High-precision low-torque servo module load adaptive method and device

Abstract

The application relates to a high-precision low-torque servo module load adaptive method, which comprises the following steps: based on the structure and working principle of a low-torque servo module, load bearing capacity theoretical calculation is carried out on each component in the low-torque servo module to obtain a load theoretical parameter set of each component; based on the load theoretical parameter set, load response characteristic testing is carried out on each component to obtain a load response characteristic set of each component; the load response characteristic set is used to correlate and analyze a load collaborative correlation parameter set among the components; based on the load collaborative correlation parameter set, a low-torque servo module load adaptive strategy is formulated to obtain a load adaptive strategy set of each component; and based on the load adaptive strategy set, load adaptive control is carried out on the low-torque servo module, so that the technical problem that in traditional design methods, load parameters of each component are often set according to experience or simple theoretical calculation, and each component is difficult to play the best performance in actual operation is solved.

Description

Technical Field

[0001] This invention relates to the field of servo module technology, and in particular to a high-precision, low-torque servo module load adaptive method and apparatus. Background Technology

[0002] In modern industrial automation, low-torque servo modules, as key components, are widely used in fields with extremely high requirements for precision and stability, such as precision assembly, electronic manufacturing, and medical devices. However, current low-torque servo modules have significant problems in load adaptability. On the one hand, traditional design methods often rely solely on experience or simple theoretical calculations to set the load parameters of each component, failing to fully consider the complex and variable load characteristics under actual working conditions. This results in components failing to perform optimally in actual operation, and even frequently malfunctioning due to improper load bearing, seriously affecting the normal operation of equipment and production efficiency. On the other hand, when facing actual working conditions, existing technologies lack comprehensive and accurate testing of the load response characteristics of each component. This makes it impossible to clearly understand the set of load coordination parameters between components, thus making it difficult to formulate scientific and reasonable load adaptive strategies. In practice, each component operates independently, failing to achieve efficient coordination, resulting in energy waste and making it difficult to ensure the high-precision operation of the entire system, thus failing to meet the growing demands of high-end manufacturing. Furthermore, due to the lack of effective load adaptive control methods, the stability and accuracy of low-torque servo modules are severely impacted by load changes during operation. For example, in precision assembly, even slight load fluctuations can lead to assembly deviations, reducing product quality and increasing the defect rate. Therefore, developing a high-precision load adaptive method for low-torque servo modules to address these problems and improve their performance and reliability has become a crucial issue that urgently needs to be addressed in the field of industrial automation. Summary of the Invention

[0003] The main objective of this invention is to provide a high-precision, low-torque servo module load adaptive method and device, which solves the technical problem that traditional design methods often rely solely on experience or simple theoretical calculations to set the load parameters of each component, resulting in the components failing to achieve optimal performance in actual operation.

[0004] To achieve the above objectives, the present invention provides a high-precision, low-torque servo module load adaptive method, comprising the following steps: Based on the structure and working principle of the low-torque servo module, the load-bearing capacity of each component in the low-torque servo module is theoretically calculated to obtain the load theoretical parameter set of each component. Under actual working conditions, based on the theoretical load parameter set, load response characteristics of each component are tested to obtain the load response characteristic set of each component; Using the aforementioned load response characteristic set, the load coordination correlation parameter set between various components is analyzed; Based on the load coordination correlation parameter set, a load adaptive strategy is formulated for the overall low torque servo module to obtain the load adaptive strategy set for each component. Based on the aforementioned load adaptive strategy set, load adaptive control is performed on the low-torque servo module.

[0005] Furthermore, based on the structure and working principle of the low-torque servo module, theoretical calculations of the load-bearing capacity of each component in the low-torque servo module are performed to obtain a set of theoretical load parameters for each component, including: Finite element analysis was performed on the structure of the low-torque servo module to obtain stress and strain distribution data for each component; Obtain the material mechanical property parameters corresponding to each component, and calculate the ultimate bearing capacity of each component based on the stress-strain distribution data and the material mechanical property parameters to obtain the ultimate bearing capacity parameter set of each component. The ultimate bearing capacity parameter set includes the maximum tensile strength, maximum compressive strength, maximum shear strength and fatigue limit. Based on the ultimate bearing capacity parameter set and the working principle, dynamic characteristic simulation is performed on each component of the low torque servo module to obtain dynamic response characteristic data of each component, and frequency domain analysis is performed on the dynamic response characteristic data to obtain the natural frequency and damping ratio parameter set of each component. Based on the natural frequency and damping ratio parameter set, a stability margin analysis is performed on each component of the low torque servo module to obtain a stability margin parameter set for each component. Based on the stability margin parameter set, the safe operating area of ​​each component under different load conditions is calculated to obtain the load theoretical parameter set for each component. The load theoretical parameter set for each component includes the rated load, maximum load, and safe load range.

[0006] Furthermore, based on the stress-strain distribution data and the material mechanical property parameters, the ultimate bearing capacity of each component is calculated to obtain the ultimate bearing capacity parameter set of each component, including: Based on the stress-strain distribution data, principal stress analysis is performed on each component to obtain principal stress distribution data for each component. Based on the principal stress distribution data and the yield strength in the material mechanical property parameters, the initial yield load set of each component is calculated. Based on the initial yield load set, plastic deformation is estimated for each component to obtain plastic deformation data for each component. Based on the plastic deformation data and the hardening coefficient in the material mechanical property parameters, the ultimate bearing capacity increment set for each component is calculated. Based on the ultimate bearing capacity increment set and the initial yield load set, the ultimate bearing capacity of each component is calculated to obtain the ultimate bearing capacity parameter set of each component.

[0007] Furthermore, based on the theoretical load parameter set, load response characteristic tests are performed on each component to obtain the load response characteristic set of each component, including: Based on the theoretical load parameter set, the actual operating load range of each component is divided to obtain the actual operating load range set of each component. Then, the load interval characteristic analysis of the actual operating load range set of each component is performed to obtain the load interval characteristic set of each component. By using the load range characteristic set of each component, the load dynamic response process of each component is monitored, and the response time feature of each component is extracted based on the load dynamic response process to obtain the response time feature set of each component. Based on the response time feature set of each component, the load response frequency characteristics of each component are analyzed, and the frequency response bandwidth of each component is determined according to the load response frequency characteristic set of each component. By utilizing the frequency response bandwidth of each component, the load response characteristics of each component are comprehensively evaluated.

[0008] Furthermore, based on the theoretical load parameter set, the actual operating load range of each component is divided to obtain the actual operating load range set of each component, including: Based on the load theoretical parameter set, load extreme values ​​are predicted for each component to obtain a load extreme value set for each component, and load thresholds are set for the load extreme value sets for each component to obtain a load threshold set for each component. By discretizing the load threshold set of each component, a load discretization result set of each component is obtained, and the working condition load level identifier of each component is marked based on the load discretization result set of each component to obtain a load level identifier set of each component. Based on the load level identifier set of each component, load gradient analysis is performed on each component to obtain the load gradient set of each component, and the load gradient interval of each component is divided according to the load gradient set of each component. Using the load gradient interval, the load range of each component is integrated to obtain the set of actual operating load ranges of each component.

[0009] Furthermore, the step of using the load response characteristic set to perform correlation analysis on the load coordination correlation parameter set among the components includes: Based on the load response characteristic set, the load input-output relationship between each component is identified, and logical relationship reasoning is performed on the load input-output relationship to obtain the load logical relationship set between each component; By using the load logic relationship set between each component, the load energy transfer path between each component is traced to obtain the load energy transfer path set between each component, and the energy loss node between each component is identified based on the load energy transfer path set between each component. Based on the energy loss node, the load distribution balance among the components is evaluated to obtain the load distribution balance set among the components, and the load coordination correlation parameter set among the components is determined according to the load distribution balance set among the components.

[0010] Furthermore, based on the load coordination correlation parameter set, a load adaptive strategy is formulated for the overall low-torque servo module to obtain a load adaptive strategy set for each component, including: Based on the load coordination correlation parameter set, the load allocation priority of each component in the overall low torque servo module is planned, and the resource allocation rationality analysis of the load allocation priority of each component is performed to obtain the resource allocation rationality set of each component. By defining the load dynamic adjustment range of each component through the resource allocation rationality set of each component, and evaluating the adjustment flexibility of each component based on the load allocation priority of each component, the adjustment flexibility set of each component is obtained. Based on the adjustment flexibility set of each component, the load adaptive mode of each component is classified to obtain the load adaptive mode set of each component, and the mode switching conditions of each component are determined according to the load adaptive mode set of each component. Using the aforementioned mode transition conditions, a set of load adaptive strategies for each component is formulated.

[0011] The present invention also provides a high-precision, low-torque servo module load adaptive device, comprising: The calculation module is used to perform theoretical calculations on the load-bearing capacity of each component in the low-torque servo module based on its structure and working principle, and to obtain the set of theoretical load parameters for each component. The testing module is used to test the load response characteristics of each component under actual working conditions based on the load theoretical parameter set, and to obtain the load response characteristic set of each component. The analysis module is used to analyze the load coordination correlation parameter set between various components using the load response characteristic set. The formulation module is used to formulate a load adaptive strategy for the overall low-torque servo module based on the load coordination correlation parameter set, and obtain the load adaptive strategy set for each component. The control module is used to perform load adaptive control on the low-torque servo module based on the load adaptive strategy set.

[0012] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0013] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0014] This invention provides a high-precision, low-torque servo module load adaptive method, comprising the following steps: performing theoretical calculations on the load-bearing capacity of each component to obtain a set of theoretical load parameters for each component; based on the set of theoretical load parameters, performing load response characteristic tests on each component to obtain a set of load response characteristics for each component; using the set of load response characteristics, performing correlation analysis on the load coordination correlation parameter set between each component to obtain a set of load coordination correlation parameters between each component; based on the set of load coordination correlation parameters, formulating a load adaptive strategy for the overall low-torque servo module to obtain a set of load adaptive strategies for each component; and performing load adaptive control on the low-torque servo module based on the set of load adaptive strategies. This method solves the technical problem that traditional design methods often rely solely on experience or simple theoretical calculations to set the load parameters of each component, leading to difficulties in achieving optimal performance of each component in actual operation. It enables the formulated strategy to accurately adjust the working state of each component according to different working conditions and load changes, achieving dynamic adaptive adjustment of the load, thereby ensuring that the low-torque servo module maintains high-precision operation under various load conditions, meeting the stringent precision requirements of high-end manufacturing. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the steps of a high-precision low-torque servo module load adaptive method in one embodiment of the present invention; Figure 2 This is a structural block diagram of a high-precision, low-torque servo module load adaptive device in one embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0016] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] like Figure 1 As shown, Figure 1 This is a schematic diagram of the steps of a high-precision, low-torque servo module load adaptive method in one embodiment of the present invention; One embodiment of the present invention provides a high-precision, low-torque servo module load adaptive method, comprising the following steps: Step S1: Based on the structure and working principle of the low-torque servo module, perform theoretical calculations on the load-bearing capacity of each component in the low-torque servo module to obtain the load theoretical parameter set of each component.

[0019] Specifically, to achieve "theoretical calculations of the load-bearing capacity of each component in a low-torque servo module based on its structure and working principle, and to obtain the theoretical load parameter set for each component," it is first necessary to deeply analyze the mechanical structure of the low-torque servo module, clarifying the specific construction and connection relationships of its transmission components (such as lead screws, guide rails, gears, etc.), drive components (motors, etc.), and feedback components (encoders, etc.). Simultaneously, it is crucial to understand its working principle of achieving precise control through servo motor drive, power transmission via transmission components, and feedback components. Based on this, according to the stress-strain relationship in mechanics of materials, the stress conditions of the transmission components under different loads are analyzed. Combining the principles of mechanical dynamics, the impact of changes in parameters such as torque and speed of the motor during the drive process on each component is calculated, thereby constructing a theoretical calculation model for load-bearing capacity. For example, in the precision assembly scenario of electronic manufacturing, by analyzing the stress on the lead screw of the low-torque servo module under frequent start-stop and micro-displacement operations, and calculating the effect of the torque generated during motor drive on the gears, the theoretical load parameter set, such as the maximum axial force that the lead screw can withstand and the allowable torque of the gears, can be obtained, providing crucial data support for subsequent work.

[0020] Step S2: Under actual working conditions, based on the load theoretical parameter set, load response characteristics of each component are tested to obtain the load response characteristic set of each component.

[0021] Specifically, after calculating the theoretical load parameter set for each component based on the structure and working principle of the low-torque servo module, the key step is to "test the load response characteristics of each component under actual working conditions, based on the theoretical load parameter set, to obtain the load response characteristic set of each component." Actual working conditions encompass the complex operating environments faced by the low-torque servo module in applications such as precision assembly in electronic manufacturing, including different operating frequencies, ambient temperatures, vibration conditions, and various dynamic load changes. Taking the chip mounting process in electronic manufacturing as an example, under these actual working conditions, based on the previously obtained theoretical load parameter set, a series of representative test conditions are set, such as simulating load changes caused by frequent start-stop operations and minute displacements during chip mounting. High-precision sensors are used to monitor the operating parameters of each component in real time under different load inputs, such as the displacement accuracy of the lead screw, the current change of the motor, and the feedback signal of the encoder. By changing factors such as load size and loading rate, repeated tests were conducted to record the response data of each component to different loads. This allowed for the analysis of the response patterns and performance of each component during actual operation, ultimately forming a load response characteristic set containing information on component dynamics and stability. This provides a reliable data basis for subsequent analysis of load coordination and correlation parameter sets between components.

[0022] Step S3: Using the load response characteristic set, perform correlation analysis on the load coordination correlation parameter set between each component.

[0023] Specifically, after obtaining the load response characteristic set of each component, by analyzing the response performance of each component to the load under actual working conditions, we can delve into the load coordination and correlation parameter set among them. Specifically, using the operating data of each component under different load conditions recorded in the load response characteristic set, such as the fluctuation of the lead screw's displacement accuracy when the motor output torque changes, and the impact of lateral force on the encoder feedback signal when the guide rail is subjected to lateral force, data mining and correlation analysis algorithms are employed to quantify the degree of correlation between the load responses of each component. Taking the precision assembly scenario of electronic manufacturing as an example, when the low-torque servo module performs the assembly operation of small parts, the transmission efficiency of the lead screw, the friction coefficient of the guide rail, and the signal feedback of the encoder will all change accordingly with the adjustment of the motor torque. By comprehensively analyzing these data, we can determine the synergistic relationship between the motor torque and the lead screw displacement accuracy and the guide rail stability, calculate the correlation weight of the load changes of each component, the response time difference, and other key parameters, thereby obtaining the load coordination and correlation parameter set among the components. This provides accurate data support for the subsequent formulation of load adaptive strategies, ensuring that the components of the low-torque servo module can operate efficiently and collaboratively in actual work.

[0024] Step S4: Based on the load coordination and correlation parameter set, a load adaptive strategy is formulated for the overall low-torque servo module to obtain a load adaptive strategy set for each component.

[0025] Specifically, after obtaining the load coordination correlation parameter set, this can be used as a basis to deeply analyze the coordination patterns of each component of the low-torque servo module under different loads, and then formulate an overall load adaptive strategy. Specifically, by analyzing parameters such as the load change correlation weights and response time differences of each component in the load coordination correlation parameter set, and combining this with the actual needs of the low-torque servo module in applications such as precision assembly in electronic manufacturing, an adaptive control model is constructed. For example, in the chip packaging stage of electronic manufacturing, when the module needs to complete high-precision chip picking and placement, based on the load-coordinated parameter set, if a change in motor torque is detected, the displacement trend of the lead screw, the force on the guide rail, and the fluctuation of the encoder feedback signal can be predicted in advance according to the correlated parameters. This allows for advance adjustment of the motor drive current, lead screw feed speed, etc., and the formulation of load adaptive strategies for various components such as motor, lead screw, and guide rail. For example, the acceleration and deceleration curve of the motor can be optimized to reduce impact, and the lubrication frequency of the lead screw can be adjusted to ensure transmission accuracy. Ultimately, a load adaptive strategy set covering the adjustment methods of each component under different working conditions is formed, ensuring that the low-torque servo module can still maintain high-precision operation in complex and variable load environments.

[0026] Step S5: Based on the load adaptive strategy set, perform load adaptive control on the low-torque servo module.

[0027] Specifically, in precision assembly scenarios in electronic manufacturing, load-adaptive control of low-torque servo modules based on a load-adaptive strategy set is a crucial step in ensuring efficient and stable module operation and achieving high-precision assembly. This process relies on real-time data acquisition from sensors, which is compared and analyzed with the strategy set to achieve precise control. In actual operation, various monitoring devices, such as force sensors, displacement sensors, and temperature sensors, are installed on components like the motor, lead screw, and guide rail of the low-torque servo module to collect data on component load magnitude, displacement changes, and operating temperature in real time. For example, during chip mounting, when the sensor detects an increase in motor load torque, the system immediately compares this data with the load-adaptive strategy set. If it determines that the system meets the mode transition conditions for high motor load in the strategy set, it quickly triggers the corresponding strategy, such as reducing motor speed, increasing drive current to improve torque output, and simultaneously adjusting the lead screw feed speed, allowing the lead screw and motor to adjust in tandem to ensure chip mounting accuracy. By using this real-time monitoring, comparative analysis, and rapid response method, the modules are dynamically adjusted according to the load adaptive strategy set to ensure that the low-torque servo modules maintain stable and efficient operation in the complex and ever-changing electronic manufacturing assembly load environment, meeting the stringent requirements of precision assembly tasks for accuracy and stability.

[0028] In a specific embodiment, based on the structure and working principle of the low-torque servo module, the load-bearing capacity of each component in the low-torque servo module is theoretically calculated to obtain a set of theoretical load parameters for each component, including: Finite element analysis was performed on the structure of the low-torque servo module to obtain stress and strain distribution data for each component; Obtain the material mechanical property parameters corresponding to each component, and calculate the ultimate bearing capacity of each component based on the stress-strain distribution data and the material mechanical property parameters to obtain the ultimate bearing capacity parameter set of each component. The ultimate bearing capacity parameter set includes the maximum tensile strength, maximum compressive strength, maximum shear strength and fatigue limit. Based on the ultimate bearing capacity parameter set and the working principle, dynamic characteristic simulation is performed on each component of the low torque servo module to obtain dynamic response characteristic data of each component, and frequency domain analysis is performed on the dynamic response characteristic data to obtain the natural frequency and damping ratio parameter set of each component. Based on the natural frequency and damping ratio parameter set, a stability margin analysis is performed on each component of the low torque servo module to obtain a stability margin parameter set for each component. Based on the stability margin parameter set, the safe operating area of ​​each component under different load conditions is calculated to obtain the load theoretical parameter set for each component. The load theoretical parameter set for each component includes the rated load, maximum load, and safe load range.

[0029] Specifically, in the field of precision assembly in electronic manufacturing, the stable operation of low-torque servo modules directly affects the assembly accuracy and production efficiency of products, and accurately calculating the load-bearing capacity of each component is a key prerequisite for ensuring its performance. The process of theoretically calculating the load-bearing capacity of each component based on the structure and working principle of the low-torque servo module, and obtaining the theoretical load parameter set, requires the comprehensive application of multidisciplinary knowledge and advanced analysis techniques to form a rigorous and scientific process. First, finite element analysis is performed on the structure of the low-torque servo module, which is the foundation of the entire calculation process. Taking the lead screw assembly in the module as an example, in precision assembly in electronic manufacturing, the lead screw needs to frequently perform linear reciprocating motion to achieve precise positioning, and its stress conditions are complex. A geometric model of the lead screw is accurately constructed using 3D modeling software, and then the model is imported into finite element analysis software. Based on the actual assembly relationship of the lead screw in the module, reasonable constraints are set, such as fixing one end of the lead screw to simulate its connection with the module base. Simultaneously, corresponding boundary conditions are applied according to the axial force, torque, and other loads that the lead screw may bear in precision assembly. Finite element analysis (FEM) calculations yield detailed stress and strain distribution data for each part of the lead screw, clearly showing the stress concentration areas and strain magnitudes under different stress conditions. For example, higher stress concentrations may occur at locations such as the root of the thread. Next, the material mechanical properties of each component, including the lead screw, are obtained. Lead screws are typically made of high-strength alloy steel; parameters such as elastic modulus, yield strength, and tensile strength are obtained by consulting material handbooks and conducting material tests. Based on the obtained stress and strain distribution data and material mechanical properties, the ultimate bearing capacity of the lead screw is calculated using strength theories and calculation formulas in mechanics of materials. For example, based on the fourth strength theory and the stress state of each part of the lead screw, the maximum tensile strength, maximum compressive strength, and maximum shear strength are calculated. Considering the frequent reciprocating motion of the lead screw in precision assembly, its fatigue limit is determined through fatigue tests or empirical formulas, thus obtaining the set of ultimate bearing capacity parameters. These parameters clarify the maximum load the lead screw can withstand under ideal conditions, providing an important basis for subsequent analysis. Then, based on the ultimate bearing capacity parameter set and the working principle of the low-torque servo module, dynamic characteristic simulations were performed on components such as the lead screw. In the precision assembly process of electronic manufacturing, the low-torque servo module needs to respond quickly to control commands to achieve high-precision motion control, making the dynamic performance of each component crucial. Using multibody dynamics simulation software, the motion process of the lead screw in actual operation was simulated, including different stages such as acceleration, deceleration, and constant speed. The influence of factors such as the torque change of the motor drive and the friction of the guide rail on the lead screw motion was considered, obtaining the dynamic response characteristic data of the lead screw, such as the curves of displacement, velocity, and acceleration over time. Frequency domain analysis was performed on these dynamic response characteristic data. Through mathematical methods such as Fourier transform, the time domain signal was converted into a frequency domain signal, thereby obtaining the natural frequency and damping ratio parameter set of the lead screw.The natural frequency reflects the vibration characteristics of the lead screw itself. If the external excitation frequency is close to the natural frequency, resonance may occur, affecting the stability and accuracy of the module. The damping ratio reflects the rate of vibration decay of the lead screw. Finally, based on the natural frequency and damping ratio parameter sets, a stability margin analysis is performed on components such as the lead screw. In the complex working conditions of precision assembly in electronic manufacturing, modules may be subject to various external disturbances and load fluctuations, thus requiring an assessment of the component's stability margin. By establishing a stability analysis model, considering factors such as the natural frequency, damping ratio, and load variations in actual operation, the stability margin parameter set of the lead screw is calculated, and its stability under different working conditions is evaluated. Based on the stability margin parameter set, combined with the ultimate bearing capacity parameter, the safe operating area of ​​the lead screw under different load conditions is further calculated. For example, the rated load that the lead screw can continuously operate under certain stability and accuracy conditions, as well as the maximum load it can withstand for a short period of time, are determined, thus obtaining a theoretical parameter set of the lead screw load that includes the rated load, maximum load, and safe load range. For other components of the low-torque servo module, such as motors and guide rails, similar calculations are used to obtain a complete set of theoretical load parameters for each component of the entire module. This provides a solid theoretical foundation for module design optimization, load adaptive strategy formulation, and load control in actual operation, ensuring that the low-torque servo module operates stably and efficiently in precision assembly of electronic manufacturing, meeting the needs of high-precision production.

[0030] In a specific embodiment, the step of calculating the ultimate bearing capacity of each component based on the stress-strain distribution data and the material mechanical property parameters to obtain the ultimate bearing capacity parameter set of each component includes: Based on the stress-strain distribution data, principal stress analysis is performed on each component to obtain the principal stress distribution data of each component. Based on the principal stress distribution data and the yield strength in the material mechanical property parameters, the initial yield load set of each component is calculated. Based on the initial yield load set, plastic deformation is estimated for each component to obtain plastic deformation data for each component. Based on the plastic deformation data and the hardening coefficient in the material mechanical property parameters, the ultimate bearing capacity increment set for each component is calculated. Based on the ultimate bearing capacity increment set and the initial yield load set, the ultimate bearing capacity of each component is calculated to obtain the ultimate bearing capacity parameter set of each component.

[0031] Specifically, in precision assembly of electronic manufacturing, each component of a low-torque servo module needs to accurately bear the load to ensure assembly accuracy. Calculating the ultimate bearing capacity based on stress-strain distribution data and material mechanical property parameters is the core step in determining the component's load-bearing capacity. This process starts with principal stress analysis and gradually delves into plastic deformation estimation and ultimate bearing capacity calculation to obtain an accurate set of ultimate bearing capacity parameters. First, principal stress analysis is performed on each component based on stress-strain distribution data. Taking the guide rail component in a low-torque servo module as an example, during precision assembly, the guide rail not only bears the vertical pressure transmitted by the slider but may also experience lateral forces due to assembly errors or movement, leading to a complex stress state. Using stress-strain distribution data obtained through finite element analysis and stress analysis methods in mechanics of materials, such as Mohr's circle theory, the magnitude and direction of the principal stresses at each point on the guide rail are calculated, yielding principal stress distribution data. The principal stresses clearly reflect the maximum and minimum directions of force on the guide rail under actual working conditions, which is crucial for determining the guide rail's strength. By combining the yield strength from the material's mechanical properties and applying yield criteria (such as the Tresca or Mises criterion), the loads corresponding to the onset of plastic deformation at various points on the guide rail are calculated, i.e., the initial yield load set. For example, if the principal stress combination in a certain region of the guide rail reaches the material's yield strength threshold, then the load corresponding to that region is the initial yield load. This data marks the critical state of the guide rail transitioning from elastic deformation to plastic deformation. Next, based on the initial yield load set, plastic deformation is estimated for each component. Once the guide rail reaches the initial yield load, it enters the plastic deformation stage. Using the plastic deformation data from the material's mechanical properties, combined with theories of metal plastic deformation, such as incremental or total deformation theories, the degree of plastic deformation of the guide rail under different initial yield loads is estimated. As the load continues to increase, the material undergoes work hardening, at which point the hardening coefficient from the material's mechanical properties is introduced. The hardening coefficient reflects the degree of strength increase during plastic deformation. Based on the plastic deformation data and the hardening coefficient, the additional load the guide rail can withstand during the plastic deformation stage is calculated, i.e., the ultimate load-bearing increment set. For example, as the load on the guide rail exceeds the initial yield load, its material strength increases due to work hardening. The hardening coefficient can be used to calculate the remaining load increment the guide rail can withstand during plastic deformation, providing crucial data for accurately assessing its ultimate load-bearing capacity. Finally, based on the ultimate load increment set and the initial yield load set, the ultimate load-bearing capacity of each component is calculated. Adding the initial yield load to the ultimate load increment yields the final ultimate load-bearing capacity of the guide rail. This ultimate load-bearing capacity comprehensively considers the transition of the guide rail material from elastic to plastic deformation, as well as the effect of work hardening during plastic deformation, and accurately reflects the maximum load the guide rail can withstand under actual working conditions.For other components of the low-torque servo module, such as the lead screw and motor shaft, the same steps are followed: first, principal stress analysis is performed to obtain the initial yield load set; then, the ultimate bearing capacity increment set is obtained by combining plastic deformation estimation with the hardening coefficient; finally, the ultimate bearing capacity of each component is calculated, forming an ultimate bearing capacity parameter set that includes parameters such as maximum tensile strength, maximum compressive strength, maximum shear strength, and fatigue limit. These parameters provide a solid data foundation for subsequent dynamic characteristic simulation, stability analysis, and load adaptive strategy formulation for each component of the low-torque servo module. This ensures that each component can reasonably withstand the load in the complex load environment of precision assembly in electronic manufacturing, guaranteeing the high-precision operation and long-term stability of the module, and meeting the stringent requirements of the electronic manufacturing industry for equipment reliability and accuracy.

[0032] In a specific embodiment, the step of testing the load response characteristics of each component based on the load theoretical parameter set to obtain the load response characteristic set of each component includes: Based on the theoretical load parameter set, the actual operating load range of each component is divided to obtain the actual operating load range set of each component. Then, the load interval characteristics of the actual operating load range set of each component are studied to obtain the load interval characteristic set of each component. By using the load range characteristic set of each component, the load dynamic response process of each component is monitored, and the response time feature of each component is extracted based on the load dynamic response process to obtain the response time feature set of each component. Based on the response time feature set of each component, the load response frequency characteristics of each component are analyzed, and the frequency response bandwidth of each component is determined according to the load response frequency characteristic set of each component. By utilizing the frequency response bandwidth of each component, the load response characteristics of each component are comprehensively evaluated.

[0033] Specifically, in the load adaptive control system of low-torque servo modules, "testing the load response characteristics of each component based on the theoretical load parameter set to obtain the load response characteristic set of each component" is a crucial transitional step in transforming theoretical parameters into practical application basis, which is particularly important in precision assembly scenarios in electronic manufacturing. Its implementation process needs to closely revolve around each component, gradually progressing from defining the load range to a comprehensive evaluation of response characteristics. First, based on the acquired theoretical load parameter set, the actual operating load range of each component is divided. In precision assembly in electronic manufacturing, low-torque servo modules face diverse operating conditions such as minute pressure loads during chip mounting and intermittent torque loads during circuit board insertion. Taking the motor component in the module as an example, based on its maximum torque, rated power, and other data in the theoretical load parameter set, combined with the motor's operating status and load changes in actual assembly tasks, its actual operating load range is divided into light load (such as the equipment no-load debugging stage), medium load (routine chip mounting operations), and heavy load (high-precision assembly of complex components), thus obtaining the actual operating load range set of each component. To gain a more detailed understanding of the characteristics of each load range, a load range characteristic study is conducted on the actual operating load range of each component. By simulating the operating conditions of different load ranges, the stress characteristics and energy losses of the components within these ranges are analyzed, thus obtaining the load range characteristic set of each component. For example, when studying the mid-load range characteristics of the lead screw assembly, the axial force distribution law of the lead screw and the wear of the threaded pair within this range can be observed. Next, the load dynamic response process of each component is monitored using the load range characteristic sets of each component. In the electronic manufacturing assembly process, when a low-torque servo module switches from one assembly task to another, the load changes dynamically. Taking the guide rail assembly as an example, the lateral force and friction force borne by the guide rail changes in real time when picking up and placing electronic components of different sizes. Using high-precision sensors, such as force sensors and displacement sensors, various parameters of the guide rail during the dynamic load change process are collected in real time, recording the changes in displacement, velocity, and force over time, thus fully presenting the load dynamic response process. Based on this process, response time features of each component are extracted, and the time required for a component to respond and reach a steady state after a load change is analyzed, resulting in a response time feature set for each component. For example, when the motor load suddenly increases, the response time required for the motor to stabilize from a sudden load change to a stable speed can be accurately extracted by analyzing the change curves of parameters such as motor speed and torque. Subsequently, based on the response time feature sets of each component, the load response frequency characteristics of each component are analyzed. In the high-speed precision assembly process of electronic manufacturing, low-torque servo modules may face high-frequency load changes. For example, in high-speed surface mount technology (SMT) processes, the motor needs to frequently start, stop, and commutate, generating high-frequency torque loads. By testing and analyzing the response of each component under different frequency loads, the load response frequency curves of the components are plotted, thereby obtaining a load response frequency characteristic set for each component.Based on this characteristic set, the frequency response bandwidth of each component is determined, i.e., the range of load frequencies that the component can effectively respond to. For example, for an encoder component, its frequency response bandwidth is determined by testing its feedback accuracy under pulse signal inputs at different frequencies, and judging whether it can meet the signal feedback requirements of the module under high-speed assembly conditions. Finally, using the frequency response bandwidth of each component, the load response characteristic set of each component is comprehensively evaluated. The frequency response bandwidth is combined with the previously obtained load range characteristics, response time characteristics, etc., to comprehensively consider the response performance of each component under different load conditions. In the precision assembly scenario of electronic manufacturing, if the frequency response bandwidth of the lead screw component is narrow, it may not be able to achieve displacement control quickly and accurately in assembly tasks with high-frequency load changes, affecting assembly accuracy. Comprehensively evaluating the load response characteristic set of each component can clearly understand the advantages and disadvantages of each component under actual working conditions, providing strong data support for subsequent development of load adaptive strategies and optimization of module performance, ensuring that the low-torque servo module operates stably and efficiently in complex and ever-changing electronic manufacturing assembly tasks, meeting the industry's stringent requirements for high precision and high reliability.

[0034] In a specific embodiment, the step of dividing the actual operating load range of each component based on the load theoretical parameter set to obtain the actual operating load range set of each component includes: Based on the load theoretical parameter set, load extreme values ​​are predicted for each component to obtain a load extreme value set for each component, and load thresholds are set for the load extreme value sets for each component to obtain a load threshold set for each component. By discretizing the load threshold set of each component, a load discretization result set of each component is obtained, and the load range of each component is marked based on the load discretization result set of each component to obtain a load range marking set of each component; Based on the load interval marking set of each component, load gradient analysis is performed on each component to obtain the load gradient set of each component, and the load gradient interval of each component is divided according to the load gradient set of each component. Using the load gradient interval, the load range of each component is integrated to obtain the set of actual operating load ranges of each component.

[0035] Specifically, when low-torque servo modules are applied to scenarios such as precision assembly in electronic manufacturing, accurately defining the actual operating load range of each component is fundamental to achieving efficient load adaptive control. The process of "dividing the actual operating load range of each component based on the aforementioned load theoretical parameter set to obtain the actual operating load range set of each component" requires multiple rigorous steps. First, based on the existing load theoretical parameter set, load extreme values ​​are predicted for each component. In precision assembly in electronic manufacturing, the loads borne by components such as motors, lead screws, and guide rails of low-torque servo modules vary significantly in different operational stages. Taking the motor as an example, during the chip mounting pick-up action, the motor must overcome the gravity of the nozzle and chip, as well as the inertial forces during acceleration and deceleration; during the placement action, it must also cope with the impact force at the moment of contact with the substrate. By applying theories of mechanical dynamics and materials mechanics, combined with the module's workflow and the functional characteristics of each component, the load changes of the motor during the entire mounting process are simulated and calculated to predict its maximum possible torque, maximum power, and other extreme load values. Similarly, the extreme load values ​​of other components such as the lead screw and guide rail are obtained, thus forming a set of extreme load values ​​for each component. To provide clear boundaries for subsequent analysis, load thresholds are set for each component's extreme load value set. Based on the component's material properties, safety factor, and equipment operating requirements, a reasonable upper and lower load limit is determined, resulting in a set of load thresholds for each component. For example, based on the lead screw material's yield strength and equipment precision requirements, a threshold for the lead screw's axial force is set to ensure that the lead screw does not undergo plastic deformation due to excessive load during operation while still meeting the precision requirements of precision assembly. Next, the load thresholds for each component are discretized. Since load changes are continuous in actual working conditions, the load values ​​within the load threshold range are discretized for ease of analysis and control. Taking the guide rail assembly as an example, assuming its load threshold range is 0-50N, this range is divided into multiple discrete load values ​​at equal intervals, such as 5N, 10N, 15N, etc., to obtain a discretized load result set for each component. Based on this result set, the load range of each component is marked, dividing adjacent discrete load values ​​into a range and assigning corresponding labels, such as [0-10N] as the light load range, [10-30N] as the medium load range, and [30-50N] as the heavy load range, thus obtaining a load range label set for each component. This labeling method can intuitively reflect the range division of different load levels, providing a clear classification basis for subsequent analysis. Subsequently, based on the load range label set of each component, load gradient analysis is performed on each component. In the actual operation of precision assembly in electronic manufacturing, the load is not constant, but changes with a certain gradient in different ranges. Taking the module performing continuous circuit board insertion tasks as an example, as the number of insertions increases, the axial force of the lead screw pushing the insertion head will gradually increase, exhibiting a certain load gradient.By analyzing the rate and trend of load change for each component in different load ranges, the load gradient is calculated, resulting in a load gradient set for each component. Based on the magnitude and variation pattern of the load gradient, the load gradient ranges for each component are further subdivided. For example, if it is found that the load change of the lead screw is slow in the light load range and rapid in the heavy load range, the portion with a smaller load gradient in the light load range can be divided into one sub-range, and the portion with a larger load gradient in the heavy load range can be divided into another sub-range, thus describing the load change characteristics in more detail. Finally, the load ranges of each component are integrated using the load gradient ranges. Taking into account factors such as load extremes, thresholds, discretization ranges, and gradient ranges, the load ranges of each component are systematically sorted and integrated. Using the entire workflow of the low-torque servo module in precision assembly of electronic manufacturing as a guide, and combining the load conditions of each component in different processes, the various dispersed load ranges and gradient ranges are rationally pieced together and categorized, ultimately obtaining a set of actual operating load ranges for each component. This set clearly defines the load range that each component may encounter in actual work, the characteristics of different load ranges, and the gradient law of load changes. It provides comprehensive and accurate basic data for subsequent load response characteristic testing and the formulation of load adaptive strategies, which helps to improve the operational stability and working efficiency of low-torque servo modules under complex working conditions such as precision assembly in electronic manufacturing.

[0036] In a specific embodiment, the step of using the load response characteristic set to correlate and analyze the load coordination correlation parameter set among the components includes: Based on the load response characteristic set, the load input-output relationship between each component is sorted out, and the logical relationship of the load input-output relationship is analyzed to obtain the load logical relationship set between each component; By using the load logic relationship set between each component, the load energy transfer path between each component is traced to obtain the load energy transfer path set between each component, and the energy loss node between each component is identified based on the load energy transfer path set between each component. Based on the energy loss node, the load distribution balance among the components is evaluated to obtain the load distribution balance set among the components, and the load coordination correlation parameter set among the components is determined according to the load distribution balance set among the components.

[0037] Specifically, in precision assembly scenarios in electronic manufacturing, the collaborative operation of all components in a low-torque servo module is essential to ensure high-precision operation. Utilizing load response characteristic set correlation analysis to determine the load collaboration parameter set between components is a core step in achieving efficient collaboration. This process begins with analyzing logical relationships, gradually delving into energy transfer, loss assessment, and load distribution analysis to clarify the load collaboration parameter set between each component. First, based on the load response characteristic set, the load input-output relationships between each component are clarified. In precision assembly in electronic manufacturing, when the low-torque servo module is running, the motor output torque drives the lead screw to rotate. The lead screw converts rotational motion into linear motion, moving the slider and assembly tools. The encoder provides real-time position information to adjust the motor's operating state. These processes constitute the complex load input-output relationships between each component. Taking the relationship between the motor and the lead screw as an example, the torque output by the motor is the load input of the lead screw, while the displacement and speed changes of the lead screw are its load output. Through detailed analysis of the module's workflow, combined with the response data of each component under different loads in the load response characteristic set, these input-output relationships are comprehensively clarified. Based on this, the logical relationship between load input and output is analyzed to clarify the sequence and causal relationships of load transfer between components. For example, the lead screw can only move at a predetermined speed and displacement when the motor outputs appropriate torque. This logical relationship, after analysis and organization, forms a set of load logical relationships between components, laying the logical foundation for subsequent research. Next, the load energy transfer path between components is traced using this set of load logical relationships. During the operation of the low-torque servo module, energy is generated by the motor and transferred to the actuator through the transmission components. The entire process involves complex energy transfer paths. Taking the gear transmission link as an example, the electrical energy output by the motor is converted into mechanical energy to drive the gear to rotate. The gear then transfers mechanical energy to the next stage gear or lead screw through meshing. Using the law of conservation of energy and the principle of mechanical transmission, combined with the load logical relationship, the energy transfer trajectory between components is traced, each transfer link and direction is determined, and a set of load energy transfer paths between components is obtained. Based on this set of paths, energy loss nodes between components are identified. Due to factors such as friction between components and elastic deformation of transmission parts, energy is lost during transmission. For example, friction between the lead screw and nut causes some mechanical energy to be converted into heat energy and lost. These points of energy loss are called energy loss nodes. Accurately identifying these nodes helps to understand the module's energy utilization efficiency and the working status of each component. Subsequently, based on the energy loss nodes, the load distribution balance among the components is evaluated. In the continuous operation of precision assembly in electronic manufacturing, if the load distribution among components is uneven, it will lead to premature wear or performance degradation of some components, affecting the overall accuracy and lifespan of the module. By analyzing the energy loss at the energy loss nodes and the response characteristics of each component under different loads, the reasonableness of the load size and proportion borne by each component is evaluated.For example, if the guide rail experiences excessive lateral force under certain operating conditions, leading to a significant increase in energy loss, while other components are relatively lightly loaded, it indicates an unbalanced load distribution. A comprehensive analysis of the load distribution of each component under different operating conditions yields a load distribution balance set among the components. Based on the load distribution reflected in this set, a set of load coordination correlation parameters among the components is determined. If the load distribution of each component is balanced, it indicates good coordination and efficient, stable operation; conversely, if the load distribution is unbalanced, the load distribution needs to be adjusted to optimize the coordination relationship between components and improve the overall module performance. Through this series of closely interconnected analyses, from the load input-output logic relationship to the energy transfer path, and then to the load distribution balance assessment, the set of load coordination correlation parameters among the components of the low-torque servo module is finally determined. This result provides a crucial basis for subsequent development of load adaptive strategies and optimization of module design, ensuring that the components of the low-torque servo module can work together effectively in complex operating conditions such as precision assembly in electronic manufacturing, achieving high-precision and high-efficiency operation goals and meeting the stringent performance requirements of the electronic manufacturing industry.

[0038] In a specific embodiment, the load adaptive strategy formulation for the overall low-torque servo module based on the load coordination correlation parameter set, resulting in a load adaptive strategy set for each component, includes: Based on the load coordination correlation parameter set, the load allocation priority of each component in the overall low torque servo module is planned, and the resource allocation rationality analysis of the load allocation priority of each component is performed to obtain the resource allocation rationality set of each component. By defining the load dynamic adjustment range of each component through the resource allocation rationality set of each component, and evaluating the adjustment flexibility of each component based on the load allocation priority of each component, the adjustment flexibility set of each component is obtained. Based on the adjustment flexibility set of each component, the load adaptive mode of each component is classified to obtain the load adaptive mode set of each component, and the mode switching conditions of each component are determined according to the load adaptive mode set of each component. Using the aforementioned mode transition conditions, a set of load adaptive strategies for each component is formulated.

[0039] Specifically, in the precision assembly scenario of electronic manufacturing, low-torque servo modules face complex and ever-changing load conditions. Developing load adaptive strategies based on a load coordination parameter set is crucial for ensuring their efficient and stable operation. This process, based on the parameter set, involves a series of interconnected operations, including priority planning, rationality analysis, range definition, flexibility assessment, and mode classification, ultimately forming a set of load adaptive strategies for each component. First, based on the load coordination parameter set, the load allocation priority of each component in the overall low-torque servo module is planned. In the chip mounting process of electronic manufacturing, the motor of the low-torque servo module serves as the power source, providing operating power to components such as lead screws and guide rails, and its load allocation priority is relatively high. While the encoder, primarily responsible for feedback of position information, is indispensable, its load allocation priority is slightly lower than that of core transmission components such as the motor. By deeply analyzing parameters such as the load change correlation weight and response time difference of each component in the load coordination parameter set, combined with the functional requirements of the module in precision assembly, the importance of each component's load under different operating conditions is clarified, thereby determining the load allocation priority. Next, a resource allocation rationality analysis is performed on the load allocation priority of each component. Resources include electrical energy, heat dissipation capacity, and lubrication resources. Taking the motor as an example, if its load allocation priority is high but there are insufficient heat dissipation resources, overheating will cause performance degradation under prolonged high-load operation, affecting the overall operation of the module. A resource allocation rationality set for each component is obtained by comprehensively considering the priority and resource configuration of each component. Then, the dynamic load adjustment range of each component is defined using this set. In precision assembly in electronic manufacturing, the load dynamically changes when the low-torque servo module performs different assembly tasks. Taking the lead screw assembly as an example, if its resource allocation is reasonable and its priority is high, it can withstand a wider range of load changes, and its dynamic load adjustment range is relatively wide; conversely, if resources are limited or the priority is low, the dynamic load adjustment range is narrower. Based on the load allocation priority of each component, the adjustment flexibility of each component is evaluated. Due to the characteristics of its design and control method, the motor can quickly respond to load changes and adjust its output torque, exhibiting high adjustment flexibility; while the guide rail, due to structural and material limitations, has relatively low adjustment flexibility. By evaluating and obtaining the adjustment flexibility sets of each component, the differences in load adjustment capabilities among different components are clarified. Then, based on these adjustment flexibility sets, each component is classified into load adaptive modes. In actual precision assembly operations in electronic manufacturing, components are categorized into load adaptive modes such as fast response and stable adjustment based on factors such as component adjustment flexibility and dynamic load adjustment range. For example, motors can be classified as fast response type, capable of rapidly adjusting output during sudden load changes; guide rails, on the other hand, can be classified as stable adjustment type, focusing more on maintaining operational stability within a certain load range. After obtaining the load adaptive mode sets of each component, the mode switching conditions for each component are determined based on its operating characteristics and performance requirements under different working conditions.For example, when the motor load exceeds 80% of its rated value and the duration reaches a certain threshold, the system switches from the normal operating mode to an energy-saving and load-reducing mode to avoid overheating and damage to the motor. Finally, using the mode switching conditions, a set of load adaptive strategies for each component is formulated. In the precision assembly process of electronic manufacturing, when the module detects a load change that triggers the mode switching conditions of a component, the corresponding adaptive strategy is immediately executed. If the load of the lead screw reaches the upper limit of its dynamic adjustment range and meets the mode switching conditions, strategies such as reducing the feed speed and increasing the lubrication frequency are executed according to its stable adjustment-type load adaptive mode to ensure that the lead screw can still operate stably under high load. If the motor enters the energy-saving and load-reducing mode, the motor speed and torque output are reduced by optimizing the control algorithm, while ensuring the accuracy of key assembly actions. In this way, a comprehensive and targeted set of load adaptive strategies is formulated for the different characteristics and operating conditions of each component of the low-torque servo module. This allows each component of the module to fully utilize its own advantages and cooperate in the complex load environment of precision assembly in electronic manufacturing, effectively improving the overall operating accuracy, stability, and reliability, and meeting the stringent requirements of the electronic manufacturing industry for high-precision equipment.

[0040] The above describes the high-precision, low-torque servo module load adaptive method in the embodiments of the present invention. The following describes the high-precision, low-torque servo module load adaptive device in the embodiments of the present invention. Please refer to [link / reference]. Figure 2 One embodiment of the high-precision low-torque servo module load adaptive device of the present invention includes: The calculation module 21 is used to perform theoretical calculations on the load-bearing capacity of each component in the low-torque servo module based on the structure and working principle of the low-torque servo module, and obtain the load theoretical parameter set of each component. Test module 22 is used to test the load response characteristics of each component under actual working conditions based on the load theoretical parameter set, and obtain the load response characteristic set of each component. Analysis module 23 is used to perform correlation analysis on the load coordination correlation parameter set between each component using the load response characteristic set, so as to obtain the load coordination correlation parameter set between each component; The formulation module 24 is used to formulate a load adaptive strategy for the overall low-torque servo module based on the load coordination correlation parameter set, and obtain a load adaptive strategy set for each component. Control module 25 is used to perform load adaptive control on the low-torque servo module based on the load adaptive strategy set.

[0041] In this embodiment, the specific implementation of each unit in the above device embodiment is described in the above method embodiment, and will not be repeated here.

[0042] Reference Figure 3This invention also provides a computer device whose internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0043] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0044] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0045] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0046] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0047] 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 structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A high-precision, low-torque servo module load adaptive method, characterized in that, Includes the following steps: Based on the structure and working principle of the low-torque servo module, the load-bearing capacity of each component in the low-torque servo module is theoretically calculated to obtain the load theoretical parameter set of each component. Under actual working conditions, based on the theoretical load parameter set, load response characteristics of each component are tested to obtain the load response characteristic set of each component; Using the load response characteristic set, a load coordination correlation parameter set between each component is analyzed; specifically, this includes: identifying the load input-output relationship between each component based on the load response characteristic set, and performing logical relationship reasoning on the load input-output relationship to obtain a load logical relationship set between each component; tracing the load energy transfer path between each component through the load logical relationship set between each component to obtain a load energy transfer path set between each component, and identifying energy loss nodes between each component based on the load energy transfer path set between each component; evaluating the load distribution balance between each component based on the energy loss nodes to obtain a load distribution balance set between each component, and determining the load coordination correlation parameter set between each component based on the load distribution balance set between each component. Based on the load coordination and correlation parameter set, a load adaptive strategy is formulated for the overall low-torque servo module, resulting in a load adaptive strategy set for each component. Specifically, this includes: planning the load allocation priority of each component in the overall low-torque servo module based on the load coordination and correlation parameter set, and performing a resource allocation rationality analysis on the load allocation priority of each component to obtain a resource allocation rationality set for each component; defining the dynamic load adjustment range of each component through the resource allocation rationality set, and evaluating the adjustment flexibility of each component based on the load allocation priority to obtain an adjustment flexibility set for each component; classifying the load adaptive modes of each component based on the adjustment flexibility set to obtain a load adaptive mode set for each component, and determining the mode transition conditions for each component according to the load adaptive mode set; and formulating a load adaptive strategy set for each component using the mode transition conditions. Based on the aforementioned load adaptive strategy set, load adaptive control is performed on the low-torque servo module.

2. The high-precision, low-torque servo module load adaptive method according to claim 1, characterized in that, Based on the structure and working principle of the low-torque servo module, theoretical calculations of the load-bearing capacity of each component in the low-torque servo module are performed to obtain a set of theoretical load parameters for each component, including: Finite element analysis was performed on the structure of the low-torque servo module to obtain stress and strain distribution data for each component; Obtain the material mechanical property parameters corresponding to each component, and calculate the ultimate bearing capacity of each component based on the stress-strain distribution data and the material mechanical property parameters to obtain the ultimate bearing capacity parameter set of each component. The ultimate bearing capacity parameter set includes the maximum tensile strength, maximum compressive strength, maximum shear strength and fatigue limit. Based on the ultimate bearing capacity parameter set and the working principle, dynamic characteristic simulation is performed on each component of the low torque servo module to obtain dynamic response characteristic data of each component, and frequency domain analysis is performed on the dynamic response characteristic data to obtain the natural frequency and damping ratio parameter set of each component. Based on the natural frequency and damping ratio parameter set, a stability margin analysis is performed on each component of the low torque servo module to obtain a stability margin parameter set for each component. Based on the stability margin parameter set, the safe operating area of ​​each component under different load conditions is calculated to obtain the load theoretical parameter set for each component. The load theoretical parameter set for each component includes the rated load, maximum load, and safe load range.

3. The high-precision, low-torque servo module load adaptive method according to claim 2, characterized in that, Based on the stress-strain distribution data and the material mechanical property parameters, the ultimate bearing capacity of each component is calculated to obtain the ultimate bearing capacity parameter set of each component, including: Based on the stress-strain distribution data, principal stress analysis is performed on each component to obtain the principal stress distribution data of each component. Based on the principal stress distribution data and the yield strength in the material mechanical property parameters, the initial yield load set of each component is calculated. Based on the initial yield load set, plastic deformation is estimated for each component to obtain plastic deformation data for each component. Based on the plastic deformation data and the hardening coefficient in the material mechanical property parameters, the ultimate bearing capacity increment set for each component is calculated. Based on the ultimate bearing capacity increment set and the initial yield load set, the ultimate bearing capacity of each component is calculated to obtain the ultimate bearing capacity parameter set of each component.

4. The high-precision, low-torque servo module load adaptive method according to claim 1, characterized in that, The load response characteristic test is performed on each component based on the load theoretical parameter set to obtain the load response characteristic set of each component, including: Based on the theoretical load parameter set, the actual operating load range of each component is divided to obtain the actual operating load range set of each component. Then, the load interval characteristic analysis of the actual operating load range set of each component is performed to obtain the load interval characteristic set of each component. By using the load range characteristic set of each component, the load dynamic response process of each component is monitored, and the response time feature of each component is extracted based on the load dynamic response process to obtain the response time feature set of each component. Based on the response time feature set of each component, the load response frequency characteristics of each component are analyzed, and the frequency response bandwidth of each component is determined according to the load response frequency characteristic set of each component. By utilizing the frequency response bandwidth of each component, the load response characteristics of each component are comprehensively evaluated.

5. The high-precision, low-torque servo module load adaptive method according to claim 4, characterized in that, Based on the theoretical load parameter set, the actual operating load range of each component is divided to obtain the actual operating load range set of each component, including: Based on the load theoretical parameter set, load extreme values ​​are predicted for each component to obtain a load extreme value set for each component, and load thresholds are set for the load extreme value sets for each component to obtain a load threshold set for each component. The load threshold set of each component is discretized to obtain the load discretization result set of each component, and the working condition load level identifier of each component is marked based on the load discretization result set of each component to obtain the load level identifier set of each component. Based on the load level identifier set of each component, load gradient analysis is performed on each component to obtain the load gradient set of each component, and the load gradient interval of each component is divided according to the load gradient set of each component. Using the load gradient interval, the load range of each component is integrated to obtain the set of actual operating load ranges of each component.

6. A high-precision, low-torque servo module load adaptive device, characterized in that, include: The calculation module is used to perform theoretical calculations on the load-bearing capacity of each component in the low-torque servo module based on its structure and working principle, and to obtain the set of theoretical load parameters for each component. The testing module is used to test the load response characteristics of each component under actual working conditions based on the theoretical load parameter set, and to obtain the load response characteristic set of each component. The analysis module is used to analyze the load coordination correlation parameter set between components using the load response characteristic set. Specifically, it includes: identifying the load input-output relationship between components based on the load response characteristic set, and performing logical relationship reasoning on the load input-output relationship to obtain the load logical relationship set between components; tracing the load energy transfer path between components through the load logical relationship set between components to obtain the load energy transfer path set between components, and identifying energy loss nodes between components based on the load energy transfer path set between components; evaluating the load distribution balance between components based on the energy loss nodes to obtain the load distribution balance set between components, and determining the load coordination correlation parameter set between components based on the load distribution balance set between components. A load adaptation strategy formulation module is used to formulate a load adaptation strategy set for the overall low-torque servo module based on the load coordination correlation parameter set, thereby obtaining a load adaptation strategy set for each component. Specifically, this includes: planning the load allocation priority of each component in the overall low-torque servo module based on the load coordination correlation parameter set, and performing resource allocation rationality analysis on the load allocation priority of each component to obtain a resource allocation rationality set for each component; defining the dynamic load adjustment range of each component through the resource allocation rationality set, and evaluating the adjustment flexibility of each component based on the load allocation priority to obtain an adjustment flexibility set for each component; classifying each component into load adaptation modes based on the adjustment flexibility set to obtain a load adaptation mode set for each component, and determining the mode transition conditions for each component according to the load adaptation mode set; and formulating a load adaptation strategy set for each component using the mode transition conditions. The control module is used to perform load adaptive control on the low-torque servo module based on the load adaptive strategy set.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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