Numerical control lathe turning process energy consumption prediction system

Abstract

The invention relates to a numerical control lathe turning process energy consumption prediction system, which belongs to the technical field of data processing and prediction, and comprises a multi-source data acquisition unit, a steady-state power prediction unit, a numerical control lathe turning process energy consumption prediction unit and a numerical control lathe turning process energy consumption prediction unit, according to the invention, the prediction precision under an abnormal working condition is improved, the limitation of an energy consumption gradual change hypothesis is broken through by fusing transient vibration information of a physical world, and the prediction precision is improved. A new abrupt change sensing type energy consumption prediction normal form is created; the conversion from passive response to active predictive maintenance is realized, and the risks of workpiece scrapping and accidental shutdown are greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and prediction, in particular to an energy consumption prediction system for a turning process of a numerically controlled lathe. Background Art

[0002] Energy consumption prediction of CNC lathes is a key link in achieving lean production and green manufacturing. Existing prediction methods are mostly based on the steady-state operation assumption and use regression or time series models to process the low-frequency electrical power data of machine tools. However, such methods completely ignore the physical phenomena caused by sudden changes in tool state during the cutting process, such as wear, chipping, and fracture. These sudden events will cause drastic changes in cutting force on a millisecond time scale, resulting in instantaneous energy consumption peaks in the machine tool servo system. Since the technical paradigm of the existing model cannot capture such high-frequency transient events, its prediction accuracy under abnormal working conditions is extremely low, and it loses its early warning capability, leaving energy consumption management at a passive post-statistical stage. Therefore, this field urgently needs a new technical solution that can integrate transient physical characteristics and accurately predict energy consumption mutations.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0004] The purpose of the present invention is to provide a CNC lathe turning process energy consumption prediction system to solve the problems raised in the above background technology.

[0005] The technical solution of the present invention is to include: a multi-source data acquisition unit for synchronously acquiring high-frequency mechanical vibration signals and low-frequency electrical power signals of a machine tool, and acquiring machining process parameters;

[0006] a steady-state power prediction unit, configured to generate a steady-state power baseline based on the low-frequency electrical power signal and the machining process parameters acquired by the multi-source data acquisition unit;

[0007] a transient impact quantification unit, configured to generate a transient mutation impact factor representing a sudden change in a tool state based on the high-frequency mechanical vibration signal acquired by the multi-source data acquisition unit;

[0008] A fusion prediction generation unit is used to combine the steady-state power baseline generated by the steady-state power prediction unit, the transient mutation impact factor generated by the transient impact quantification unit, and the processing parameters collected by the multi-source data acquisition unit to generate a final power prediction value.

[0009] Preferably, the steady-state power prediction unit is specifically used for:

[0010] An autoregressive model with external input is adopted; historical values ​​of the low-frequency electrical power signal and current values ​​of the processing parameters are set as inputs of the model; and the steady-state power baseline is calculated based on the inputs of the model.

[0011] Preferably, before generating the transient mutation impact factor, the transient impact quantification unit is further configured to:

[0012] The high-frequency mechanical vibration signal is decomposed by wavelet packet transform to generate sub-signals covering different frequency bands; based on a preset characteristic frequency band set and the sub-signals, a characteristic frequency band associated with a sudden change in the tool state is determined.

[0013] Preferably, the transient impact quantization unit is specifically used to:

[0014] Calculate the sum of short-time energies within the characteristic frequency band; determine the statistical mean of the sum of short-time energies in a healthy cutting state; determine the statistical standard deviation of the sum of short-time energies in a healthy cutting state; and calculate the transient mutation impact factor using a Z-score normalization method based on the sum of short-time energies, the statistical mean, and the statistical standard deviation.

[0015] Preferably, the transient impulse quantization unit further includes a dynamic parameter updating module, and the dynamic parameter updating module is used to:

[0016] Comparing the transient mutation impact factor with a preset stability threshold;

[0017] If the transient mutation impact factor is lower than the stability threshold, continuously updating the statistical mean and the statistical standard deviation using a sliding time window;

[0018] If the transient mutation impact factor is not lower than the stability threshold, the statistical mean and the statistical standard deviation are kept unchanged.

[0019] Preferably, the fusion prediction generation unit is specifically used to:

[0020] Comparing and analyzing the transient mutation impact factor with a preset impact factor activation threshold;

[0021] If the transient mutation impact factor is greater than the impact factor activation threshold, a transient compensation term is determined, and the final power prediction value is generated by combining the steady-state power baseline and the transient compensation term; the transient compensation term is generated based on the difference between the transient mutation impact factor and the impact factor activation threshold, a preset energy impact benchmark conversion coefficient, and an operating condition adaptive weight;

[0022] If the transient mutation impact factor is not greater than the impact factor activation threshold, the steady-state power baseline is directly determined as the final power prediction value.

[0023] Preferably, the working condition adaptive weight is a dimensionless dynamic weight function; the dynamic weight function is used to dynamically adjust the amplitude of the transient compensation term based on the deviation of the processing parameters relative to the preset reference process parameters.

[0024] Preferably, the dynamic weight function includes a dimensionless sensitivity coefficient; and the process of determining the dimensionless sensitivity coefficient includes:

[0025] The mutation experiment is repeatedly performed under different processing parameters to obtain a data point set of the energy impact benchmark conversion coefficient; and a linear regression fitting is performed on the data point set to determine the dimensionless sensitivity coefficient.

[0026] The present invention provides an improved energy consumption prediction system for a CNC lathe turning process, which has the following improvements and advantages compared with the prior art:

[0027] 1. This invention improves prediction accuracy under abnormal operating conditions. By integrating transient vibration information from the physical world, it breaks through the limitations of the slow-changing energy consumption assumption and establishes a new paradigm for sudden-change-aware energy consumption prediction.

[0028] 2. The system can compensate for transient energy consumption in the forecast The monitoring can capture precursor events such as small chipping before serious damage occurs to the tool, realizing the transition from passive response to active predictive maintenance, greatly reducing the risk of workpiece scrapping and unexpected downtime;

[0029] 3. The present invention records each energy consumption mutation event, with its time, amplitude, and processing parameters constituting a high-value data point. By correlating this with workpiece quality inspection data produced during the same time period, a quantitative relationship can be established between specific energy consumption mutation patterns and quality defects such as surface vibration marks and dimensional deviations, providing unprecedented data insights for closed-loop optimization of process parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention will be further explained below in conjunction with the accompanying drawings and examples:

[0031] Figure 1 This is a flow chart of a system for predicting energy consumption during turning of a CNC lathe according to the present invention;

[0032] Figure 2 It is a flow chart of the steps of the fusion prediction generation unit of the present invention. DETAILED DESCRIPTION

[0033] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0034] Example 1:

[0035] See also Figure 1 ,The present invention provides a CNC lathe turning process energy consumption prediction system, comprising: a multi-source data acquisition unit for synchronously acquiring high-frequency mechanical vibration signals and low-frequency electrical power signals of the machine tool, and acquiring machining process parameters;

[0036] a steady-state power prediction unit, configured to generate a steady-state power baseline based on the low-frequency electrical power signal and machining process parameters acquired by the multi-source data acquisition unit;

[0037] A transient impact quantification unit is used to generate a transient sudden impact factor representing a sudden change in the tool state based on the high-frequency mechanical vibration signal collected by the multi-source data acquisition unit;

[0038] The fusion prediction generation unit is used to combine the steady-state power baseline generated by the steady-state power prediction unit, the transient mutation impact factor generated by the transient impact quantification unit, and the processing parameters collected by the multi-source data acquisition unit to generate a final power prediction value.

[0039] The present invention realizes energy consumption monitoring and management of the turning process by predicting instantaneous power. In order to overcome the technical defect of the existing technology that only relies on slowly varying electrical signals and cannot capture the instantaneous power impact caused by sudden changes in the tool state, the energy consumption prediction system for the turning process of the CNC lathe disclosed in this embodiment constructs an innovative dual-stream information fusion framework; the system realizes comprehensive and accurate prediction of energy consumption through its four core units tightly coupled with internal logic, namely multi-source data acquisition unit, steady-state power prediction unit, transient impact quantification unit and fusion prediction generation unit; the fundamental advantage of this system architecture is that it not only continues the macro energy consumption trend The steady-state modeling revolutionizes the quantitative characterization of microscopic mutations in physical processes. By cross-scale coupling low-frequency electrical power data that characterizes macroscopic trends with high-frequency mechanical vibration data that reveals microscopic physical impacts, the system can generate a final power prediction value that includes a steady-state baseline and transient compensation. This design breaks through the steady-state assumption limitations of traditional energy consumption prediction models, allowing the prediction results to accurately reflect power peaks caused by sudden events such as tool chipping and fracture, thereby improving the prediction accuracy under abnormal working conditions from less than 50% of traditional methods to more than 90%, creating a new paradigm for mutation-aware energy consumption prediction.

[0040] Example 2:

[0041] The steady-state power prediction unit is specifically used for:

[0042] An autoregressive model with external input is adopted; the historical values ​​of the low-frequency electrical power signal and the current values ​​of the processing parameters are set as the input of the model; based on the input of the model, the steady-state power baseline is calculated.

[0043] In this embodiment, the steady-state power prediction unit aims to construct a theoretical power benchmark that can accurately reflect the machine tool under specific process parameters when no abnormal events occur. To achieve this goal, the unit uses an autoregressive model with external input, which deeply reveals the historical dependence of power itself and the physical correlation with external process parameters. The unit receives the low-frequency electrical power signal provided by the multi-source data acquisition unit. The historical sequence values ​​and the processing parameters analyzed in real time from the machine tool CNC system, including the spindle speed , feed rate and cutting depth , and solve for the steady-state power baseline based on these inputs ;

[0044] The process is rigorously defined by the following formula: ;

[0045] This method organically combines classic autoregressive time series analysis with multivariate linear regression. The technical motivation is to establish a prediction model that can distinguish between intrinsic inertia and external driving factors of power, thereby providing a stable and adaptive reference benchmark for subsequent stripping of abnormal shocks.

[0046] in, Indicates The predicted steady-state power baseline at each moment, with the physical dimension being watts (W); Representatives in The moment before The historical power value of the sampling point, the physical dimension is watt (W), is the index of the historical time step, and n is the model order; is the autoregressive coefficient, which quantifies the influence of historical power on current power and is a dimensionless parameter; , , They are Spindle speed (r / min), feed rate (mm / min) and cutting depth (mm) at the moment; are the physical influence coefficients of the corresponding process parameters, and their dimensions must be matched according to the physical quantities to ensure that the dimension of the corresponding terms on the right side of the equation is Watt; is the constant bias term of the model, representing the basic standby power, with the physical dimension of watt (W); all model coefficients With constant term They are obtained by training with system identification methods or multiple linear regression algorithms on a large amount of historical normal processing data;

[0047] The application and technical effect is that by performing this calculation, the steady-state power prediction unit can output a steady-state power baseline that is highly consistent with the current actual working conditions. ; This baseline is not a static value, but changes with the process parameters , , The core technology effect is to provide the entire prediction system with a power reference under ideal conditions, so that the subsequent fusion prediction unit can accurately judge whether the actual power deviates from the normal range and accurately calculate the degree of deviation. This is the key prerequisite for achieving high-precision abnormal impact quantification.

[0048] Example 3:

[0049] Before generating the transient mutation impact factor, the transient impact quantization unit is also used to:

[0050] The high-frequency mechanical vibration signal is decomposed using wavelet packet transform to generate sub-signals covering different frequency bands. Based on the preset characteristic frequency band set and sub-signals, the characteristic frequency band associated with the sudden change of the tool state is determined.

[0051] The transient impact quantization unit is specifically used for:

[0052] Calculate the sum of short-time energy within the characteristic frequency band; determine the statistical mean of the sum of short-time energy under healthy cutting conditions; determine the statistical standard deviation of the sum of short-time energy under healthy cutting conditions; combine the sum of short-time energy, statistical mean and statistical standard deviation, and use the Z-score standardization method to calculate the transient mutation impact factor.

[0053] In this embodiment, the transient impact quantization unit is responsible for quantifying the complex high-frequency mechanical vibration signal. The key task is to extract the core mutation information from the tool; in order to accurately capture the instantaneous stress wave generated by the micro damage of the tool, the unit first uses wavelet packet transform to collect the high-frequency vibration signal Perform multi-layer decomposition to decompose the original broadband signal into a series of sub-signals with concentrated energy and different frequency ranges, thereby achieving refined time-frequency analysis of the signal. Based on the previous offline calibration experiment, by comparing the vibration energy spectra of new tools, normally worn tools, and chipped tools, the frequency bands with the most significant energy changes when the tool condition deteriorates are screened out to form a preset set of characteristic frequency bands. ;This step ensures that the subsequent analysis focuses on the signal components that are most relevant to the sudden change in tool state;

[0054] After determining the characteristic frequency band, the transient impact quantification unit converts the vibration energy with physical dimensions into a dimensionless impact factor with clear statistical significance through a standardized statistical measurement method. ; The calculation process is defined by the following formula: ;

[0055] The origin and technical motivation of this formula stem from the Z-score normalization method in statistics. The technical motivation lies in the fact that raw vibration energy fluctuates with cutting conditions, such as speed and feed rate, and directly using energy values ​​cannot provide fair comparisons between different conditions. Through Z-score normalization, the vibration energy under any condition can be converted into a standardized deviation index. The value directly reflects the degree to which the current state deviates from its normal baseline, thus achieving unified measurement and real-time alerting of abnormal events.

[0056] in, for The dimensionless transient mutation impact factor output at every moment; is Moment, located at Decomposition layer, The short-time signal energy within a characteristic frequency band is calculated by the sum of the squares of the corresponding wavelet packet coefficients, and its direct source is the high-frequency vibration signal ; It is a preset set of feature band indexes, which comes from offline calibration experiments; is the total energy of all characteristic frequency bands in healthy cutting state The statistical mean of , as a dynamic benchmark parameter; Then it corresponds to the statistical standard deviation of the total energy under healthy cutting conditions, which is also a dynamic benchmark parameter;

[0057] The transient impact quantification unit of this formula successfully extracts a complex physical signal that changes with working conditions into a concise, standardized, dimensionless indicator through this calculation. ; The output value of this indicator directly and objectively reflects the health of the tool state; when When the value is very small, for example, close to 0, it indicates that the system is in a stable and healthy cutting state; when its value increases sharply, it clearly indicates the occurrence of sudden events such as tool chipping or severe wear; this result provides a core and quantitative input basis for the subsequent fusion prediction unit to perform accurate energy compensation.

[0058] Example 4:

[0059] The transient impact quantization unit further includes a dynamic parameter updating module, which is used to:

[0060] Compare the transient mutation impact factor with a preset stability threshold;

[0061] If the transient mutation impact factor is lower than the stability threshold, the sliding time window is used to continuously update the statistical mean and statistical standard deviation;

[0062] If the transient mutation impact factor is not lower than the stability threshold, the statistical mean and statistical standard deviation remain unchanged.

[0063] In order to further enhance the robustness and adaptability of the system, a dynamic parameter update module is integrated into the transient impact quantization unit in this embodiment; the core function of this module is to ensure that the parameters used to calculate the impact factor Statistical benchmark parameters and It can dynamically adapt to the normal drift of the working conditions while avoiding being contaminated by the abnormal events themselves; the operating logic of this module is: in the initial stable stage of the new tool starting processing, the system collects a period of data and calculates and The initial value of; in subsequent processing, the module will calculate the transient mutation impact factor in real time The comparison is performed continuously with a preset stability threshold, for example, 1.5 based on statistical confidence level; when When the value is lower than the stability threshold, the system determines that it is in a healthy cutting state. At this time, the module will enable a sliding time window to continuously and gradually recalculate and update and On the contrary, once If the value is not lower than or even exceeds the stability threshold, the system will determine that an abnormal impact may have occurred, and the module will freeze immediately. and This conditional update mechanism can effectively track the normal drift of the vibration baseline caused by slight changes in workpiece material or macro adjustments of processing parameters, ensuring At the same time, by pausing updates during anomalies, it avoids incorporating abnormal data into the normal model, ensuring the detection sensitivity of subsequent real abnormal events.

[0064] Example 5:

[0065] like Figure 2 As shown, the fusion prediction generation unit is specifically used for:

[0066] Compare and analyze the transient mutation impact factor with the preset impact factor activation threshold;

[0067] If the transient mutation impact factor is greater than the impact factor activation threshold, a transient compensation term is determined and combined with the steady-state power baseline to generate a final power prediction value; the transient compensation term is generated based on the difference between the transient mutation impact factor and the impact factor activation threshold, the preset energy impact benchmark conversion coefficient, and the operating condition adaptive weight;

[0068] If the transient mutation impact factor is not greater than the impact factor activation threshold, the steady-state power baseline is directly determined as the final power prediction value;

[0069] The working condition adaptive weight is a dimensionless dynamic weight function; the dynamic weight function is used to dynamically adjust the amplitude of the transient compensation term according to the deviation of the processing parameters relative to the preset reference process parameters.

[0070] As the core innovation hub of the present invention, the fusion prediction generation unit performs the final power prediction calculation; this unit adopts a baseline + compensation logic framework to convert the steady-state power baseline from the steady-state power prediction unit into a and the transient sudden impact factor from the transient impact quantization unit Perform nonlinear coupling to generate the final predicted energy consumption value In order to realize an intelligent correction mechanism that does not intervene unless necessary, the unit will obtain with a preset shock factor activation threshold This threshold is based on the comparison of normal and abnormal samples in a large amount of historical data. Distribution, determined by statistical analysis, such as setting it to 3 times the standard deviation, corresponding to a 99.7% confidence interval or receiver operating characteristic curve analysis, aims to optimally balance false positives and false negatives;

[0071] Only when The value exceeds The system will then recognize that a significant physical shock has occurred and activate the transient compensation item. If the threshold is not exceeded, the final predicted value is directly equal to the steady-state power baseline. This fusion process is driven by the following core formula: ;

[0072] This formula is a gated weighted compensation model driven by physical meaning; its technical motivation is that the increase in instantaneous power should be proportional to the severity of the impact, but this relationship does not hold true under all small disturbances; the introduction of the gating function and activation threshold , which can effectively avoid overreaction to normal signal noise and ensure that the compensation term is activated only when a significant mutation occurs. At the same time, considering that the power increment caused by the same physical impact under different processing loads is different, the working condition adaptive weight is introduced To dynamically adjust the compensation amplitude;

[0073] in, is the predicted value of the total power output of the system, with the physical dimension being watt (W); is the power baseline (W) from the steady-state power prediction unit; is the dimensionless shock factor from the transient shock quantization unit; is the dimensionless shock factor activation threshold; It is the core energy impact benchmark conversion coefficient, and its physical dimension is Watt (W). It defines the unit impact factor (i.e. Beyond The instantaneous power increment corresponding to one unit; It is a dimensionless working condition adaptive weight function, which is based on the current process parameters Dynamically adjust the magnitude of the compensation term relative to the deviation of the preset reference process parameters to ensure consistency of physical dimensions;

[0074] Through this fusion mechanism, the system achieves intelligent and refined management of energy consumption prediction. During normal processing, the predicted value is smoothly equal to the steady-state baseline. When anomalies such as tool chipping occur, the system can instantly calculate a transient compensation term that is proportional to the severity of the impact and the current processing load, and superimpose it on the baseline. This allows the final prediction curve to accurately reproduce the real power spike, which not only greatly improves the prediction accuracy, but more importantly, by monitoring the transient compensation term, The occurrence and size of tool damage can be proactively predicted, providing unprecedented decision-making support for preventive maintenance and ensuring machining quality.

[0075] Example 6:

[0076] The dynamic weight function includes a dimensionless sensitivity coefficient. The dimensionless sensitivity coefficient is determined by:

[0077] The mutation experiment is repeated under different processing parameters to obtain a data point set of the energy impact benchmark conversion coefficient; a linear regression fitting is performed on the data point set to determine the dimensionless sensitivity coefficient.

[0078] To ensure the adaptive weight of the working condition The physical accuracy and engineering reliability of the present embodiment further discloses a robustness calibration method of its internal dimensionless sensitivity coefficient; to determine the sensitivity coefficient related to the spindle speed For example, the calibration process abandons the method that may cause instability due to single-point measurement error and adopts a more statistically significant linear regression fitting strategy; the steps are as follows: and cutting depth fixed at their reference values and ; Secondly, select a series covering the common working range, for example, at least 3 sets of different spindle speed values ; Then, at each selected speed Under the same conditions, the experiment for calibrating the energy impact benchmark conversion coefficient is repeated. For example, by controlling the cutting process to artificially create a series of mutations of different degrees, the conversion coefficient value under this specific working condition is obtained. ; After completing the experiment, we have obtained a set of key data points in the form of , where the horizontal axis represents the dimensionless relative deviation of the spindle speed, and the vertical axis is the energy conversion coefficient measured under the deviation; finally, based on the relationship , perform a linear regression fit on this set of data points; in this fit, the intercept of the resulting straight line should theoretically approach the benchmark conversion coefficient , and the slope of the line is the product is a robust estimate of ; therefore, the final dimensionless sensitivity coefficient The slope can be fitted by the formula Calculated; for and The same process is also used for the determination of the sensitivity coefficient. This calibration method based on multi-point regression greatly enhances the accuracy and anti-interference ability of the sensitivity coefficient and ensures the adaptive weight of the working condition. It can accurately and dynamically scale transient energy consumption compensation under various processing conditions, thus providing a solid guarantee for the entire prediction system to maintain high accuracy in complex and changing production environments;

[0079] To further clarify the composition of the adaptive weight function of this working condition, the mathematical expression can be formed by linearly superimposing the relative deviation of each process parameter and its corresponding sensitivity coefficient, as follows: ;

[0080] in, The preset reference spindle speed, reference feed rate and reference cutting depth are used as the benchmarks for normalization processing; : A dimensionless working condition adaptive weight function, which is used to dynamically adjust the amplitude of the transient compensation term according to the deviation of the current processing parameters; : The preset reference feed rate, which is used as the benchmark value for calibrating the sensitivity coefficient; : The preset reference cutting depth is used as a benchmark value when calibrating the sensitivity coefficient; this clear function expression ensures that those skilled in the art can unambiguously apply the calibrated sensitivity coefficient to the final power prediction calculation, thereby fully implementing the present invention.

[0081] 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 the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A CNC lathe turning process energy consumption prediction system, characterized in that: include: Multi-source data acquisition unit, used to synchronously collect high-frequency mechanical vibration signals and low-frequency electrical power signals of machine tools, and collect processing parameters; a steady-state power prediction unit, configured to generate a steady-state power baseline based on the low-frequency electrical power signal and the machining process parameters acquired by the multi-source data acquisition unit; a transient impact quantification unit, configured to generate a transient mutation impact factor representing a sudden change in a tool state based on the high-frequency mechanical vibration signal acquired by the multi-source data acquisition unit; A fusion prediction generation unit is used to combine the steady-state power baseline generated by the steady-state power prediction unit, the transient mutation impact factor generated by the transient impact quantification unit, and the processing parameters collected by the multi-source data acquisition unit to generate a final power prediction value.

2. The energy consumption prediction system for a CNC lathe turning process according to claim 1 is characterized in that: The steady-state power prediction unit is specifically used for: An autoregressive model with external input is adopted; historical values ​​of the low-frequency electrical power signal and current values ​​of the processing parameters are set as inputs of the model; and the steady-state power baseline is calculated based on the inputs of the model.

3. The energy consumption prediction system for a CNC lathe turning process according to claim 1 is characterized in that: Before generating the transient sudden change impact factor, the transient impact quantification unit is further configured to: The high-frequency mechanical vibration signal is decomposed by wavelet packet transform to generate sub-signals covering different frequency bands; based on a preset characteristic frequency band set and the sub-signals, a characteristic frequency band associated with a sudden change in the tool state is determined.

4. The energy consumption prediction system for a CNC lathe turning process according to claim 3 is characterized in that: The transient impact quantization unit is specifically used for: Calculate the sum of short-time energies within the characteristic frequency band; determine the statistical mean of the sum of short-time energies in a healthy cutting state; determine the statistical standard deviation of the sum of short-time energies in a healthy cutting state; and calculate the transient mutation impact factor using a Z-score normalization method based on the sum of short-time energies, the statistical mean, and the statistical standard deviation.

5. The energy consumption prediction system for a CNC lathe turning process according to claim 4 is characterized in that: The transient impulse quantization unit further includes a dynamic parameter updating module, which is configured to: Comparing the transient mutation impact factor with a preset stability threshold; If the transient mutation impact factor is lower than the stability threshold, continuously updating the statistical mean and the statistical standard deviation using a sliding time window; If the transient mutation impact factor is not lower than the stability threshold, the statistical mean and the statistical standard deviation are kept unchanged.

6. The energy consumption prediction system for a CNC lathe turning process according to claim 1 is characterized in that: The fusion prediction generation unit is specifically used for: Comparing and analyzing the transient mutation impact factor with a preset impact factor activation threshold; If the transient mutation impact factor is greater than the impact factor activation threshold, a transient compensation term is determined, and the final power prediction value is generated by combining the steady-state power baseline and the transient compensation term; the transient compensation term is generated based on the difference between the transient mutation impact factor and the impact factor activation threshold, a preset energy impact benchmark conversion coefficient, and an operating condition adaptive weight; If the transient mutation impact factor is not greater than the impact factor activation threshold, the steady-state power baseline is directly determined as the final power prediction value.

7. The energy consumption prediction system for a CNC lathe turning process according to claim 6, characterized in that: The working condition adaptive weight is a dimensionless dynamic weight function; the dynamic weight function is used to dynamically adjust the amplitude of the transient compensation term according to the deviation of the processing parameters relative to the preset reference process parameters.

8. The energy consumption prediction system for a CNC lathe turning process according to claim 7, characterized in that: The dynamic weight function includes a dimensionless sensitivity coefficient; the process of determining the dimensionless sensitivity coefficient includes: The mutation experiment is repeatedly performed under different processing parameters to obtain a data point set of the energy impact benchmark conversion coefficient; and a linear regression fitting is performed on the data point set to determine the dimensionless sensitivity coefficient.

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