Intelligent equipment rack processing method capable of monitoring material state in real time

Abstract

This invention discloses a method for processing intelligent equipment racks that can monitor material conditions in real time. The method includes: collecting stress signal data of the material during processing using a sensor array to generate an initial stress distribution map; denoising and feature extraction of the signal using signal processing technology based on the initial stress distribution map to obtain stress change trend data; calling a pre-established stress distribution model based on the stress change trend data; determining the priority direction of parameter adjustment by locating deformation risk points; if the priority direction of parameter adjustment deviates from a preset range, generating an adjustment command to obtain an optimized processing parameter configuration scheme; and applying the optimized processing parameter configuration scheme. This invention achieves full-process control of the intelligent equipment rack processing by real-time acquisition of stress signals, accurate feature extraction, model-based risk analysis, and dynamic parameter optimization, ensuring stable material conditions and product quality compliance.

Description

Technical Field

[0001] This invention relates to the field of intelligent equipment rack processing technology, and more specifically to an intelligent equipment rack processing method that can monitor the material status in real time. Background Technology

[0002] As the core load-bearing structure, the rack of intelligent equipment requires extremely high precision in machining, structural strength, and stability. The machining method directly affects the quality, performance, and production efficiency of the product. However, despite continuous advancements in machining technology, existing methods still have significant limitations, primarily in their insufficient ability to dynamically adapt to the material's state during machining. Many traditional solutions rely on preset parameters or empirical values, making it difficult to cope with the complex and ever-changing mechanical environment during machining. This results in difficulties in guaranteeing machining quality and stability when facing different material or process requirements.

[0003] Looking deeper, the core challenge in this field lies in effectively controlling material stress changes during processing. As processing progresses, materials generate complex stress distributions during operations such as cutting, stamping, or welding. If these changes cannot be monitored in real time, stress concentration problems can easily occur, leading to uncontrolled material deformation or product defects. This uncontrollability of stress changes further exacerbates the difficulty of dynamically adjusting processing parameters. Due to the lack of precise feedback on the material's state, parameter adjustments are often delayed or inaccurate, making it difficult to ensure that product strength remains at a reasonable level at critical moments.

[0004] Therefore, how to monitor the stress changes and deformation of materials in real time during processing, and dynamically adjust processing parameters according to the mechanical requirements at different stages, has become a key issue in ensuring product strength and quality. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a smart equipment rack processing method that can monitor the material status in real time, so as to solve the problems of material status monitoring lag, insufficient data processing accuracy, lack of targeted parameter adjustment, lack of risk prediction, and insufficient closed-loop control capability in the prior art. By collecting stress signals in real time, accurately extracting features, analyzing risks in a model, and dynamically optimizing parameters, the invention achieves full-process control of the smart equipment rack processing, ensuring stable material status and product quality.

[0006] To address the aforementioned technical problems, this invention provides a smart equipment rack processing method capable of real-time monitoring of material status, the method comprising: The stress signal data of the material during the processing is collected by a sensor array to generate an initial stress distribution map. Based on the initial stress distribution map, signal processing techniques are used to denoise and extract features from the signal to obtain stress change trend data. Based on the stress change trend data, a pre-established stress distribution model is invoked to analyze the stress concentration areas in material processing and determine the deformation risk points; By locating the deformation risk points and combining them with the operating status of the processing equipment, the correlation data between the processing parameters and the stress distribution is obtained, and the priority direction for parameter adjustment is determined. If the priority direction of the parameter adjustment deviates from the preset range, an adjustment command is generated to obtain an optimized processing parameter configuration scheme; Based on the optimized processing parameter configuration scheme, a dynamic adjustment signal is sent to the processing equipment to synchronously update the mechanical output value during the processing and determine the stability index of the material state.

[0007] Furthermore, the step of acquiring stress signal data of the material during processing through a sensor array and generating an initial stress distribution map includes: real-time monitoring of the material during processing using a sensor array to obtain raw stress signal data; denoising the acquired raw stress signal data using signal processing techniques to obtain a purified stress signal dataset; determining the correspondence between stress signals and material positions using a pre-established mapping model to obtain preliminary stress distribution information; identifying outliers in the preliminary stress distribution information by comparing them with a preset threshold range to determine whether the outliers are valid data, thus obtaining a filtered stress distribution dataset; smoothing the data using an interpolation method to obtain continuous stress distribution features; generating a visualized initial stress map using continuous stress distribution features combined with distribution mapping techniques; and classifying the stress change trends in the generated initial stress map using a support vector machine algorithm to determine the stress distribution patterns in key areas.

[0008] Further, based on the initial stress distribution map, signal processing techniques are used to denoise and extract features from the signal to obtain stress change trend data. This includes: acquiring raw signal data from the initial stress distribution map and obtaining denoised first signal data using a filtering method; decomposing the signal using wavelet transform based on the first signal data, extracting multi-scale feature information, and determining key feature points; performing time series analysis on the key feature points to determine the change pattern of the feature points in the time dimension; if the change amplitude of the feature points exceeds a preset threshold, they are marked as anomalies, resulting in a labeled feature dataset; and analyzing the distribution of the anomalies using the labeled feature dataset.

[0009] Furthermore, based on the stress change trend data, a pre-established stress distribution model is invoked to analyze stress concentration areas during material processing and determine deformation risk points. This includes: acquiring real-time stress change information from the material processing process through a sensor acquisition system and storing it as an initial dataset to obtain stress change records during the processing; based on the stress change records, the pre-built stress distribution model is invoked to analyze the correlation between the stress changes and the processing process, and to determine the distribution characteristics of the stress concentration areas; based on the distribution characteristics of the stress concentration areas, the finite element analysis method is used to simulate the degree of stress concentration during the material processing and to identify potential deformation risk areas.

[0010] Furthermore, by locating the deformation risk points and combining them with the operating status of the processing equipment, correlation data between processing parameters and stress distribution is obtained to determine the priority direction for parameter adjustment. This includes: obtaining data on the processing equipment under different operating states by accurately locating the deformation risk points to determine the initial stress distribution characteristics; analyzing the correlation data between processing parameters and stress distribution based on the initial stress distribution characteristics to obtain the distribution law of parameter influence; using the distribution law as a basis and combining it with the operating status information of the processing equipment, determining the priority direction for processing parameter adjustment; if the influence of a certain parameter on the stress distribution exceeds a preset threshold... If the parameter is not selected, it is adjusted first to obtain a preliminary adjustment strategy. Using this preliminary strategy, simulated stress distribution data after parameter adjustment is obtained to determine if the adjustment strategy meets the requirements for deformation risk control. If the simulated stress distribution data does not meet the preset control standard, the parameter adjustment strategy is optimized using a support vector machine algorithm, combined with equipment status and related data, to obtain an optimized adjustment scheme. Based on the optimized adjustment scheme, the changing trend of deformation risk points is analyzed to determine the actual impact of the adjustment strategy on risk control. Using the analysis results of the changing trends, the final processing parameter adjustment scheme is obtained to determine the control effect of deformation risk points.

[0011] Furthermore, if the priority direction of the parameter adjustment deviates from the preset range, an adjustment instruction is generated to obtain an optimized processing parameter configuration scheme, including: analyzing the specific performance of the adjustment direction based on the standardized parameter dataset, triggering a direction deviation judgment mechanism, and determining the specific value of the deviation degree; comparing the specific value of the deviation degree using pre-established decision rules, and generating corresponding adjustment instruction content if it exceeds the preset threshold; generating an adapted configuration scheme by logically verifying the adjustment instruction content and combining it with the target requirements of parameter optimization, and determining the final configuration content; predictively analyzing the stability of the configuration scheme for the final configuration content and obtaining prediction results.

[0012] Furthermore, based on the optimized processing parameter configuration scheme, a dynamic adjustment signal is sent to the processing equipment to synchronously update the mechanical output values ​​during the processing and determine the stability index of the material state. This includes: collecting mechanical output data based on the response state of the processing equipment to determine the mechanical change trend during the processing; analyzing the fluctuation of the output data in response to the mechanical change trend; if the fluctuation exceeds a preset threshold, triggering a dynamic adjustment mechanism to obtain the adjusted mechanical output data; evaluating the change characteristics of the material state through the adjusted mechanical output data to determine whether the material state meets the expected standard; if the material state does not meet the standard, generating an adjustment signal to update the operating instructions of the processing equipment and synchronously transmitting them to relevant modules to obtain the final processing stability index.

[0013] The beneficial effects of this invention are as follows: 1. Real-time monitoring is more accurate and risk prediction is more proactive: Stress signals are collected in real time through a sensor array, and combined with signal processing technology and mapping model, a visualized initial stress spectrum is generated to realize real-time tracking of material state; with the help of stress distribution model and finite element analysis, stress concentration areas and deformation risk points are accurately located, and risk prediction is transformed from "post-event detection" to "pre-event warning", effectively avoiding material defects.

[0014] 2. Intelligent data processing and precise feature extraction: Employing multiple technologies such as filtering and denoising, wavelet transform, and support vector machine algorithms, the system purifies complex stress signals, extracts multi-scale features, and classifies change trends, reducing the impact of interference signals, improving the accuracy and efficiency of data processing, and providing reliable data support for subsequent analysis.

[0015] 3. Dynamic parameter adjustment and enhanced adaptability: Based on the correlation analysis between processing parameters and stress distribution, the priority direction of parameter adjustment is determined. Through simulation optimization and dynamic adjustment mechanisms, adaptive optimization of processing parameters is achieved, effectively coping with the complex and ever-changing mechanical environment during processing and adapting to different material and process requirements.

[0016] 4. Closed-loop integrated management and control, and stable processing quality: Construct a closed-loop management and control system for the entire process of "signal acquisition - data processing - risk analysis - parameter optimization - dynamic adjustment". Each link is seamlessly connected and linked in real time to ensure that the material condition is always stable within the expected range, which significantly improves the processing accuracy, structural strength and quality stability of the intelligent equipment frame.

[0017] 5. Wide range of applicable scenarios and outstanding practicality: It is compatible with various intelligent equipment frame processing technologies such as cutting, stamping, and welding, and can adapt to the processing needs of frames of different materials and specifications. It does not require major modification of existing equipment, has low promotion and application costs, and has both versatility and specialization. Attached Figure Description

[0018] Figure 1 This is a flowchart of the present invention.

[0019] Figure 2 This is a schematic diagram of the specific process of step 3 of the present invention.

[0020] Figure 3 This is a schematic diagram of the specific process of step 6 of the present invention. Detailed Implementation

[0021] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0022] Reference Figures 1 to 3 As shown, an embodiment of the intelligent equipment frame processing method of the present invention, which can monitor the material state in real time, is based on the synergistic effect of sensor array, signal processing technology, stress distribution model, and parameter optimization algorithm to achieve real-time monitoring and dynamic control of the material state during processing. The specific implementation process is as follows: The intelligent equipment frame processing method that can monitor the material status in real time includes six core steps: stress signal acquisition and initial spectrum generation, signal denoising and stress change trend extraction, stress concentration analysis and deformation risk point determination, processing parameter correlation analysis and adjustment direction determination, adjustment command generation and processing parameter optimization configuration, dynamic adjustment and material status stability confirmation.

[0023] The stress signal acquisition and initial stress map generation steps include: acquiring raw stress signal data in real time through a sensor array; after denoising, filtering, and smoothing, generating an initial stress map by combining a mapping model and distribution mapping technology; and then classifying stress change trends using a support vector machine algorithm. The sensor array monitors the material during processing in real time to acquire raw stress signal data. Signal processing techniques are used to denoise the raw stress signal data, resulting in a purified stress signal dataset. A pre-established mapping model is called to determine the correspondence between stress signals and material locations, obtaining preliminary stress distribution information. A preset threshold range is compared to determine whether outliers in the preliminary stress distribution information are valid data, resulting in a filtered stress distribution dataset. Interpolation methods are used to smooth the filtered dataset, obtaining continuous stress distribution features. Distribution mapping technology is used to transform the continuous stress distribution features into a visualized initial stress map. A support vector machine algorithm is used to classify the stress change trends in the initial stress map, determining the stress distribution patterns in key areas.

[0024] The signal denoising and stress change trend extraction steps include: filtering and denoising the signal of the initial stress spectrum, extracting multi-scale features using wavelet transform, marking outliers through time series analysis, and refining stress change trend data; based on the initial stress distribution spectrum, using signal processing techniques to denoise and extract features from the signal to obtain stress change trend data; collecting raw signal data from the initial stress distribution spectrum and obtaining the denoised first signal data using filtering methods; decomposing the first signal data using wavelet transform to extract multi-scale feature information and determine key feature points; performing time series analysis on the key feature points to determine the change pattern of the feature points in the time dimension; if the change amplitude of the feature points exceeds a preset threshold, they are marked as outliers, obtaining the labeled feature dataset; analyzing the distribution of outliers in the labeled feature dataset and refining stress change trend data.

[0025] The steps for stress concentration analysis and deformation risk point determination include: 1) calling a stress distribution model based on stress change records, simulating the degree of stress concentration using finite element analysis, and locating deformation risk points; 2) calling a pre-established stress distribution model based on stress change trend data, analyzing stress concentration areas during material processing, and determining deformation risk points; 3) acquiring real-time stress change information from the material processing process through a sensor acquisition system, storing it as an initial dataset, and obtaining stress change records during processing; 4) calling a pre-constructed stress distribution model based on the stress change records, analyzing the correlation between stress changes and the processing process, and determining the distribution characteristics of stress concentration areas; 5) using finite element analysis to simulate the degree of stress concentration during material processing, identifying potential deformation risk areas, and accurately locating deformation risk points.

[0026] The steps for analyzing the correlation between processing parameters and determining the direction of adjustment include: analyzing the correlation between processing parameters and stress distribution to determine the priority direction of parameter adjustment, and obtaining the final adjustment scheme through simulation optimization; by locating deformation risk points and combining them with the operating status of the processing equipment, obtaining correlation data between processing parameters and stress distribution, and determining the priority direction of parameter adjustment; accurately locating deformation risk points, obtaining data of the processing equipment under different operating states, and determining the initial stress distribution characteristics; based on the initial stress distribution characteristics, analyzing the correlation data between processing parameters and stress distribution to obtain the distribution law of parameter influence; using the distribution law as a basis, combined with the operating status information of the processing equipment, determining the adjustment of processing parameters. The process involves several steps: First, the priority direction of adjustment is determined. If the influence of a certain parameter on stress distribution exceeds a preset threshold, that parameter is adjusted first to obtain a preliminary adjustment strategy. Second, simulated stress distribution data after parameter adjustment is obtained using this preliminary strategy to determine if the adjustment strategy meets the requirements for deformation risk control. If the simulated stress distribution data does not meet the preset control standard, the parameter adjustment strategy is optimized using a support vector machine algorithm, combined with equipment status and related data, to obtain an optimized adjustment scheme. Third, based on the optimized adjustment scheme, the changing trend of deformation risk points is analyzed to determine the actual impact of the adjustment strategy on risk control. Finally, the final processing parameter adjustment scheme is obtained through the analysis of the changing trend, and the control effect of deformation risk points is determined.

[0027] The steps for generating adjustment instructions and optimizing processing parameters include: determining the degree of deviation in the parameter adjustment direction, generating and verifying adjustment instructions, and forming an optimized processing parameter configuration scheme; if the priority direction of parameter adjustment deviates from a preset range, generating adjustment instructions to obtain an optimized processing parameter configuration scheme; analyzing the specific performance of the adjustment direction based on the standardized parameter dataset, triggering a deviation judgment mechanism, and determining the specific value of the deviation degree; comparing the specific value of the deviation degree using pre-established decision rules, and generating corresponding adjustment instructions if it exceeds a preset threshold; generating an appropriate configuration scheme by logically verifying the adjustment instructions and combining them with the target requirements of parameter optimization, and determining the final configuration content; predicting and analyzing the stability of the configuration scheme for the final configuration content and obtaining prediction results.

[0028] The dynamic adjustment and material state stability confirmation steps include: sending adjustment signals to the processing equipment, collecting mechanical output data, and dynamically optimizing until the material state stability index is determined; sending dynamic adjustment signals to the processing equipment according to the optimized processing parameter configuration scheme, synchronously updating the mechanical output values ​​during the processing, and determining the material state stability index; collecting mechanical output data according to the response status of the processing equipment, and determining the mechanical change trend during the processing; analyzing the fluctuation of the output data for the mechanical change trend, and if the fluctuation exceeds a preset threshold, triggering the dynamic adjustment mechanism to obtain the adjusted mechanical output data; evaluating the change characteristics of the material state through the adjusted mechanical output data, and determining whether the material state meets the expected standard; if the material state does not meet the standard, generating an adjustment signal, updating the operation instructions of the processing equipment, and synchronously transmitting them to the relevant modules to obtain the final processing stability index.

[0029] The specific operation process of this invention is as follows: 1. Preliminary preparations and system initialization (1) Equipment and sensor deployment Based on the processing technology of the intelligent equipment frame (such as cutting, stamping, welding), sensor arrays are deployed at key workstations of the processing equipment. Sensor types include stress sensors, strain gauges, temperature sensors, etc., to ensure coverage of the core area of ​​material processing and achieve comprehensive acquisition of stress signals. The sensor arrays are then connected to the data acquisition module, signal processing module, control module, and display module to build a complete processing monitoring system.

[0030] (2) Model and parameter preset A mapping model (for associating stress signals with material locations), a stress distribution model (built based on frame material properties and processing technology), and decision rules (for determining the degree of deviation in parameter adjustment direction) are pre-established. Key parameters such as signal denoising threshold, feature point change threshold, parameter influence threshold, and mechanical output fluctuation threshold are set. Training samples (historical data of different stress distribution patterns) are input into the support vector machine algorithm to complete the algorithm training and ensure classification accuracy.

[0031] (3) Pretreatment before processing Pre-treat the materials used for processing the intelligent device rack (e.g., rust removal, straightening) to ensure that the initial state of the materials meets the processing requirements; debug the processing equipment to the initial operating state, calibrate the acquisition accuracy of the sensor array, and verify the stability of the data transmission link; input the processing requirements of the rack (e.g., dimensional accuracy, structural strength standards) into the control module as the target basis for parameter optimization.

[0032] 2. Real-time monitoring and data processing of the processing procedure (1) Stress signal acquisition and initial spectrum generation The processing equipment and sensor array are started. The sensor array collects raw stress signal data in real time during material processing and transmits it to the data acquisition module. After preliminary processing of the raw data, the data acquisition module sends it to the signal processing module for noise reduction (using Kalman filtering or adaptive filtering algorithms) to obtain a purified stress signal dataset. The mapping model is called to determine the correspondence between stress signals and material positions, generating preliminary stress distribution information. Valid data is filtered and invalid outliers are removed by comparing with preset thresholds. Interpolation methods (such as cubic spline interpolation) are used to smooth the data and obtain continuous stress distribution characteristics. A visual initial stress map is generated using distribution mapping techniques (such as heat map mapping). The support vector machine algorithm classifies the stress change trends in the initial stress map, identifies the stress distribution patterns in key areas (such as corners and welds), and transmits the data synchronously to the display module for operators to view.

[0033] (2) Extraction of stress variation trend and risk analysis The signal processing module extracts raw signal data from the initial stress spectrum and performs filtering and noise reduction to obtain the first signal data. It then uses wavelet transform to decompose the first signal data into multiple scales, extracting multi-scale feature information in the time and frequency domains to determine key feature points of stress change. Time series analysis is performed on these key feature points to track their changes during processing. If the change amplitude of a feature point exceeds a preset threshold, it is marked as an anomaly. The distribution of anomalies is statistically analyzed, and stress change trend data is extracted. This stress change trend data is input into the stress distribution model and, combined with the real-time operating status of the processing equipment (such as cutting speed, stamping pressure, and welding current), the distribution characteristics of stress concentration areas are analyzed. Finite element analysis is used to simulate the degree of stress concentration, accurately locating deformation risk points (such as areas with a stress concentration coefficient exceeding 1.5). The risk point information and stress change trend data are then synchronously transmitted to the control module.

[0034] 3. Optimization and dynamic adjustment of processing parameters (1) Parameter correlation analysis and adjustment direction determination After receiving information on deformation risk points, the control module retrieves the operating status data of the processing equipment (such as current processing parameters and equipment load), analyzes the correlation between processing parameters (cutting speed, feed rate, stamping pressure, etc.) and stress distribution, and obtains the influence law of each parameter on stress changes. If the influence of a certain parameter on stress distribution exceeds a preset threshold, it is listed as a priority adjustment item, forming a preliminary adjustment strategy. The simulation module simulates the implementation effect of the preliminary strategy and obtains simulated stress distribution data. If the simulated data does not meet the deformation risk control requirements (i.e., the risk points are not eliminated or mitigated), the support vector machine algorithm is used to optimize the preliminary strategy (adjusting parameter values ​​and adjustment timing) to obtain an optimized adjustment strategy. The simulated stress distribution data corresponding to the optimized strategy is analyzed to confirm the control effect of deformation risk points and determine the final parameter adjustment direction.

[0035] (2) Adjusting command generation and parameter configuration The control module determines whether the parameter adjustment direction deviates from the preset range (based on decision rules). If the deviation exceeds the preset threshold, it generates an adjustment instruction (specifying the adjustment parameters, adjustment range, and adjustment timing). The adjustment instruction undergoes logical verification (verifying whether it conforms to the operating capabilities of the processing equipment and the material characteristics). Combined with the frame processing technology requirements, an optimized processing parameter configuration scheme is formed. The configuration scheme undergoes stability prediction analysis (simulating its execution effect in subsequent processing). If the prediction result meets the requirements, the configuration scheme is sent to the execution module. If the prediction result does not meet the requirements, it returns to re-optimize until a feasible configuration scheme is formed.

[0036] (3) Dynamic adjustment and status confirmation The execution module receives the optimized machining parameter configuration scheme and sends a dynamic adjustment signal to the machining equipment to adjust the machining parameters (such as reducing the cutting speed in stress concentration areas and adjusting the stamping pressure). The sensor array collects the adjusted stress signals and mechanical output data (such as the strain value of the material and the load change of the machining equipment) in real time and transmits them to the control module. The control module analyzes the fluctuation of the mechanical output data. If the fluctuation is within the preset threshold range, the current parameters are maintained and machining continues. If the fluctuation exceeds the threshold, the dynamic adjustment mechanism is triggered to generate a secondary adjustment signal to fine-tune the machining parameters. The above adjustment process is repeated until the mechanical output data stabilizes within the preset range, and the stability indicators of the material state (such as stress uniformity and deformation error value) are determined to ensure that it meets the quality requirements of the frame machining.

[0037] 4. Post-processing quality verification and data traceability (1) Quality Inspection After processing, the intelligent equipment frame undergoes quality inspection, including dimensional accuracy inspection (using a coordinate measuring machine), structural strength inspection (using a tensile testing machine), and defect inspection (using ultrasonic testing) to verify whether the frame meets the preset standards. The quality inspection results are compared with the stability indicators during the processing to evaluate the control effect of the method.

[0038] (2) Data traceability and optimization The control module stores data from the entire processing process (including raw stress signals, processed data, parameter adjustment records, and stability indicators) for at least 180 days to facilitate subsequent quality traceability and problem investigation. It performs statistical analysis on historical data to optimize the parameters of the mapping model and stress distribution model, as well as the training samples for the support vector machine algorithm, continuously improving the monitoring accuracy and parameter optimization efficiency of the method. Based on processing feedback from different racks, it updates decision rules and preset thresholds to enhance the adaptability of the method.

[0039] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A method for processing intelligent equipment frames capable of real-time monitoring of material status, characterized in that, The method includes the following steps: (1) Collect stress signal data of materials during processing through a sensor array to generate an initial stress distribution map; (2) Based on the initial stress distribution map, signal processing techniques are used to denoise and extract features from the signal to obtain stress change trend data; (3) Based on the stress change trend data, call the pre-established stress distribution model to analyze the stress concentration area in material processing and determine the deformation risk points; (4) By locating the deformation risk points and combining them with the operating status of the processing equipment, obtain the correlation data between the processing parameters and the stress distribution, and determine the priority direction for parameter adjustment; (5) If the priority direction of the parameter adjustment deviates from the preset range, an adjustment command is generated to obtain an optimized processing parameter configuration scheme; (6) Based on the optimized processing parameter configuration scheme, send a dynamic adjustment signal to the processing equipment, update the mechanical output value during the processing synchronously, and determine the stability index of the material state.

2. The intelligent equipment frame processing method for real-time monitoring of material status as described in claim 1, characterized in that, The step of acquiring stress signal data of materials during processing through a sensor array and generating an initial stress distribution map includes: real-time monitoring of the materials during processing using the sensor array to obtain raw stress signal data; denoising the acquired raw stress signal data using signal processing techniques to obtain a purified stress signal dataset; determining the correspondence between stress signals and material positions using a pre-established mapping model to obtain preliminary stress distribution information; identifying outliers in the preliminary stress distribution information by comparing them to a preset threshold range to determine whether the outliers are valid data, thus obtaining a filtered stress distribution dataset; smoothing the filtered stress distribution dataset using interpolation methods to obtain continuous stress distribution features; generating a visualized initial stress map using continuous stress distribution features combined with distribution mapping techniques; and classifying the stress change trends in the generated initial stress map using a support vector machine algorithm to determine the stress distribution patterns in key areas.

3. The intelligent equipment frame processing method for real-time monitoring of material status as described in claim 2, characterized in that, Based on the initial stress distribution map, signal processing techniques are used to denoise and extract features from the signal to obtain stress change trend data. This includes: acquiring raw signal data from the initial stress distribution map and obtaining denoised first signal data using a filtering method; decomposing the signal using wavelet transform based on the first signal data, extracting multi-scale feature information, and determining key feature points; performing time series analysis on the key feature points to determine the change pattern of the feature points in the time dimension; if the change amplitude of the feature points exceeds a preset threshold, they are marked as anomalies, resulting in a labeled feature dataset; and analyzing the distribution of the anomalies using the labeled feature dataset.

4. The intelligent equipment frame processing method for real-time monitoring of material status as described in claim 3, characterized in that, Based on the stress change trend data, a pre-established stress distribution model is invoked to analyze stress concentration areas during material processing and determine deformation risk points. This includes: acquiring real-time stress change information from the material processing process through a sensor acquisition system and storing it as an initial dataset to obtain stress change records during the processing; based on the stress change records, the pre-built stress distribution model is invoked to analyze the correlation between the stress changes and the processing process, and to determine the distribution characteristics of the stress concentration areas; based on the distribution characteristics of the stress concentration areas, the finite element analysis method is used to simulate the degree of stress concentration during the material processing and to identify potential deformation risk areas.

5. The intelligent equipment frame processing method for real-time monitoring of material status as described in claim 4, characterized in that, By locating the deformation risk points and combining them with the operating status of the processing equipment, the correlation data between processing parameters and stress distribution is obtained to determine the priority direction of parameter adjustment. This includes: accurately locating the deformation risk points to obtain data on the processing equipment under different operating states and determining the initial stress distribution characteristics; analyzing the correlation data between processing parameters and stress distribution based on the initial stress distribution characteristics to obtain the distribution law of parameter influence; using the distribution law as a basis and combining it with the operating status information of the processing equipment, determining the priority direction of processing parameter adjustment; if the influence of a certain parameter on the stress distribution exceeds a preset threshold, then adjusting that parameter first to obtain a preliminary adjustment strategy; using the preliminary strategy, obtaining simulated stress distribution data after the processing parameters are adjusted to determine whether the adjustment strategy meets the requirements of deformation risk control; if the simulated stress distribution data does not meet the preset control standard, then combining the equipment status and correlation data, using a support vector machine algorithm to optimize the parameter adjustment strategy to obtain an optimized adjustment scheme; based on the optimized adjustment scheme, analyzing the changing trend of the deformation risk points to determine the actual impact of the adjustment strategy on risk control; and using the analysis results of the changing trend to obtain the final processing parameter adjustment scheme and determine the control effect of the deformation risk points.

6. The intelligent equipment frame processing method for real-time monitoring of material status as described in claim 5, characterized in that, If the priority direction of the parameter adjustment deviates from the preset range, an adjustment instruction is generated to obtain an optimized processing parameter configuration scheme, including: analyzing the specific performance of the adjustment direction based on the standardized parameter dataset, triggering a direction deviation judgment mechanism, and determining the specific value of the deviation degree; comparing the specific value of the deviation degree using pre-established decision rules, and generating corresponding adjustment instruction content if it exceeds the preset threshold; generating an adapted configuration scheme by logically verifying the adjustment instruction content and combining it with the target requirements of parameter optimization, and determining the final configuration content; predictively analyzing the stability of the configuration scheme for the final configuration content and obtaining prediction results.

7. The intelligent equipment frame processing method for real-time monitoring of material status as described in claim 6, characterized in that, Based on the optimized processing parameter configuration scheme, a dynamic adjustment signal is sent to the processing equipment to synchronously update the mechanical output values ​​during the processing and determine the stability index of the material state. This includes: collecting mechanical output data based on the response status of the processing equipment to determine the mechanical change trend during the processing; analyzing the fluctuation of the output data based on the mechanical change trend; if the fluctuation exceeds a preset threshold, triggering a dynamic adjustment mechanism to obtain the adjusted mechanical output data; evaluating the change characteristics of the material state through the adjusted mechanical output data to determine whether the material state meets the expected standard; if the material state does not meet the standard, generating an adjustment signal to update the operating instructions of the processing equipment and synchronously transmitting them to relevant modules to obtain the final processing stability index.

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