angle data, the valid angle value is obtained through data screening. These effective angle values ​​represent the actual rotation of the screw during the locking process. The compensated torque value is matched with the effective angle value in time sequence, and the torque-angle data pair is obtained through data alignment operation, which reflects the torque and corresponding rotation angle changes at each instant in the locking process. In order to analyze the dynamic behavior of the screw during the locking process, the torque-angle data pair is classified according to the thread type of the screw. Different types of threads show different torque and angle change rules during the locking process. Classifying them helps to describe the locking characteristics more accurately. In the classification, the data of each type of thread is segmented to obtain several characteristic intervals. Each characteristic interval represents a certain stage in the locking process, such as the initial locking stage, the intermediate transition stage, and the final locking stage. The mechanical behavior of each stage is different. The data in the characteristic interval is integrated and the parameters of the characteristic curve are obtained by accumulation. The integral operation describes the overall behavior of the screw during the locking process by accumulating the changes in torque and angle. The integral result reflects the cumulative effect of torque and angle over time. Based on the characteristic curve parameters, the function fitting method is used to reconstruct the curve to generate a torque-locking angle characteristic curve to describe the relationship between torque and angle of the screw during the locking process.

[0017] S5, performing a locking state evaluation based on the torque-locking angle characteristic curve to obtain a state evaluation index, performing a deviation analysis between the state evaluation index and the set locking threshold, and obtaining a system correction matrix through multi-dimensional parameter collaborative calculation; Among them, data analysis is performed based on the torque-locking angle characteristic curve. By analyzing the characteristic curves of different stages in the screw assembly process, key nodes are extracted to obtain a locking state sequence describing the entire locking process. These key nodes represent important events or state changes in the locking process, such as initial locking, screw tightening to a predetermined torque, and final locking. Based on the locking state sequence, the accuracy of the torque is monitored online. By analyzing the torque range, torque state data reflecting the locking state is obtained. These data are used to describe the stability and change trend of the torque output during the locking process, so as to evaluate the quality and reliability of the locking. The torque state data is input into the evaluation unit to conduct a comprehensive state evaluation of the screw locking process. The evaluation unit monitors the abnormalities in the locking process in real time based on the fault alarm and counting functions, and generates state evaluation indicators. During the evaluation process, three dimensions are monitored, namely, material shortage, material jamming, and torque abnormality. Lack of material means that the screws are not supplied in time, resulting in the inability to continue the locking process; material jamming means that the screws are blocked or stuck during the feeding process, affecting the normal locking process; and torque abnormality may indicate that the electric screwdriver fails to output the appropriate torque during the locking process, all of which will lead to substandard final assembly quality. By monitoring these anomalies, an abnormal identification matrix is ​​generated based on the threshold detection method to identify and record all abnormal conditions in the locking process. The state evaluation index and the abnormal identification matrix are fused, and the state comprehensive score of the entire locking process is calculated through weighted operation. Different weights are assigned to different anomalies according to their severity, reflecting the overall impact of each anomaly on the locking quality. The state comprehensive score is a quantitative assessment of the overall quality of the locking process, indicating the quality of the current locking state. The state comprehensive score is compared with the pre-set locking threshold, and the deviation parameter group is obtained by difference calculation. If the comprehensive score deviates from the set locking threshold, it means that there are certain abnormalities in the current locking process, and these deviations need to be corrected through further adjustments. Based on the deviation parameter group, a multidimensional state space is constructed to conduct collaborative analysis of various deviations in the locking process. The multidimensional state space contains various state variables, such as torque, speed, angle, temperature, etc., which together describe the dynamic characteristics of the locking process. Through collaborative analysis in the multidimensional state space, the mutual influence between the various state variables can be accurately found, and the corresponding parameter corrections can be calculated. These parameter corrections are used to dynamically adjust the working state of the system in order to eliminate abnormal phenomena in the locking process and ensure that the entire process is carried out within the predetermined range. The parameter corrections are processed by dimensionality reduction according to the dynamic model. Through dimensionality reduction, the calculation process is simplified, redundant information is removed, and more concise and effective adjustment parameters are obtained. After the dimensionality reduction process is completed, the system correction matrix is ​​obtained by matrix transformation. The system correction matrix is ​​used to make overall corrections and optimizations to various parameters in the control system in order to further improve the stability and reliability of the locking process.Each element in the correction matrix represents the adjustment range of different state variables. By dynamically adjusting these variables, the feeding system realizes real-time adaptive control of multiple parameters such as torque, pressure, and speed, thereby improving the accuracy and consistency of the screw locking process.

[0018] S6, input the system correction matrix into the Lagrangian control model, update the dynamic weight coefficient, and output the optimized control instruction set.

[0019] Specifically, the system correction matrix is ​​substituted into the motion equation of the electric screwdriver torque output system and the air pressure-speed function relationship to perform matrix operations. Through this process, the initial weight data is obtained by combining the correction matrix and the existing dynamic model. The initial weight data reflects the key parameter relationship of the system in the current state. The substitution of the system correction matrix and the matrix operation enable these initial weight data to reflect the actual dynamic characteristics of the current feeding system. The initial weight data is subjected to feature analysis. By analyzing the characteristics of the initial weights, the parameters that play a key role in the system dynamics can be identified. Combined with the control reference parameter group, the weight data is optimized to obtain an updated coefficient matrix. The update coefficient matrix corrects the initial weight data in multiple optimization operations to make the dynamic response characteristics of the system more accurate and meet the predetermined control requirements. The update coefficient matrix is ​​subjected to weight iteration operation, and the dynamic weight coefficient is obtained through repeated adjustment and iteration. The update and iteration of the weight coefficient is intended to optimize the overall control performance of the system, so that the feeding and locking processes can respond quickly to external changes and achieve stable operation effects. In order to ensure that the dynamic weight coefficient can adapt to the changes in the actual feeding and locking process, the weight coefficient is corrected online according to the speed and pressure parameters of the feeder to obtain the optimized control parameter group. The speed and pressure parameters of the feeder have a direct impact on the feeding effect and the final locking quality of the screw. Through online correction, it is ensured that the control parameter group is always consistent with the actual operating state of the feeder, and the dynamic response capability of the system is improved. The optimized control parameter group is substituted into the Lagrangian control model, and the control variable sequence is obtained through parameter mapping in combination with the previously obtained parameter relationship. The control variable sequence is used to describe the state of each control link in the feeding and locking process to ensure that the control variable of each step of operation changes within the optimal range. The control variable sequence is analyzed and processed, and parameter matching is performed in combination with the torque-locking angle characteristic curve. Through this process, it is ensured that the control variables can reflect the actual locking process characteristics and obtain the benchmark instruction set. The benchmark instruction set is the basis of the entire control system, including the operation instructions of each step of feeding and locking. The benchmark instruction set is functionally divided according to electric batch control and feeding control. The electric batch control instructions are used to manage the torque output, speed adjustment and angle control of the electric batch, ensuring that the screws can be gradually tightened according to the set path and torque; the feeding control instructions are used to control the supply of compressed air, the speed adjustment of the feeder and the delivery of the screws, ensuring that the screws can accurately reach the locking position when needed. Through instruction parsing, the control tasks are decomposed into independent sub-item control instructions, making the entire control process more detailed and modular, facilitating precise adjustment and real-time response. The sub-item control instructions are dynamically combined, and all instructions are integrated through coordinated operations to output the optimized control instruction set.The purpose of coordination operation is to achieve coordination between electric batch control and feeding control, so that feeding and locking operations can be carried out synchronously, avoiding problems such as untimely feeding or insufficient locking torque, thereby ensuring the stability and efficiency of system operation. The optimized control instruction set comprehensively considers the current state of the system, environmental changes, and real-time data feedback during the locking process, and can achieve optimal control of the feeding and locking process under different working conditions.

[0020] In one example, screw specification parameters of a screw feeding device are extracted, and the speed signal of an electric screwdriver and the initial torque signal are collected to construct a Lagrangian control model, and a control benchmark parameter group is obtained, including: The screw specification parameters are input through the LCD screen control system input interface, and the data is classified according to the preset specification range to obtain the initial matrix of screw parameters; The ratio of the total length L of the screw to the nut diameter D in the initial matrix of screw parameters is calculated, and the effective parameter group with L / D greater than 1.3 is screened out. At the same time, the no-load speed signal of the electric screwdriver collected by the encoder is digitized, and the speed digital quantity is obtained through signal sampling and conversion; The displacement data collected by the angular displacement sensor is differentiated to obtain the real-time rotational speed change rate, and the temperature data collected by the temperature sensor is input into the compensation function to obtain the initial torque coefficient, and the initial torque coefficient is temperature corrected to obtain the corrected torque coefficient; The motion equation of the torque output system of the electric screwdriver is established based on the effective parameter group, the digital value of the speed, and the speed change rate. The speed and pressure parameters of the feeder in the compressed air pipeline are sampled, and the pressure-speed function relationship is obtained through data fitting. The motion equation of the electric screwdriver torque output system, the corrected torque coefficient, and the air pressure-speed function relationship are substituted into the Lagrangian control model to construct a dynamic model and obtain a control benchmark parameter group.

[0021] In this example, the specification parameters of the screws are entered through the input interface of the LCD control system. The LCD control system is the interactive interface between the operator and the feeding system. The operator intuitively enters various screw parameters through the LCD screen, such as the total length of the screw. , the diameter of the nut , thread type, material, and quality, etc. After these parameters are entered, the screws are classified according to the preset specification range to construct a data set containing all screw characteristics, namely the screw parameter initial matrix. The initial matrix is ​​expressed as: ; in, Indicates The total length of the screws, Indicates the diameter of the nut, For thread type, Screw material, is the screw mass. The initial matrix contains the basic specification information of all screws. and nut diameter Perform ratio calculation to screen out effective parameter groups suitable for feeding equipment. The ratio calculation is completed by the following formula: ; For all screws, if the ratio , then these screws are regarded as valid screw parameter groups, recorded as the effective matrix At the same time, the speed signal of the electric screwdriver in the no-load state is collected by the encoder. These speed signals are originally analog signals. Through signal sampling and conversion, the no-load speed signal collected by the encoder is converted into a digital quantity, which is recorded as At the same time, the angular displacement sensor collects the displacement of the chuck of the electric screwdriver in real time to obtain displacement data By performing first-order differential operation on the displacement data, the real-time speed change rate of the electric screwdriver is obtained, that is: ; in, represents the real-time speed, reflecting the speed change of the system at different time points. These data are used to describe the dynamic behavior of the electric screwdriver and provide a basis for establishing the motion equation. In order to consider the impact of temperature on system performance, a temperature sensor is introduced to collect ambient temperature data, denoted as The temperature data is input into the compensation function for correction, which is used to calculate the initial torque coefficient Considering the influence of temperature on material properties and equipment performance, the initial torque coefficient needs to be corrected by temperature. The corrected torque coefficient is expressed as: ; in, is the corrected torque coefficient, is the temperature correction coefficient, is the reference temperature value. This correction takes into account the effect of temperature increase or decrease on torque output, thus ensuring that the torque output remains stable under different temperature environments. , Digital speed and real-time speed change rate , establish the motion equation of the electric screwdriver torque output system. The dynamic behavior of the electric screwdriver is described by the following equation: ; in, Represents the moment of inertia of the electric screwdriver. represents the damping coefficient, It represents the load torque generated by the friction between the screw and the material during the locking process. This equation describes the dynamic response of the electric screwdriver when the torque is applied. At the same time, the speed and pressure parameters of the feeder in the compressed air pipeline are sampled to understand the response of the system under different air pressure conditions. By fitting these data, the functional relationship between air pressure and speed is obtained, which is recorded as: ; in, Indicates that the feeder is under pressure The speed of , , is the fitting coefficient. This functional relationship describes the effect of compressed air on the speed of the feeder, which helps to achieve precise control of the feeding process. The motion equation of the electric screwdriver torque output system, the corrected torque coefficient, and the air pressure-speed function relationship are substituted into the Lagrangian control model to construct the dynamic model of the entire system. The Lagrangian control model is used to describe the energy conversion and motion characteristics of the system. The control benchmark parameter group is obtained through the Lagrangian equation to guide the subsequent control strategy. The Lagrangian equation is expressed as: ; in, is the Lagrangian function, defined as the kinetic energy of the system With potential energy The difference is and denote the generalized coordinates and generalized velocity respectively, Represents generalized force. In this system, generalized coordinates are represented by the displacement and rotation angle of the screw, and generalized speed is the speed of the electric screwdriver. Through the above equations, the motion equation, the corrected torque and the air pressure function are combined to obtain the control reference parameter group of the entire dynamic system. These control reference parameters include a series of variables such as the speed of the electric screwdriver, torque output, and air pressure.

[0022] In one example, real-time air pressure data stream is obtained, and the conveying cycle deviation and screw impact stress value are calculated according to the control reference parameter group, and the air pressure control instruction is obtained through difference compensation operation, including: Data is collected through a distributed pressure sensor arranged in the compressed air supply pipeline, initial air pressure data is obtained through signal conversion, and the initial air pressure data is input into a digital filter to eliminate environmental noise interference, thereby obtaining a real-time air pressure data stream; According to the feeder speed parameter in the control reference parameter group, the real-time air pressure data stream is analyzed periodically to obtain the conveying time value of a single screw, and the period calculation is performed based on the conveying time value of a single screw. The calculation result is compared with the preset 0.6 second standard period to obtain the conveying period deviation; The force applied when the screw reaches the chuck of the electric screwdriver is detected by a high-precision torque sensor, and the screw impact stress value is obtained by stress conversion. A threshold judgment is made on the screw impact stress value, and the stress compensation amount is calculated based on the air pressure function in the control reference parameter group. The conveying cycle deviation and stress compensation amount are input into the adaptive algorithm module, and the air pressure correction value is obtained through weighted calculation. According to the difference between the air pressure correction value and the current air pressure data, the air pressure control instruction is generated through compensation calculation.

[0023] In this example, multiple pressure sensors are arranged in the feed pipe. These sensors are used to capture the pressure changes of compressed air in the feed pipe and obtain the original air pressure signal, which is recorded as Since these signals will be disturbed by environmental noise during the collection process, such as mechanical vibration, pipeline resonance, etc., the collected initial air pressure data is digitized through the signal conversion module and input into the digital filter to eliminate environmental noise interference and obtain the real-time air pressure data stream after filtering. The real-time air pressure data is periodically analyzed according to the feeder speed parameter in the control reference parameter group. The speed of the feeder is an important factor in determining the screw conveying speed, and the change of air pressure directly affects the speed of the feeder. Perform cycle analysis to obtain the time required for a single screw to be transported from the feeder to the target position, which is recorded as the transport time. This time value is obtained by observing the air pressure fluctuation cycle, because each time a screw is delivered to its place, a characteristic fluctuation in air pressure will occur. The delivery time of a single screw is calculated using the following formula: ; in, Indicates the frequency of air pressure fluctuations during the feeding process. After obtaining the conveying time, the cycle calculation is performed based on this time, and the calculation result is compared with the preset standard cycle. Compare the time in seconds to get the deviation of the conveying cycle

[0024] ; If there is a deviation between the delivery time and the standard time, it means that there is an abnormality in the current feeding process, such as insufficient air pressure causing the feeding speed to slow down, or too high air pressure causing the screw to be delivered too quickly. At the same time, in order to ensure the stability of the screw when it is delivered to the electric screwdriver chuck, a high-precision torque sensor is used to detect the force applied when the screw reaches the electric screwdriver chuck. The magnitude of this force can reflect the impact strength of the screw during the delivery process. Through stress conversion, the impact stress value generated by the screw when it hits the chuck is obtained, which is recorded as The stress conversion formula is: ; in, Indicates the impact force when the screw reaches the chuck, Indicates the cross-sectional area of ​​the screw in contact with the chuck. Through calculation, the impact strength of the screw when it is delivered in place can be accurately evaluated. The threshold value of the screw impact stress value is judged. With the set safety stress threshold For comparison. , it means that the screw impact strength is too large, which will affect the integrity of the screw or the reliability of the locking. Based on the air pressure function in the control reference parameter group, calculate the required stress compensation , to reduce or increase the air pressure to ensure a smoother screw delivery process: ; in, is the stress compensation coefficient, which is used to convert the stress difference into the air pressure compensation amount. and stress compensation Input into the adaptive algorithm module, and obtain a final pressure correction value through weighted calculation. : ; in, and They are the weight coefficients of the conveying cycle deviation and the stress compensation amount, respectively, which are used to control the influence of these two factors in the air pressure correction process. This correction value dynamically adjusts the air pressure in the feed pipeline to ensure that the system can adaptively respond to various changes in the feed process. With current real-time air pressure data The difference between the two is calculated to obtain the compensation amount : ; This difference reflects the deviation between the actual air pressure of the current feeding system and the expected air pressure. Based on this deviation, the air pressure control instruction is generated through compensation calculation. , used to adjust the air pressure of the compressed air supply pipeline: ; Through the air pressure control instructions, the air pressure in the feeding pipeline is adjusted in real time, so that the feeding process is always in an optimal state, to ensure that each screw can be delivered to the right place accurately and smoothly, to ensure the efficiency and reliability of the entire locking process.

[0025] In one example, the air pressure control command is transmitted to the electric batch driver, and the torque-speed-temperature mapping function is constructed in combination with the real-time displacement data collected by the angular displacement sensor, and the torque output parameters are calculated by the least squares method, including: Input the air pressure control command into the electric batch driver, and segment the data according to the preset pressure range to obtain the electric batch control reference value; The rotation state of the electric screwdriver chuck is detected in real time through the angular displacement sensor, and the real-time displacement data is obtained through the signal conditioning circuit. The real-time displacement data is analyzed in time series, and the real-time speed value and acceleration value of the electric screwdriver are obtained through differential calculation. The ambient temperature data collected by the temperature sensor is input into the compensation calculation unit, and the temperature compensation factor is obtained through correction calculation; The speed response equation is constructed based on the electric batch control reference value and the real-time speed value, and the temperature compensation factor is substituted into the equation to obtain the corrected speed curve. A dynamic characteristic equation is established according to the corrected speed curve and acceleration value, and a torque-speed-temperature mapping function is obtained through function conversion; The torque-speed-temperature mapping function is calculated by least square method, and the torque initial value sequence is obtained through error iteration operation. The torque initial value sequence is weighted averaged to obtain the torque output parameter.

[0026] In this example, the generated air pressure control command The air pressure control command input represents the pressure change during the feeding process and is used to adjust the movement of the electric screwdriver chuck under different load conditions. In order to achieve precise control of the air pressure, the air pressure data is segmented according to the preset pressure range of the system to achieve more precise control of the electric screwdriver. Set the air pressure range to arrive , the control reference value of the electric batch is obtained by segmentation , used to guide the operation of the electric batch under different pressure conditions. The control reference value after segmentation is determined by the following formula: ; in, Indicates the segmentation coefficient, which ranges from 0 to 1. , to achieve flexible control of different pressure ranges. In order to monitor the motion state of the electric screwdriver chuck, an angular displacement sensor is used to collect the rotation state of the electric screwdriver chuck in real time. The original displacement signal obtained by the angular displacement sensor After being processed by the signal conditioning circuit, environmental noise and signal distortion are eliminated to obtain accurate real-time displacement data. In order to analyze the motion state of the electric screwdriver, these displacement data are analyzed in time series, and the real-time speed of the electric screwdriver is obtained by first-order difference calculation. : ; Furthermore, by performing a second-order differential calculation on the speed, the real-time acceleration of the electric screwdriver is obtained.

[0027] ; in, Represents the chuck at time The instantaneous speed at the moment, represents the instantaneous angular acceleration of the electric screwdriver. By calculating and monitoring these parameters, the changes in the speed and acceleration of the electric screwdriver during the locking process can be monitored in real time. At the same time, the ambient temperature data is collected through the temperature sensor , and input it into the compensation calculation unit to eliminate the adverse effects of temperature on torque and speed. Through the compensation function, a temperature compensation factor is calculated. , which is used to correct the temperature deviation of the system. The calculation formula of the compensation factor is as follows: ; in, represents the temperature sensitivity coefficient, Indicates the reference temperature. When the ambient temperature deviates from the reference temperature, the compensation factor The corresponding adjustment will be made to ensure that the torque and speed output of the electric screwdriver can remain stable under different temperature conditions. and real-time speed , construct the speed response equation. This equation is used to describe the relationship between the speed of the electric screwdriver and the applied air pressure, and its form is: ; in, Represents the moment of inertia of the electric screwdriver. represents the damping coefficient of the system, Indicates the air pressure loss due to friction, etc. The temperature compensation factor Substituting into the speed response equation, the corrected speed curve is obtained, which reflects the dynamic response characteristics of the electric screwdriver under different temperature and pressure conditions. , establish the dynamic characteristic equation of the electric screwdriver. This equation describes the mechanical characteristics of the electric screwdriver during the locking process, and the specific form is: ; in, Indicates the time when the electric batch The torque output at the moment. Through the joint analysis of the speed response equation and the dynamic characteristic equation, a mapping function including the relationship between torque, speed and temperature is established, namely the torque-speed-temperature mapping function. This function is used to describe the torque output characteristics of the electric screwdriver under different speed and temperature conditions. In order to optimize the torque output, the torque-speed-temperature mapping function is calculated by the least squares method. The least squares method is a data fitting technology. By fitting the actual collected data, the function form that best fits these data is found to obtain the torque initial value sequence, which is recorded as The initial value sequence represents the initial torque output of the electric screwdriver under different working conditions. Through error iteration operation, these initial values ​​are adjusted multiple times to continuously reduce the deviation between the calculated value and the actual value, thereby improving the accuracy of the fitting. The weighted average calculation of the torque initial value sequence is performed to obtain the final torque output parameter : ; in, Indicates By assigning different weights to each initial value, different working states are tilted in the calculation to obtain a torque output that is more in line with the dynamic characteristics of the system.

[0028] In one example, adaptive compensation calculation is performed on the torque output parameter to obtain the torque compensation parameter, and the torque compensation parameter is integrated with the locking angle sensor data to generate a torque-locking angle characteristic curve, including: The torque output parameters are sampled and analyzed, and data is calibrated based on the torque range of the electric screwdriver to obtain a torque reference sequence. A compensation calculation model is constructed based on the torque reference sequence, and the no-load speed data is substituted into the compensation calculation model to obtain the torque compensation parameters. The torque accuracy is dynamically compensated according to the torque compensation parameters, and the compensated torque value is obtained through data correction; The lock angle sensor data collected by the lock angle sensor is sampled, and the effective angle value is obtained through data screening, and the compensated torque value and the effective angle value are time-matched, and the torque-angle data pair is obtained through data alignment; The torque-angle data pairs are classified according to the thread type, and the characteristic intervals are obtained by data segmentation; The data in the characteristic interval are integrated and calculated, and the characteristic curve parameters are obtained by cumulative operation. Function fitting is performed based on the characteristic curve parameters, and the torque-lock angle characteristic curve is generated by curve reconstruction.

[0029] In this example, the torque output parameters of the electric screwdriver are sampled and analyzed to ensure that the torque generated by the electric screwdriver during operation can accurately match the preset torque requirements. In order to better control the torque output, data calibration is performed based on the torque range of the electric screwdriver. The maximum and minimum torques of the electric screwdriver are recorded as and These values ​​are usually determined based on the design parameters of the electric screwdriver and the actual use conditions. , based on these data, the torque benchmark sequence is obtained These benchmark data are used as the basis for subsequent compensation calculations. By comparing and calibrating the real-time collected torque data with the preset torque range, a torque benchmark sequence suitable for different working conditions is obtained: ; The no-load speed data Substitute it into the constructed compensation calculation model to determine the compensation caused by external factors (such as air pressure changes, friction, etc.) to the torque output. Usually, the speed data recorded when the electric screwdriver motor is idling under no load reflects the speed response of the electric screwdriver under different air pressure and environmental conditions. The compensation calculation model adopts the following form: ; in, is the compensation coefficient, It is the rated speed, which indicates the speed of the electric screwdriver under standard load. This is the torque value after compensation. Through this step, the torque output of the electric screwdriver is dynamically compensated to make it more accurately match the set working conditions. Data correction is performed to ensure the torque value after compensation. It is effective in actual operation. Data correction is performed by comparing with the torque data during the actual screw tightening process. Assume that an actual torque data sequence is obtained through experiment By comparing these actual data with the compensated torque values , use the following error correction formula to correct the data: ; in, is the correction error, calculated by the least square method or other error correction methods. When performing data correction, considering the changes in different screws and assembly environments, multiple experiments are required to determine the appropriate error correction amount. The corrected torque value As the basis for subsequent analysis. Perform correlation analysis between angle and torque. In the process of screw tightening, the screw tightening angle is a key parameter that directly affects the tightening quality of the screw. By using the screw tightening angle sensor, the angle data of the screw tightening process can be collected in real time. These data are screened and cleaned to remove noise and invalid data, and obtain valid angle values The screening process is performed by setting a threshold and , excluding data that do not conform to actual operating conditions. For example, the locking angle may fluctuate abnormally in some cases, and the screening step ensures that only valid angle values ​​are used for subsequent analysis. and effective locking angle value Perform timing matching and obtain torque-angle data pairs through data alignment. Classify the torque-angle data pairs according to thread type. The thread type of the screw has a great influence on the torque-angle relationship during the locking process. In data processing, it is classified according to different thread types. Thread types include different pitches, thread angles, etc., which will affect the mechanical properties during the locking process. Assume that the data is divided into The torque-angle data pairs are segmented according to different thread types to obtain the characteristic intervals under each category. ,in Indicates the classification index of different thread types. After obtaining the characteristic intervals of various types of data, the characteristic curve parameters in each interval are obtained by integral calculation. The integral calculation adopts the accumulation method to accumulate the data in each characteristic interval to obtain the characteristic parameters of the interval. : ; in, The function that represents the relationship between torque and angle can be set as a polynomial, exponential function or other form of function according to the actual situation. Based on the characteristic curve parameters , the data in the characteristic interval are fitted by function fitting to obtain a continuous torque-lock angle characteristic curve. Assume that the fitting function is , then the final torque-locking angle characteristic curve is obtained by the following formula: ; This characteristic curve can accurately describe the relationship between torque and locking angle under different thread types.

[0030] In one example, the locking state is evaluated based on the torque-locking angle characteristic curve to obtain a state evaluation index, the state evaluation index is analyzed for deviation from the set locking threshold, and a system correction matrix is ​​obtained through multi-dimensional parameter collaborative calculation, including: Data analysis is performed on the torque-locking angle characteristic curve, key nodes are extracted according to the screw assembly state, and the locking state sequence is obtained. The torque accuracy is monitored online according to the locking state sequence, and the torque state data is obtained through torque range analysis. The torque status data is input into the evaluation unit, and the status evaluation index is obtained based on the fault alarm and counting function. The three dimensions of material shortage, material jamming and torque abnormality in the locking process are monitored, and the abnormal identification matrix is ​​obtained through threshold detection; The state evaluation index and the abnormal identification matrix are fused, and the comprehensive state score is obtained through weighted operation. The comprehensive state score is compared with the set lock threshold, and the deviation parameter group is obtained through difference calculation; A multidimensional state space is constructed based on the deviation parameter group, and the parameter correction is obtained through collaborative analysis. The parameter correction is reduced in dimension according to the dynamic model, and the system correction matrix is ​​obtained through matrix transformation.

[0031] In this example, the torque-locking angle characteristic curve is analyzed to extract the key locking nodes. These key nodes represent the significant change points of the torque-angle relationship during the screw locking process, such as when the screw contacts the workpiece, when the screw starts to be pressed in, when the predetermined tightening force is reached, etc. Assume that the torque-locking angle data pair is ,in Indicates The torque value at the moment, is the corresponding locking angle value. By analyzing these data, these key nodes are extracted through numerical differentiation, trend detection or inflection point identification methods. These key nodes reflect the locking state of the screw. After sorting these nodes, the locking state sequence is obtained. , where each state Each corresponds to a specific locking process stage, such as "initial contact", "locking start", "torque approaching set value" or "locking completed". Based on the locking state sequence, the torque accuracy is monitored online. By analyzing the torque range, that is, by comparing the real-time torque data With preset maximum torque and minimum torque , get torque state data , reflecting whether the torque in the current screw assembly process is within the normal range. The calculation of torque state data is expressed as:

[0032] Among them, 1 indicates normal torque and 0 indicates abnormal torque. By monitoring the torque status data, it is possible to know in real time whether there is any deviation in the locking process. The torque status data is input into the evaluation unit for status evaluation. The quality of the entire screw locking process is evaluated based on real-time data, and it is determined whether there is a fault or abnormality. In this process, the fault alarm and counting functions are key parts, and based on these functions, status evaluation indicators in multiple dimensions are obtained. For example, during the locking process, three typical abnormal conditions occur: material shortage, material jamming, and torque abnormality. Material shortage refers to insufficient supply of screws, resulting in the inability of the screws to enter the chuck normally or to continue to lock; material jamming refers to obstruction of the screws during the conveying or locking process, resulting in obstruction of the conveying or locking process; and torque abnormality means that during the locking process, the torque data exceeds the preset range, indicating that the locking operation has a deviation. Based on the data of these three dimensions, the threshold detection method is used to detect abnormal conditions in each dimension. Assume that the set thresholds are respectively and For angle, and For torque, an anomaly identification matrix is ​​constructed by comparing the real-time data with these thresholds. , whose value represents the abnormal situation of each dimension at the moment. According to this matrix, the abnormal identification of each dimension at each moment is obtained. The state evaluation index is fused with the abnormal identification matrix. The abnormal information of each dimension is integrated and a comprehensive state score is evaluated. Assume that the weight of each dimension is , corresponding to material shortage, material jam and torque abnormality, and the comprehensive status score Calculated by the following formula: ; in, It is an abnormality mark at a specific time point, indicating the state of material shortage, material jam and torque abnormality. The value is greater than the set lock threshold , it means that there is a major abnormality in the system and further adjustment or alarm processing is required. , it means that the current system status is normal. By calculating the difference between the status comprehensive score and the set lock threshold, a deviation parameter group is obtained. , used for further optimization and correction. The deviation parameter group represents the gap between the current and ideal state of the system, and is calculated as: ; The deviation parameter group reflects the error in the current screw tightening process. Based on this, the parameters are corrected to restore the system to the optimal working state. To achieve this goal, a multidimensional state space is constructed based on the deviation parameter group, and the parameter correction amount is obtained through collaborative analysis. The construction of the multidimensional state space is usually achieved by integrating the deviations of each dimension to form a multidimensional vector. Each dimension in the vector represents a deviation in a specific aspect of the system, such as material shortage, material jamming, or torque abnormality. Through collaborative analysis, a correction amount vector is obtained. , which indicates how the system should be adjusted in each dimension. Assume that the correction vector is , represents the correction value for material shortage, material jamming and torque abnormality. By reducing the dimension of the correction value vector, it is simplified into a single correction value, and the matrix transformation method is used to complete this operation. By reducing the dimension of the correction value vector, the final system correction matrix is ​​obtained , which represents the amount of correction the system needs to make to each control parameter. Through matrix transformation, the calculation formula of the correction matrix is: ; in, It is a weighted matrix that represents the weight of each control parameter. By adjusting these control parameters, the performance of the system can be restored to the ideal state.

[0033] In one example, the system correction matrix is ​​input into the Lagrangian control model, the dynamic weight coefficient is updated, and the optimized control instruction set is output, including: Substitute the system correction matrix into the motion equation of the electric screwdriver torque output system and the air pressure-speed function relationship, and obtain the initial weight data through matrix operation; Perform feature analysis on the initial weight data, optimize the weights in combination with the control benchmark parameter group to obtain an update coefficient matrix, and perform weight iteration operation on the update coefficient matrix to obtain the dynamic weight coefficients; The dynamic weight coefficient is corrected online according to the speed and pressure parameters of the feeder to obtain an optimized control parameter group, and the optimized control parameter group is substituted into the Lagrangian control model to obtain a control variable sequence through parameter mapping; The control variable sequence is analyzed and processed, and the parameters are matched in combination with the torque-lock angle characteristic curve to obtain the benchmark instruction set; The benchmark instruction set is functionally divided into electric batch control and feeding control, and sub-item control instructions are obtained through instruction analysis. The sub-item control instructions are dynamically combined, and the optimized control instruction set is output through coordinated operation.

[0034] In this example, the system correction matrix It is obtained through multi-dimensional analysis and collaborative calculation of the system state. The matrix contains all the control parameters that need to be adjusted, such as torque, speed, air pressure, etc. Substitute the matrix into the motion equation of the electric screwdriver torque output system and the air pressure-speed function relationship to obtain the initial weight data. Assume that the motion equation of the electric screwdriver torque output system is: ; in, is the total force applied to the system, is the mass, a is the acceleration, is the damping matrix, is the speed. The air pressure-speed function relationship is expressed as: ; in, is the rotation speed, is the air pressure, a function of Describes the relationship between air pressure and speed. By changing the system correction matrix Substituting into the above equation, we get the initial control weight data, which represents the influence of each control parameter on the dynamic response of the system. The initial weight data is represented as a vector , each element represents a parameter Contribution to system performance. Perform feature analysis on the initial weight data and optimize the weights in combination with the control benchmark parameter group. The purpose of feature analysis is to discover the regularity and importance in the initial weight data, evaluate the contribution of each control parameter, and adjust the weight accordingly. Assume that the control benchmark parameter group Contains preset ideal control parameters, which can be used as a reference for optimization. The feature analysis method uses principal component analysis or other statistical analysis methods to convert the initial weight data Projected into the feature space of the benchmark parameter group, the importance score of each control parameter is obtained. After feature analysis, the update coefficient matrix is ​​obtained Represents the optimized control weight. The calculation process of updating the coefficient matrix is ​​solved iteratively by the least squares method or gradient descent algorithm, and the formula is expressed as: ; in, is the learning rate, is the loss function Weight The gradient of the loss function Reflects the error between the current control system and the preset target. In the process of weight iteration, the dynamic weight coefficient is obtained by continuously updating the weight data. These coefficients are used to describe the final impact of various control parameters (such as air pressure, speed, etc.) on system performance. The dynamic weight coefficient is corrected online according to the feeder speed and pressure parameters. The weight coefficient is dynamically adjusted through real-time data so that the system can adapt to changes in the external environment. For example, the speed and pressure of the feeder will be affected by the system load or other factors, and dynamic adjustments are made through real-time sampling data. Assuming the feeder speed is , the air pressure is , online correction is performed through the following relationship: ; in, is the correction factor, represents the corrected dynamic weight coefficient. Through this online correction method, the system can adapt to the changes in air pressure and speed in real time and achieve more precise control. Substitute into the Lagrangian control model to generate the final control instruction set. The Lagrangian control model is a mathematical model that describes the motion of the system. The control instructions are derived through the variational method. The control instruction set is the core of the system to achieve precise control. The optimized control parameters are mapped to specific control variables through parameter mapping. Assume that the control variable sequence is , these variables represent specific signals output by the system controller, such as the torque and speed of the electric batch or the air pressure of the feeder. After the control variable sequence is analyzed, the parameters are matched in combination with the torque-locking angle characteristic curve. The torque-locking angle characteristic curve is a curve that represents the relationship between torque and locking angle, which is obtained through experiments or theoretical calculations. During the analysis process, the control variables are matched with the characteristic curve to ensure that the system can achieve the ideal torque value at the predetermined locking angle. Through this matching process, the benchmark instruction set is obtained. , which contains all the basic control commands that need to be executed. Baseline Instruction Set The functions will be divided according to electric batch control and feeder control. The complex control task will be decomposed into multiple subtasks, each of which corresponds to a control link in the system. For example, electric batch control is divided into torque control and speed control, while feeder control includes air pressure and feeder speed control. By parsing the benchmark instruction set, the sub-item control instructions for each subtask are obtained. By dynamically combining these sub-item control instructions and performing coordinated operations, an optimized control instruction set is obtained. This instruction set will be transmitted to the electric batch driver and feeder control system to achieve precise screw locking control.

[0035] Reference Figure 2 , this embodiment provides a screw feeding pressure adaptive adjustment device, comprising: Acquisition module 1 is used to extract screw specification parameters of the screw feeding equipment, and collect the electric screwdriver speed signal and initial torque signal to build a Lagrangian control model to obtain a control reference parameter group; Calculation module 2 is used to obtain real-time air pressure data flow, and calculate the conveying cycle deviation and screw impact stress value according to the control reference parameter group, and obtain the air pressure control instruction through difference compensation operation; Building module 3, used to transmit the air pressure control command to the electric batch driver, build the torque-speed-temperature mapping function in combination with the real-time displacement data collected by the angular displacement sensor, and calculate the torque output parameter by the least square method; The compensation module 4 is used to perform adaptive compensation calculation on the torque output parameter to obtain the torque compensation parameter, and perform integration operation on the torque compensation parameter and the locking angle sensor data to generate a torque-locking angle characteristic curve; Analysis module 5, used to evaluate the locking state based on the torque-locking angle characteristic curve, obtain the state evaluation index, perform deviation analysis between the state evaluation index and the set locking threshold, and obtain the system correction matrix through multi-dimensional parameter collaborative calculation; The output module 6 is used to input the system correction matrix into the Lagrangian control model, update the dynamic weight coefficient, and output the optimized control instruction set.

[0036] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0037] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0038] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0039] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0040] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0041] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0042] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for self-adapting screw feeding pressure, characterized in that: The following steps are involved: Extract screw specification parameters from screw feeding equipment, collect electric screwdriver speed signal and initial torque signal to build Lagrangian control model, and obtain control benchmark parameter group; Acquire real-time air pressure data stream, calculate the conveying cycle deviation and screw impact stress value according to the control reference parameter group, and obtain air pressure control instructions through difference compensation operation; The air pressure control command is transmitted to the electric batch driver, and the torque-speed-temperature mapping function is constructed in combination with the real-time displacement data collected by the angular displacement sensor, and the torque output parameter is calculated by the least square method; Performing adaptive compensation calculation on the torque output parameter to obtain a torque compensation parameter, and integrating the torque compensation parameter with the locking angle sensor data to generate a torque-locking angle characteristic curve; Based on the torque-lock angle characteristic curve, a locking state evaluation is performed to obtain a state evaluation index, a deviation analysis is performed between the state evaluation index and a set locking threshold, and a system correction matrix is ​​obtained through multi-dimensional parameter collaborative calculation; The system correction matrix is ​​input into the Lagrangian control model, the dynamic weight coefficient is updated, and the optimized control instruction set is output.

2. The method for self-adapting screw feeding pressure according to claim 1, characterized in that: The screw feeding device is subjected to screw specification parameter extraction, and the electric screwdriver speed signal and the initial torque signal are collected to construct a Lagrangian control model, and a control reference parameter group is obtained, including: The screw specification parameters are input through the LCD screen control system input interface, and the data is classified according to the preset specification range to obtain the initial matrix of screw parameters; The ratio of the total length L of the screw to the nut diameter D in the initial matrix of screw parameters is calculated to select a valid parameter group with L / D greater than 1.

3. At the same time, the no-load speed signal of the electric screwdriver collected by the encoder is digitized to obtain the digital speed value through signal sampling conversion; Perform differential operation on the displacement data collected by the angular displacement sensor to obtain the real-time rotational speed change rate, input the temperature data collected by the temperature sensor into the compensation function to obtain the initial torque coefficient, perform temperature correction on the initial torque coefficient to obtain the corrected torque coefficient; Based on the effective parameter group, the speed digital quantity, and the speed change rate, a motion equation of the electric screw torque output system is established, and the speed and pressure parameters of the feeder in the compressed air pipeline are sampled, and the air pressure-speed function relationship is obtained by data fitting; The motion equation of the electric screw torque output system, the corrected torque coefficient, and the air pressure-speed function relationship are substituted into the Lagrangian control model to construct a dynamic model and obtain a control reference parameter group.

3. The method for self-adapting screw feeding pressure according to claim 2, characterized in that: The real-time air pressure data stream is obtained, and the conveying cycle deviation and the screw impact stress value are calculated according to the control reference parameter group, and the air pressure control instruction is obtained through difference compensation operation, including: Data is collected by a distributed pressure sensor arranged in a compressed air supply pipeline, initial air pressure data is obtained by signal conversion, and the initial air pressure data is input into a digital filter to eliminate environmental noise interference, thereby obtaining a real-time air pressure data stream; According to the feeder speed parameter in the control reference parameter group, the real-time air pressure data stream is periodically analyzed to obtain a conveying time value of a single screw, and a period calculation is performed based on the conveying time value of the single screw, and the calculation result is compared with a preset standard period of 0.6 seconds to obtain a conveying period deviation; The force applied when the screw reaches the chuck of the electric screwdriver is detected by a high-precision torque sensor, and the screw impact stress value is obtained by stress conversion, and a threshold value is judged for the screw impact stress value, and the stress compensation amount is calculated based on the air pressure function in the control reference parameter group; The conveying cycle deviation and the stress compensation amount are input into the adaptive algorithm module, and the air pressure correction value is obtained through weighted calculation. According to the difference between the air pressure correction value and the current air pressure data, the air pressure control instruction is generated through compensation calculation.

4. The method for self-adapting screw feeding pressure according to claim 3, characterized in that: The air pressure control command is transmitted to the electric batch driver, the torque-speed-temperature mapping function is constructed in combination with the real-time displacement data collected by the angular displacement sensor, and the torque output parameter is calculated by the least square method, including: Input the air pressure control command into the electric batch driver, and segment the data according to the preset pressure range to obtain the electric batch control reference value; The rotation state of the electric screwdriver chuck is detected in real time by an angular displacement sensor, real-time displacement data is obtained by a signal conditioning circuit, and the real-time displacement data is analyzed in time series, and the real-time rotation speed value and acceleration value of the electric screwdriver are obtained by differential calculation; The ambient temperature data collected by the temperature sensor is input into the compensation calculation unit, and the temperature compensation factor is obtained through correction calculation; Constructing a speed response equation based on the electric batch control reference value and the real-time speed value, and substituting the temperature compensation factor into the equation to obtain a corrected speed curve; A dynamic characteristic equation is established according to the corrected speed curve and the acceleration value, and a torque-speed-temperature mapping function is obtained through function conversion; The torque-speed-temperature mapping function is calculated by least square method, and a torque initial value sequence is obtained through error iteration operation. The torque initial value sequence is then weighted averaged to obtain a torque output parameter.

5. The method for self-adapting screw feeding pressure according to claim 4, characterized in that: The adaptive compensation calculation is performed on the torque output parameter to obtain a torque compensation parameter, and the torque compensation parameter is integrated with the locking angle sensor data to generate a torque-locking angle characteristic curve, including: Sampling and analyzing the torque output parameters, calibrating the data based on the torque range of the electric batch, obtaining a torque reference sequence, and constructing a compensation calculation model based on the torque reference sequence, substituting the no-load speed data into the compensation calculation model, and obtaining a torque compensation parameter; Dynamically compensate the torque accuracy according to the torque compensation parameter, and obtain the compensated torque value through data correction; Sampling the locking angle sensor data collected by the locking angle sensor, obtaining a valid angle value through data screening, and performing time sequence matching on the compensated torque value and the valid angle value, and obtaining a torque-angle data pair through data alignment; Classifying the torque-angle data pairs according to thread types, and obtaining characteristic intervals through data segmentation; The data in the characteristic interval are integrated and calculated, characteristic curve parameters are obtained by cumulative operation, and function fitting is performed based on the characteristic curve parameters, and a torque-lock angle characteristic curve is generated by curve reconstruction.

6. The method for self-adapting screw feeding pressure according to claim 5, characterized in that: The locking state is evaluated based on the torque-locking angle characteristic curve to obtain a state evaluation index, the state evaluation index is analyzed for deviation from a set locking threshold, and a system correction matrix is ​​obtained through collaborative calculation of multi-dimensional parameters, including: Performing data analysis on the torque-locking angle characteristic curve, extracting key nodes according to the screw assembly state, obtaining a locking state sequence, and performing online monitoring of the torque accuracy according to the locking state sequence, and obtaining torque state data through torque range analysis; The torque state data is input into the evaluation unit, and the state evaluation index is obtained based on the fault alarm and counting function. The three dimensions of material shortage, material jamming and torque abnormality in the locking process are monitored, and the abnormal identification matrix is ​​obtained through threshold detection; The state evaluation index is data-fused with the abnormal identification matrix, a comprehensive state score is obtained by weighted operation, and the comprehensive state score is compared with a set lock threshold, and a deviation parameter group is obtained by difference calculation; A multidimensional state space is constructed based on the deviation parameter group, and a parameter correction amount is obtained through collaborative analysis. The parameter correction amount is subjected to dimensionality reduction processing according to a dynamic model, and a system correction matrix is ​​obtained through matrix transformation.

7. The method for self-adapting screw feeding pressure according to claim 6, characterized in that: The step of inputting the system correction matrix into the Lagrangian control model, updating the dynamic weight coefficient, and outputting an optimized control instruction set includes: Substituting the system correction matrix into the motion equation of the electric screw torque output system and the air pressure-speed function relationship, and obtaining initial weight data through matrix operation; Performing feature analysis on the initial weight data, performing weight optimization in combination with the control reference parameter group to obtain an update coefficient matrix, and performing weight iteration operation on the update coefficient matrix to obtain a dynamic weight coefficient; The kinetic weight coefficient is corrected online according to the speed and pressure parameters of the feeder to obtain an optimized control parameter group, and the optimized control parameter group is substituted into the Lagrangian control model to obtain a control variable sequence through parameter mapping; Analyze the control variable sequence, perform parameter matching in combination with the torque-lock angle characteristic curve, and obtain a reference instruction set; The reference instruction set is functionally divided according to electric batch control and feeding control, and sub-item control instructions are obtained through instruction analysis. The sub-item control instructions are dynamically combined, and an optimized control instruction set is output through coordinated calculation.

8. A screw feeding pressure self-adapting regulating device, characterized in that: For implementing the steps of the method according to any one of claims 1 to 7, the device comprises: The acquisition module is used to extract screw specification parameters from the screw feeding equipment, and to collect the electric screwdriver speed signal and initial torque signal to construct the Lagrangian control model to obtain the control reference parameter group; A calculation module is used to obtain a real-time air pressure data stream, and calculate the conveying cycle deviation and the screw impact stress value according to the control reference parameter group, and obtain an air pressure control instruction through a difference compensation operation; A construction module is used to transmit the air pressure control command to the electric batch driver, construct a torque-speed-temperature mapping function in combination with the real-time displacement data collected by the angular displacement sensor, and obtain the torque output parameter by calculating the least square method; A compensation module, used for performing adaptive compensation calculation on the torque output parameter to obtain a torque compensation parameter, and performing an integration operation on the torque compensation parameter and the locking angle sensor data to generate a torque-locking angle characteristic curve; An analysis module, for evaluating the locking state based on the torque-locking angle characteristic curve, obtaining a state evaluation index, performing deviation analysis between the state evaluation index and a set locking threshold, and obtaining a system correction matrix through collaborative calculation of multi-dimensional parameters; The output module is used to input the system correction matrix into the Lagrangian control model, update the dynamic weight coefficient, and output the optimized control instruction set.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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