Torque intelligent control method and system for impact wrench

Abstract

The invention discloses a torque intelligent control method and system for an impact wrench, and relates to the technical field of torque intelligent control, and the method comprises the steps: obtaining a real-time torque output data set of a target impact wrench; calling a plurality of working condition data of the target impact wrench, performing fluctuation calculation, and determining an error value interval; judging the real-time torque output data set to generate a torque error judgment result; establishing a mapping network, performing control analysis on the target impact wrench, and formulating a torque control strategy; performing torque prediction on the target impact wrench, and drawing a torque change trend chart; and a torque control strategy is dynamically optimized, and intelligent control is carried out. The technical problems that in the prior art, the torque error of the impact wrench is not accurately judged, a torque control strategy is difficult to adapt to dynamic changes of working conditions, and consequently intelligent torque control cannot be achieved are solved, and the technical effect of improving intelligence, accuracy and dynamic optimization capacity of torque control of the target impact wrench is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent torque control, and in particular to an intelligent torque control method and system for an impact wrench. Background Art

[0002] In the field of industrial production and assembly, impact wrenches are widely used in bolt tightening and other operations as a key tool. With the continuous improvement of the manufacturing industry's requirements for product quality and production efficiency, higher standards are put forward for the accuracy and stability of impact wrench torque control. However, there are many limitations in the current impact wrench torque control technology. On the one hand, when determining the error between the real-time torque output and the target torque, there is often a lack of accurate judgment methods, relying on simple threshold judgments, and unable to adapt to the torque fluctuations under complex working conditions, resulting in inaccurate error judgments. On the other hand, different working conditions such as bolt material, working environment temperature and humidity have complex effects on torque output. It is difficult for existing technologies to comprehensively and deeply analyze the intrinsic connection between working condition data and real-time torque output, and it is impossible to establish an accurate mapping relationship. In addition, in the face of dynamic changes in working conditions, traditional torque control strategies are often fixed and lack the ability to dynamically optimize according to the real-time torque change trend, which makes it easy for impact wrenches to have torque control deviations in actual operations, affecting product assembly quality and production efficiency.

[0003] The existing technology has technical problems such as inaccurate judgment of the torque error of the impact wrench and difficulty in adapting the torque control strategy to dynamic changes in working conditions, resulting in the inability to achieve intelligent torque control. Summary of the invention

[0004] The present application provides a torque intelligent control method and system for an impact wrench, which is used to solve the technical problems in the prior art that the impact wrench torque error judgment is inaccurate and the torque control strategy is difficult to adapt to dynamic changes in working conditions, resulting in the inability to achieve intelligent torque control.

[0005] In view of the above problems, the present application provides a torque intelligent control method and system for an impact wrench.

[0006] In a first aspect of the present application, a torque intelligent control method for an impact wrench is provided, the method comprising:

[0007] Acquire a real-time torque output data set of a target impact wrench; retrieve multiple working condition data of the target impact wrench, set a target torque value according to the multiple working condition data, perform fluctuation calculation based on the target torque value, and determine an error value interval; compare the real-time torque output data set with the target torque value, determine the real-time torque output data set according to the error value interval, and generate a torque error determination result; establish a mapping network between the multiple working condition data and the real-time torque output data set according to the torque error determination result, perform control analysis on the target impact wrench based on the mapping network, and formulate a torque control strategy; perform torque prediction on the target impact wrench by traversing the mapping network based on the multiple working condition data, and draw a torque change trend graph; dynamically optimize the torque control strategy according to the torque change trend graph, generate a torque optimization control strategy for the target impact wrench, and intelligently control the torque of the target impact wrench through the torque optimization control strategy.

[0008] A second aspect of the present application provides a torque intelligent control system for an impact wrench, the system comprising:

[0009] An output data set acquisition module is used to acquire a real-time torque output data set of a target impact wrench; an error value interval determination module is used to retrieve multiple working condition data of the target impact wrench, set a target torque value according to the multiple working condition data, perform fluctuation calculation based on the target torque value, and determine the error value interval; an error judgment result generation module is used to compare the real-time torque output data set with the target torque value, judge the real-time torque output data set according to the error value interval, and generate a torque error judgment result; a torque control strategy formulation module is used to establish a mapping network between the multiple working condition data and the real-time torque output data set according to the torque error judgment result, perform control analysis on the target impact wrench based on the mapping network, and formulate a torque control strategy; a torque change trend graph drawing module is used to traverse the mapping network based on the multiple working condition data to predict the torque of the target impact wrench and draw a torque change trend graph; an intelligent control module is used to dynamically optimize the torque control strategy according to the torque change trend graph, generate a torque optimization control strategy for the target impact wrench, and intelligently control the torque of the target impact wrench through the torque optimization control strategy.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The real-time torque output data set of the target impact wrench is obtained; multiple working condition data of the target impact wrench are retrieved, the target torque value is set, fluctuation calculation is performed, and the error value interval is determined; the real-time torque output data set is judged according to the error value interval, and a torque error judgment result is generated; a mapping network is established, control analysis is performed on the target impact wrench, and a torque control strategy is formulated; torque prediction is performed on the target impact wrench, and a torque change trend chart is drawn; the torque control strategy is dynamically optimized according to the torque change trend chart, and a torque optimization control strategy of the target impact wrench is generated, and the torque of the target impact wrench is intelligently controlled through the torque optimization control strategy. The technical effect of improving the intelligence, accuracy and dynamic optimization capability of the torque control of the target impact wrench is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic flow chart of a torque intelligent control method for an impact wrench provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of a torque intelligent control system for an impact wrench provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: output data set acquisition module 10 , error value interval determination module 20 , error determination result generation module 30 , torque control strategy formulation module 40 , torque change trend diagram drawing module 50 , intelligent control module 60 . DETAILED DESCRIPTION

[0016] The present application provides a torque intelligent control method and system for an impact wrench, aiming to solve the technical problems in the prior art of inaccurate torque error judgment of the impact wrench and difficulty in the torque control strategy to adapt to dynamic changes in working conditions, resulting in the inability to achieve intelligent torque control.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1, as Figure 1As shown, the present application provides a torque intelligent control method for an impact wrench, the method comprising:

[0019] Step S100: acquiring a real-time torque output data set of a target impact wrench.

[0020] Specifically, with the help of advanced sensor technology, high-precision torque sensors are precisely installed on the power output shaft or key transmission components of the target impact wrench. These sensors have high sensitivity and fast response characteristics, and can accurately capture subtle changes in torque when the impact wrench is running. As the impact wrench operation continues, the sensor collects torque data at extremely short time intervals, such as once per millisecond. The collected data is transmitted to the data acquisition card through a data line with strong anti-interference ability. The data acquisition card converts the analog signal into a digital signal and transmits it to a dedicated microprocessor. The microprocessor performs preliminary screening and sorting of the data, removes outliers and noise interference, and finally forms a complete and accurate torque output data set that can reflect the real-time working status of the impact wrench, laying a solid data foundation for the subsequent torque intelligent control process.

[0021] Step S200: Retrieving a plurality of working condition data of a target impact wrench, setting a target torque value according to the plurality of working condition data, performing fluctuation calculation based on the target torque value, and determining an error value interval.

[0022] Specifically, a database storing a large amount of working condition data is accessed to accurately screen out information closely related to the target impact wrench, covering key factors such as bolt material (such as high-strength alloy steel, ordinary carbon steel, etc.), bolt size specifications (including detailed parameters such as diameter and pitch), workpiece surface roughness, ambient temperature and humidity. Based on these multi-dimensional working condition data, a multivariate regression algorithm, such as the least squares method, is used for multivariate linear regression analysis. The above working condition factors are used as independent variables, and the torque value is used as the dependent variable to construct a regression model. Through training and fitting of a large amount of historical data, multiple target torque values ​​are obtained. Then, the core target working condition data is extracted from the working condition data. For example, if the current processing is an alloy steel bolt with a specific hardness and is in a high temperature and high humidity environment, the pre-set scene adaptation rules are used to evaluate the multiple target torque values ​​obtained before, and the fitness of each torque value under the target working condition is calculated. The fitness calculation comprehensively considers the deviation of the torque value from the ideal tightening torque, the stability and reliability of the equipment under the working condition, and other factors. Subsequently, the multiple target torque values ​​are arranged in descending order according to the fitness level to form a torque value sequence. Take the first-order torque value in the sequence and determine it as the final target torque value. After determining the target torque value, consider the uncertainty factors in actual operation and perform fluctuation calculation. Collect a large amount of historical torque output data of this model of impact wrench under similar working conditions, use statistical methods to calculate the standard deviation and mean of these data, center on the mean, and determine a reasonable fluctuation range based on the standard deviation and actual engineering experience, such as taking the mean plus or minus three times the standard deviation as the upper and lower limits to determine the error value range. This range will serve as an important basis for subsequent judgment of whether the actual torque output meets the requirements, ensuring the accuracy and reliability of torque control.

[0023] Step S300: Compare the real-time torque output data set with the target torque value, determine the real-time torque output data set according to the error value interval, and generate a torque error determination result.

[0024] Specifically, each torque value in the real-time torque output data set is extracted one by one, and the difference operation is performed between it and the determined target torque value. For example, if the target torque value is 50N·m, and the torque value at a certain moment in the real-time torque output data set is 48N·m, then the difference between the two is -2N·m. Then, these differences are compared with the pre-set error value interval. Assuming that the error value interval is [45N·m, 55N·m], when the difference is within this interval, it indicates that the torque output is within a reasonable fluctuation range. In the judgment process, the proportion of the number of torque values ​​in the error interval to the total data volume is counted. If the proportion exceeds the set threshold (such as 80%), a torque feasible signal is generated, indicating that the overall torque output is relatively stable and reliable. If the proportion does not reach the threshold, or there is a difference that exceeds the error interval, a torque infeasible signal is generated. For the torque infeasible signal, further in-depth analysis is performed. Through data visualization technology, a curve of torque value changes over time is drawn to observe the time period and trend of abnormal torque fluctuations. At the same time, a cluster analysis algorithm is used to group abnormal torque values ​​to find possible abnormal modes or fault characteristics. For example, if the torque value is found to be continuously lower than the lower limit of the error range within a certain period of time, it may indicate that there is an energy loss problem in the power transmission system. These analysis results are summarized to finally generate a comprehensive torque error determination result, providing a detailed basis for subsequent control strategy adjustments.

[0025] Step S400: establishing a mapping network between the plurality of working condition data and the real-time torque output data set according to the torque error determination result, performing control analysis on the target impact wrench based on the mapping network, and formulating a torque control strategy.

[0026] Specifically, first, according to the torque error judgment result, the key torque data points and the corresponding working condition data are screened out. For the abnormal data in the torque error judgment result, the time, frequency and degree of deviation from the target torque value are deeply analyzed. At the same time, the working condition data corresponding to these abnormal data are extracted, such as the bolt material, specification, ambient temperature and humidity at that time. Then, the feature engineering technology in machine learning is used to extract and transform the screened working condition data and torque data. For example, for the bolt material, it is converted into the corresponding material strength grade value; for the ambient temperature and humidity, it is normalized. Next, a suitable neural network model, such as a recurrent neural network (RNN), is used to train the model with the processed working condition data as the input layer and the torque data as the output layer. During the training process, the weights and biases of the network are continuously adjusted to minimize the error between the predicted torque and the actual torque. After training and optimization of a large amount of data, a mapping network between multiple working condition data and real-time torque output data sets is constructed. Based on this mapping network, by inputting the current working condition data, the network can quickly predict the corresponding torque output. When formulating the torque control strategy, the prediction results of the mapping network are combined with engineering control theory and practical operation experience. If the predicted torque is higher than the target torque value, the driving voltage of the impact wrench can be appropriately reduced or the impact frequency can be adjusted to reduce the torque output; conversely, if the predicted torque is lower than the target torque value, the driving voltage can be increased or the impact parameters can be adjusted to achieve precise control of the target impact wrench torque, ensuring that it can work stably under different working conditions and achieve the ideal tightening effect.

[0027] Step S500: traversing the mapping network based on the multiple working condition data to predict the torque of the target impact wrench and draw a torque change trend graph.

[0028] Specifically, multiple working condition data are standardized so that their value range is between 0 and 1. For example, for bolt materials, they can be mapped to corresponding value intervals according to their hardness grade. Then, the standardized working condition data are arranged in time series or operation order as the input sequence of the LSTM network. The LSTM network can effectively capture the long-term dependencies and dynamic change characteristics in the working condition data through its internal memory unit and gating mechanism. In the training stage, a large amount of existing working condition data and the corresponding actual torque values ​​are used to train the LSTM network. The weights and biases of the network are continuously adjusted through the back propagation algorithm to minimize the mean square error between the predicted torque value and the actual torque value. After sufficient training, the current working condition data of the target impact wrench is input into the trained LSTM network, and the network will output a series of torque prediction values. As time or operation progresses, these prediction values ​​are continuously collected, and the Matplotlib library or other drawing tools in Python are used to draw a torque change trend chart with time as the horizontal axis and torque prediction value as the vertical axis. In the figure, the torque change trend under different working conditions can be represented by lines of different colors. At the same time, annotations and legends can be added to clearly show the law of torque change with working conditions, such as the downward trend of torque when the temperature rises, or the rising curve of torque as the bolt tightening progresses, etc., thereby providing strong support for subsequent analysis and control strategy optimization.

[0029] Step S600: dynamically optimizing the torque control strategy according to the torque variation trend diagram to generate a torque optimization control strategy for a target impact wrench, and intelligently controlling the torque of the target impact wrench through the torque optimization control strategy.

[0030] Specifically, the torque change trend chart is first analyzed in depth, and image recognition technology and data analysis algorithms are used to accurately identify nodes and time periods with large torque changes in the chart, as well as abnormal intervals where the torque value deviates from the target value. For example, if it is found that the torque shows a trend of continuous increase and exceeds the predetermined upper limit in a certain period of time, or the torque fluctuates sharply when a specific working condition is converted. Then, the optimization program is started for these abnormal situations, and intelligent retrieval and matching are performed in the existing torque control strategy library according to the characteristics of the torque change and the corresponding working condition information. For the problem of too fast torque rise, the control strategy options of reducing the speed of the drive motor or adjusting the impact frequency are selected; for abnormal torque fluctuations, the strategy of increasing the sensitivity of feedback regulation or optimizing the stability of power transmission may be selected. Then, the selected strategies are pre-evaluated using simulation technology. By building a virtual impact wrench working environment, inputting current working condition data and different candidate strategies, observing the simulated changes in torque output, and calculating key performance indicators such as torque stability and energy consumption. Based on the simulation evaluation results, the strategy combination with the best performance is selected as the torque optimization control strategy. Finally, this optimization strategy is transmitted to the controller of the target impact wrench in real time. The controller dynamically adjusts key control parameters such as motor drive current and impact rhythm according to the parameter adjustment instructions in the strategy, thereby realizing intelligent and precise control of the target impact wrench torque, ensuring that a stable and efficient working state can be maintained under various complex working conditions, thereby improving the quality and efficiency of tightening operations.

[0031] In a possible implementation, step S200 further includes:

[0032] Step S210: performing multivariate regression on the target impact wrench based on the multiple working condition data to determine multiple target torque values.

[0033] Step S220: extracting target operating condition data based on the multiple operating condition data, performing scenario adaptation on the multiple target torque values ​​according to the target operating condition data, and obtaining multiple fitness levels.

[0034] Step S230: Processing the multiple target torque values ​​in descending order according to the multiple fitness levels to generate a torque value sequence, and extracting the first-order torque value as the target torque value based on the torque value sequence.

[0035] Step S240: performing torque peak deviation calculation according to the target operating condition data and the target torque value to determine an upper limit value of the fluctuation error.

[0036] Step S250: performing torque trough deviation calculation according to the target operating condition data and the target torque value to determine a lower limit of the fluctuation error.

[0037] Step S260: Taking the upper limit of the fluctuation error as the positive extreme value boundary and the lower limit of the fluctuation error as the negative extreme value boundary, the error value interval is established.

[0038] Specifically, first of all, we comprehensively collect various working condition data related to the target impact wrench, including the material characteristics of the tightened bolts (such as different strength grades of carbon steel, composition ratio of alloy steel), dimensional parameters (precise diameter, pitch value), surface treatment (presence of anti-rust coating, coating type and thickness), and detailed information of the operating environment (specific values ​​of ambient temperature and humidity, and even vibration frequency and amplitude of the workplace, etc.). These rich and diverse working condition data are used as independent variables, and torque value is used as dependent variable. Multivariate regression analysis methods such as principal component regression algorithm are introduced, and regression models are constructed using a large amount of historical experimental data and actual operation records. During the model training process, the weight coefficients of various working condition factors are continuously adjusted to minimize the error between the predicted torque value and the actual torque value. After multiple iterations and optimizations, based on the current input of multiple working condition data, the model can accurately calculate and output multiple target torque values. These target torque values ​​reflect the theoretical torque required to achieve the ideal tightening effect under different working condition combinations, laying the foundation for further screening and determination of the final target torque value.

[0039] Using the data screening algorithm, the target working condition data can be accurately extracted from the existing large amount of working condition data according to the set key feature screening conditions. For example, by matching keywords and limiting the value range of the data field, the bolt material, specifications, and ambient temperature and humidity data that meet the current operation are screened out. For the scene adaptation of the target torque value and the target working condition data, an adaptation model based on material mechanics and engineering experience is established. For the bolt material, if it is high-strength alloy steel, according to its material performance parameter library, determine the influence coefficient of its hardness, elastic modulus and other parameters on torque transmission. For bolts of different specifications, such as diameter and pitch, the thread mechanics calculation formula is used to calculate the force distribution during the tightening process, and then the corresponding torque adaptation coefficient is obtained. Considering the environmental temperature and humidity factors, a temperature and humidity-torque correction curve is established based on the existing experimental data. When the temperature is 30°C and the humidity is 40%, find the corresponding torque correction value from the curve. The coefficients and correction values ​​corresponding to these different factors are comprehensively calculated to obtain the fitness score of each target torque value under the current target working conditions. Through weighted summation, the material coefficient weight is set to 0.4, the specification coefficient weight is 0.4, and the temperature and humidity correction coefficient weight is 0.2. Finally, the fitness value of each target torque value is calculated, thereby completing the scene adaptation process and obtaining multiple fitness levels.

[0040] Call the sorting algorithm to sort multiple target torque values ​​according to the size of the fitness. During the sorting process, compare the corresponding fitness values ​​of all target torque values ​​one by one. The larger the fitness value, the higher the rationality and adaptability of the target torque value under the current working conditions. As the comparison and sorting continue, a torque value sequence arranged from high to low in terms of fitness is eventually formed. In this sequence, the torque value at the top is extracted as the target torque value because it has the highest fitness. This target torque value will serve as a key reference in the subsequent torque control process, and will be used for comparison and adjustment with the actual torque output to ensure that the impact wrench can output the most appropriate torque under the current working conditions, and achieve efficient and reliable tightening operations.

[0041] According to the target working condition data, the key factors affecting torque fluctuation are comprehensively analyzed. For the bolt material in the target working condition, such as high-strength alloy steel, because its material properties are relatively stable but there is still a possibility of slight deformation under extreme stress, the fluctuation calculation model is used to estimate the deviation under high torque extreme value conditions by combining the elastic modulus and yield strength of material mechanics and the target torque value. Considering the operating environment temperature, if it is in a high temperature environment, the thermal expansion and contraction of the material will change the tightness of the fit between the bolt and the nut, thereby affecting the stability of torque transmission. According to the empirical formula of the influence of temperature on material properties and a large amount of experimental data, the torque peak deviation caused by temperature factors is calculated. In terms of the mechanical properties of the equipment itself, the influence of factors such as the power transmission system accuracy and gear clearance of the impact wrench on the torque output is analyzed. For example, it is known that the transmission error of the power transmission system is within a certain range, and combined with the target torque value, the upper limit of the torque fluctuation caused by the mechanical structure is determined. Combining these factors, through precise mathematical calculations and engineering experience judgment, the result of the torque peak deviation calculation, that is, the upper limit of the fluctuation error, is determined.

[0042] The calculation of torque trough deviation is based on the bolt material, environmental factors and equipment performance in the target working condition data. For example, for bolts with softer materials, local yield deformation may occur during the tightening process, resulting in a sudden drop in torque. The possible torque lower limit deviation is calculated by using the plastic deformation characteristics of the material and related mechanical models, combined with the target torque value. Taking into account the impact of ambient humidity on the lubrication effect of the equipment, if the humidity is high, it may cause increased friction between components, resulting in energy loss during torque transmission, thereby reducing the torque value. Through the research data on the relationship between humidity and friction and the analysis of the equipment lubrication coefficient, the torque trough deviation caused by humidity factors is determined, and then combined with other mechanical characteristics of the equipment, the lower limit of the fluctuation error is finally determined, providing a reasonable error range for subsequent torque control.

[0043] The calculated upper limit of the fluctuation error is used as the positive extreme value boundary, and the lower limit of the fluctuation error is used as the negative extreme value boundary to establish the error value range. This range provides a quantitative standard range for subsequent judgment of whether the actual torque output meets the requirements, ensuring that in actual operation, even if the torque fluctuates to a certain extent, as long as it is within this error range, it can be considered to be within an acceptable range, thereby ensuring the accuracy and reliability of the impact wrench torque control.

[0044] In a possible implementation, step S300 further includes:

[0045] Step S310: subtracting the target torque value as a target reference value from the real-time torque output data set to obtain a plurality of real-time torque error values.

[0046] Step S320: Projecting the multiple real-time torque error values ​​to the error value interval to generate an error projection result.

[0047] Step S330: According to the error projection result, projection determination is performed on the multiple real-time torque error values ​​and the error value interval to generate the torque error determination result.

[0048] Specifically, starting from the determined target torque value, it is set as the standard reference, and each data point in the real-time torque output data set is subtracted one by one. For example, if the target torque value is 50N·m, and the value at a certain moment in the real-time torque output data set is 48N·m, the subtraction between the two will obtain a real-time torque error value of -2N·m. This cycle is repeated to process the entire data set, thereby obtaining a series of real-time torque error values, which intuitively reflect the deviation between the actual torque output and the target value.

[0049] A professional data analysis algorithm is used to map the multiple real-time torque error values ​​obtained above to a preset error value range. For example, if the error value range is [47N·m, 53N·m], and a real-time torque error value is -1N·m, the algorithm is used to quantify its relative position and relationship in the range, and generate an error projection result, which clearly shows the specific distribution state of each error value within the allowable error range.

[0050] Based on the generated error projection results, a detailed projection judgment is performed. By judging whether the real-time torque error value is completely within the error value range, as well as factors such as the distribution density and discreteness within the range, the stability and accuracy of the torque output are comprehensively evaluated. If most of the error values ​​are tightly concentrated within the error range and meet certain proportion requirements, a torque feasible signal indicating that the torque output is more reliable is generated and included in the torque error judgment result; conversely, if there are many error values ​​that exceed the range or the distribution is abnormal, a torque infeasible signal is generated, and the cause of the abnormality is further analyzed, such as whether there is equipment failure or sudden change in working conditions, etc., to finally form a comprehensive and accurate torque error judgment result, providing a key basis for subsequent control strategy adjustments.

[0051] In a possible implementation, step S330 further includes:

[0052] Step S331: According to the error projection result, determine whether there are overlapping values ​​between the multiple real-time torque error values ​​and the error value interval.

[0053] Step S332: If there are overlapping values ​​between the multiple real-time torque error values ​​and the error value interval, the numerical ratio of the overlapping values ​​is calculated to determine whether the overlapping numerical ratio is greater than or equal to the expected overlapping numerical ratio; if the overlapping numerical ratio is greater than or equal to the expected overlapping numerical ratio, a torque feasible signal is generated and the torque feasible signal is added to the torque error judgment result.

[0054] Step S333: If there is no overlapping value between the multiple real-time torque error values ​​and the error value interval or the overlapping value ratio is less than the expected overlapping value ratio, a torque unfeasible signal is generated.

[0055] Step S334: performing abnormality analysis on the multiple real-time torque error values ​​based on the torque unfeasible signal, and adding the abnormality analysis result to the torque error determination result.

[0056] Specifically, the error projection results are extracted and sorted to obtain all the real-time torque error values ​​and the corresponding error value interval boundary information. Then, using the numerical comparison algorithm, starting from the first real-time torque error value, it is compared with the lower limit and upper limit of the error value interval one by one. If the error value is greater than or equal to the lower limit and less than or equal to the upper limit, it means that there is an overlapping value between this error value and the error value interval. Subsequently, in the same way, each subsequent real-time torque error value is judged in turn until the entire real-time torque error value set is checked, thereby comprehensively determining whether there are overlapping values ​​between multiple real-time torque error values ​​and the error value interval, providing key basic information for the subsequent judgment process.

[0057] After determining that there are overlapping values ​​between multiple real-time torque error values ​​and the error value interval, the calculation of the overlapping value ratio is immediately carried out. The total number of all real-time torque error values ​​is counted, and this total number represents the total number of samples. Then, the number of overlapping values ​​falling within the error value interval is accurately counted, and the number of overlapping values ​​is divided by the total number of real-time torque error values. The result is the value ratio of the overlapping values. After obtaining the ratio, it is compared with the preset expected overlapping value ratio, which is determined based on a large amount of historical data, engineering experience, and the performance requirements of the target impact wrench. If the calculated overlapping value ratio is greater than or equal to the expected overlapping value ratio, it means that the proportion of the real-time torque output of the current target impact wrench within the error allowable range meets or exceeds the expected standard. At this time, a torque feasible signal is generated, which indicates that the current torque output is in good condition and the equipment is running relatively stably. Finally, this torque feasible signal is added to the torque error judgment result to provide a clear basis for subsequent analysis and decision-making.

[0058] After analyzing multiple real-time torque error values ​​and error value intervals, if it is found that there is no overlapping value between the two, that is, all real-time torque error values ​​are outside the error value interval, or although there are overlapping values, the calculated overlapping value ratio is less than the preset expected overlapping value ratio, both of these situations indicate that the current real-time torque output is not ideal. Based on such a judgment, a torque infeasible signal will be generated, which means that the torque output of the current target impact wrench may not meet the actual work requirements, and further inspection and adjustment are required to ensure that the torque output can be within a reasonable error range to ensure the normal operation and work quality of the equipment.

[0059] After the torque infeasible signal is generated, a comprehensive and in-depth abnormal analysis is carried out on multiple real-time torque error values. These error values ​​are observed from the perspective of time series to see whether the error values ​​show a specific trend of change, such as whether they continue to increase or decrease over time, or whether there are periodic fluctuations. This helps to determine whether the abnormality is gradually accumulated or caused by periodic factors. The distribution of error values ​​is analyzed, such as whether the error values ​​are concentrated in a certain range of values ​​or present a discrete state. If the error values ​​are concentrated in a certain range, it may imply that a specific factor is dominating the abnormal situation; while a discrete distribution may mean that multiple factors work together. In addition, the working condition data of the target impact wrench, such as bolt material, specification, operating environment and other factors, are combined to analyze the correlation between these factors and the real-time torque error value. For example, whether bolts of a specific material are more likely to have torque abnormalities, or whether the error values ​​are more significant under specific ambient temperature and humidity conditions. Through detailed analysis of these aspects, the possible causes of the abnormality are summarized, the abnormality analysis results are formed, and they are added to the torque error judgment results.

[0060] In a possible implementation, step S334 further includes:

[0061] Step S3341: abnormally locate the multiple real-time torque error values ​​through the torque infeasibility signal to obtain multiple abnormal torque error values.

[0062] Step S3342: Perform numerical calculation based on the multiple abnormal torque error values ​​to obtain multiple numerical distribution frequencies and multiple numerical distribution probabilities.

[0063] Step S3343: Draw a numerical distribution histogram according to the multiple numerical distribution frequencies and the multiple numerical distribution probabilities.

[0064] Step S3344: performing cluster analysis on the multiple abnormal torque error values ​​according to the numerical distribution histogram to determine multiple abnormal value concentration intervals.

[0065] Step S3345: Add the multiple abnormal value concentration intervals to the abnormal analysis results.

[0066] Specifically, when a torque infeasible signal is received, the abnormal location process of multiple real-time torque error values ​​is started. Each real-time torque error value is screened with the error value interval as the reference standard. If a real-time torque error value falls outside the error value interval, it is judged to be an abnormal situation, and the error value will be marked as abnormal. By checking all real-time torque error values ​​one by one, a series of such abnormal values ​​are finally screened out, and these screened abnormal values ​​together constitute multiple abnormal torque error values.

[0067] For each different abnormal torque error value, start to count the number of times it appears in the entire abnormal torque error value set. By traversing all abnormal torque error values, each time a specific error value is encountered, the counter of the corresponding number of occurrences is increased by one. After the statistics are completed, the number of occurrences of each error value is divided by the total number of abnormal torque error values. The result is the numerical distribution frequency of the error value. These frequency data reflect the relative frequency of occurrence of different abnormal torque error values ​​in the set. Then, based on the probability statistics theory, the numerical distribution probability of each abnormal torque error value is calculated. This calculation process takes into account the overall characteristics of the abnormal torque error value set and the relationship between the error values. The maximum likelihood estimation method is used to comprehensively consider the distribution form of the data, the number of samples and other factors to accurately estimate the probability of occurrence of each abnormal torque error value. Through these calculations, multiple numerical distribution probabilities are obtained. These probability values ​​provide more comprehensive quantitative indicators for in-depth understanding of the distribution characteristics of abnormal torque error values, which is helpful for further analysis of abnormal situations in the future.

[0068] First, determine the horizontal axis as the abnormal torque error value interval, divide the small intervals according to the actual data, and then determine the vertical axis scale according to the frequency and probability of the numerical distribution. Then, for each small interval, draw a rectangle according to the frequency and probability corresponding to the abnormal torque error value falling into it, and finally form a numerical distribution histogram that intuitively displays the distribution of abnormal torque error values.

[0069] In the face of the numerical distribution histogram, a cluster analysis method is used to process multiple abnormal torque error values. First, based on the density of abnormal torque error value distribution intuitively presented by the rectangular height in the histogram, possible dense areas are preliminarily screened out. Then, a density-based clustering algorithm, such as the DBSCAN algorithm, is used. The algorithm sets a neighborhood radius and a minimum number of points to examine the points corresponding to each abnormal torque error value. If the number of points contained in a point within its neighborhood radius reaches or exceeds the minimum number of points, this point is identified as a core point. Starting with the core point, the surrounding points are classified into the same category through the density connection relationship. In this process, the algorithm will continue to scan the points in the histogram, continuously merge the density-connected areas, and finally identify the areas with dense numerical distribution. These areas are determined as multiple abnormal value concentration intervals in the value range of the abnormal torque error value, so as to clarify the concentrated distribution range of the abnormal torque error value and provide key clues for the subsequent identification of the abnormal cause.

[0070] After completing the determination of multiple abnormal value concentration intervals, these interval information are integrated into the abnormal analysis results. By recording the boundary values, interval ranges and other detailed information of each abnormal value concentration interval in a specific data format, it is supplemented to the existing abnormal analysis report or data set. As an important part of abnormal analysis, these interval data provide a key and intuitive basis for further diagnosis of the cause of torque abnormality, evaluation of the impact of abnormalities on impact wrench performance, and formulation of targeted solutions, which improves the entire abnormal analysis process and makes the analysis results more comprehensive and accurate.

[0071] In a possible implementation, step S400 further includes:

[0072] Step S410: performing principal component analysis on the real-time torque output data set according to the multiple abnormal value concentration intervals to determine multiple real-time torque features.

[0073] Step S420: traverse the plurality of operating condition data to perform scene feature analysis to obtain a plurality of operating condition features.

[0074] Step S430: performing data alignment based on the multiple operating condition characteristics and the multiple real-time torque characteristics, normalizing the multiple operating condition characteristics and the multiple real-time torque characteristics according to the alignment result, and generating a normalized result.

[0075] Step S440: performing association training on the plurality of operating condition data and the real-time torque output data set according to the normalization result to construct the mapping network.

[0076] Specifically, principal component analysis is performed on the real-time torque output data set based on the determined multiple outlier concentration intervals. Principal component analysis aims to extract the most representative information from a complex data set, and by reducing the dimensionality of the data, find the components that can best explain the data variance, thereby determining multiple real-time torque features. These features can effectively summarize the key characteristics of the real-time torque output data and provide a core data basis for subsequent analysis.

[0077] We started to traverse multiple working condition data and conduct in-depth scene feature analysis. For each working condition data, we considered different dimensions, such as the material and specifications of the bolts, the temperature and humidity of the operating environment, etc., and extracted the significant features closely related to the working condition, thereby obtaining multiple working condition features. These working condition features fully reflect the characteristics of different working condition scenarios, which are crucial to understanding the relationship between torque output and working conditions.

[0078] Based on the data alignment of multiple working condition features and multiple real-time torque features that have been acquired, the working condition features are matched one by one with the real-time torque features according to the inherent logic and physical meaning between the features. For example, the working condition feature representing the material of the bolt is matched with the real-time torque feature corresponding to the material condition to ensure that each working condition feature can find a real-time torque feature closely associated with it. After completing the data alignment, in order to eliminate the differences in dimensions and numerical ranges between different features, so as to facilitate subsequent analysis and modeling, these features are normalized and all data are compressed to the range of [0, 1]. All working condition features and real-time torque features are normalized to generate normalized results. These normalized data allow different features to be represented at the same scale, laying a good foundation for the subsequent association training of multiple working condition data with the real-time torque output data set and mining the potential relationship between the two.

[0079] The association training of multiple working condition data and real-time torque output data sets is carried out. The normalized working condition characteristics and real-time torque characteristics are used as the basic data. A suitable neural network model, such as a recurrent neural network (RNN), is used. During the training process, the working condition data is used as input and the real-time torque output data is used as the expected output. By continuously adjusting the parameters in the network, the real-time torque output predicted by the model is made as close as possible to the actual real-time torque output data set. In each iteration, the algorithm calculates the loss value based on the difference between the predicted result and the actual result, and then updates the network parameters based on the back propagation of the loss value. After a large number of training iterations, the model gradually learns the complex mapping relationship between the working condition data and the real-time torque output, thereby constructing a mapping network that can accurately describe the relationship between the two. This network can be used to subsequently predict the real-time torque output under different working conditions, providing strong support for torque control and optimization.

[0080] In a possible implementation, step S600 further includes:

[0081] Step S610: traverse the torque variation trend graph to perform trend analysis and determine the torque variation amplitude value.

[0082] Step S620: When the torque variation amplitude value is greater than a preset critical point, the optimization module is activated, and the torque variation trend graph is annotated by the optimization module to determine a plurality of labels to be optimized.

[0083] Step S630: using the multiple tags to be optimized as indexes to traverse the torque control strategy for search and matching, and determining multiple control data to be optimized.

[0084] Step S640: performing source tracing update based on the multiple control data to be optimized to generate multiple optimized replacement control data, optimizing the torque control strategy according to the multiple optimized replacement control data, and generating the torque optimization control strategy of the target impact wrench.

[0085] Specifically, the torque change trend diagram is traversed comprehensively and meticulously. During the traversal process, for the curve in the diagram representing the change of torque over time or other related variables, the difference in torque values ​​between different key nodes is accurately calculated, and combined with the time intervals between these nodes or the change amplitude of the variables, the torque change amplitude value is determined.

[0086] Once it is determined that the torque change amplitude value is greater than the preset critical point, the optimization mechanism will be triggered and an activation instruction will be sent to the optimization module, which is pre-equipped with a series of algorithms and tools for analyzing and annotating torque change trend graphs. The optimization module first parses the data of the torque change trend graph and converts it into an operable digital signal or data structure. For example, if the trend graph represents the torque value in a time series, the optimization module will obtain the torque data corresponding to each time point. Subsequently, the pattern recognition algorithm is used to conduct in-depth analysis of these data. For example, a sliding window algorithm is used to slide a window of fixed length on the torque data sequence to analyze the torque change pattern in each window. If the torque change in the window shows a specific abnormal pattern, such as a sharp increase or decrease in torque in a short period of time, and the change amplitude exceeds a certain threshold (the threshold can be set based on experience or historical data), the time period corresponding to the window is marked as a possible optimization point. At the same time, the optimization module also combines machine learning models, such as classification models trained based on historical torque data and corresponding optimization results, to classify and judge the current torque change trend. If the model predicts that the current torque change trend belongs to the category that needs to be optimized, it will be marked at the corresponding position on the trend graph. These marked points are sorted and analyzed, and are identified as multiple tags to be optimized based on their characteristics and locations. For example, for the marked points where the torque rises sharply, they are marked as "torque sharp rise points" and the specific location information of the point in the trend chart (such as time point, corresponding torque value, etc.) is attached, so that the torque control strategy can be accurately optimized based on these tags later.

[0087] These multiple tags to be optimized are used as indexes to perform comprehensive search and matching in the torque control strategy. The torque control strategy contains a series of rules and data for regulating the torque of the impact wrench. By corresponding to the tags to be optimized, the control data associated with these tags can be accurately found, and multiple control data to be optimized can be determined.

[0088] The traceability and update work is carried out for multiple control data to be optimized. First, by querying historical data records, analyzing control algorithm logic, and referring to relevant technical documents, the source and initial setting basis of these control data to be optimized are traced. For example, if a control data to be optimized involves the motor speed adjustment parameter of the impact wrench, it is necessary to find out the motor performance indicators, expected working intensity of the wrench and other factors that were referenced when the parameter was initially set. After clarifying the data source and setting background, these data are updated according to the latest performance requirements, working condition changes and optimization goals of the target impact wrench. For example, considering that the new working scenario has higher requirements for torque stability, the motor speed adjustment parameters are recalculated and adjusted based on a more accurate motor dynamics model and actual test data to generate optimized replacement control data. After completing the generation of multiple optimized replacement control data, they are applied to the original torque control strategy, replacing the corresponding control data to be optimized in the original strategy one by one, and adaptively adjusting the relevant control logic and algorithm. For example, if the original torque control strategy performs PID adjustment based on specific torque sensor feedback values ​​and control data, after replacing the optimized replacement control data, the PID parameters and adjustment rules are recalibrated to ensure the consistency and effectiveness of the entire control strategy. After such a comprehensive optimization process, the torque optimization control strategy of the target impact wrench is finally generated to improve the torque control accuracy and stability of the wrench in actual work.

[0089] Embodiment 2 is based on the same inventive concept as the torque intelligent control method for an impact wrench in the above embodiment. Figure 2 As shown, the present application provides a torque intelligent control system for an impact wrench, and the system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0090] The output data set acquisition module 10 is used to acquire a real-time torque output data set of a target impact wrench.

[0091] The error value interval determination module 20 is used to retrieve multiple working condition data of the target impact wrench, set a target torque value according to the multiple working condition data, perform fluctuation calculation based on the target torque value, and determine the error value interval.

[0092] The error determination result generating module 30 is used to compare the real-time torque output data set with the target torque value, determine the real-time torque output data set according to the error value interval, and generate a torque error determination result.

[0093] The torque control strategy formulation module 40 is used to establish a mapping network between the multiple working condition data and the real-time torque output data set according to the torque error judgment result, perform control analysis on the target impact wrench based on the mapping network, and formulate a torque control strategy.

[0094] The torque variation trend diagram drawing module 50 is used to traverse the mapping network to perform torque prediction on the target impact wrench based on the multiple working condition data and draw a torque variation trend diagram.

[0095] The intelligent control module 60 is used to dynamically optimize the torque control strategy according to the torque change trend diagram, generate a torque optimization control strategy for the target impact wrench, and intelligently control the torque of the target impact wrench through the torque optimization control strategy.

[0096] Furthermore, the error value interval determination module 20 also includes:

[0097] The target torque value determination unit is used to perform multivariate regression on the target impact wrench based on the multiple working condition data to determine multiple target torque values.

[0098] The fitness acquisition unit is used to extract target operating condition data based on the multiple operating condition data, and perform scene adaptation on the multiple target torque values ​​according to the target operating condition data to obtain multiple fitness levels.

[0099] The torque value sequence generating unit is used to process the multiple target torque values ​​in descending order according to the multiple fitness levels to generate a torque value sequence, and extract the first-order torque value as the target torque value based on the torque value sequence.

[0100] The fluctuation error upper limit value determining unit is used to perform torque peak deviation calculation according to the target operating condition data in combination with the target torque value to determine the fluctuation error upper limit value.

[0101] The fluctuation error lower limit value determination unit is used to perform torque trough deviation calculation according to the target operating condition data in combination with the target torque value to determine the fluctuation error lower limit value.

[0102] The error value interval establishing unit is used to establish the error value interval by taking the upper limit value of the fluctuation error as the positive extreme value boundary and taking the lower limit value of the fluctuation error as the negative extreme value boundary.

[0103] Furthermore, the error determination result generating module 30 further includes:

[0104] The real-time torque error value acquisition unit is used to take the target torque value as a target reference value and perform subtraction with the real-time torque output data set to obtain a plurality of real-time torque error values.

[0105] The error projection result generating unit is used to project the multiple real-time torque error values ​​to the error value interval to generate an error projection result.

[0106] The projection determination unit is used to perform projection determination on the multiple real-time torque error values ​​and the error value interval according to the error projection result to generate the torque error determination result.

[0107] Furthermore, the projection determination unit further includes:

[0108] The overlapping value judgment unit is used to judge whether there are overlapping values ​​between the multiple real-time torque error values ​​and the error value interval according to the error projection result.

[0109] The torque feasible signal generating unit is used to calculate the numerical ratio of the overlapping numerical values ​​if there are overlapping numerical values ​​between the multiple real-time torque error values ​​and the error value interval, and determine whether the overlapping numerical ratio is greater than or equal to the expected overlapping numerical ratio. If the overlapping numerical ratio is greater than or equal to the expected overlapping numerical ratio, a torque feasible signal is generated, and the torque feasible signal is added to the torque error judgment result.

[0110] The torque unfeasible signal generating unit is used to generate a torque unfeasible signal if there is no overlapping value between the multiple real-time torque error values ​​and the error value interval or the overlapping value ratio is less than the expected overlapping value ratio.

[0111] The abnormality analysis unit is used to perform abnormality analysis on the multiple real-time torque error values ​​based on the torque unfeasible signal, and add the abnormality analysis result to the torque error determination result.

[0112] Furthermore, the abnormality analysis unit also includes:

[0113] The abnormal torque error value acquisition unit is used to perform abnormal location on the multiple real-time torque error values ​​through the torque unfeasible signal to obtain multiple abnormal torque error values.

[0114] The numerical calculation unit is used to perform numerical calculation based on the multiple abnormal torque error values ​​to obtain multiple numerical distribution frequencies and multiple numerical distribution probabilities.

[0115] The value distribution histogram drawing unit is used to draw a value distribution histogram according to the multiple value distribution frequencies combined with the multiple value distribution probabilities.

[0116] The abnormal value concentration interval determination unit is used to perform cluster analysis on the multiple abnormal torque error values ​​according to the numerical distribution histogram to determine multiple abnormal value concentration intervals.

[0117] The abnormal analysis result adding unit is used to add the multiple abnormal value concentration intervals to the abnormal analysis result.

[0118] Furthermore, the torque control strategy formulation module 40 also includes:

[0119] The real-time torque feature determination unit is used to perform principal component analysis on the real-time torque output data set according to the multiple abnormal value concentration intervals to determine multiple real-time torque features.

[0120] The operating condition feature acquisition unit is used to traverse the multiple operating condition data to perform scene feature analysis and obtain multiple operating condition features.

[0121] The normalization result generating unit is used to perform data alignment based on the multiple operating condition characteristics and the multiple real-time torque characteristics, normalize the multiple operating condition characteristics and the multiple real-time torque characteristics according to the alignment result, and generate a normalization result.

[0122] An association training unit is used to perform association training on the multiple operating condition data and the real-time torque output data set according to the normalization result to construct the mapping network.

[0123] Furthermore, the intelligent control module 60 also includes:

[0124] The torque variation amplitude value determining unit is used to traverse the torque variation trend diagram to perform trend analysis and determine the torque variation amplitude value.

[0125] The unit for determining the label to be optimized is used to activate the optimization module when the torque variation amplitude value is greater than a preset critical point, and to mark the torque variation trend diagram through the optimization module to determine a plurality of labels to be optimized.

[0126] The control data to be optimized determining unit is used to use the multiple tags to be optimized as indexes to traverse the torque control strategy for search and matching, and determine multiple control data to be optimized.

[0127] The torque control strategy optimization unit is used to perform traceability update based on the multiple control data to be optimized, generate multiple optimized replacement control data, optimize the torque control strategy according to the multiple optimized replacement control data, and generate the torque optimization control strategy of the target impact wrench.

[0128] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0130] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A torque intelligent control method for an impact wrench, characterized in that: The method comprises: Acquire a real-time torque output data set of a target impact wrench; Retrieving a plurality of working condition data of a target impact wrench, setting a target torque value according to the plurality of working condition data, performing fluctuation calculation based on the target torque value, and determining an error value interval; comparing the real-time torque output data set with the target torque value, determining the real-time torque output data set according to an error value interval, and generating a torque error determination result; Establishing a mapping network between the plurality of working condition data and the real-time torque output data set according to the torque error determination result, performing control analysis on the target impact wrench based on the mapping network, and formulating a torque control strategy; Based on the multiple working condition data, the mapping network is traversed to predict the torque of the target impact wrench, and a torque change trend graph is drawn; The torque control strategy is dynamically optimized according to the torque variation trend diagram to generate a torque optimization control strategy for a target impact wrench, and the torque of the target impact wrench is intelligently controlled by the torque optimization control strategy.

2. The torque intelligent control method for an impact wrench according to claim 1, characterized in that: Retrieving multiple working condition data of a target impact wrench, setting a target torque value according to the multiple working condition data, performing fluctuation calculation based on the target torque value, and determining an error value interval, the method includes: Performing multivariate regression on the target impact wrench based on the multiple working condition data to determine multiple target torque values; Extracting target operating condition data based on the multiple operating condition data, performing scenario adaptation on the multiple target torque values ​​according to the target operating condition data, and obtaining multiple fitness levels; Processing the plurality of target torque values ​​in descending order according to the plurality of fitnesses to generate a torque value sequence, and extracting a first-order torque value as the target torque value based on the torque value sequence; Perform torque peak deviation calculation according to the target operating condition data combined with the target torque value to determine an upper limit value of fluctuation error; Perform torque trough deviation calculation according to the target operating condition data combined with the target torque value to determine a lower limit of the fluctuation error; The error value interval is established by taking the upper limit value of the fluctuation error as the positive extreme value boundary and the lower limit value of the fluctuation error as the negative extreme value boundary.

3. The torque intelligent control method for an impact wrench according to claim 1, characterized in that: The real-time torque output data set is compared with the target torque value, and the real-time torque output data set is judged according to the error value interval to generate a torque error judgment result, the method comprising: Taking the target torque value as a target reference value and performing subtraction with the real-time torque output data set, a plurality of real-time torque error values ​​are obtained; Projecting the multiple real-time torque error values ​​to the error value interval to generate an error projection result; According to the error projection result, projection judgment is performed on the multiple real-time torque error values ​​and the error value interval to generate the torque error judgment result.

4. The intelligent torque control method for an impact wrench according to claim 3, characterized in that: According to the error projection result, projection determination is performed on the multiple real-time torque error values ​​and the error value interval to generate the torque error determination result, the method comprising: According to the error projection result, determining whether the multiple real-time torque error values ​​and the error value interval have overlapping values; If there are overlapping values ​​between the multiple real-time torque error values ​​and the error value interval, then the value ratio of the overlapping values ​​is calculated, and it is determined whether the overlapping value ratio is greater than or equal to the expected overlapping value ratio; if the overlapping value ratio is greater than or equal to the expected overlapping value ratio, a torque feasible signal is generated, and the torque feasible signal is added to the torque error determination result; If there is no overlapping value between the multiple real-time torque error values ​​and the error value interval or the overlapping value ratio is less than the expected overlapping value ratio, a torque unfeasible signal is generated; The plurality of real-time torque error values ​​are analyzed for abnormality based on the torque unfeasible signal, and the abnormality analysis result is added to the torque error determination result.

5. The intelligent torque control method for an impact wrench according to claim 4, characterized in that: Based on the torque infeasible signal, the multiple real-time torque error values ​​are analyzed for abnormality, and the abnormality analysis result is added to the torque error determination result. The method includes: Performing abnormal location on the multiple real-time torque error values ​​by using the torque infeasible signal to obtain multiple abnormal torque error values; Performing numerical calculation based on the multiple abnormal torque error values ​​to obtain multiple numerical distribution frequencies and multiple numerical distribution probabilities; Draw a numerical distribution histogram according to the multiple numerical distribution frequencies combined with the multiple numerical distribution probabilities; Performing cluster analysis on the multiple abnormal torque error values ​​according to the numerical distribution histogram to determine multiple abnormal value concentration intervals; The plurality of abnormal value concentration intervals are added to the abnormality analysis result.

6. The intelligent torque control method for an impact wrench according to claim 5, characterized in that: A mapping network between the plurality of operating condition data and the real-time torque output data set is established according to the torque error determination result, the method comprising: Performing principal component analysis on the real-time torque output data set according to the multiple abnormal value concentration intervals to determine multiple real-time torque features; Traversing the plurality of operating condition data to perform scene feature analysis to obtain a plurality of operating condition features; Performing data alignment on the multiple operating condition characteristics and the multiple real-time torque characteristics, and normalizing the multiple operating condition characteristics and the multiple real-time torque characteristics according to the alignment result to generate a normalized result; The plurality of operating condition data are associated with the real-time torque output data set for training according to the normalization result to construct the mapping network.

7. The intelligent torque control method for an impact wrench according to claim 1, characterized in that: The torque control strategy is dynamically optimized according to the torque change trend diagram to generate a torque optimization control strategy for a target impact wrench, the method comprising: Traversing the torque change trend graph to perform trend analysis and determine the torque change amplitude value; When the torque variation amplitude value is greater than a preset critical point, activating an optimization module, annotating the torque variation trend graph through the optimization module, and determining a plurality of labels to be optimized; Using the multiple tags to be optimized as indexes to traverse the torque control strategy for search and matching, and determining multiple control data to be optimized; Based on the multiple control data to be optimized, traceability update is performed to generate multiple optimized replacement control data, and the torque control strategy is optimized according to the multiple optimized replacement control data to generate the torque optimization control strategy of the target impact wrench.

8. Intelligent torque control system for impact wrench, characterized in that: The system is used to implement the torque intelligent control method for an impact wrench according to any one of claims 1 to 7, and the system comprises: An output data set acquisition module, used to acquire a real-time torque output data set of a target impact wrench; An error value interval determination module is used to retrieve multiple working condition data of a target impact wrench, set a target torque value according to the multiple working condition data, perform fluctuation calculation based on the target torque value, and determine the error value interval; an error determination result generating module, used for comparing the real-time torque output data set with the target torque value, determining the real-time torque output data set according to the error value interval, and generating a torque error determination result; A torque control strategy formulation module, used to establish a mapping network between the plurality of working condition data and the real-time torque output data set according to the torque error determination result, perform control analysis on the target impact wrench based on the mapping network, and formulate a torque control strategy; A torque variation trend diagram drawing module, used for traversing the mapping network to perform torque prediction on a target impact wrench based on the plurality of working condition data and drawing a torque variation trend diagram; The intelligent control module is used to dynamically optimize the torque control strategy according to the torque change trend diagram, generate a torque optimization control strategy for the target impact wrench, and intelligently control the torque of the target impact wrench through the torque optimization control strategy.

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