A digital-twin-based electric tool whole life cycle management system

Abstract

The application discloses a digital-twin-based electric tool whole-life-cycle management system, belongs to the technical field of digital twin and intelligent device management in the field of industrial manufacturing, and comprises a data acquisition module used for acquiring multi-dimensional data streams from an electric tool physical entity and a working environment in real time; the multi-dimensional data streams comprise real-time motor current, real-time environment temperature and processing material hardness; a state evaluation and life prediction module is used for calculating a real-time degradation rate based on the multi-dimensional data streams and generating a remaining effective life based on the real-time degradation rate; the real-time degradation rate reflects a thermal-mechanical coupling effect; a control strategy generation module is used for generating adaptive control instructions according to the remaining effective life and combining a preset operation target decision rule set; the adaptive control instructions are used for regulating and controlling the peak output torque of the electric tool, and the application provides factual basis for realizing accurate life prediction and performance regulation and improves the delicacy and accuracy of management.

Description

Technical Field

[0001] This invention relates to the field of digital twins and intelligent equipment management in industrial manufacturing, specifically to a digital twin-based power tool lifecycle management system. Background Technology

[0002] In precision manufacturing scenarios, traditional power tool management methods mainly treat their service life as a static setpoint based on factory parameters. This management approach usually adopts a full-load operation mode until failure, ignoring the complex working conditions that the tool is subjected to during actual use.

[0003] This situation has led to two core problems: First, the inability to account for the coupling effect of dynamic changes such as ambient temperature and processing load results in a serious discrepancy between the actual and theoretical lifespan of the tools, making lifespan predictions extremely inaccurate; second, the lack of an adaptive performance control mechanism leads to a serious imbalance between the immediate production performance and long-term service life of the tools.

[0004] The reason for the above problems is that traditional methods have failed to effectively quantify and model the accelerated impact of thermo-mechanical coupling effects on tool degradation under real working conditions. Due to the lack of real-time acquisition and analysis capabilities for multi-dimensional data such as motor current, ambient temperature, and load hardness, managers cannot dynamically assess the true health status of tools. As a result, power tools often fail prematurely due to overload or suddenly fail due to sudden changes in working conditions, causing unexpected downtime and ultimately significantly affecting the production reliability and economic benefits of precision manufacturing.

[0005] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a digital twin-based power tool lifecycle management system to solve the problems mentioned in the background art.

[0007] The technical solution of the present invention includes:

[0008] The data acquisition module is used to acquire multi-dimensional data streams in real time from the physical entity of the power tool and the working environment; the multi-dimensional data streams include real-time motor current, real-time ambient temperature and hardness of the processed material;

[0009] The condition assessment and lifetime prediction module is used to calculate the real-time degradation rate based on multi-dimensional data streams and generate the remaining effective lifetime based on the real-time degradation rate; the real-time degradation rate reflects the thermo-mechanical coupling effect.

[0010] The control strategy generation module is used to generate adaptive control commands based on the remaining effective life and in combination with a preset set of operating target decision rules; the adaptive control commands are used to regulate the peak output torque of the power tool.

[0011] Preferably, the real-time degradation rate is determined by the following steps:

[0012] Based on the real-time current of the motor, according to the formula Calculate the baseline degradation rate; where, This is the motor current. The inherent degradation coefficient, The current stress index;

[0013] Calculate the dynamic stress correction factor;

[0014] Combining the baseline degradation rate and the dynamic stress correction factor, according to the formula Generate real-time degradation rates; where, For real-time degradation rate, As a baseline degradation rate, This is the dynamic stress correction factor.

[0015] Preferably, the dynamic stress correction factor is determined through the following steps:

[0016] Calculate the thermal overload stress term based on real-time ambient temperature;

[0017] Calculate the load fluctuation stress term based on the real-time motor current;

[0018] Combining the thermal overload stress term and the load fluctuation stress term, according to the formula Generate dynamic stress correction factors; where, This is the thermal overload stress term. This is the load fluctuation stress term.

[0019] Preferably, the thermal overload stress term is based on the formula Determined; among them, This is the thermal overload stress term. For temperature sensitivity coefficient, For real-time ambient temperature, This is the preset thermal overload threshold temperature.

[0020] Preferably, the load fluctuation stress term is determined through the following steps:

[0021] The real-time current signal of the motor is decomposed into low-frequency and high-frequency components by using a digital low-pass filter.

[0022] Calculate the standard deviation of high-frequency components within a sliding time window;

[0023] Combining the mean of the low-frequency components and the standard deviation of the high-frequency components, according to the formula... Generate the load fluctuation stress term; where, For load fluctuation stress term, For load fluctuation sensitivity coefficient, For the high-frequency standard deviation of current, This is the low-frequency average value of the current.

[0024] Preferably, the remaining effective lifespan is determined according to the formula... Determined; among them, For the current moment The remaining effective lifespan, This represents the real-time degradation rate at a historical moment.

[0025] Preferably, the control strategy generation module generates adaptive control instructions through the following steps:

[0026] The remaining effective lifespan is compared with the first preset lifespan threshold and the second preset lifespan threshold; the first preset lifespan threshold is higher than the second preset lifespan threshold.

[0027] If the remaining effective lifespan is higher than the first preset lifespan threshold, then the peak output torque is set to 100% of the design value.

[0028] If the remaining effective lifespan is not higher than the first preset lifespan threshold but higher than the second preset lifespan threshold, and a preset severe working condition is detected, then the peak output torque is limited to a preset first percentage.

[0029] If the remaining effective lifespan is not higher than the second preset lifespan threshold, then the peak output torque is forcibly limited to the preset second percentage; the second percentage is lower than the first percentage.

[0030] Preferably, the inherent degradation coefficient and the current stress exponent are determined through the following steps:

[0031] Multiple sets of accelerated life tests under constant current were conducted on tools of the same model to obtain test data.

[0032] The least squares regression analysis was performed on the test data to calibrate and obtain the parameters.

[0033] This invention provides an improved digital twin-based power tool lifecycle management system, which has the following improvements and advantages compared to existing technologies:

[0034] 1. This invention constructs a closed-loop management architecture from data acquisition and status assessment to control decision-making, overcoming the fundamental defect of existing technologies that treat tool life as a static fixed value, leading to inaccurate predictions. Existing technologies typically estimate life based solely on factory parameters, failing to adapt to the dynamic impact of environmental and load coupling effects on tools in precision manufacturing scenarios. This invention, by acquiring multi-dimensional data such as motor current, ambient temperature, and the hardness of processed materials in real time, treats tool life as a variable dynamically affected by actual working conditions. This design enables the system to accurately capture the real-time wear and tear of tool life caused by thermo-mechanical coupling effects, providing a factual basis for achieving accurate life prediction and performance regulation, and improving the precision and accuracy of management.

[0035] 2. This invention proposes a two-step method that decomposes degradation rate calculation into baseline determination and dynamic correction, greatly improving the adaptability and physical realism of the life prediction model. This method determines a baseline degradation rate under ideal operating conditions by analyzing the real-time current of the motor. This baseline rate is based on rigorous accelerated life test data, ensuring the model has a solid physical foundation. Furthermore, the system introduces a dynamic stress correction factor to quantify the additional losses caused by thermal stress and mechanical load fluctuations under real operating conditions. This design, which equates the impact of complex operating conditions to dynamic correction of the baseline rate, makes the model structure clear, its physical meaning explicit, and the generated real-time degradation rate accurately reflects the actual aging speed of the tool at a specific instant.

[0036] 3. The present invention cleverly decomposes the fuzzy thermo-mechanical coupling effect into two independently quantifiable and modelable physical processes in its construction of the dynamic stress correction factor, achieving high-performance and cost-effective measurement. In terms of thermal stress assessment, the system introduces the concept of a thermal overload threshold and draws upon the laws describing the relationship between chemical reaction rates and temperature, enabling nonlinear quantification of the accelerated aging effect of exceeding the threshold high temperature on key components such as tool insulation materials. Regarding mechanical load fluctuation assessment, the present invention innovatively uses filtering and statistical analysis of the motor current signal to indirectly assess the severity of impact loads by combining the high-frequency standard deviation and low-frequency mean of the current. This avoids the need for expensive external sensors, robustly and economically solving the problem of the difficulty in directly measuring mechanical impacts through a purely internal sensing method based on electrical signal analysis.

[0037] 4. This invention determines the remaining effective lifespan of a tool by integrating the real-time degradation rate over time, ensuring that the assessment results comprehensively reflect the impact of all historical operating conditions of the tool. This method accumulates and calculates every instantaneous loss since the tool was put into use, thereby obtaining a quantitative value of the current health status that takes into account historical burdens. This means that even if the tool is currently under light load, its remaining lifespan assessment result will be correspondingly reduced due to the harsh operating conditions it has experienced. This assessment method is fully consistent with physical reality and provides a reliable and intuitive decision-making basis for achieving a truly meaningful full life cycle management.

[0038] 5. This invention establishes an adaptive control strategy based on hierarchical management of remaining effective life, achieving an optimal balance between tool performance and lifespan. This strategy abandons the traditional single mode of running at full load until failure and introduces the concept of risk management. The system divides the tool's life cycle into different intervals such as healthy, warning, and dangerous, and applies differentiated peak output torque limits according to the interval. When the tool is healthy, the system ensures its maximum production efficiency. When signs of aging appear and harsh working conditions are encountered, the system actively intervenes to avoid overload risks. When the tool is nearing the end of its lifespan, ensuring reliable task completion is the primary objective. This intelligent regulation not only maximizes the overall service life of the tool but also significantly improves the stability and predictability of the production process by actively avoiding high-risk operations, bringing significant economic benefits to users. Attached Figure Description

[0039] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0040] Figure 1 This is a flowchart of the system of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0042] Example 1

[0043] Please see Figure 1 This invention provides a digital twin-based power tool lifecycle management system, comprising:

[0044] The data acquisition module is used to acquire multi-dimensional data streams in real time from the physical entity of the power tool and the working environment; the multi-dimensional data streams include real-time motor current, real-time ambient temperature and hardness of the processed material;

[0045] The condition assessment and lifetime prediction module is used to calculate the real-time degradation rate based on multi-dimensional data streams and generate the remaining effective lifetime based on the real-time degradation rate; the real-time degradation rate reflects the thermo-mechanical coupling effect.

[0046] The control strategy generation module is used to generate adaptive control commands based on the remaining effective life and in combination with a preset set of operating target decision rules; the adaptive control commands are used to regulate the peak output torque of the power tool.

[0047] A digital twin-based power tool lifecycle management system aims to address the problems of inaccurate tool life prediction and performance-life imbalance caused by the coupling effect of environment and load in precision manufacturing scenarios. In this embodiment, the system is constructed as a closed-loop adaptive management architecture that includes data acquisition, status assessment, and control decision-making.

[0048] The system's operational logic begins with the data acquisition module. This module's function is to acquire multi-dimensional raw data streams in real time from the physical entity of the power tool and its operating environment, providing factual basis for subsequent analysis and decision-making. In this embodiment, this module is configured to continuously acquire three types of core data:

[0049] This is the real-time current of the motor, which serves as a direct quantitative indicator of the motor's electromagnetic load and main heat source.

[0050] The real-time ambient temperature data determines the tool's passive heat dissipation efficiency and is a key input for assessing thermal stress.

[0051] This data reflects the mechanical load on the tool and is an important parameter for assessing mechanical stress, indicating the hardness of the material being processed.

[0052] The collected multi-dimensional data stream is transmitted to the status assessment and lifetime prediction module. The purpose of this module is to accurately calculate the current health status of the tool and predict its remaining effective lifetime based on the real-time input data. In this embodiment, the module has a core algorithm that can calculate the real-time degradation rate that reflects the thermo-mechanical coupling effect. By integrating the historical values ​​of this rate from the initial operation time of the tool, a quantitative assessment of the remaining effective lifetime of the tool is generated.

[0053] The remaining effective lifespan output by the condition assessment and lifespan prediction module is further passed to the control strategy generation module. The purpose of this module is to dynamically generate adaptive control commands based on the current health status of the tool and in combination with a set of preset operating target decision rules, so as to achieve the optimal balance between performance and lifespan. In this embodiment, the generated adaptive control commands are used to directly regulate the peak output torque of the power tool. In this way, the system forms a complete closed loop from perception to analysis to decision-making to execution, realizing intelligent management of the entire life cycle of the power tool.

[0054] This embodiment achieves accurate prediction of power tool lifespan and adaptive performance control through the collaborative work of the aforementioned modules. The technical advantage lies in that it no longer treats tool lifespan as a static value determined solely by factory parameters, but rather as a variable dynamically affected by real-world working conditions. By quantifying the impact of thermo-mechanical coupling effects on lifespan in real time and proactively adjusting tool performance output accordingly, this system can maximize tool lifespan while ensuring production efficiency, effectively preventing unexpected downtime due to sudden tool failure, and significantly improving economic benefits and production reliability in precision manufacturing scenarios.

[0055] Example 2

[0056] The real-time degradation rate is determined by the following steps:

[0057] Based on the real-time current of the motor, according to the formula Calculate the baseline degradation rate; where, This is the motor current. The inherent degradation coefficient, The current stress index; The baseline degradation rate;

[0058] Calculate the dynamic stress correction factor;

[0059] Combining the baseline degradation rate and the dynamic stress correction factor, according to the formula Generate real-time degradation rates; where, For real-time degradation rate, As a baseline degradation rate, This is a dynamic stress correction factor;

[0060] The inherent degradation coefficient and the current stress exponent are determined through the following steps:

[0061] Multiple sets of accelerated life tests under constant current were conducted on tools of the same model to obtain test data.

[0062] The least squares regression analysis was performed on the test data to calibrate and obtain the parameters;

[0063] In this embodiment, based on the condition assessment and lifetime prediction module, the process of determining the real-time degradation rate is further specified; this process aims to combine the baseline model under ideal operating conditions with the dynamic disturbances under real operating conditions to obtain a high-precision degradation rate.

[0064] To achieve this objective, the technical solution of this process includes:

[0065] The baseline degradation rate is calculated based on the real-time motor current; the baseline degradation rate... This refers to the core aging rate determined solely by the internal electrical stress of the motor under ideal operating conditions, i.e., constant ambient temperature and mechanical load. Its function is to provide a stable and reproducible starting point for subsequent corrections under complex operating conditions. In this embodiment, the baseline degradation rate... It is calculated using the following formula:

[0066]

[0067] In this formula, Represents motor current, which physically means the real-time current value flowing through the motor coil. The data type is floating point, and the source is the real-time output of the data acquisition module. The inherent degradation coefficient is the physical value of the base degradation rate under unit electrical stress, expressed in % / (hour·A^γ) to ensure dimensional consistency. The current stress index is a dimensionless parameter that represents the physical sensitivity of the degradation rate to changes in current.

[0068] To ensure the inherent degradation coefficient With current stress index The physical validity of the calibration was determined by a rigorous experimental procedure. This procedure included: conducting multiple sets of accelerated life tests under constant current on the same type of tool to obtain tool life data under different fixed electrical stress levels; and performing least squares regression analysis on the test data to fit the experimental data to the parameter that best describes the life-current relationship. and value;

[0069] One non-limiting calibration method is as follows: Select at least three groups, for example, four groups, of brand-new power tools of the same model, each group containing at least five samples, and conduct continuous operation tests at multiple constant stress levels above the rated current, for example, 120%, 150%, 180%, and 210% of the rated current; set a clear lifespan endpoint criterion, for example, when the tool's peak output torque drops to 80% of its initial value, and record its operating time. This yields multiple sets of current stresses. Corresponding lifespan The data points; assuming that lifetime and current follow an inverse power law model, i.e.:

[0070]

[0071] in, :constant; Current stress; Current stress index;

[0072] By taking the logarithm of both sides of the formula to make it linear, we obtain:

[0073]

[0074] in, Natural logarithm function; Lifespan or operating time; :constant; Current stress index;

[0075] Then use the least squares method to By performing linear regression on the data points, the slope can be obtained. and intercept The baseline degradation rate can be considered as the reciprocal of the lifetime, and the inherent degradation coefficient. Then with constant The correlation can be further determined through dimensional analysis and data fitting.

[0076] Calculate the dynamic stress correction factor; Dynamic stress correction factor The definition and calculation method will be detailed in the following embodiments; the purpose is to quantify the additional life loss caused by real working conditions compared to ideal working conditions.

[0077] By combining the baseline degradation rate with the dynamic stress correction factor, a real-time degradation rate is generated; real-time degradation rate This refers to the instantaneous aging rate of a tool that fully considers the combined effects of electrical stress, thermal stress, and mechanical load fluctuations; in this embodiment, the real-time degradation rate... It is generated using the following formula:

[0078]

[0079] In this formula, It is the baseline degradation rate calculated from the preceding steps. It is a dynamic stress correction factor; the motivation for this calculation logic is to equate the comprehensive impact of complex working conditions to an amplification effect on the baseline degradation rate, with a clear model structure and explicit physical meaning.

[0080] The gain effect of this embodiment is that by decomposing the calculation of degradation rate into two steps, benchmark determination and dynamic correction, the adaptability and accuracy of the model are greatly improved. The calculation of the benchmark degradation rate is based on rigorous experimental data, which ensures the physical basis of the model. The introduction of the dynamic correction factor enables the model to respond to changes in the environment and load in real time. This two-step design makes lifetime prediction no longer a static estimate, but a dynamic process that can accurately reflect the actual working conditions.

[0081] Example 3

[0082] The dynamic stress correction factor is determined through the following steps:

[0083] Calculate the thermal overload stress term based on real-time ambient temperature;

[0084] Calculate the load fluctuation stress term based on the real-time motor current;

[0085] Combining the thermal overload stress term and the load fluctuation stress term, according to the formula:

[0086]

[0087] The dynamic stress correction factor is generated using the above formula; where... This is the thermal overload stress term. This refers to the load fluctuation stress term; Dynamic stress correction factor;

[0088] The thermal overload stress term is based on the formula Determined; among them, This is the thermal overload stress term. For temperature sensitivity coefficient, For real-time ambient temperature, The preset thermal overload threshold temperature; Exponential function; :Maximum value function;

[0089] The load fluctuation stress term is determined through the following steps:

[0090] The real-time current signal of the motor is decomposed into low-frequency and high-frequency components by using a digital low-pass filter.

[0091] In this embodiment, a preferred approach is to use a second-order Butterworth low-pass filter, because it has the flattest frequency response within its passband and a low cutoff frequency. This is a key parameter, and its selection principle is to effectively separate the main current component determined by the load mean from the disturbance component caused by rapid load changes. A non-limiting method for determining this parameter is to perform Fast Fourier Transform analysis on the current signal under various typical operating conditions, such as no-load, stable heavy load, and impulsive load. Typically, the main power spectrum under stable load is concentrated in a lower frequency range, such as below 5Hz, while impulsive and fluctuating loads introduce higher frequency components. Therefore, the cutoff frequency can be... Set it near the upper limit of this low-frequency range, for example, it can be set to 10Hz;

[0092] Calculate the standard deviation of high-frequency components within a sliding time window;

[0093] Combining the mean of the low-frequency components and the standard deviation of the high-frequency components, according to the formula... Generate the load fluctuation stress term; where, For load fluctuation stress term, For load fluctuation sensitivity coefficient, For the high-frequency standard deviation of the current, This is the low-frequency average value of the current; It is a tiny positive constant set to prevent the denominator from being zero. For example, it can be 0.1% of the rated current to enhance the computational robustness of the model under no-load or near-no-load conditions.

[0094] In this embodiment, based on the real-time degradation rate calculation process, a dynamic stress correction factor is used. The determination process is further specified; the technical motivation for this factor design is to mathematically quantify the accelerated degradation effect caused by the combined effects of environmental thermal stress and mechanical load fluctuations; in this embodiment, The method is constructed as a linear superposition of the excess degradation effects caused by two independent stress terms; it is based on the engineering approximation assumption that the excess degradation contributions caused by high temperature and variable load are approximately independent and additive.

[0095] The specific steps of this determination process are as follows:

[0096] Calculate the thermal overload stress term based on real-time ambient temperature; thermal overload stress term The aim is to quantify the accelerated aging effect on critical components such as tool insulation materials when the ambient temperature exceeds a certain safety threshold. The mathematical form borrows from the Arrhenius equation, which describes the relationship between chemical reaction rates and temperature, because material aging is essentially a chemical process. In this embodiment, the thermal overload stress term... It is determined according to the following formula:

[0097]

[0098] In this formula, It is the temperature sensitivity coefficient, which physically represents the sensitivity of the degradation rate to temperatures exceeding a threshold temperature, and its unit is 1000 kJ / m². The data was determined by performing accelerated life tests at multiple constant high temperatures and fitting the life-temperature data.

[0099] Multiple sets of tool samples can be placed in constant temperature chambers at different temperatures, for example, above the thermal overload threshold. of , , Accelerated life testing was conducted under constant electrical load, and the average lifespan at each temperature level was recorded. Based on the Arrhenius model, the relationship between degradation rate and temperature can be expressed as:

[0100]

[0101] in Activation energy; Degradation rate; Exponential function; Boltzmann constant; Temperature; Formula in this embodiment:

[0102]

[0103] This model is an engineering simplification within the overthreshold range; by fitting different overthreshold temperatures. By using the reciprocal of lifespan, which represents the degradation rate, the most suitable temperature sensitivity coefficient can be determined. ;

[0104] It is the real-time ambient temperature, which is the real-time output of the data acquisition module; It is a preset thermal overload threshold temperature, which is set based on a large amount of historical working condition data statistics. That is, when the ambient temperature is continuously higher than this value, the probability of tool failure shows a significant non-linear increase.

[0105] Statistical analysis of historical failure data can be performed, such as modeling tool life data under different ambient temperatures using the Weibull distribution. By comparing the failure rate function curves at different temperatures, the following can be observed: The temperature at which the failure rate begins to show a significant nonlinear acceleration is set; this point is usually an inflection point on the lifetime-temperature relationship curve, marking the beginning of the thermal aging mechanism becoming dominant.

[0106] The use of the max function ensures that the thermal acceleration effect is activated only when the ambient temperature exceeds the preset threshold, which is consistent with physical reality.

[0107] Furthermore, based on the real-time motor current, the load fluctuation stress term is calculated; the load fluctuation stress term The aim is to quantify the additional loss of motor life caused by impact loads due to uneven hardness of the processed materials. To improve the economy and feasibility of the solution, this embodiment indirectly evaluates load fluctuations by performing in-depth analysis of the collected motor current signal, avoiding the need to add an external hardness sensor. The method uses a digital low-pass filter to decompose the real-time motor current signal into low-frequency and high-frequency components. The principle of this step is that a uniform load generates a stable current, mainly reflected in the low-frequency component, while drastic load fluctuations will cause high-frequency disturbances in the current signal. The standard deviation of the high-frequency component is calculated within a sliding time window.

[0108] The width of this time window needs to balance the timeliness of the response and the stability of the calculation. In this embodiment, the window width can be set between 0.5 seconds and 2 seconds; for example, choosing a 1-second window width means that the system will assess the severity of the current load fluctuation based on high-frequency current data from the past second; this width is sufficient to cover the entire process of most processing shock events while avoiding excessive delays.

[0109] This standard deviation is used as a quantitative indicator of the severity of load fluctuations; combining the mean of the low-frequency components and the standard deviation of the high-frequency components, the load fluctuation stress term... It is generated according to the following formula:

[0110]

[0111] In this formula, It is the load fluctuation sensitivity coefficient, which physically represents the sensitivity of the degradation rate to load fluctuation. It is a dimensionless parameter, and its value needs to be determined through special experiments, that is, the tool is run under multiple sets of working conditions with different known load fluctuation levels, and regression analysis is performed on the lifetime-current characteristic data. : Standard deviation of current at high frequency; Low-frequency average current;

[0112] To achieve this calibration, a non-limiting experimental method is to apply loads with varying degrees of fluctuation to the power tool using a programmable dynamometer or power supply. For example, multiple sets of experiments can be designed, with the average current of each set being... The relative fluctuation index of the current remains unchanged, but by superimposing sinusoidal or random noise signals of different amplitudes and frequencies, the relative fluctuation index of the current is made possible. These are preset values, such as 0.05, 0.10, 0.15, etc.; the time it takes for the tool to reach its end-of-life is recorded at each fluctuation level, and these lifespan data are analyzed using regression analysis with the corresponding... The load fluctuation sensitivity coefficient can be determined by this value. ;

[0113] It is the high-frequency standard deviation of the current, which is calculated from the aforementioned signal processing sub-steps; It is the low-frequency average value of the current, which is also calculated by the aforementioned signal processing sub-steps; this formula transforms the mechanical shock problem, which is difficult to measure directly, into an internal sensing problem based entirely on electrical signal analysis;

[0114] Based on the above calculation results, a dynamic stress correction factor is generated by combining the thermal overload stress term and the load fluctuation stress term; in this embodiment, the dynamic stress correction factor... It is generated according to the following formula:

[0115]

[0116] In this formula, The thermal overload stress term is obtained from temperature calculations. This is the load fluctuation stress term calculated from the current fluctuation; the model is configured to ensure that when there is no additional stress, i.e. and hour, and The values ​​are all 1, at this time Also 1, real-time degradation rate It can smoothly degrade to the baseline degradation rate. ;

[0117] The gain technology in this embodiment is effective in decomposing the fuzzy thermo-mechanical coupling effect into two measurable and modelable physical processes by constructing a structured dynamic stress correction factor. The modeling of thermal stress takes into account the nonlinear threshold effect, while the modeling of load fluctuations cleverly uses electrical signal analysis to replace expensive mechanical sensing. Thus, in a cost-effective and robust manner, it achieves accurate quantification of complex stress factors under real working conditions, providing core support for the final life prediction accuracy.

[0118] Example 4

[0119] The remaining effective lifespan is calculated using the formula: Determined; among them, For the current moment The remaining effective lifespan, The real-time degradation rate at a historical moment; : The current moment; Time integration; A historic moment;

[0120] In this embodiment, the condition assessment and life prediction module calculates the real-time degradation rate that can dynamically reflect the actual operating conditions. Afterwards, the remaining effective life of the tool was finally determined; remaining effective life The definition is that the tool is at the current moment The quantitative assessment of the health status is usually expressed as a percentage; its purpose is to provide an intuitive and easy-to-understand decision-making basis for the upper-level control strategy module; in this embodiment, the remaining effective lifespan is determined according to the following formula:

[0121]

[0122] In this formula, Represents the current moment The remaining effective lifespan; Represents the time since the tool was enabled, i.e. Time, up to the present moment The historical instantaneous degradation rate at any historical moment between these points originates from the continuous calculation results of preceding steps. The calculation logic of this formula is as follows: by integrating the real-time degradation rate over time, the total cumulative wear and tear of the tool from its activation to the current moment is obtained. Subtracting this cumulative wear and tear from the initial 100% lifetime yields the current remaining lifetime. In a digital management system, this integration process is discretized into the integration of each calculation... The summation operation after multiplying the value by the time interval;

[0123] The gain effect of this embodiment lies in providing a scientific method to transform instantaneous degradation rate into cumulative health status. Through integration or summation, the model can account for the impact of all historical operating conditions of the tool, rather than relying solely on the current state. This means that even if the tool is currently under light load, its remaining lifespan assessment will be reduced accordingly due to the severe operating conditions it has experienced. This assessment method that takes into account historical burdens improves the final output of the remaining effective lifespan. It is more in line with physical reality and provides reliable quantitative indicators for true full life cycle management.

[0124] The control strategy generation module generates adaptive control instructions through the following steps:

[0125] The remaining effective lifespan is compared with the first preset lifespan threshold and the second preset lifespan threshold; the first preset lifespan threshold is higher than the second preset lifespan threshold.

[0126] If the remaining effective lifespan is higher than the first preset lifespan threshold, then the peak output torque is set to 100% of the design value.

[0127] If the remaining effective lifespan is not higher than the first preset lifespan threshold but is higher than the second preset lifespan threshold, and a preset severe working condition is detected, then the peak output torque is limited to a preset first percentage.

[0128] If the remaining effective lifespan is not higher than the second preset lifespan threshold, then the peak output torque is forcibly limited to a preset second percentage; the second percentage is lower than the first percentage.

[0129] In this embodiment, the control strategy generation module is based on the accurate remaining effective lifetime output by the state assessment and lifetime prediction module. It executes a set of preset decision rules to generate adaptive control instructions; the process aims to balance immediate efficiency with long-term costs by adjusting the performance output of the tool at different stages of its lifespan.

[0130] The control logic of this module is implemented through the following steps: The underlying logic is to compare the remaining effective lifespan with a first preset lifespan threshold and a second preset lifespan threshold; The first preset lifespan threshold and the second preset lifespan threshold are key health status nodes determined based on statistical analysis of the performance and failure data of a large number of similar tools throughout their entire lifespan; In this embodiment, the first preset lifespan threshold is set to 50% to define the healthy zone and the warning zone; The second preset lifespan threshold is set to 20% to define the warning zone and the danger zone, wherein the first preset lifespan threshold is higher than the second preset lifespan threshold;

[0131] The comparison result will trigger the corresponding control rules:

[0132] If the remaining effective lifespan is higher than the first preset lifespan threshold, for example This indicates that the tool is in good condition. At this time, the system will set the peak output torque to 100% of the design value to ensure maximum production efficiency.

[0133] If the remaining effective lifespan is not higher than the first preset lifespan threshold but is higher than the second preset lifespan threshold, for example... This indicates that the tool has entered the warning zone; within this zone, the system will further determine whether a preset severe operating condition has been detected; the preset severe operating condition is defined as ambient temperature. Exceeding the thermal overload threshold And the current fluctuates relatively Greater than the preset fluctuation threshold Operating condition combinations; preset fluctuation threshold The value of is determined based on statistical analysis of current fluctuation characteristics that are significantly related to accelerated tool aging in a large amount of historical data;

[0134] The relative fluctuations in current can be obtained by collecting a large amount of historical operating data. The values ​​were calculated and their probability distribution histograms were plotted; simultaneously, these data were correlated with the tool's accelerated aging or early failure events. Setting it at a high percentile of the distribution, such as the 95th percentile, means that any fluctuation exceeding this value is statistically considered a rare and potentially damaging condition that could significantly impair lifespan.

[0135] If this demanding operating condition is detected, the system will proactively limit the peak output torque to a preset first percentage, such as 85% of the design value;

[0136] If the remaining effective lifespan is not higher than the second preset lifespan threshold, for example This indicates that the tool has entered a dangerous zone; at this time, in order to ensure the reliable completion of the current task and avoid unexpected shutdown, the system will forcibly limit the peak output torque to a preset second percentage, such as 70% of the design value, and this second percentage is set to be lower than the first percentage;

[0137] The gain effect of this embodiment lies in establishing a clear and implementable adaptive control strategy. This strategy is no longer a simple full-load operation to failure mode, but introduces the concept of risk management. By classifying and managing the health status of the tool and applying different performance limiting strategies at different stages, the system can go all out when the tool is healthy, be conservative and robust when signs of aging appear, and ensure a good end to its life when it is nearing the end of its life. This intelligent regulation not only extends the overall life of the tool, but also significantly improves the stability and predictability of the production process by actively avoiding overload operation under high-risk conditions.

[0138] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A digital-twin-based electric tool full-life-cycle management system, characterized by, The application relates to a method for generating adaptive control strategy for power tools, comprising: a data acquisition module for acquiring multi-dimensional data stream from physical entity of power tools and working environment in real time; the multi-dimensional data stream comprises real-time current of motor, real-time environmental temperature and hardness of processing material; a state evaluation and life prediction module for calculating real-time degradation rate based on the multi-dimensional data stream and generating residual effective life based on the real-time degradation rate; the real-time degradation rate reflects thermal-mechanical coupling effect; a control strategy generation module for generating adaptive control instruction according to the residual effective life and combining preset operation target decision rule set; the adaptive control instruction is used for regulating and controlling peak output torque of the power tool; the real-time degradation rate is determined by the following steps: Based on the real-time current of the motor, the reference degradation rate is calculated according to the formula ; wherein, is the motor current, is the inherent degradation coefficient, is the current stress index; calculating a dynamic stress correction factor; The real-time degradation rate is generated according to the formula in combination with the dynamic stress correction factor, is the real-time degradation rate, is the reference degradation rate, is the dynamic stress correction factor; the dynamic stress correction factor is determined by the following steps: calculating a thermal overload stress term based on real-time environmental temperature; calculating a load fluctuation stress term based on real-time current of the motor; combining the thermal overload stress term and the load fluctuation stress term, according to the formula generating a dynamic stress correction factor; wherein, is the thermal overload stress term, is the load fluctuation stress term; The thermal overload stress term is according to the formula is determined; wherein, is a thermal overload stress term, is a temperature sensitivity coefficient, is a real-time ambient temperature, is a preset thermal overload threshold temperature; the load fluctuation stress term is determined by the following steps: decomposing the real-time current signal of the motor into low-frequency component and high-frequency component through a digital low-pass filter; calculating the standard deviation of the high-frequency component in a sliding time window; combining the mean of the low frequency component and the standard deviation of the high frequency component according to the formula generating a load fluctuation stress term; wherein, is the load fluctuation stress term, is the load fluctuation sensitivity coefficient, is the high frequency standard deviation of the current, is the low frequency mean of the current, is a small positive number set to prevent the denominator from being zero.

2. The digital-twin-based electric tool whole life cycle management system according to claim 1, characterized in that, The remaining useful life is determined according to the formula wherein, is the remaining useful life at the current time t, is the real-time degradation rate at the historical time.

3. The digital-twin-based power tool whole life cycle management system according to claim 1, characterized in that, the control strategy generation module generates the adaptive control instruction by the following steps: comparing the residual effective life with a first preset life threshold and a second preset life threshold; the first preset life threshold is higher than the second preset life threshold; if the residual effective life is higher than the first preset life threshold, the peak output torque is set to 100% of the design value; if the residual effective life is not higher than the first preset life threshold but higher than the second preset life threshold, and a preset severe working condition is detected, the peak output torque is limited to a preset first percentage; if the residual effective life is not higher than the second preset life threshold, the peak output torque is forcibly limited to a preset second percentage; the second percentage is lower than the first percentage.

4. The digital-twin-based electric tool whole life cycle management system according to claim 1, characterized in that, the intrinsic degradation coefficient and the current stress index are determined by the following steps: performing accelerated life test on the same type of tools under multiple groups of constant current to obtain test data; performing regression analysis on the test data by using the least square method to calibrate and obtain parameters.

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