Automobile fastener production energy consumption optimization control method and system

Abstract

The invention discloses an automobile fastener production energy consumption optimization control method and system, and relates to the technical field of fastener production, and the method comprises the following steps: obtaining a real-time current waveform signal and a power grid fundamental wave voltage signal of an inversion unit in an energy feedback link, building a phase dynamic drift model through a time difference analysis algorithm, and carrying out the calculation of the phase dynamic drift model; continuous mapping of energy flow direction change is formed on a time axis; and based on the established phase dynamic drift model, extracting an offset rate continuous change section between the current waveform and the power grid fundamental wave, constructing a phase trend prediction function by using a sliding window algorithm, and converting the time characteristic of the energy flow direction change into a phase prediction signal trajectory. Through phase dynamic drift modeling and lead compensation control, synchronization of feedback current and power grid voltage is realized, and ripple current and element stress are reduced; and through energy density change rate extraction and dynamic filtering regulation and control, the energy flow is stable and continuous, and efficient and energy-saving control over fastener production is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fastener production, in particular to a method and system for optimizing energy consumption in automobile fastener production. BACKGROUND

[0002] Optimizing energy consumption in automobile fastener production refers to dynamically monitoring, data modeling and intelligently controlling energy consumption links in the production line during the manufacturing process of automobile fasteners (such as bolts, nuts, screws, etc.), so as to minimize the consumption of electricity, heat, compressed air and cooling water, etc. under the premise of ensuring product quality and production capacity. The core idea is to use sensors and industrial Internet of Things technology to collect energy consumption data and process parameters (such as temperature, torque, speed, heating time, etc.) of each device in real time, combine energy consumption distribution model and optimization algorithm, and implement adaptive energy distribution and load balancing control for key links such as heating, forming, heat treatment, electroplating and testing. For example, when the heat treatment furnace is in a low load state, the system can automatically adjust the heating power or delay the start time to avoid energy waste; in the forming process, the system dynamically matches the driving energy according to the material deformation resistance to realize on-demand energy supply. This method can effectively improve the overall energy efficiency by 10%~30%, prolong the service life of the equipment, and realize green and intelligent production.

[0003] The prior art has the following disadvantages: In the prior art, energy feedback mode is generally used in fastener production lines to improve power utilization, but this mode has potential risks of phase detection response lag in the operation environment of multiple processes and high-frequency start-stop. When the phase detection of the inverter unit is delayed beyond the set threshold, the current phase in the energy feedback link will deviate from the power grid fundamental wave, causing the feedback energy flow direction to be reversed, resulting in a large amount of ripple current in a short time. Such abnormal ripples not only make the filter capacitor, power tube and other components of the inverter unit bear transient stress exceeding the design limit, but also may cause overheating of the energy recovery module, out-of-sync of the drive circuit, even equipment burning, which seriously affects the stability and safety of energy consumption optimization control.

[0004] Therefore, how to effectively suppress the energy flow reversal and abnormal ripple current caused by phase detection delay in the energy feedback process has become a key technical problem to be solved in the prior art.

[0005] The above information disclosed in the background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the present application is to provide a method and system for optimizing energy consumption in automobile fastener production to solve the problems in the background.

[0007] In order to achieve the above object, the present application provides the following technical solutions: a car fastener production energy consumption optimization control method, comprising the following steps: Step one, obtain the real-time current waveform signal of the inverter unit in the energy feedback link and the grid fundamental voltage signal, use the time difference analysis algorithm to establish a phase dynamic drift model, form a continuous mapping of the change of the energy flow direction on the time axis, and provide a stable reference baseline for phase prediction; Step two, based on the established phase dynamic drift model, extract the continuous change section of the offset rate between the current waveform and the grid fundamental, use the sliding window algorithm to construct the phase trend prediction function, convert the time characteristics of the change of the energy flow direction into a phase prediction signal trajectory, so as to realize the early identification of the instantaneous segment that may occur phase delay; Step three, according to the phase prediction signal trajectory generated by the phase trend prediction function, use the phase advance compensation algorithm to dynamically adjust the pulse width modulation trigger time of the inverter unit, so that the phase of the feedback current and the phase of the grid voltage keep synchronous flow, thereby weakening the forming conditions of the reverse impact current in the feedback link; Step four, based on the feedback current signal adjusted by the phase advance compensation algorithm, extract the energy density change rate, and use the dynamic filtering gating algorithm to reduce the high-frequency oscillation components of the feedback energy signal, so as to form a smooth and stable energy transmission path, and ensure the continuity of the energy flow under different load fluctuations; Step five, according to the smooth energy signal output by the dynamic filtering gating algorithm, real-time update the parameters of the phase dynamic drift model, realize the adaptive adjustment of the energy feedback link, so that the system continuously maintains the stability and safety of the energy flow direction under complex working conditions, thereby realizing efficient energy feedback control.

[0008] Preferably, the step of obtaining the real-time current waveform signal of the inverter unit in the energy feedback link and the grid fundamental voltage signal comprises: Current detection points and voltage detection points are arranged at the output side and the grid connection side of the inverter unit in the energy feedback link respectively, so as to synchronously obtain the real-time current waveform signal of the inverter unit output end and the fundamental voltage signal of the grid side, and ensure that the sampling period is triggered at the same time under the same time reference; The current waveform signal and the grid fundamental voltage signal are compared point by point on the time axis, the phase difference at each sampling time is determined, and the phase difference time sequence with time as the horizontal axis and phase offset angle as the vertical axis is formed; The phase difference time sequence is mapped corresponding to the time axis, a continuous curve of the change of the energy flow direction is formed, and the instantaneous trend of the change of the energy flow direction is identified according to the phase difference change rate; A phase dynamic drift model is established based on the continuous curve of the energy flow direction change, and a phase dynamic drift model that can be continuously updated according to the running state is formed by comparing the phase change reference point with the local extreme point.

[0009] Preferably, the step of extracting the continuous change section of the offset rate between the current waveform and the power grid fundamental wave based on the phase dynamic drift model comprises: The phase offset amount between the current waveform and the power grid fundamental wave voltage is continuously extracted in the established phase dynamic drift model, and the section with a higher phase change rate is identified on the time axis to form a phase offset rate change identification sequence; The phase change curve in the offset rate change section is continuously segmented and analyzed to extract the phase change directionality and stability characteristics, and unstable factors caused by power grid fluctuations or current jitter are excluded; The phase change directionality curve is correspondingly mapped with the time axis, the time law of the energy flow direction change is converted into a continuous trend signal trajectory, and the acceleration interval and the stable interval of the phase change are identified; The trend signal trajectory is analyzed and dynamically monitored in real time, when the trajectory slope sharply rises in a short time, the time period is marked as a potential phase delay interval, so as to realize the early identification of the possible phase delay instantaneous segment.

[0010] Preferably, the step of dynamically adjusting the pulse width modulation trigger time of the inverter unit according to the phase prediction signal trajectory generated by the phase trend prediction function comprises: The phase synchronization state of the feedback current and the power grid voltage is analyzed in real time according to the phase prediction signal trajectory, so as to determine the deviation direction and degree of the current phase relative to the power grid fundamental wave voltage phase; After the analysis of the phase deviation direction and degree is completed, the pulse width modulation trigger time of the inverter unit is preliminarily adjusted and prepared, and the best advance amount of the conduction trigger time is determined according to the position of the phase change inflection point in the phase prediction signal trajectory; After the adjustment range of the trigger time is determined, the pulse width modulation signal of the inverter unit is continuously and dynamically adjusted, so that the phase of the feedback current waveform is time-synchronized with the power grid voltage waveform and real-time calibration is realized; After the dynamic adjustment of the pulse width modulation trigger time is completed, the phase difference, peak value corresponding relationship and waveform symmetry of the adjusted feedback current waveform and power grid voltage waveform are verified in real time, so as to ensure that the phase synchronization effect of the energy feedback process is continuously stable.

[0011] Preferably, during the continuous dynamic adjustment of the pulse width modulation triggering time of the inverter unit, the triggering time changes of multiple consecutive drive cycles are smoothed to make the triggering adjustment have a gradual change characteristic, so as to avoid waveform oscillation. When the phase difference fluctuation is detected to exceed the preset range, the phase prediction signal trajectory is updated again to calculate a new trigger advance, thereby ensuring that the feedback current and the grid voltage are in phase synchronization and stable.

[0012] Preferably, the steps of extracting the energy density change rate based on the feedback current signal adjusted by the phase lead compensation algorithm and performing high-frequency oscillation component reduction processing include: Instantaneous energy density characteristics are extracted from the feedback current signal after phase lead compensation adjustment, and a distribution curve of energy density change is established on the time axis to reflect the transmission state of energy flow in the feedback link. Continuous difference analysis was performed on the energy density change distribution curve to extract the time series of energy density change rate and identify the dynamic trend of energy flow intensity changing with time. Dynamic smoothing is applied to the time series of energy density change rate to reduce high-frequency oscillation components and form a gentle trend curve of energy change, so that the energy flow curve maintains a continuous transition state. The smoothed energy signal is remapped on the time axis to construct a stable energy transmission path, and the continuity of the path is verified in real time to ensure the smoothness and continuity of energy flow under different load fluctuations.

[0013] Preferably, in the step of dynamically smoothing the time series of energy density change rate, the smoothing process is gradual. By reducing high-frequency oscillation components while retaining low-frequency energy change trends, the energy transmission path maintains a continuous transition on the time axis. The time scale of the smoothing interval is automatically adjusted when the energy flow direction changes, so as to ensure that the energy feedback link maintains a stable flow state under load change conditions.

[0014] Preferably, the step of updating the phase dynamic drift model parameters in real time based on the smoothed energy signal output by the dynamic filtering gating algorithm includes: Based on the smooth energy signal output after dynamic filtering and gating, the current operating status of the energy feedback link is identified and features are extracted to obtain the energy flow amplitude, energy flow direction change rate, energy flow stable range, and the location of energy flow abrupt change points. The energy flow characteristic parameters extracted from the smoothed energy signal are compared with the historical parameters in the phase dynamic drift model to identify the deviation between the energy flow state and the phase change model, and to calculate the time difference between the energy flow characteristic change and the phase shift change. After identifying the deviation, the parameters of the phase dynamic drift model are updated step by step, and the energy flow intensity, direction change rate and stable range are continuously fed back into the model to achieve adaptive adjustment of the model parameters and maintain a smooth transition. After completing the real-time parameter update, the synchronization relationship between the phase dynamic drift model output and the smoothed energy signal before and after the update is verified to ensure the stability of the energy flow direction and the safety of electrical operation.

[0015] Preferably, during the gradual update of the phase dynamic drift model parameters, when the rate of change of energy flow direction is detected to exceed a preset threshold, the phase drift rate and direction correction coefficient in the model are adjusted first, and the parameter smooth transition time is automatically extended at the energy flow abrupt change point to prevent phase fluctuations caused by excessive model response, thereby ensuring the stable operation of the energy feedback link under high-frequency start-stop conditions.

[0016] The energy consumption optimization and control system for automotive fastener production includes a phase dynamic modeling module, a phase trend prediction module, a phase synchronization compensation module, an energy signal smoothing module, and an adaptive adjustment module. The phase dynamic modeling module acquires the real-time current waveform signal of the inverter unit in the energy feedback link and the fundamental voltage signal of the power grid. It uses the time difference analysis algorithm to establish a phase dynamic drift model, forming a continuous mapping of the change in energy flow direction on the time axis. The phase trend prediction module extracts the continuously changing segment of the offset rate between the current waveform and the fundamental wave of the power grid based on the established phase dynamic drift model, and uses the sliding window algorithm to construct the phase trend prediction function, which transforms the time characteristics of the energy flow direction change into the phase prediction signal trajectory. The phase synchronization compensation module dynamically adjusts the pulse width modulation triggering time of the inverter unit based on the phase prediction signal trajectory generated by the phase trend prediction function and the phase lead compensation algorithm, so that the phase of the feedback current and the phase of the grid voltage are kept synchronized. The energy signal smoothing module extracts the energy density change rate based on the feedback current signal adjusted by the phase lead compensation algorithm, and uses a dynamic filtering gating algorithm to reduce the high-frequency oscillation component of the feedback energy signal to form an energy transmission path. The adaptive adjustment module updates the parameters of the phase dynamic drift model in real time based on the smoothed energy signal output by the dynamic filtering gating algorithm, and adaptively adjusts the energy feedback link.

[0017] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention establishes a dynamic phase drift model and combines it with phase trend prediction and advance compensation control to ensure that the current phase in the energy feedback link remains continuously synchronized with the grid voltage, effectively avoiding the reverse energy flow impact caused by phase response lag. This control method can correct phase offset in real time under complex production environments with multiple processes and high-frequency start-stop cycles, ensuring that the feedback current waveform remains in a stable conducting state. This significantly reduces ripple current amplitude, decreases transient stress on the inverter unit's filter capacitors and power transistors, and improves the stability of energy feedback operation and the lifespan of the equipment.

[0018] This invention introduces an energy density change rate extraction and dynamic filtering gating mechanism into the feedback current signal processing, ensuring the energy transmission path remains smooth and continuous under different load fluctuations, thus achieving real-time adaptive adjustment of energy flow. This method dynamically balances the energy flow between energy feedback and equipment load, significantly improving energy utilization and feedback efficiency, while avoiding heat loss and oscillation risks caused by energy accumulation or sudden changes. This allows the entire fastener production line to achieve a dynamically optimal state in terms of energy-saving control and operational safety. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a flowchart of the energy consumption optimization and control method for automotive fastener production according to the present invention.

[0021] Figure 2 This is a schematic diagram of the module of the energy consumption optimization control system for automotive fastener production of the present invention. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0023] This invention provides, for example Figure 1 The energy consumption optimization and control method for automotive fastener production shown includes the following steps: Step 1: Obtain the real-time current waveform signal of the inverter unit in the energy feedback link and the fundamental voltage signal of the power grid. Use the time difference analysis algorithm to establish a phase dynamic drift model and form a continuous mapping of the energy flow direction change on the time axis to provide a stable reference baseline for phase prediction. The specific implementation method for this step is as follows: Current and voltage detection points are set at the inverter unit output side and grid connection side of the energy feedback link, respectively, to synchronously acquire the real-time current waveform signal at the inverter unit output and the fundamental voltage signal at the grid side. To ensure the authenticity and time consistency of the signal data, the current detection point should be located close to the inverter unit output bus, and the voltage detection point should be directly connected to the grid input node, so that the acquired signal covers the complete energy feedback path from the inverter unit output to the grid input. During data acquisition, the sampling period of the current and voltage signals is strictly set to trigger simultaneously under the same time reference, thereby avoiding phase errors caused by asynchronous sampling times. To ensure that subtle changes in the current waveform can be accurately recorded, the sampling resolution should be sufficient to reflect the small amplitude changes and transition edge characteristics of the waveform. For example, when the inverter unit rapidly switches between light load and full load, the current waveform will show a sudden change in slope and waveform distortion in a short time. High-resolution sampling can capture these transition details completely. At the same time, the acquisition of the voltage signal should ensure waveform integrity and avoid distortion of the fundamental waveform due to electromagnetic interference or sampling noise. By synchronously acquiring current and voltage signals across the entire time domain, two sets of data sequences with a unified timestamp can be obtained, providing a stable data foundation for establishing a time mapping of energy flow direction.

[0024] After synchronously acquiring the current and voltage signals, their correspondence on the time axis is compared point by point to determine the phase difference at each sampling moment. During this process, the zero-crossing points of the voltage waveform and the grid voltage waveform are time-aligned, and the degree of lead or lag of the current waveform relative to the voltage waveform is calculated within each sampling interval. This time-series comparison yields an initial phase difference sequence with time as the horizontal axis and phase offset angle as the vertical axis. In fastener production lines, the phase difference between the current and voltage waveforms changes significantly within a very short time when the energy feedback device frequently starts and stops. This change is related to multiple factors such as load switching, heat treatment furnace heating, and molding machine startup. To ensure the continuity of this sequence, the phase difference within each sampling period needs to be smoothly transitioned to the result of the previous period to eliminate phase jump errors caused by signal abrupt changes or noise. The resulting phase difference time series not only reflects the current response lag characteristics during energy feedback but also provides an accurate time reference for subsequent analysis of energy flow direction trends.

[0025] After obtaining the phase difference time series of the current and voltage waveforms, this series is mapped one-to-one with the time axis to form a continuous curve showing the change in energy flow direction. This mapping process requires matching each time sampling point of the current waveform with the corresponding voltage waveform sampling point, and determining the energy flow direction based on the positive or negative direction of the phase difference. When the phase of the current waveform leads the voltage waveform, it indicates that energy flows from the inverter unit to the grid, which is positive energy feedback; when the phase of the current waveform lags the voltage waveform, it indicates that energy flows from the grid into the inverter unit, which is reverse energy absorption. On fastener production lines with multiple processes operating in parallel, these two energy flow states alternate frequently, especially during the rapid start-up and shutdown of the molding machine or the heating stage of the heat treatment equipment. The switching of energy flow direction often occurs within a millisecond timescale. To accurately characterize this rapid switching process, it is necessary to mark the boundary points of energy flow direction change on the mapping curve and calculate the instantaneous trend of energy flow direction switching based on the rate of change of phase difference at these boundary points. When the slope of the mapping curve changes from positive to negative, it indicates that the energy flow direction has changed from output to absorption; when the slope changes from negative to positive, it indicates that energy feedback has restarted. In this way, a continuous mapping trajectory of the energy flow direction change is formed on the time axis, which can intuitively reflect the energy flow pattern throughout the entire feedback process. This mapping curve can not only reflect the stability of the energy flow, but also reveal the dynamic response characteristics of the energy flow under different load changes.

[0026] After obtaining a continuous mapping of energy flow direction changes, the phase difference change characteristics across the entire time axis are modeled to form a dynamic phase drift model. This model, based on the continuous mapping of energy flow direction, multi-dimensionally correlates the phase difference value, phase change rate, and energy flow direction identifier at each time point, thereby establishing a time-domain model capable of dynamically describing the trend of energy flow direction changes. During model building, reference points for phase changes on the time axis are first determined; these reference points typically correspond to critical moments in energy flow direction changes. By comparing these reference points with local extreme points in the continuous mapping curve, the key time windows for the energy flow direction to transition from positive feedback to reverse absorption can be identified. Next, the phase change rate within these time windows is continuously tracked, enabling the model to reflect the inertia and delay characteristics of phase changes in the energy feedback link. When the energy feedback link is affected by load disturbances, grid fluctuations, or the inverter unit's own temperature rise, the dynamic phase drift model automatically adjusts its drift trend on the time axis to maintain the real-time correspondence between phase difference changes and energy flow direction. Finally, by time-calibrating the phase drift characteristics updated each time with the mapping results of the previous cycle, a phase dynamic drift model that is continuously updated with the operating state can be formed, so that the mapping curve of the energy flow direction maintains continuous, smooth and high-precision characteristics during long-term operation.

[0027] It should be noted that: The time difference analysis algorithm refers to a calculation method that determines the phase shift between the inverter unit's output current waveform and the grid's fundamental voltage waveform at any sampling time by accurately aligning and calculating their relative timing in the time domain, and thus establishing a dynamic phase change relationship. Its core idea is to convert the time shift into a phase difference value by comparing the time difference between the zero-crossing points, peak points, and transition edges of two sets of synchronously sampled signals, thereby forming a continuously traceable phase difference curve on the time axis.

[0028] For example, during the operation of an energy feedback link, when the inverter unit outputs energy to the grid, theoretically the current waveform should be in phase with the grid voltage waveform (i.e., the peaks and zero-crossings of both should occur simultaneously). If the peak of the current signal appears later than the peak of the voltage signal, for example, in a 50Hz grid cycle, the voltage peak appears at 10ms while the current peak appears at 10.5ms, then there is a 0.5ms time difference. Using a time difference analysis algorithm, this 0.5ms time difference is converted into a phase angle difference, i.e., a phase lag angle of approximately 9° (calculated as: phase angle = time difference / cycle × 360°). This phase lag angle reflects the dynamic delay of the current response relative to voltage changes. If this time difference further increases to 0.8ms in subsequent sampling cycles, it indicates that the phase lag trend of the energy feedback link is intensifying, and the system may face the risk of reverse energy flow or increased current ripple.

[0029] In practical applications, the time difference analysis algorithm not only calculates the phase difference at a single moment but also tracks the rate of change of the time difference over continuous sampling periods, forming a dynamic trajectory of phase change over time to identify minute drift trends in the energy flow direction. For example, in the heat treatment process of fasteners, when the heating load suddenly switches, the waveform of the energy feedback current will briefly mismatch. The time difference analysis algorithm can instantly capture the transient change from 0.3ms to 0.7ms and record this phase drift in the phase dynamic drift model. In this way, the system can accurately characterize the dynamic change law of the energy flow direction, achieving high-precision monitoring and modeling of the energy feedback phase state.

[0030] Through the above sequential implementation steps, a comprehensive perception of the phase relationship between the inverter unit output current and the grid voltage in the energy feedback link can be achieved, forming a precise mapping of the energy flow direction on the time axis. This process not only reveals the actual energy flow state in the feedback link but also stably tracks the phase change trend under the complex operating conditions of high-frequency start-stop of the production line, thus providing a solid foundation for subsequent phase synchronization and energy consumption optimization. Through the established phase dynamic drift model, the energy feedback link can maintain a stable correspondence between current and voltage phases during operation, avoiding abrupt changes in energy flow direction and current reverse impact caused by phase detection delays. This makes energy feedback control in the automotive fastener production process safer, smoother, and more efficient.

[0031] Step 2: Based on the established phase dynamic drift model, extract the continuously changing segment of the offset rate between the current waveform and the grid fundamental wave, and use the sliding window algorithm to construct a phase trend prediction function to transform the time characteristics of the energy flow direction change into the phase prediction signal trajectory, so as to realize the early identification of instantaneous segments that may have phase delay. The specific implementation method for this step is as follows: In the established phase dynamic drift model, the phase offset between the current waveform and the grid fundamental voltage is continuously extracted, and segments with high phase change rates are marked on the time axis. By analyzing the continuous mapping results of energy flow direction changes in the phase dynamic drift model, it can be clearly observed that the phase offset exhibits periodic or non-periodic trends within a certain time period. When the energy feedback link is in a stable energy flow state, the phase difference between the current waveform and the grid voltage waveform remains within a relatively constant range, and the slope of the offset rate change curve is small. However, when the load state in the production line changes abruptly, such as when the molding machine changes from standby to stamping or the heat treatment furnace changes from constant temperature to heating stage, the rate of change of the phase difference suddenly increases, and the offset rate curve shows a significant steep rise or fall. At this time, it is necessary to mark these drastically changing time segments on the time axis of the phase dynamic drift model to form a preliminary phase offset rate change identification sequence, providing a data foundation for subsequent trend identification. To ensure the continuity of identification, the sampling time interval needs to be precisely controlled so that the time interval between each sampling point is less than the phase change response time, thereby ensuring that the complete phase change trajectory can still be captured when the energy flow direction changes rapidly.

[0032] After initially identifying the offset rate variation segments, the phase change curves within these segments are continuously segmented for analysis to extract the directional and stability characteristics of the phase change. Specifically, the phase change curves are divided into multiple isochronous segments according to time sequence, and the direction of phase offset change is calculated within each segment. When the phase offset continuously increases over time, it indicates that the current waveform is gradually lagging behind the grid voltage, and the energy flow direction may be changing from positive feedback to reverse absorption; when the phase offset decreases over time, it indicates that the current waveform is gradually leading, and the energy flow direction is in the transition process from absorption to feedback. Through continuous analysis of the phase change direction within each segment, a time curve describing the phase change trend can be formed. This curve can reflect the directional characteristics of the phase difference changing over time in the phase dynamic drift model. In addition, the smoothness of the phase change rate should also be analyzed within each time segment. When irregular fluctuations appear in the rate of change curve, it indicates the presence of instantaneous disturbances within that time period, such as short-term fluctuations in grid voltage or current jitter in the inverter unit output. Such unstable factors need to be identified and eliminated to ensure that subsequent trend prediction is based on stable phase change data.

[0033] After obtaining a stable phase change directional curve, time feature mapping is performed on these continuous change segments to transform the temporal regularity of energy flow direction changes into a predictable signal trajectory. In this process, the phase offset rate at each time point in the phase change curve needs to be compared with the trend of change in the time intervals before and after it to determine the phase change trend stage at that time point. For example, when the phase change rate continues to rise and the phase offset approaches the stability threshold set in the model, it indicates that the system is about to enter the phase delay stage; when the phase change rate begins to decrease and the offset falls back, it indicates that the system is recovering phase synchronization. In this way, the temporal characteristics of phase change can be transformed into a continuous trend signal trajectory, which reflects the acceleration, deceleration, and stabilization intervals of phase change on the time axis. Especially during the rapid switching phase of the energy feedback process, this trend signal trajectory can clearly identify the moment when the phase change is about to reach the hysteresis critical point, providing a direct basis for subsequent early identification. To ensure the continuity of the trajectory, the time series needs to be dynamically smoothed to ensure that the phase trend change forms a natural transition on the time axis without abrupt breaks.

[0034] After obtaining the phase trend signal trajectory, it is analyzed and dynamically monitored in real time to identify instantaneous segments where phase delay may occur. By continuously observing the slope changes of the trend trajectory, when the slope changes from a slow increase to a sharp increase in a short period of time, it indicates that the rate of change in the energy flow direction has accelerated significantly, and the system has entered the risk region of phase response lag. At this time, this time period can be marked as a potential phase delay interval and recorded in the model. When the energy feedback link is running continuously, these marked time intervals will form a set of dynamically updated time segments, each segment corresponding to a potential risk of phase change anomaly. By continuously tracking these time segments, moments when phase asynchrony may occur can be identified in advance, thus providing a time-based early warning signal for the regulation of the energy feedback link. To make this identification process continuous and adaptive, the trend trajectory needs to be continuously updated with the latest current and voltage waveform data during the operation of the energy feedback link to maintain the timeliness of phase trend prediction. When the load condition, energy flow intensity, or grid fluctuation state changes, the phase trend signal trajectory can automatically extend or contract, thereby reflecting the latest trend of change in the energy flow direction.

[0035] It should be noted that: Constructing a phase trend prediction function using the sliding window algorithm involves dividing continuously acquired current waveforms and the phase offset data of the grid fundamental voltage signal into multiple fixed-length, partially overlapping time windows based on a phase dynamic drift model. The phase change rate and directionality within each window are dynamically calculated and fitted to fit the trend, thus forming a continuously predictable phase change trend function in the time dimension. The core of this method lies in transforming phase change trend detection from "discrete detection" to "continuous prediction" through segmented analysis and continuous updating of the sliding window, enabling the system to identify the formation process of phase delay or phase drift in advance.

[0036] For example, in the operation of an automotive fastener production line, when the heat treatment furnace switches from a constant temperature state to a heating stage, the phase of the feedback energy current will show a slow lag trend. Direct analysis of the full-time data is easily affected by local abrupt changes, leading to distorted trend judgments. The sliding window algorithm, however, divides the time axis into multiple overlapping windows, such as each window being 10ms long with a 5ms overlap. Within each window, the average value, rate of change, and directional characteristics of the phase shift are calculated, and a local trend curve of phase change is fitted linearly or nonlinearly. When the next window slides in, the system automatically discards the old data from the previous window and adds new sampled values, ensuring the phase trend prediction function remains continuous and real-time. If the phase change rate continues to increase in adjacent windows, the prediction function will show an intensified phase delay trend, allowing the system to predict in advance that the feedback current will lag behind the grid voltage.

[0037] This sliding window-based trend prediction mechanism can smooth out short-term noise and preserve long-term trends, enabling energy feedback control to dynamically identify phase anomaly intervals within millisecond response times. For example, when the phase offset rate exceeds a set threshold for three consecutive windows, the system can trigger compensation control in advance to prevent energy flow reversal or sudden increases in ripple current, thereby achieving real-time prediction and proactive defense against phase response lag.

[0038] Through the above specific implementation steps, based on the established phase dynamic drift model, the deviation rate change between the current waveform and the fundamental voltage of the power grid can be continuously identified and its trend extracted, and the temporal law of energy flow direction change can be transformed into an intuitive and predictable signal trajectory. This process not only enables early identification of phase change trends in the energy feedback link, but also effectively predicts the possible time period of phase response lag, giving energy feedback control predictive adjustment capability under dynamic operating conditions. This avoids the energy back shock phenomenon caused by phase asynchrony, ensuring that the automotive fastener production line can maintain stable and safe energy flow even in the complex operating environment of high-frequency start-stop of multiple processes.

[0039] Step 3: Based on the phase prediction signal trajectory generated by the phase trend prediction function, the phase lead compensation algorithm is used to dynamically adjust the pulse width modulation triggering time of the inverter unit so that the phase of the feedback current flows synchronously with the phase of the grid voltage, thereby weakening the formation conditions of the reverse impact current in the feedback link. The specific implementation method for this step is as follows: Based on the aforementioned phase prediction signal trajectory, a real-time comparative analysis of the phase synchronization status between the feedback current and the grid voltage is performed. By reading the temporal characteristics of phase changes in the signal trajectory, the direction and degree of deviation of the current feedback current phase relative to the grid fundamental voltage phase can be determined. When the phase prediction signal trajectory shows that the phase offset rate of the current waveform is in the rising stage, it indicates that the phase of the feedback current is gradually lagging behind the grid voltage. If no adjustment is made, there may be a trend of energy flow reversal. When the trajectory shows that the phase offset rate is in the falling stage, it indicates that the phase of the feedback current is gradually leading, and there is a risk of excessive energy feedback in the system. By continuously monitoring the directional change of this phase deviation, the moment when the feedback current and the grid voltage are about to become out of sync can be determined on the time axis. To ensure the accuracy of the analysis results, it is necessary to ensure that the data of the phase prediction signal trajectory is strictly synchronized with the real-time waveform of the energy feedback link. Each sampling point must correspond to the current actual voltage and current state to avoid misjudgment due to time drift.

[0040] After analyzing the direction and degree of phase deviation, preliminary adjustments are made to the pulse width modulation (PWM) triggering time of the inverter unit. The core of this adjustment lies in determining the optimal trigger point for each inverter drive cycle, ensuring a time correspondence between the inverter unit's turn-on time and the grid voltage peak position. Specifically, the time series of the phase prediction signal trajectory is analyzed to extract the inflection points of phase change within each cycle. These inflection points reflect the critical boundary of the synchronization state between the current and voltage phases. By matching these inflection point times with the inverter unit's drive triggering cycle, the optimal trigger advance can be determined for the current energy flow direction. When the energy flow direction changes rapidly and the phase lag trend is significant, the inverter unit's turn-on triggering time should be appropriately advanced to ensure that the rising edge of the output current coincides with the peak value of the positive half-cycle waveform of the grid voltage, thus achieving synchronous current phase following. Conversely, when the energy flow direction shows a slight leading trend, the triggering time should be appropriately delayed to avoid overcompensation caused by the current waveform exceeding the voltage waveform prematurely. Throughout this process, each triggering time adjustment relies on the time information of the phase prediction signal trajectory, making the adjustment process forward-looking and dynamically responsive.

[0041] After determining the adjustment range for the trigger timing, the pulse width modulation (PWM) signal of the inverter unit is continuously and dynamically adjusted to gradually align the phase of the feedback current waveform with the grid voltage waveform. During this process, each conduction cycle of the inverter unit requires synchronous correction based on the real-time changes in the phase prediction signal trajectory, ensuring a constant time difference between the rising edge of the PWM signal and the zero-crossing point of the grid voltage waveform. When an increasing trend of current phase lag is detected, the adjustment logic will trigger the inverter unit's conduction signal earlier, shifting the overall feedback current waveform forward and reducing the current lag angle. When a trend of current phase lead is detected, the trigger signal is appropriately delayed, allowing the peak value of the feedback current to realign with the grid voltage waveform. Through this continuous dynamic trigger correction method, the phase of the feedback current and the grid voltage can be calibrated in real time during operation. To avoid waveform oscillations during trigger adjustment, the trigger timing changes across multiple consecutive drive cycles are smoothed, resulting in a gradual change in trigger adjustment characteristics and ensuring the stability of the energy feedback link. Through this process, the feedback current waveform can accurately follow the fluctuations of the grid voltage on the time axis, achieving true phase synchronization.

[0042] After dynamically adjusting the pulse width modulation trigger timing, the adjusted feedback current waveform is verified in real time to ensure that the phase synchronization effect during energy feedback is maintained. The verification process includes comparing the phase difference, peak value correspondence, and waveform symmetry between the current waveform and the grid voltage waveform before and after adjustment. When the verification results show that the phase difference between the current waveform and the voltage waveform remains constant and in the same direction throughout the entire cycle, it indicates that the feedback link is in a stable synchronized state. When a phase difference fluctuation exceeds the preset range within a short period, it indicates that the trigger adjustment has not completely offset the impact of load disturbances or grid fluctuations, and the phase prediction signal trajectory needs to be updated to recalculate the trigger advance for the next cycle. Through this continuous verification and feedback method, the synchronization relationship between the feedback current and the grid voltage can be continuously corrected during operation, thus maintaining long-term stability. After the energy feedback link has been running for a long time, this dynamic adjustment mechanism can automatically adapt to changes in different operating conditions in the production line, such as frequent start-stop of the molding machine, load fluctuations in heat treatment equipment, and slight grid voltage imbalances, ensuring that the feedback current and grid voltage always maintain phase consistency and avoiding energy backlash caused by phase delay.

[0043] It should be noted that: Phase lead compensation algorithm refers to predicting and correcting the real-time offset trend of the current phase relative to the grid voltage phase, adjusting the pulse width modulation (PWM) triggering time of the inverter unit in advance, so that the phase of the feedback current slightly leads the phase of the grid voltage. This compensates in advance when energy flow is about to lag, ensuring that the current and voltage flow synchronously. Essentially, it is a dynamic advance conduction control mechanism based on time prediction and phase correction, aiming to reduce phase delay and reverse impact during energy feedback.

[0044] For example, during the operation of an automotive fastener production line, when the heat treatment equipment suddenly starts up, causing a sharp increase in load power, the output current of the inverter unit often lags behind the grid voltage by about 5° to 10°. At this time, the system detects a rapid increase in the offset rate in the phase prediction signal trajectory, indicating that the current phase is about to lag further. The phase lead compensation algorithm then fine-tunes the PWM trigger time in advance, so that the inverter unit starts conducting about 0.5ms before the grid voltage peak arrives. In this way, the rising edge of the feedback current can be aligned with the peak position of the grid voltage in advance, thereby reducing the phase lag angle from 10° to about 2°. If the grid voltage subsequently shows a downward trend, the algorithm automatically delays the trigger time based on the trend prediction result, avoiding overcompensation caused by the current waveform exceeding the voltage waveform prematurely.

[0045] Through this dynamic "pre-correction - real-time verification - continuous smoothing" approach, the system can automatically maintain phase synchronization within millisecond cycles, enabling the current waveform to accurately follow changes in grid voltage. This not only effectively avoids reverse energy impact and current ripple amplification caused by phase delay, but also maintains the stable operation of the energy feedback link in complex scenarios such as multi-process start-up and shutdown and rapid load fluctuations.

[0046] Through the above sequential implementation steps, the phase prediction signal trajectory and the pulse width modulation triggering logic of the inverter unit can form a real-time closed loop, thereby maintaining a high degree of phase synchronization between the feedback current and the grid voltage during energy feedback. This process not only effectively weakens the formation conditions of reverse surge current in the energy feedback link, but also significantly reduces the transient stress borne by the power devices of the inverter unit, improving the stability of the energy feedback process and the service life of the equipment. Through this implementation method, the automotive fastener production line can achieve continuous and stable energy feedback control in a multi-process, high-frequency start-stop operation environment, achieving a dynamic balance between energy saving and safety in the production process.

[0047] Step 4: Based on the feedback current signal adjusted by the phase lead compensation algorithm, the energy density change rate is extracted, and the high-frequency oscillation component of the feedback energy signal is reduced by the dynamic filtering gating algorithm to form a smooth and stable energy transmission path and ensure the continuity of energy flow under different load fluctuations. The specific implementation method for this step is as follows: Instantaneous energy density characteristics are extracted from the feedback current signal after phase lead compensation adjustment, and a preliminary distribution curve of energy density change is established on the time axis. Since phase lead compensation synchronizes the feedback current waveform with the grid voltage waveform, the feedback current signal acquired at this stage has high time accuracy and phase consistency, accurately reflecting the energy transfer state in the feedback link. In practice, the feedback current signal needs to be mapped point-by-point to the corresponding grid voltage signal to calculate the transient energy change trend at each moment, thus obtaining an energy density sequence reflecting the energy flow intensity changing over time. In the multi-process operation environment of a production line, such as during the pressurization stage of molding equipment, the heating stage of heat treatment equipment, or the stabilization stage of electroplating, energy density fluctuates with load changes. Therefore, when establishing the energy density curve, the sampling frequency should be high enough to capture these short-term changes. By continuously arranging the energy density data at each moment on the time axis, a complete energy density change distribution curve can be formed. The fluctuation amplitude and slope of this curve reflect the stability of the energy flow at different time periods, providing a basis for subsequent rate of change extraction.

[0048] Continuous difference analysis is performed on the established energy density variation distribution curve to extract the time series of the energy density change rate. The key to this step is capturing the dynamic trend of energy density change, not just the static value. By analyzing the differences between adjacent time points in the energy density curve, the rate of increase or decrease of energy density over time can be identified. When the energy density change rate is positive and large, it indicates a rapid increase in energy within a short period, potentially leading to energy accumulation in the feedback loop. When the energy density change rate is negative and fast, it indicates rapid energy release, potentially causing energy flow attenuation in the loop. When the change rate is close to zero, it indicates that the energy flow is in equilibrium, representing an ideal steady-state operation. To ensure the accuracy of the change rate, sudden noise or short-term disturbances need to be eliminated during the calculation process to prevent them from affecting the overall trend judgment. In multi-process alternating operation, fastener production lines frequently experience alternating processes of energy release and absorption; therefore, the energy density change rate curve usually exhibits periodic fluctuations. By continuously tracking this curve, the unstable range of energy flow can be accurately identified.

[0049] After obtaining the time series of energy density change rate, the series is dynamically smoothed to reduce high-frequency oscillation components and form a gentle trend curve of energy change. This step is the core of energy signal optimization, aiming to eliminate energy fluctuations caused by equipment start-up and shutdown, grid disturbances, or load abrupt changes. In practice, the energy density change rate curve is divided into time periods to identify time intervals with abnormally high rates of change. These intervals often correspond to stages where oscillations occur in the energy flow. For example, when fastener forming equipment frequently starts up under high-speed stamping conditions, the rising and falling edges of the current waveform trigger rapid changes in energy density, causing high-frequency energy oscillations. By detecting and smoothing these abrupt changes on the time axis, the sharp peaks in energy density change can be weakened, allowing the energy flow curve to exhibit a continuous transition. Furthermore, to maintain the timeliness and balance of the energy signal, the smoothing process should be gradual, that is, eliminating high-frequency oscillations while preserving low-frequency energy trends, thus retaining the macroscopic characteristics of energy flow changes. The smoothed energy change curve clearly shows the overall variation law of energy flow under different operating conditions, providing a stable signal input for the subsequent construction of energy transmission paths.

[0050] Finally, after smoothing the energy density change rate, the processed energy signal is used to construct a stable energy transmission path, and its continuity is verified. By remapping the smoothed energy signal on the time axis, a stable trajectory of energy flow can be formed, reflecting the energy transfer pattern between the inverter unit output and the grid input. When the slope of the energy flow trajectory is stable and the direction is consistent, it indicates that the energy is transmitted smoothly in the link without reverse impact; when the slope of the trajectory fluctuates briefly or reverses, it indicates that the energy flow is disturbed and the compensation process needs to be readjusted. To ensure the continuity of the energy transmission path, the smoothed energy signal needs to be continuously tracked over time to ensure that it can maintain continuous flow under different load fluctuations. Especially in the case of multiple processes operating in parallel on a fastener production line, load changes in heat treatment, electroplating, and forming equipment can cause energy flow to fluctuate in multiple directions. By maintaining the stability of the energy transmission path, energy interruption or local energy accumulation in the energy feedback link can be avoided. Ultimately, through continuous smoothing signal correction and path tracing, the energy flow in the energy feedback link can be kept in a stable and controllable state, so that the entire energy feedback process has high stability and high continuity under different operating conditions.

[0051] It should be noted that: Dynamic filtering and gating algorithms are signal smoothing methods used in energy feedback signal processing that adaptively adjust the filtering range and threshold control conditions based on the real-time fluctuation characteristics of the energy density change rate. The core principle is to maintain a continuous and stable energy flow over time by identifying and dynamically suppressing high-frequency oscillation components in the feedback current signal, while avoiding energy delays or signal distortion caused by traditional fixed filtering. This algorithm is characterized by "dynamic response, threshold control, and gradual smoothing," and can flexibly switch the filtering intensity between energy abrupt changes and steady-state conditions.

[0052] For example, in an automotive fastener production line, when the forming machine enters the high-speed stamping stage, the inverter unit's output current will experience high-amplitude oscillations within a very short time, resulting in spikes in the energy density change rate curve. If a fixed-parameter filter is used, the system will either react too slowly to effectively weaken the high-frequency oscillations, or react too quickly, causing excessive weakening of the actual trend of the energy signal. A dynamic filtering gating algorithm, however, dynamically sets the filtering threshold by monitoring the amplitude and rate of change of the energy density in real time: when the rate of change exceeds a set threshold (e.g., the energy density change rate exceeds three times the steady-state range), a strong filtering gating is automatically activated to suppress high-frequency components with high intensity; when the rate of change falls back to the stable range, the algorithm automatically reduces the filtering intensity, retaining low-frequency energy components to maintain the integrity of the energy flow trend.

[0053] For example, when a heat treatment device switches from the heating stage to the isothermal stage, the energy feedback current waveform changes from an unstable oscillating state to a stable periodic signal. At this time, the dynamic filtering gating algorithm gradually weakens the filtering threshold by identifying the decreasing trend of the energy density change rate, allowing the energy signal to resume its natural flow. Through this process, high-frequency spikes in the feedback energy curve are effectively reduced, while the overall energy flow trend is preserved, thus forming a smooth and continuous energy transmission path. The advantage of this algorithm is that it can adaptively adjust the signal smoothness under different operating conditions, achieving intelligent energy signal optimization control that neither filters critical information nor fails to effectively suppress high-frequency disturbances.

[0054] Through the above implementation steps, the quality of the energy flow signal can be further optimized based on phase lead compensation, fundamentally reducing the energy feedback instability caused by high-frequency oscillations. After this process, the energy flow not only remains continuous in time but also transmits more evenly in space, thereby effectively improving the utilization rate of feedback energy, reducing the electrical stress on the inverter unit, and enhancing the energy consumption control stability and safety of the entire automotive fastener production line under multi-process high-frequency start-stop conditions.

[0055] Step 5: Based on the smooth energy signal output by the dynamic filtering gating algorithm, update the parameters of the phase dynamic drift model in real time to achieve adaptive adjustment of the energy feedback link, so that the system can continuously maintain the stability and safety of the energy flow direction under complex working conditions, thereby achieving efficient energy feedback control. The specific implementation method for this step is as follows: Based on the smoothed energy signal output after dynamic filtering and gating, the current operating status of the energy feedback link is identified and its features are extracted. The smoothed energy signal, after reducing high-frequency oscillation components, accurately reflects the stable changes in feedback energy over time. Therefore, at this stage, it is necessary to extract key characteristic parameters representing the energy flow state from the smoothed signal, including energy flow amplitude, rate of change of energy flow direction, stable energy flow range, and the location of energy flow abrupt change points. Continuous acquisition of these features can form a real-time operating status map of the energy feedback link. To ensure the accuracy of status identification, the signal sampling must be consistent with the time reference, ensuring that each energy signal segment corresponds precisely in time to the corresponding current waveform and grid voltage signal. Especially under the high-frequency start-stop conditions of fastener production lines, the energy flow state often exhibits periodic fluctuations. By continuously extracting the temporal characteristics of these fluctuations, the current energy flow trend and its stability can be accurately reflected, providing basic data for updating the parameters of the phase dynamic drift model.

[0056] The energy flow characteristic parameters extracted from the smoothed energy signal are compared with historical parameters in the existing phase dynamic drift model to identify deviations between the current energy flow state and the phase change model. Specifically, the phase dynamic drift model describes the dynamic change of the feedback current phase relative to the grid voltage phase, while the smoothed energy signal reflects the actual state of energy transmission intensity and direction. When the two are consistent, it indicates stable system operation; when they differ, it indicates a drift in the phase mapping of the current energy feedback link. In this case, it is necessary to calculate the time difference between the energy flow characteristic change and the phase shift change to determine the degree of model response lag. For example, if the energy flow direction has changed but the model still maintains the old phase relationship, it indicates that the dynamic response of the model parameters is lagging, and the phase drift rate and direction correction coefficient in the model need to be adjusted immediately. In this way, a dynamic correspondence can be established between the energy signal and the phase model in the time dimension, enabling the phase dynamic drift model to perceive changes in the energy flow state in real time and identify the deviation trend of phase drift.

[0057] After identifying the deviation between the energy flow state and the phase model, the parameters of the phase dynamic drift model are progressively updated to enable the model to adaptively adjust. This update process is driven by the real-time characteristics of the energy flow, continuously feeding back the energy flow intensity, direction change rate, and stable range into the model to correct the time constant and drift slope of the phase change. Specifically, when a rapid increase in energy flow is detected within a short period, the phase change rate of the feedback link should be accelerated to prevent delayed response in the energy flow direction; when the energy flow is stable and the rate of change is low, the phase drift rate of the model should be slowed down accordingly to maintain the balance between energy flow and phase change. Furthermore, near abrupt changes in energy flow, the model's phase parameters should be automatically adjusted to advance or delay the phase response by a certain time to offset the abrupt effect of the energy flow change. Through this real-time update method, the model parameters are no longer fixed but are continuously corrected with the dynamic changes in energy flow, enabling the phase dynamic drift model to maintain self-adaptive characteristics under different load conditions. To prevent frequent adjustments from causing model instability, a smooth transition should be performed after each parameter update to ensure that the phase change curve of the model output is continuous and stable on the time axis, avoiding overcorrection or response oscillation.

[0058] After completing the real-time update of the phase dynamic drift model parameters, the overall operational performance of the energy feedback link is dynamically verified to ensure that the stability and safety of the energy flow direction are maintained after the model update. The verification process includes comparing the coupling relationship between the phase dynamic drift model output and the smoothed energy signal before and after the update, and judging the effectiveness of the model update by analyzing their synchronicity on the time axis. When the phase change curve of the model output is completely consistent with the energy flow direction of the smoothed energy signal and the synchronization delay time is kept within the preset threshold, it indicates that the model update is successful and the energy feedback link has achieved adaptive adjustment. When a slight delay in the phase response of the model output or a momentary deviation in the energy flow direction is detected, it indicates that the model update amplitude is insufficient or the parameter response is incomplete, and recalibration is required. During long-term operation, the model parameter update should form a continuous self-looping mechanism, so that every change in the energy signal can trigger the fine-tuning of the phase model. Especially during fastener production, when multiple devices such as forming machines, heat treatment furnaces, and electroplating tanks operate alternately, the energy feedback link faces complex and variable operating conditions. Only through continuous parameter updates can the reverse energy flow direction be prevented, avoiding excessive electrical stress in the inverter unit caused by instantaneous energy backflow. Ultimately, through continuous real-time updates and verification, the energy feedback link can maintain stable energy flow direction and safe electrical operation under complex load conditions, achieving truly efficient energy feedback control.

[0059] Through the specific implementation steps described above, the smoothed energy signal can be tightly integrated with the phase dynamic drift model, enabling the model to adaptively update during operation. This ensures that the energy feedback link maintains stable energy flow under different loads, grid conditions, and operating cycles. This process not only improves the control accuracy and dynamic response speed of energy feedback but also significantly enhances the disturbance resistance and safety of the energy feedback process. It allows the automotive fastener production line to maximize energy utilization and maintain long-term equipment stability even under high-frequency start-stop and multi-process collaborative operation environments.

[0060] This invention establishes a dynamic phase drift model and combines it with phase trend prediction and advance compensation control to ensure that the current phase in the energy feedback link remains continuously synchronized with the grid voltage, effectively avoiding the reverse energy flow impact caused by phase response lag. This control method can correct phase offset in real time under complex production environments with multiple processes and high-frequency start-stop cycles, ensuring that the feedback current waveform remains in a stable conducting state. This significantly reduces ripple current amplitude, decreases transient stress on the inverter unit's filter capacitors and power transistors, and improves the stability of energy feedback operation and the lifespan of the equipment.

[0061] This invention introduces an energy density change rate extraction and dynamic filtering gating mechanism into the feedback current signal processing, ensuring the energy transmission path remains smooth and continuous under different load fluctuations, thus achieving real-time adaptive adjustment of energy flow. This method dynamically balances the energy flow between energy feedback and equipment load, significantly improving energy utilization and feedback efficiency, while avoiding heat loss and oscillation risks caused by energy accumulation or sudden changes. This allows the entire fastener production line to achieve a dynamically optimal state in terms of energy-saving control and operational safety.

[0062] This invention provides, for example Figure 2 The automotive fastener production energy consumption optimization control system shown includes a phase dynamic modeling module, a phase trend prediction module, a phase synchronization compensation module, an energy signal smoothing module, and an adaptive adjustment module. The phase dynamic modeling module acquires the real-time current waveform signal of the inverter unit in the energy feedback link and the fundamental voltage signal of the power grid. It uses the time difference analysis algorithm to establish a phase dynamic drift model, forming a continuous mapping of the change in energy flow direction on the time axis. The phase trend prediction module extracts the continuously changing segment of the offset rate between the current waveform and the fundamental wave of the power grid based on the established phase dynamic drift model, and uses the sliding window algorithm to construct the phase trend prediction function, which transforms the time characteristics of the energy flow direction change into the phase prediction signal trajectory. The phase synchronization compensation module dynamically adjusts the pulse width modulation triggering time of the inverter unit based on the phase prediction signal trajectory generated by the phase trend prediction function and the phase lead compensation algorithm, so that the phase of the feedback current and the phase of the grid voltage are kept synchronized. The energy signal smoothing module extracts the energy density change rate based on the feedback current signal adjusted by the phase lead compensation algorithm, and uses a dynamic filtering gating algorithm to reduce the high-frequency oscillation component of the feedback energy signal to form an energy transmission path. The adaptive adjustment module updates the parameters of the phase dynamic drift model in real time based on the smoothed energy signal output by the dynamic filtering gating algorithm, and adaptively adjusts the energy feedback link.

[0063] The energy consumption optimization and control method for automotive fastener production provided in this embodiment of the invention is implemented through the aforementioned energy consumption optimization and control system for automotive fastener production. For details of the specific methods and processes of the energy consumption optimization and control system for automotive fastener production, please refer to the embodiments of the aforementioned energy consumption optimization and control method for automotive fastener production, which will not be repeated here.

[0064] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing energy consumption control in automobile fastener production, characterized by, The method comprises the following steps: Step 1: Obtain the real-time current waveform signal of the inverter unit in the energy feedback link and the grid fundamental voltage signal, and establish a phase dynamic drift model by using a time difference analysis algorithm to form a continuous mapping of the energy flow direction change on the time axis; Step 2: Based on the established phase dynamic drift model, extract the continuous change section of the offset rate between the current waveform and the grid fundamental, and use a sliding window algorithm to construct a phase trend prediction function to convert the time characteristics of the energy flow direction change into a phase prediction signal trajectory; Step 3: According to the phase prediction signal trajectory generated by the phase trend prediction function, use a phase advance compensation algorithm to dynamically adjust the pulse width modulation trigger time of the inverter unit, so that the phase of the feedback current and the phase of the grid voltage remain synchronous; Step 4: Based on the feedback current signal adjusted by the phase advance compensation algorithm, extract the energy density change rate, and use a dynamic filtering gating algorithm to reduce the high-frequency oscillation components of the feedback energy signal to form an energy transmission path; Step 5: According to the smoothed energy signal output by the dynamic filtering gating algorithm, real-time update the parameters of the phase dynamic drift model, and adaptively adjust the energy feedback link.

2. The automobile fastener production energy consumption optimization control method according to claim 1, characterized by, The step of obtaining the real-time current waveform signal of the inverter unit in the energy feedback link and the grid fundamental voltage signal comprises: Setting current detection points and voltage detection points on the output side and the grid connection side of the inverter unit in the energy feedback link respectively, and synchronously obtaining the real-time current waveform signal of the inverter unit output end and the fundamental voltage signal of the grid side; Point-by-point comparison of the current waveform signal and the grid fundamental voltage signal on the time axis is performed to determine the phase difference at each sampling time, and a phase difference time sequence with time as the horizontal axis and phase offset angle as the vertical axis is formed; The phase difference time sequence is mapped corresponding to the time axis to form a continuous curve of energy flow direction change, and the instantaneous trend of energy flow direction switching is identified according to the phase difference change rate; Based on the continuous curve of energy flow direction change, a phase dynamic drift model is established, and by comparing the phase change reference points and the local extreme points, a phase dynamic drift model that can be continuously updated according to the running state is formed.

3. The automobile fastener production energy consumption optimization control method according to claim 2, characterized by, The step of extracting the continuous change section of the offset rate between the current waveform and the grid fundamental based on the phase dynamic drift model comprises: In the established phase dynamic drift model, the phase offset between the current waveform and the grid fundamental voltage is continuously extracted, and the section with high phase change rate on the time axis is identified to form a phase offset rate change identification sequence; The phase change curve in the offset rate change section is continuously analyzed and segmented to extract the phase change directionality and stability characteristics, and the unstable factors caused by grid fluctuations or current jitter are excluded; The phase change directionality curve is mapped corresponding to the time axis to convert the time law of energy flow direction change into a continuous trend signal trajectory, and the acceleration interval and the stable interval of phase change are identified; Real-time analysis and dynamic monitoring are performed on the trend signal trajectory, and when the trajectory slope sharply rises in a short time, the time period is marked as a potential phase delay interval.

4. The automobile fastener production energy consumption optimization control method according to claim 3, characterized by, The step of dynamically adjusting the pulse width modulation trigger time of the inverter unit according to the phase prediction signal trajectory generated by the phase trend prediction function comprises: comparing the phase synchronization state of the feedback current and the grid voltage in real time according to the phase prediction signal trajectory, and determining the deviation direction and degree of the current phase relative to the grid fundamental voltage phase; after completing the analysis of the phase deviation direction and degree, preliminarily adjusting the pulse width modulation trigger time of the inverter unit, and determining the optimal advance of the conduction trigger time according to the position of the phase change inflection point in the phase prediction signal trajectory; after determining the adjustment range of the trigger time, continuously and dynamically adjusting the pulse width modulation signal of the inverter unit, so that the phase of the feedback current waveform is time-synchronized with the grid voltage waveform and real-time calibration is achieved; after completing the dynamic adjustment of the pulse width modulation trigger time, real-time verifying the phase difference, peak value corresponding relationship and waveform symmetry of the adjusted feedback current waveform and the grid voltage waveform.

5. The automobile fastener production energy consumption optimization control method according to claim 4, characterized by, In the process of continuously and dynamically adjusting the pulse width modulation trigger time of the inverter unit, the trigger time changes of continuous multiple driving periods are smoothed, so that the trigger adjustment presents a slow change characteristic, and when the phase difference fluctuation exceeds the preset range, the phase prediction signal trajectory is updated to calculate a new trigger advance.

6. The automobile fastener production energy consumption optimization control method according to claim 4, characterized by, The step of extracting the energy density change rate and performing high-frequency oscillation component reduction processing based on the feedback current signal adjusted by the phase advance compensation algorithm comprises: extracting the instantaneous energy density characteristics in the feedback current signal adjusted by the phase advance compensation, and establishing an energy density change distribution curve on the time axis to reflect the transmission state of the energy flow in the feedback link; performing continuous difference analysis on the energy density change distribution curve, extracting the energy density change rate time sequence and identifying the dynamic trend of the energy flow intensity change over time; performing dynamic smoothing processing on the energy density change rate time sequence, reducing the high-frequency oscillation component and forming a gentle trend curve of energy change, so that the energy flow curve remains in a continuous transition state; re-mapping the smoothed energy signal on the time axis to construct a stable energy transmission path, and verifying the continuity of the path in real time.

7. The automobile fastener production energy consumption optimization control method according to claim 6, characterized by, In the step of performing dynamic smoothing processing on the energy density change rate time sequence, the low-frequency energy change trend is retained while the high-frequency oscillation component is reduced, so that the energy transmission path remains continuous and transitional on the time axis, and the time scale of the smoothing interval is automatically adjusted when the energy flow direction changes.

8. The automobile fastener production energy consumption optimization control method according to claim 6, characterized by, The step of real-time updating the phase dynamic drift model parameters according to the smoothed energy signal output by the dynamic filtering gating algorithm comprises: based on the smoothed energy signal output after the dynamic filtering gating processing, identifying and extracting the energy flow features of the current energy feedback link to obtain the energy flow amplitude, energy flow direction change rate, energy flow stable interval and energy flow abrupt point position; comparing the energy flow feature parameters extracted from the smoothed energy signal with the historical parameters in the phase dynamic drift model, identifying the deviation between the energy flow state and the phase change model, and calculating the time difference between the energy flow feature change and the phase offset change; After identifying the deviation, the parameters of the phase dynamic drift model are updated step by step, and the energy flow intensity, the direction change rate and the stable interval are continuously fed back to the model to realize the adaptive adjustment of the model parameters and keep smooth transition; After completing the real-time updating of the parameters, the synchronization relationship between the phase dynamic drift model output before and after updating and the smoothed energy signal is verified.

9. The automobile fastener production energy consumption optimization control method according to claim 8, characterized by, During the step-by-step updating process of the phase dynamic drift model parameters, when it is detected that the energy flow direction change rate exceeds the preset threshold, the phase drift rate and the direction correction coefficient in the model are adjusted preferentially, and the parameter smoothing transition time is automatically extended at the energy flow mutation point to prevent phase fluctuations caused by excessive response of the model.

10. The system for optimizing energy consumption control in the production of automotive fasteners, for implementing the method for optimizing energy consumption control in the production of automotive fasteners according to any one of claims 1 to 9, characterized in that, The method comprises a phase dynamic modeling module, a phase trend prediction module, a phase synchronization compensation module, an energy signal smoothing module and an adaptive adjustment module. The phase dynamic modeling module acquires real-time current waveform signals of an inverter unit in an energy feedback link and grid fundamental voltage signals, and establishes a phase dynamic drift model using a time difference analysis algorithm to form a continuous mapping of energy flow direction changes on a time axis. The phase trend prediction module extracts a continuous change section of the offset rate between the current waveform and the grid fundamental based on the established phase dynamic drift model, and constructs a phase trend prediction function using a sliding window algorithm to convert the time characteristics of energy flow direction changes into a phase prediction signal trajectory. The phase synchronization compensation module generates a phase prediction signal trajectory based on the phase trend prediction function, and dynamically adjusts the pulse width modulation trigger time of the inverter unit using a phase lead compensation algorithm to make the phase of the feedback current and the phase of the grid voltage keep synchronous flow. The energy signal smoothing module extracts the energy density change rate based on the feedback current signal adjusted by the phase lead compensation algorithm, and uses a dynamic filtering gating algorithm to reduce high-frequency oscillation components of the feedback energy signal to form an energy transmission path. The adaptive adjustment module updates the parameters of the phase dynamic drift model in real time based on the smoothed energy signal output by the dynamic filtering gating algorithm to adaptively adjust the energy feedback link.

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