A spinning machine power flash stop adaptive optimization method and system

Abstract

The present application belongs to the field of spinning machine power flash stop, and relates to a spinning machine power flash stop adaptive optimization method and system. The system comprises a parameter acquisition module, a parameter analysis module, a parameter optimization module, a parameter configuration module and a running monitoring module. The parameter acquisition module collects current parameter of the frequency converter and real-time data of the power grid voltage; the parameter analysis module evaluates the equipment response condition of the current parameter in voltage flash stop; the parameter optimization module generates and outputs parameter adjustment strategy according to the analysis result; the parameter configuration module writes the optimized parameter into the frequency converter and completes the system setting; and the running monitoring module monitors the running state of the spinning machine and the voltage stability in real time, and feeds back the optimization effect. The present application can realize adaptive parameter adjustment and continuous stable running of the spinning machine under the condition of power flash stop.

Description

Technical Field

[0001] This invention relates to the field of power supply interruption in spinning frames, and specifically to an adaptive optimization method and system for power supply interruption in spinning frames. Background Technology

[0002] As a key piece of equipment in textile production, the continuous and stable operation of the spinning frame is crucial for ensuring production efficiency and yarn quality. Current technologies typically address spinning frame shutdowns caused by power outages by adding voltage stabilizing devices or mechanical buffer mechanisms to mitigate the impact of voltage fluctuations. While these solutions have made some progress in improving the equipment's resistance to voltage fluctuations, the following technical problems remain: During frequent or brief voltage outages, the spinning frame still triggers a protective shutdown, leading to complete yarn breakage, production interruption, increased bellows wear, and damage to electrical components, severely impacting production continuity and economic efficiency.

[0003] In view of this, it is very necessary to provide an adaptive optimization method and system for power supply interruption of a spinning machine to solve the above-mentioned defects in the prior art. Summary of the Invention

[0004] The purpose of this invention is to solve the technical problem in the prior art where frequent or brief voltage interruptions trigger protective shutdown of the spinning machine, leading to complete machine breakage, production interruption, increased bellows wear, and damage to electrical components. The invention provides a method and system for adaptive optimization of power supply interruption for spinning machines to solve the technical problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides an adaptive optimization method for power supply interruption of a spinning frame, comprising the following steps: Step S1: Based on the current first operating parameters of the spinning machine frequency converter, determine the second operating parameters related to the power supply interruption response, and obtain the parameter data set corresponding to the power supply interruption event through preprocessing; Step S2: Based on historical power supply interruption records, voltage fluctuation data, and parameter data sets, analyze the response performance of the second operating parameter in the interruption event and generate analysis results; Step S3: Generate parameter optimization schemes based on the analysis results, including adjusting the instantaneous stop response mode, increasing the voltage tolerance limit, and setting the recovery integral time; Step S4: Write the optimized parameters into the corresponding storage area of ​​the frequency converter to complete the parameter configuration update; Step S5: After the parameters are updated, monitor the operating status of the spinning machine and the changes in the power grid voltage in real time, verify the optimization effect, and record the operating data.

[0006] Secondly, the present invention also provides a power supply interruption adaptive optimization system for a spinning frame, comprising: The parameter acquisition module is used to collect the current parameters of the frequency converter and real-time grid voltage data. The parameter analysis module is used to evaluate the device's response to current parameters during a voltage flashover. The parameter optimization module is used to generate and output parameter adjustment strategies based on the analysis results. The parameter configuration module is used to write the optimized parameters into the frequency converter and complete the system settings. The operation monitoring module is used to monitor the operating status and voltage stability of the spinning machine in real time and provide feedback on the optimization effect.

[0007] The modules work together to achieve adaptive parameter adjustment and continuous stable operation of the spinning machine in the event of a power outage.

[0008] The beneficial effects of this invention are as follows: This invention, based on the existing operating parameters of the frequency converter of the spinning machine, filters and determines the operating parameters related to power supply interruption response, and preprocesses them to form a parameter data set corresponding to power supply interruption events. This enables the spinning machine to have clear parameter support when the voltage is frequently or briefly abnormal, avoiding unnecessary protective shutdowns triggered by disordered or mismatched parameters.

[0009] This invention combines historical power supply interruption records, voltage fluctuation data, and parameter data sets to analyze the operational response of a spinning machine during an interruption. This allows the adjustment of operating parameters to be based on real power grid fluctuations and equipment performance, effectively identifying key response characteristics that lead to complete machine breakage and production interruption, and improving adaptability to power supply interruption conditions.

[0010] Based on the analysis results, this invention generates targeted parameter optimization schemes. By adjusting the instantaneous stop response mode, voltage tolerance limit, and recovery integral time, the spinning machine can delay or avoid triggering protective shutdown when frequent or brief voltage flash stops occur, thereby reducing the probability of yarn breakage and reducing the increase of bellows defects during the production process.

[0011] This invention writes the optimized operating parameters into the corresponding storage area of ​​the frequency converter, so that the spinning machine can operate directly according to the optimized parameters in the event of a power outage, reducing the impact of repeated shutdowns on production continuity and electrical components, and reducing the risk of damage to electrical components caused by frequent start-stops.

[0012] This invention monitors the operating status of the spinning machine and changes in the power grid voltage in real time after parameter updates, and records relevant operating data to verify the effect of parameter optimization. This enables the spinning machine to maintain stable operation in complex power supply environments and further reduces production interruptions and equipment damage caused by voltage flashovers.

[0013] The method proposed in this invention establishes a systematic response mechanism for frequent or brief voltage interruptions by screening, analyzing, optimizing, and continuously verifying the relevant operating parameters of the spinning machine's power supply interruptions. This mechanism enables the spinning machine to maintain continuous and stable operation under power supply fluctuations, reducing the occurrence of complete machine breakage and production interruptions caused by protective shutdowns. It also reduces the risk of increased bellows defects and damage to electrical components due to repeated impacts, thereby improving the overall reliability and production efficiency of the spinning machine in unstable power supply environments.

[0014] Therefore, it is evident that the present invention has outstanding substantive features and significant progress compared with the prior art, and the beneficial effects of its implementation are also obvious. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 This is a flowchart of an adaptive optimization method for power supply interruption of a spinning frame. Figure 2 This is a schematic diagram of a power supply interruption adaptive optimization system for a spinning machine. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following implementation methods.

[0018] Example 1: like Figure 1 As shown in the figure, the adaptive optimization method for power supply interruption of a spinning machine provided in this embodiment includes the following steps: Step S1: Based on the current first operating parameters of the spinning machine frequency converter, determine the second operating parameters related to the power supply interruption response, and obtain the parameter data set corresponding to the power supply interruption event through preprocessing; Step S2: Based on historical power supply interruption records, voltage fluctuation data, and parameter data sets, analyze the response performance of the second operating parameter in the interruption event and generate analysis results; Step S3: Generate parameter optimization schemes based on the analysis results, including adjusting the instantaneous stop response mode, increasing the voltage tolerance limit, and setting the recovery integral time; Step S4: Write the optimized parameters into the corresponding storage area of ​​the frequency converter to complete the parameter configuration update; Step S5: After the parameters are updated, monitor the operating status of the spinning machine and the changes in the power grid voltage in real time, verify the optimization effect, and record the operating data.

[0019] In step S1: based on the parameter definition of the spinning machine frequency converter and the power supply interruption control principle, the operating parameters related to the power supply interruption response are determined, and the current first operating parameters of the spinning machine frequency converter are obtained.

[0020] Based on the current first operating parameters of the spinning frame frequency converter, the parameter descriptions for instantaneous power outage control in the spinning frame frequency converter's instruction manual, and the statistical analysis results of power supply flashover phenomena during long-term operation of the spinning frame, the second operating parameters related to power supply flashover response are determined. The second operating parameters include instantaneous stop response mode parameters, voltage threshold parameters, and instantaneous stop integral time parameters, specifically instantaneous stop response mode parameter b050, instantaneous stop non-stop start voltage parameter b051, instantaneous stop non-stop threshold voltage parameter b052, and instantaneous stop non-stop integral time parameter b056.

[0021] The current set value of the second operating parameter is read through the parameter display interface, operation panel, and communication interface of the spinning machine frequency converter. Simultaneously, the operating status signal of the spinning machine and the voltage sampling data on the input side of the frequency converter are collected to characterize the operating condition of the spinning machine under the current parameter configuration. Then, the acquired second operating parameter and voltage sampling data are preprocessed. Preprocessing includes filtering and smoothing the voltage sampling data to reduce the impact of transient interference on parameter judgment; normalizing or standardizing the second operating parameters of different dimensions to facilitate subsequent analysis and comparison; and organizing the preprocessed second operating parameter and voltage sampling data in chronological order to form a parameter data set corresponding to the power supply interruption event.

[0022] In step S2: based on historical power supply interruption event records, voltage fluctuation data, and the parameter data set corresponding to the power supply interruption event, the response performance during the interruption event is analyzed, and analysis results are generated, specifically: Historical power supply interruption records and voltage fluctuation data were acquired through the spinning machine operation log, inverter historical data recorder, and workshop voltage monitoring equipment. These data included the interruption time, voltage drop magnitude, duration, and spinning machine operating status information. The historical power supply interruption event records, voltage fluctuation data, and corresponding parameter data sets were compiled into an event dataset for analyzing the operational performance corresponding to each interruption. The event dataset was then input into a trained multilayer feedforward neural network for analysis. Input nodes included instantaneous stop response mode parameters, voltage threshold parameters, integration time parameters, and corresponding voltage fluctuation characteristics. The output was the analysis results of a spinning machine interruption event under given parameters and voltage fluctuation conditions. The analysis results output by the multilayer feedforward neural network were used to verify the impact of a second operating parameter on the interruption event response capability and to provide a reference for subsequent optimization.

[0023] The basic principle of the multilayer feedforward neural network is to transmit the input signal through multiple neuron layers in a forward propagation manner, and adjust the connection weights and thresholds in reverse according to the output error to learn and predict the nonlinear mapping between the input parameters and the instantaneous stop response. In the multilayer feedforward neural network, the input layer nodes include instantaneous stop response mode parameters b050, instantaneous stop-without-stop start voltage b051, instantaneous stop-without-stop threshold voltage b052, instantaneous stop-without-stop integration time b056, and voltage fluctuation characteristics; the hidden layer consists of two layers, with 8 neurons in the first hidden layer and 4 neurons in the second hidden layer, using the ReLU activation function; the output layer has 1 neuron, with the Sigmoid activation function, used to output the probability that the spinning machine will experience abnormal stop or complete spindle breakage under the current parameters and voltage fluctuation conditions. During training, supervised learning was performed using historical stop records and corresponding parameter data. Each event was labeled as abnormal stopping / complete vehicle breakage (1) and normal operation (0). The weights and thresholds were updated using the backpropagation algorithm, and the loss function was the binary cross-entropy function. ; in, Let i be the actual labeled values ​​of the samples. The neural network predicts the output value, where N is the total number of samples; the weight update formula is: ; in, Let η be the weight between the j-th neuron in layer l and the k-th neuron in the previous layer, and η be the learning rate. The number of training iterations is generally 1000-5000, or convergence is determined when the loss function changes less than a preset threshold ε for several consecutive iterations. Early stopping can be used to prevent overfitting.

[0024] The analysis results output by the neural network include the probability of abnormal parking and the probability of the entire vehicle breaking off corresponding to the second operating parameter under each flash stop event, the evaluation of the contribution to the flash stop response performance, and the identifiable high-risk or low-risk situations of parameter configuration, providing a quantitative basis for subsequent parameter optimization.

[0025] Through the above steps, the neural network can verify the operating effect of the second operating parameter under power supply interruption conditions, rather than generating parameters, thereby providing reliable data support for parameter optimization.

[0026] In step S3: Based on the analysis results, a parameter optimization scheme is generated for the spinning machine under power supply interruption conditions to improve the operational stability of the spinning machine during interruption events and reduce the risk of yarn breakage. Based on the analysis results, the second operating parameters can be adjusted in a targeted manner, including adjusting the instantaneous stop response mode, increasing the voltage tolerance limit, and setting the recovery integral time.

[0027] When the threshold voltage b051 or the threshold voltage b052 for instantaneous stop is too low and easily triggers abnormal stop, its setting value can be appropriately increased so that the spinning machine can continue to operate under short-term voltage fluctuations. When the integral time b056 is too short and causes a short-term flash stop to be mistakenly judged as an abnormal stop, the integral time can be extended to increase the tolerance to short-term voltage fluctuations, thereby reducing the risk of false stop. Specifically, the sub-parameter under the instantaneous stop response mode parameter b050 is adjusted from "02, instantaneous stop without stopping and no recovery" to "03, instantaneous stop without stopping and with recovery", enabling the spinning machine to automatically resume operation after a short-term power supply interruption; the instantaneous stop without stopping start voltage parameter b051 is increased from 440V to 470V, raising the spinning machine's tolerance limit for short-term input voltage drops; the instantaneous stop without stopping threshold voltage parameter b052 is adjusted from 720V to 510V to prevent instantaneous voltage fluctuations from being mistakenly judged as an inoperable state; and the instantaneous stop without stopping integral time parameter b056 is extended from 0.100s to 0.500s, enhancing the spinning machine's adaptability to the duration of short-term interruptions.

[0028] During the parameter optimization scheme generation process, the prediction results of the trained multi-layer feedforward neural network are used to prioritize the adjustment of parameters that contribute more to the probability of abnormal parking. The adjustment range and direction of each parameter are quantitatively determined, and a parameter adjustment list is formed to provide guidance for subsequent parameter writing and implementation.

[0029] By following the steps above, targeted parameter configurations can be formed based on different power outage characteristics and historical data, enabling highly reliable operation of the spinning machine in the event of a power outage, and minimizing yarn breakage and production loss.

[0030] In step S4, the second operating parameters determined in the optimization scheme are written into the corresponding storage area of ​​the spinning machine inverter to complete the parameter configuration update. Specifically, the optimized instantaneous stop response mode parameter b050, instantaneous stop-without-stop start voltage parameter b051, instantaneous stop-without-stop threshold voltage parameter b052, and instantaneous stop-without-stop integral time parameter b056 are written into the corresponding storage unit of the inverter through the inverter's parameter display interface, operation panel, or communication interface, and made effective immediately. During the writing process, each parameter is verified to ensure that the parameter value is within the inverter's allowable range, avoiding equipment malfunction due to exceeding the threshold. After writing, the actual setting value of each parameter is confirmed by reading the data returned by the inverter's display interface or communication interface, and the spinning machine's operating status and input voltage signal are monitored simultaneously to ensure that the updated parameters have been correctly applied and the equipment is operating stably. The parameter writing and confirmation process ensures that the adjustments in the optimization scheme can be completely and reliably applied to the inverter, providing a reliable basis for the subsequent operating status verification and instantaneous stop response effect evaluation in step S5.

[0031] In step S5: After the optimized parameters are written and take effect, the operating status of the spinning machine and the changes in the power grid voltage are monitored in real time to verify the optimization effect and record the operating data. Specifically, the operating status parameters of the spinning machine and the power grid voltage change parameters, including the spinning machine speed, tension change, yarn breakage, voltage fluctuation amplitude, flash stop duration and recovery slope, are collected in real time through the operating status signal of the spinning machine frequency converter, sensor data, and power grid voltage acquisition equipment. The real-time collected parameter data is compared with the prediction results corresponding to the second operating parameters obtained in step S2 to determine whether the spinning machine can maintain normal operation and automatically recover under different types of power supply flash stop events under the optimized parameter configuration, thereby verifying the effectiveness of the optimization scheme.

[0032] All collected data is recorded in time series and associated with corresponding stop-start event identifiers to form a complete operational data set. The recorded data can be used to evaluate optimization effectiveness, such as the reduction rate of abnormal stops and the decrease in the probability of complete yarn breakage, and can also serve as a reference for further training and parameter optimization of the neural network model, thus forming a closed-loop continuous improvement mechanism. Through this real-time monitoring and recording method, the reliability of optimization parameters under actual production conditions can be ensured, and a quantitative basis for the stable operation of the spinning machine under different stop-start conditions can be provided.

[0033] Example 2: like Figure 2 As shown in this embodiment, a power supply interruption adaptive optimization system for a spinning frame is provided, comprising: Parameter acquisition module 1, based on the parameter definition of the spinning frame inverter and the power supply interruption control principle, determines the operating parameters related to the power supply interruption response and acquires the current first operating parameter of the spinning frame inverter. Combining the parameter descriptions for instantaneous power outage control in the spinning frame inverter's instruction manual and the statistical analysis results of power supply interruptions generated during long-term operation, it determines the second operating parameter directly related to the power supply interruption response. The current set value of the second operating parameter is read through the spinning frame inverter's parameter display interface, operation panel, or communication interface. Simultaneously, it collects the spinning frame's operating status signals and voltage sampling data from the inverter's input side. After data acquisition, it preprocesses the second operating parameter and voltage sampling data to reduce the impact of instantaneous electromagnetic interference or abnormal fluctuations on the judgment results. It normalizes or standardizes second operating parameters of different dimensions to improve the consistency of subsequent analysis. Finally, it organizes the continuously collected data in chronological order to form a parameter data set corresponding to the power supply interruption event, providing a reliable data foundation for subsequent parameter analysis and optimization.

[0034] The second operating parameters include instantaneous stop response mode parameters, voltage threshold parameters, and instantaneous stop integration time parameters, specifically instantaneous stop response mode parameter b050, instantaneous stop non-stop start voltage parameter b051, instantaneous stop non-stop threshold voltage parameter b052, and instantaneous stop non-stop integration time parameter b056. Preprocessing includes filtering and smoothing the voltage sampling data.

[0035] Parameter analysis module 2, based on historical power supply interruption event records and voltage fluctuation data, analyzes the response performance of the parameter data set corresponding to the power supply interruption event in the interruption event, forming an event parameter data set. This set is input into a multilayer feedforward neural network for analysis, and the output is the analysis result of the spinning machine experiencing an interruption event under given parameters and voltage fluctuation conditions. This multilayer feedforward neural network establishes a nonlinear mapping relationship between the second operating parameter and the interruption response result through forward propagation, and adjusts the network connection weights and thresholds through an error backpropagation algorithm to learn and predict the interruption response characteristics. This analysis provides quantitative and reliable data support for the subsequent parameter optimization module.

[0036] Based on the generated analysis results, parameter optimization module 3 proposes parameter optimization schemes, including changing the instantaneous stop response mode from "instantaneous stop without recovery" to "instantaneous stop with recovery," increasing the instantaneous stop start voltage from 440V to 470V, adjusting the instantaneous stop threshold voltage from 720V to 510V, and increasing the instantaneous stop integration time from 0.100 seconds to 0.500 seconds. This improves the spinning machine's tolerance to grid voltage fluctuations and enhances the stability and recovery capability of the spinning machine's power supply interruption response system during power supply interruptions. During the optimization scheme generation process, the prediction results of the trained multi-layer feedforward neural network are used to prioritize the adjustment of parameters that contribute more to the probability of abnormal shutdowns. The adjustment magnitude and direction of each parameter are quantified, and a parameter adjustment list is generated to guide subsequent parameter writing and implementation.

[0037] The parameter configuration module 4 writes the optimized instantaneous stop response mode parameter b050, instantaneous stop-without-stop start voltage parameter b051, instantaneous stop-without-stop threshold voltage parameter b052, and instantaneous stop-without-stop integral time parameter b056 into the corresponding storage unit of the frequency converter via the parameter display interface, operation panel, or communication interface of the spinning machine frequency converter, and makes them effective immediately. During the writing process, each parameter is verified to ensure that the parameter value is within the allowable range of the frequency converter, avoiding equipment malfunctions due to exceeding the threshold. After the writing is completed, the actual setting value of each parameter is confirmed by reading the data returned by the frequency converter display interface or communication interface, and the operating status of the spinning machine and the input voltage signal are monitored at the same time. This module ensures that the entire parameter update process is reliable and accurate, avoids parameter configuration errors due to operational mistakes, and ensures that the optimized parameters take effect in a timely manner, thereby improving the operating efficiency and stability of the system.

[0038] The operation monitoring module 5 monitors the real-time operating status of the spinning machine and the voltage stability of the power grid, and verifies the optimization effect. It collects real-time operating status parameters of the spinning machine and power grid voltage change parameters through the operating status signals of the spinning machine's frequency converter, sensor data, and power grid voltage acquisition equipment. The real-time collected parameter data is compared with the predicted results to determine whether the optimized parameters have successfully improved the stability of the spinning machine during power outages. This module analyzes the optimized operating effect by comparing the data with that before optimization and provides feedback on the optimization results to provide data support for subsequent continuous improvement and parameter adjustment.

[0039] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the systems disclosed in the embodiments; relevant details can be found in the method section.

[0040] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0041] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0042] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0043] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit.

[0044] Similarly, in the various embodiments of the present invention, each processing unit can be integrated into a functional module, or each processing unit can exist physically, or two or more processing units can be integrated into a functional module.

[0045] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0046] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0047] The above-disclosed embodiments are merely preferred embodiments of the present invention, but the present invention is not limited thereto. Any non-creative variations that can be conceived by those skilled in the art, as well as any improvements and modifications made without departing from the principles of the present invention, should fall within the protection scope of the present invention.

Claims

1. A power supply interruption adaptive optimization method for a spinning frame, characterized in that, Includes the following steps: Step S1: Based on the current first operating parameters of the spinning machine frequency converter, determine the second operating parameters related to the power supply interruption response, and obtain the parameter data set corresponding to the power supply interruption event through preprocessing; Step S2: Based on historical power supply interruption records, voltage fluctuation data, and parameter data sets, analyze the response performance of the second operating parameter in the interruption event and generate analysis results; Step S3: Generate parameter optimization schemes based on the analysis results, including adjusting the instantaneous stop response mode, increasing the voltage tolerance limit, and setting the recovery integral time; Step S4: Write the optimized parameters into the corresponding storage area of ​​the frequency converter to complete the parameter configuration update; Step S5: After the parameters are updated, monitor the operating status of the spinning machine and the changes in the power grid voltage in real time, verify the optimization effect, and record the operating data.

2. The adaptive optimization method for power supply interruption of a spinning frame according to claim 1, characterized in that, In step S1: Based on the current first operating parameters of the spinning frame inverter, the parameter descriptions regarding instantaneous power outage control in the spinning frame inverter's instruction manual, and the statistical analysis results of power supply interruption phenomena during long-term operation of the spinning frame, the second operating parameters related to power supply interruption response are determined; the current set value of the second operating parameters is read through the parameter display interface, operation panel, and communication interface of the spinning frame inverter; the spinning frame operating status signal and inverter input voltage sampling data are simultaneously collected; the acquired second operating parameters and voltage sampling data are preprocessed; and the preprocessed second operating parameters and voltage sampling data are organized in chronological order to form a parameter data set corresponding to the power supply interruption event.

3. The adaptive optimization method for power supply interruption of a spinning frame according to claim 1 or 2, characterized in that, The second operating parameters include instantaneous stop response mode parameters, voltage threshold parameters, and instantaneous stop integration time parameters, specifically instantaneous stop response mode parameter b050, instantaneous stop non-stop start voltage parameter b051, instantaneous stop non-stop threshold voltage parameter b052, and instantaneous stop non-stop integration time parameter b056; preprocessing includes filtering and smoothing the voltage sampling data, and normalizing or standardizing the second operating parameters of different dimensions to facilitate subsequent analysis and comparison.

4. The adaptive optimization method for power supply interruption of a spinning frame according to claim 3, characterized in that, In step S2: historical power supply interruption event records, voltage fluctuation data, and parameter data sets corresponding to power supply interruption events are organized to form an event data set. The event data set is then input into a trained multi-layer feedforward neural network for analysis. The input nodes include instantaneous stop response mode parameters, voltage threshold parameters, integral time parameters, and corresponding voltage fluctuation characteristics. The output is the analysis result of a flash stop event occurring on the spinning machine under given parameters and voltage fluctuation conditions.

5. The adaptive optimization method for power supply interruption of a spinning frame according to claim 4, characterized in that, The historical power supply interruption records and voltage fluctuation data are obtained through the spinning machine operation log, the inverter historical data recorder, and the workshop voltage monitoring equipment. The data includes the time of the interruption, the voltage drop magnitude, the duration, and the spinning machine operating status information. The analysis results include the abnormal shutdown probability and the probability of the entire machine breaking off corresponding to the second operating parameter under each interruption event, the contribution evaluation to the interruption response performance, and the identifiable high-risk or low-risk parameter configuration situations.

6. The adaptive optimization method for power supply interruption of a spinning frame according to claim 5, characterized in that, In step S3: Based on the analysis results, a parameter optimization scheme is generated for the spinning machine under the condition of power supply interruption: the sub-parameter under the instantaneous stop response mode parameter b050 is adjusted from "02, instantaneous stop without stopping and no recovery" to "03, instantaneous stop without stopping and recovery"; the instantaneous stop without stopping start voltage parameter b051 is increased from 440V to 470V; the instantaneous stop without stopping threshold voltage parameter b052 is adjusted from 720V to 510V; the instantaneous stop without stopping integration time parameter b056 is extended from 0.100s to 0.500s; During the parameter optimization process, the prediction results of the trained multi-layer feedforward neural network are used to prioritize the adjustment of parameters that contribute more to the probability of abnormal parking. The adjustment range and direction of each parameter are quantitatively determined, and a parameter adjustment list is formed.

7. The adaptive optimization method for power supply interruption of a spinning frame according to claim 6, characterized in that, In step S4: the optimized instantaneous stop response mode parameter b050, instantaneous stop non-stop start voltage parameter b051, instantaneous stop non-stop threshold voltage parameter b052, and instantaneous stop non-stop integral time parameter b056 are written into the corresponding storage unit of the frequency converter through the parameter display interface, operation panel, or communication interface of the spinning machine frequency converter, and made effective immediately; during the writing process, each parameter is verified; after the writing is completed, the actual setting value of each parameter is confirmed by reading the data returned by the frequency converter display interface or communication interface, and the operating status and input voltage signal of the spinning machine are monitored.

8. The adaptive optimization method for power supply interruption of a spinning frame according to claim 7, characterized in that, In step S5: the operating status signal of the spinning machine inverter, sensor data, and power grid voltage acquisition equipment are used to collect the operating status parameters of the spinning machine and the power grid voltage change parameters in real time. The real-time collected parameter data is compared with the prediction results to determine whether the spinning machine can maintain normal operation and automatically recover under different types of power supply interruption events under optimized parameter configuration.

9. A power supply interruption adaptive optimization system for a spinning frame, characterized in that, include: The module includes a parameter acquisition module (1), a parameter analysis module (2), a parameter optimization module (3), a parameter configuration module (4), and a runtime monitoring module (5). The parameter acquisition module (1) collects the current parameters of the frequency converter and real-time data of the grid voltage. The parameter analysis module (2) evaluates the device response to the current parameters during a voltage flashover. The parameter optimization module (3) generates and outputs parameter adjustment strategies based on the analysis results; The parameter configuration module (4) writes the optimized parameters into the frequency converter and completes the system settings; The operation monitoring module (5) monitors the operating status and voltage stability of the spinning machine in real time and provides feedback on the optimization effect.

10. The adaptive optimization system for power supply interruption of a spinning frame according to claim 9, characterized in that, The parameter acquisition module (1) determines the operating parameters related to the power supply interruption response based on the parameter definition of the spinning machine frequency converter and the power supply interruption control principle, and acquires the current first operating parameter of the spinning machine frequency converter. Combined with the parameter description of instantaneous power outage control in the spinning machine frequency converter instruction manual and the power supply interruption statistical analysis results formed during the long-term operation of the spinning machine, it determines the second operating parameter directly related to the power supply interruption response. Through the parameter display interface, operation panel or communication interface of the spinning machine frequency converter, it reads the current set value of the second operating parameter; synchronously collects the operating status signal of the spinning machine and the voltage sampling data on the input side of the frequency converter. After completing the data acquisition, it preprocesses the second operating parameter and the voltage sampling data and organizes the continuously collected data in chronological order to form a parameter data set corresponding to the power supply interruption event. The parameter analysis module (2) analyzes the response performance of the parameter data set corresponding to the power supply flash stop event in the flash stop event based on historical power supply flash stop event records and voltage fluctuation data, forming an event parameter data set, which is input into a multi-layer feedforward neural network for analysis, and outputs the analysis results of the flash stop event of the spinning machine under given parameters and voltage fluctuation conditions; the multi-layer feedforward neural network establishes a nonlinear mapping relationship between the second operating parameter and the flash stop response result through forward propagation, and adjusts the network connection weights and thresholds through the error backpropagation algorithm; The parameter optimization module (3) proposes a parameter optimization scheme based on the generated analysis results, including changing the instantaneous stop response mode from "instantaneous stop without stopping and no recovery" to "instantaneous stop without stopping and with recovery", increasing the instantaneous stop start voltage from 440V to 470V, adjusting the instantaneous stop threshold voltage from 720V to 510V, and increasing the instantaneous stop integration time from 0.100 seconds to 0.500 seconds. During the generation of the optimization scheme, the parameters that contribute more to the probability of abnormal parking are adjusted first by using the prediction results of the trained multilayer feedforward neural network, quantifying and determining the adjustment range and direction of each parameter, and forming a parameter adjustment list. The parameter configuration module (4) writes the optimized instantaneous stop response mode parameter b050, instantaneous stop non-stop start voltage parameter b051, instantaneous stop non-stop threshold voltage parameter b052, and instantaneous stop non-stop integral time parameter b056 into the corresponding storage unit of the frequency converter through the parameter display interface, operation panel, or communication interface of the spinning machine frequency converter, and makes it effective immediately; during the writing process, each parameter is verified, and after the writing is completed, the actual setting value of each parameter is confirmed by reading the data returned by the frequency converter display interface or communication interface, and the running status and input voltage signal of the spinning machine are monitored at the same time; The operation monitoring module (5) collects the operating status parameters of the spinning machine and the grid voltage change parameters in real time through the operating status signal of the spinning machine frequency converter, sensor data and grid voltage acquisition equipment. It compares the real-time collected parameter data with the prediction results to determine whether the optimized parameters have successfully improved the stability of the spinning machine in the power supply interruption event.

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