Intelligent metal cutting processing method and system

Abstract

The invention provides an intelligent metal cutting processing method and system, and the method comprises the steps: collecting material processing path data through scanning equipment, obtaining path length and corner number as path feature data, and carrying out the grouping of the path feature data through a data grouping method, and obtaining a path complexity classification result; extracting a starting section proportion threshold according to the path complexity classification result, judging whether the path length exceeds a preset threshold, if so, increasing the starting section proportion, otherwise, maintaining the starting section proportion, and determining a starting section length range; and updating whole-course temperature track data according to the self-adaptive proportion adjustment result, and transmitting the updated track data to processing equipment by adopting a control module to realize temperature dynamic matching in the material processing process.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to an intelligent metal cutting and processing method and system. Background Technology

[0002] Metal cutting is a fundamental process in high-end equipment manufacturing, aerospace, and automotive industries. Its processing accuracy, surface quality, and efficiency directly determine the service life of parts and the performance of the whole machine, and therefore it has always been regarded as one of the core competitive advantages. Although traditional laser or plasma cutting is fast, it is plagued by problems such as a large heat-affected zone of the material and the easy generation of burrs and deformation. Emerging intelligent cutting technology attempts to solve these defects through real-time control, but has exposed deeper contradictions in actual production.

[0003] While most current methods achieve temperature adjustment, they generally employ a uniform temperature setting throughout the entire process or simple segmented fixed values. This rigid strategy struggles to balance the smoothness of the initial cut and the overall processing stability when dealing with materials of varying thicknesses and path lengths. This results in difficulty in cutting short paths and a high failure rate in arc initiation, while in long paths, the accumulation of temperature later on easily causes edge overheating and burrs. Especially when the cutting path contains many corners or complex contours, the temperature cannot dynamically match the actual stroke, leading to severely uneven heat input distribution and drastic fluctuations in processing quality.

[0004] A more critical challenge lies in the fact that the temperature requirements throughout the cutting process exhibit strong phase-specific variations as the cutting motion progresses: the initial stage requires a higher temperature to quickly melt the material for a smooth cut; the intermediate, stable stage necessitates maintaining a constant temperature to ensure a consistent kerf width; and the final stage requires a rapid temperature reduction to prevent residual heat from causing molten spatter and burrs upon tool withdrawal. This fluctuating temperature requirement directly conflicts with the control method of maintaining a single, stable temperature throughout the entire process. Furthermore, the optimal length ratio required for each stage varies drastically depending on the total length and complexity of the cutting path, yet there is a lack of effective means for adaptive segmentation. Consequently, regardless of the fixed ratio employed, it is impossible to simultaneously meet the dual requirements of efficiency and quality.

[0005] Therefore, how to automatically divide the proportions of the starting segment, stable segment, and ending segment according to the actual path length and complexity during a single cutting process, and how to accurately plan the entire temperature trajectory for each stage that can quickly preheat and cut in, stably cut, and safely exit, while comprehensively balancing energy consumption, efficiency, and quality, has become a key issue restricting the true implementation of intelligent metal cutting technology and its large-scale replacement of traditional processes. Summary of the Invention

[0006] This invention provides an intelligent metal cutting and processing method, mainly including:

[0007] Material processing path data is collected using a scanning device, and the path length and number of corners are obtained as path feature data. The path feature data is grouped using a data grouping method to obtain path complexity classification results. Based on the path complexity classification results, a starting segment ratio threshold is extracted. It is then determined whether the path length exceeds a preset threshold; if it does, the starting segment ratio is increased; otherwise, the starting segment ratio is maintained, thus determining the starting segment length range. From the starting segment length range, the starting point of the stable segment is obtained, and an optimization calculation method is used to calculate the constant temperature value of the stable segment. The upper limit of the stable segment temperature is adjusted based on the number of corners in the path feature data, resulting in a stable segment temperature distribution sequence. For the end point of the stable segment temperature distribution sequence, the starting position of the termination segment is obtained, and it is determined whether the number of corners in the path complexity classification results exceeds a preset threshold. If it does, the starting point is increased. If the temperature decrease rate in the termination segment is reduced, the rate of temperature decrease is maintained; otherwise, the temperature decrease rate is maintained, and the temperature gradient curve of the termination segment is determined. By combining the initial segment length range and the stable segment temperature distribution sequence with the temperature gradient curve of the termination segment, full-process temperature trajectory data is generated. A real-time feedback sensor monitors the temperature deviation in the trajectory data to obtain an optimized version of the adjusted temperature trajectory. The proportional parameters for each stage are extracted from the optimized version of the adjusted temperature trajectory. It is determined whether the preheating time of the initial high-heat stage is less than the proportional threshold of the stable isothermal stage. If it is less, the initial segment length is extended; otherwise, the termination cooling stage length is shortened, resulting in an adaptive proportional adjustment result. The full-process temperature trajectory data is updated based on the adaptive proportional adjustment result, and the updated trajectory data is transmitted to the processing equipment using a control module to achieve dynamic temperature matching during material processing.

[0008] This invention provides an intelligent metal cutting and processing system, mainly comprising: a path data acquisition and grouping module, used to acquire material processing path data through a scanning device, obtain path length and number of corners as path feature data, and group the path feature data using a data grouping method to obtain a path complexity classification result; a starting segment ratio adjustment module, used to extract a starting segment ratio threshold based on the path complexity classification result, determine whether the path length exceeds a preset threshold, and if it does, increase the starting segment ratio; otherwise, maintain the starting segment ratio to determine the starting segment length range; a stable segment temperature calculation module, used to obtain the starting point of the stable segment from the starting segment length range, calculate the constant temperature value of the stable segment using an optimization calculation method, adjust the upper limit of the stable segment temperature based on the number of corners in the path feature data, and obtain a stable segment temperature distribution sequence; and a termination segment temperature gradient determination module, used to obtain the starting position of the termination segment for the end point of the stable segment temperature distribution sequence, and determine the path complexity classification result. The system checks whether the number of corners exceeds a preset threshold. If it does, the temperature drop rate of the termination segment is reduced; otherwise, the temperature drop rate is maintained, and the temperature gradient curve of the termination segment is determined. A full-process temperature trajectory generation and adjustment module generates full-process temperature trajectory data by combining the initial segment length range and the stable segment temperature distribution sequence with the termination segment temperature gradient curve. A real-time feedback sensor monitors the temperature deviation in the trajectory data to obtain an optimized version of the adjusted temperature trajectory. An adaptive proportional adjustment module extracts proportional parameters for each stage from the optimized version of the adjusted temperature trajectory, determines whether the preheating time of the initial high-heat stage is less than the proportional threshold of the stable constant-temperature stage. If it is less, the initial segment length is extended; otherwise, the termination cooling stage length is shortened, resulting in an adaptive proportional adjustment. A temperature trajectory update and control module updates the full-process temperature trajectory data based on the adaptive proportional adjustment result. The control module transmits the updated trajectory data to the processing equipment to achieve dynamic temperature matching during material processing. The technical solution provided by this embodiment of the invention may include the following beneficial effects:

[0009] This invention discloses an adaptive optimization method for material processing temperature trajectory based on processing path complexity. It uses a scanning device to collect processing path length and number of corners in real time as feature data, classifying path complexity accordingly. In high-complexity paths, the method automatically extends the length of the initial segment, increases its proportion, lowers the upper temperature limit of the stable segment, and slows down the cooling rate of the final segment, thereby avoiding heat accumulation and overheating risks caused by sharp corners. In low-complexity paths, the method shortens the initial segment and accelerates cooling to improve processing efficiency. Subsequently, a real-time temperature feedback sensor is used to correct deviations in the generated temperature trajectory, and an adaptive proportional adjustment mechanism dynamically optimizes the proportions of the three stages: initial high-heat, stable constant temperature, and final cooling. This ultimately forms a full-process temperature trajectory highly matched to the path characteristics, achieving precise dynamic control of the processing temperature, effectively improving the surface quality and dimensional accuracy of complex parts while maintaining processing efficiency. Attached Figure Description

[0010] Figure 1 This is a flowchart of an intelligent metal cutting and processing method according to the present invention.

[0011] Figure 2 This is a schematic diagram of an intelligent metal cutting and processing method according to the present invention.

[0012] Figure 3 This is another schematic diagram of an intelligent metal cutting and processing method according to the present invention.

[0013] Figure 4 This is a schematic diagram of the structure of an intelligent metal cutting and processing system according to the present invention. Detailed Implementation

[0014] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] like Figures 1-4 This embodiment of an intelligent metal cutting and processing method and system may specifically include:

[0016] Step S101: Collect material processing path data through scanning equipment, obtain path length and number of corners as path feature data, and group the path feature data using a data grouping method to obtain path complexity classification results.

[0017] Material processing path trajectory data is collected in real time using scanning equipment to obtain the original path point sequence. The total path length is obtained by calculating and summing the Euclidean distances between adjacent points based on the original path point sequence. The angle between adjacent three-point vectors is detected by traversing the path point sequence; if the angle deviates from 180 degrees and exceeds a preset threshold, it is marked as a corner, and the number of corners is counted. The total path length and the number of corners are combined to construct a two-dimensional path feature vector. The K-means clustering algorithm is used to group and cluster all path feature vectors, resulting in multiple path complexity clusters. Based on the average total path length and average number of corners within each cluster, the cluster numbers are sorted from low to high to determine the path complexity level label. The feature vector of each processing path is assigned to the nearest cluster center to obtain the complexity classification result of that path.

[0018] Based on the trajectory sampling frequency and the point sequence noise level, a low-noise path point sequence is obtained by smoothing the point sequence using median filtering.

[0019] Step 1: Acquire raw path data using a trajectory sampling device. Store the collected point sequence data initially to obtain an initial point sequence set. Step 2: Analyze the distribution of point sequence noise based on the initial point sequence set. Calculate the noise level using statistical methods to determine the range of noise interference. Step 3: For point sequence data with high noise levels, smooth the data using median filtering to obtain low-noise path point sequence data. Step 4: Reassemble the low-noise path point sequence data to obtain continuous path segments and assess their integrity. Step 5: If path segments are interrupted or missing, supplement the missing parts using interpolation methods to obtain a complete path data sequence. Step 6: Based on the complete path data sequence, analyze the continuity between point sequences. Optimize the path direction using the smoothed point sequence to obtain the final path data result.

[0020] Step S102: Extract the starting segment ratio threshold based on the path complexity classification result, determine whether the path length exceeds the preset threshold, and if it does, increase the starting segment ratio; otherwise, maintain the starting segment ratio to determine the starting segment length range.

[0021] Based on the path complexity classification results, obtain the corresponding preset starting segment ratio threshold table records. Read the initial starting segment ratio threshold and the adjacent higher-level ratio threshold corresponding to the current complexity level from the preset starting segment ratio threshold table records. Obtain the current total processing path length value. By comparing the total path length value with the preset length threshold, determine whether the total path length value exceeds the preset length threshold, and obtain the length comparison result. If the length comparison result shows an excess, select the adjacent higher-level ratio threshold as the adjusted ratio; otherwise, directly use the initial starting segment ratio threshold as the adjusted ratio. Multiply the adjusted ratio by the total path length value to obtain the upper limit value of the starting segment length. Multiply the adjusted ratio by a fixed downward floating factor of the total path length value to obtain the lower limit value of the starting segment length.

[0022] Step S103: Obtain the starting point of the stable segment from the starting segment length range, calculate the constant temperature value of the stable segment using an optimization calculation method, adjust the upper limit of the stable segment temperature by the number of corners in the path feature data, and obtain the stable segment temperature distribution sequence.

[0023] The starting position of the stable section is determined based on the length range of the initial section. The number of corners per unit length is statistically analyzed using path feature data to obtain the corner density. The corner density is multiplied by a preset temperature influence coefficient to obtain the temperature baseline adjustment. The temperature baseline value is then added to the temperature baseline adjustment to obtain the initial temperature constant value. The initial temperature constant value is curve-fitted along the path coordinates using the least squares method to obtain the temperature distribution sequence. The sum of squares of the temperature differences between adjacent sampling points is calculated from the temperature distribution sequence and divided by the total number of sampling points to obtain the temperature fluctuation variance. If the temperature fluctuation variance exceeds a preset variance threshold, the initial temperature constant value is lowered by a fixed amount to obtain the adjusted upper temperature limit value; otherwise, the initial temperature constant value is directly used as the upper temperature limit value.

[0024] Based on path characteristics and corner density data, combined with path coordinates and length range analysis, and through temperature distribution and fluctuation variance calculation, an accurate determination of stable starting position and constant value is obtained.

[0025] Step 1: Acquire path feature data and corner density information, and perform preliminary processing based on path coordinate data. Determine the basic information set through data integration. Step 2: Based on the basic information set, process the path coordinates and range analysis. If the path coordinate coverage exceeds a preset threshold, segment the range boundary to obtain preliminary region divisions. Step 3: Analyze the relationship between corner density and temperature distribution through preliminary region division. Use statistical tools to segment the temperature distribution data and determine the temperature distribution fluctuation range. Step 4: Calculate the specific value of the fluctuation variance based on the temperature distribution fluctuation range. If the fluctuation variance exceeds a preset standard, perform stratified sampling of the fluctuation range to determine the fluctuation control range. Step 5: Based on the fluctuation control range, combined with data from stable initiation and location determination, analyze the impact of path features on the starting position to obtain the specific coordinates of the stable initiation. Step 6: Based on the specific coordinates of the stable initiation, combined with the processing of constant values ​​and difference calculations, use the least squares method to smooth the values ​​to obtain the final constant reference value. Step 7: Using the final constant reference value, combined with the output results and data integration process, link and store the path characteristics and temperature distribution data to determine the complete path analysis results.

[0026] Step S104: For the end point of the stable segment temperature distribution sequence, obtain the starting position of the termination segment, determine whether the number of corners in the path complexity classification result exceeds a preset threshold. If it exceeds the threshold, reduce the temperature drop rate of the termination segment; otherwise, maintain the temperature drop rate and determine the temperature gradient curve of the termination segment.

[0027] The specific coordinates of the final point are extracted from the temperature distribution data of the stable segment. A data reading tool is used to obtain the starting position of the termination segment, determining its initial boundary. For the starting position of the termination segment, the number of corners in the path complexity classification results is obtained. A data comparison tool is used to determine if the number of corners exceeds a preset threshold, thus determining the path complexity. If the path complexity determination shows that the number of corners exceeds the preset threshold, the temperature decrease rate of the termination segment is reduced. A rate adjustment tool is used to calculate the new decrease rate value, determining the adjusted rate parameter. If the path complexity determination shows that the number of corners does not exceed the preset threshold, the original temperature decrease rate of the termination segment is maintained. A data retention tool is used to record the current rate value, obtaining a stable rate parameter. Based on the adjusted or stable rate parameter, a curve generation tool is used to construct the temperature gradient curve of the termination segment, obtaining preliminary gradient distribution data. For the preliminary gradient distribution data, a data smoothing tool is used to process local fluctuations in the temperature gradient curve, obtaining smoothed gradient curve data. Based on the smoothed gradient curve data, a data storage tool is used to associate and save it with the starting position of the termination segment, determining the final temperature gradient distribution record.

[0028] Based on the starting position and boundary point data of the termination segment, corner number and complexity analysis are used. By adjusting the temperature drop and drop rate, gradient curve and distribution number are constructed, the fluctuation area is processed to obtain smooth value, and the final temperature gradient distribution record is determined.

[0029] Based on the starting position of the terminating segment and the coordinates of the ending position of the stable segment, the initial temperature value of the terminating segment and the number of path corners are obtained. The path complexity level is determined based on the number of path corners and a preset threshold. If the number of corners exceeds the preset threshold, the path complexity level is marked as high; otherwise, it is marked as low. If the path complexity level is high, the temperature decrease rate is reduced, and the adjusted rate value is calculated; otherwise, the original rate value is directly recorded. Based on the adjusted or original rate value, combined with the terminating segment length, a terminating segment temperature gradient sequence is generated. A sliding window is used to scan the temperature gradient sequence, detecting local abrupt changes and marking all fluctuation intervals. For the marked fluctuation intervals, a low-pass filter is applied to process the temperature gradient sequence to obtain a smoothed gradient distribution. Based on the smoothed gradient distribution and the starting position of the terminating segment, the final temperature gradient curve is constructed and stored in the temperature distribution record.

[0030] Step S105: By combining the starting segment length range and the stable segment temperature distribution sequence with the ending segment temperature gradient curve, full-process temperature trajectory data is generated. A real-time feedback sensor is used to monitor the temperature deviation in the trajectory data to obtain an adjusted and optimized version of the temperature trajectory.

[0031] An initial full trajectory sequence is generated by splicing the starting segment length, stable segment sequence, and ending segment curve. A continuous full trajectory sequence is obtained by smoothly connecting the three data segments using cubic spline interpolation. A real-time feedback sensor collects the actual temperature sequence at a fixed sampling period to obtain a real-time temperature data point sequence. The temperature deviation sequence at the current moment is obtained by subtracting the corresponding positions of the initial full trajectory sequence from the real-time temperature data point sequence. If the absolute value of the temperature deviation sequence exceeds a preset deviation threshold, deviation compensation calculation is initiated; otherwise, the current full trajectory sequence remains unchanged. A proportional-integral-derivative controller calculates the real-time deviation compensation sequence using the temperature deviation sequence as input. The real-time deviation compensation sequence is added point by point to the corresponding positions of the initial full trajectory sequence to obtain the compensated trajectory sequence. A Kalman filter is used to fuse the compensated trajectory sequence and the real-time temperature data point sequence as observation inputs to obtain the optimized temperature trajectory sequence.

[0032] Step S106: Extract the proportional parameters of each stage from the adjusted temperature trajectory optimization version, determine whether the preheating time of the initial high-heat stage is less than the proportional threshold of the stable constant temperature stage, if it is less, extend the length of the initial segment, otherwise shorten the length of the termination cooling stage, and obtain the adaptive proportional adjustment result.

[0033] By obtaining the proportional parameters of each stage from the optimized version of the temperature trajectory, and comparing the data of the initial high-temperature stage and the stable isothermal stage, it is determined whether the proportion of preheating time meets the preset threshold standard. If the preheating time is lower than the proportional threshold of the stable isothermal stage, the starting length of the initial high-temperature stage is incrementally adjusted to generate adjusted starting segment data. If the preheating time is higher than the proportional threshold of the stable isothermal stage, the length of the ending cooling stage is incrementally adjusted to obtain adjusted ending segment data. Based on the adjusted starting and ending segment data, the entire temperature trajectory data sequence is reconstructed to obtain the updated trajectory version. The updated trajectory version is smoothed using the Kalman filter method to obtain a smoothed temperature trajectory sequence. Through the smoothed temperature trajectory sequence, it is analyzed whether the proportional parameters of each stage match the adaptive requirements, and whether the final length result meets the business requirements. Based on the final length result, adaptively adjusted temperature trajectory data is generated to determine the final entire trajectory configuration.

[0034] Step S107: Update the entire temperature trajectory data according to the adaptive proportional adjustment result, and use the control module to transmit the updated trajectory data to the processing equipment to realize dynamic temperature matching during the material processing process.

[0035] Based on the adaptive proportional adjustment results, an updated full-process temperature trajectory sequence is generated. The control module sends the updated full-process temperature trajectory sequence to the processing equipment. The actual temperature sampling point sequence returned by the processing equipment is obtained. The deviation value is calculated point by point between the actual temperature sampling point sequence and the updated full-process temperature trajectory sequence to obtain the deviation sequence. It is determined whether there are any deviation points in the deviation sequence that exceed the preset threshold range. If the deviation sequence exceeds the preset threshold range, a trajectory correction command is generated and the out-of-range position is marked. Based on the trajectory correction command, the temperature trajectory sequence of the interval corresponding to the out-of-range position is scaled up and down using a proportional fine-tuning method to obtain the corrected temperature trajectory sequence. The corrected temperature trajectory sequence is transmitted to the processing equipment to overwrite the original trajectory sequence. The new round of actual temperature sampling point sequence after the processing equipment executes the corrected temperature trajectory sequence is obtained. The deviation sequence is recalculated between the new round of actual temperature sampling point sequence and the corrected temperature trajectory sequence until the entire deviation sequence falls within the preset threshold range, thus obtaining the real-time temperature tracking and matching status.

[0036] This invention provides an intelligent metal cutting and processing system, mainly comprising: a path data acquisition and grouping module, used to acquire material processing path data through a scanning device, obtain path length and number of corners as path feature data, and group the path feature data using a data grouping method to obtain a path complexity classification result; a starting segment ratio adjustment module, used to extract a starting segment ratio threshold based on the path complexity classification result, determine whether the path length exceeds a preset threshold, and if it does, increase the starting segment ratio; otherwise, maintain the starting segment ratio to determine the starting segment length range; a stable segment temperature calculation module, used to obtain the starting point of the stable segment from the starting segment length range, calculate the constant temperature value of the stable segment using an optimization calculation method, adjust the upper limit of the stable segment temperature based on the number of corners in the path feature data, and obtain a stable segment temperature distribution sequence; and a termination segment temperature gradient determination module, used to obtain the starting position of the termination segment for the end point of the stable segment temperature distribution sequence, and determine the path complexity classification result. The system checks whether the number of corners exceeds a preset threshold. If it does, the temperature drop rate of the termination segment is reduced; otherwise, the temperature drop rate is maintained, and the temperature gradient curve of the termination segment is determined. A full-process temperature trajectory generation and adjustment module generates full-process temperature trajectory data by combining the initial segment length range and the stable segment temperature distribution sequence with the termination segment temperature gradient curve. A real-time feedback sensor monitors the temperature deviation in the trajectory data to obtain an optimized version of the adjusted temperature trajectory. An adaptive proportional adjustment module extracts proportional parameters for each stage from the optimized version of the adjusted temperature trajectory, determines whether the preheating time of the initial high-heat stage is less than the proportional threshold of the stable constant-temperature stage. If it is less, the initial segment length is extended; otherwise, the termination cooling stage length is shortened, resulting in an adaptive proportional adjustment. A temperature trajectory update and control module updates the full-process temperature trajectory data based on the adaptive proportional adjustment result. The control module transmits the updated trajectory data to the processing equipment to achieve dynamic temperature matching during material processing. The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method of intelligent metal cutting processing, characterized by, The method comprises: acquiring material processing path data by a scanning device, obtaining path length and corner number as path feature data, grouping the path feature data using a data grouping method to obtain path complexity classification results; extracting a starting segment proportion threshold value according to the path complexity classification results, judging whether the path length exceeds a preset threshold value, if yes, increasing the starting segment proportion, otherwise maintaining the starting segment proportion, determining a starting segment length range; obtaining a stable segment starting point from the starting segment length range, calculating a temperature constant value of the stable segment using an optimization calculation method, adjusting the upper limit of the stable segment temperature through the corner number in the path feature data, obtaining a stable segment temperature distribution sequence; for the end point of the stable segment temperature distribution sequence, obtaining a termination segment starting position, judging whether the corner number in the path complexity classification results exceeds a preset threshold value, if yes, reducing the termination segment temperature drop rate, otherwise maintaining the temperature drop rate, determining a termination segment temperature gradient curve; generating whole-process temperature trajectory data by combining the starting segment length range and the stable segment temperature distribution sequence with the termination segment temperature gradient curve, monitoring temperature deviation in the trajectory data using a real-time feedback sensor, obtaining an adjusted temperature trajectory optimization version; extracting each stage proportion parameter from the adjusted temperature trajectory optimization version, judging whether the preheating time of the starting high-temperature stage is less than the proportion threshold value of the stable constant-temperature stage, if yes, lengthening the starting segment length, otherwise shortening the termination cooling stage length, obtaining an adaptive proportion adjustment result; updating the whole-process temperature trajectory data according to the adaptive proportion adjustment result, transmitting the updated trajectory data to a processing device using a control module, and realizing temperature dynamic matching in the material processing process.

2. The intelligent metal cutting process method of claim 1, wherein, The method comprises: acquiring material processing path data by a scanning device, obtaining path length and corner number as path feature data, grouping the path feature data using a data grouping method to obtain path complexity classification results, comprising: real-time acquisition of material processing path trajectory data by a scanning device to obtain an original path point sequence; calculating the Euclidean distance accumulation between adjacent points according to the original path point sequence to obtain the total path length; detecting the angle between adjacent three points by traversing the path point sequence, if the angle deviates from 180 degrees by more than a preset threshold value, marking it as a corner, and counting the number of corners; combining the total path length and the number of corners to form a two-dimensional path feature vector; grouping and clustering all path feature vectors using a K-means clustering algorithm to obtain multiple path complexity clusters; sorting the cluster serial numbers from low to high according to the mean value of the total path length and the mean value of the number of corners in each cluster to determine the path complexity level label; The characteristic vector of each processing path is assigned to the nearest cluster center to obtain the complexity classification result to which the path belongs; further comprising: according to the trajectory sampling frequency and the point sequence noise level, the point sequence is smoothed by median filtering to obtain a low-noise path point sequence, specifically comprising: step one: obtaining the original path data through the trajectory sampling device, and storing the collected point sequence data to obtain an initial point sequence set; step two: according to the initial point sequence set, analyzing the distribution of point sequence noise, calculating the noise level by statistical method, and determining the degree range of noise interference; step three: for point sequence data with high noise level, the median filtering method is used for smoothing processing to obtain low-noise path point sequence data; step four: through sequence recombination of low-noise path point sequence data, continuous path segments are obtained, and the integrity of the path segments is judged; step five: if the path segment is interrupted or missing, the interpolation method is used to supplement the missing part to obtain the complete path data sequence; step six: according to the complete path data sequence, the continuity between point sequences is analyzed, the path trend is optimized by using the smoothed point sequence, and the final path data result is obtained.

3. The intelligent metal cutting process method of claim 1, wherein, The path complexity classification result is used to extract the starting segment proportion threshold, and it is judged whether the path length exceeds the preset threshold; if it exceeds, the starting segment proportion is increased, otherwise the starting segment proportion is maintained, the starting segment length range is determined, including: According to the path complexity classification result, the corresponding preset starting segment proportion threshold table record is obtained; The initial starting segment proportion threshold corresponding to the current complexity level and the adjacent high-level proportion threshold are read from the preset starting segment proportion threshold table record; The total length value of the current processing path is obtained; By comparing the total length value of the path with the preset length threshold, it is judged whether the total length value of the path exceeds the preset length threshold, and the length comparison result is obtained; If the length comparison result shows that it exceeds, the adjacent high-level proportion threshold is selected as the adjusted proportion, otherwise the initial starting segment proportion threshold is directly used as the adjusted proportion; The adjusted proportion is multiplied by the total length value of the path to obtain the upper limit value of the starting segment length; The adjusted proportion is multiplied by the fixed down coefficient of the total length value of the path to obtain the lower limit value of the starting segment length.

4. The intelligent metal cutting process method of claim 1, wherein, The starting point of the stable segment is obtained from the starting segment length range, the temperature constant value of the stable segment is calculated by using the optimization calculation method, the upper limit of the stable segment temperature is adjusted by the number of corners in the path characteristic data, and the stable segment temperature distribution sequence is obtained, including: The starting position of the stable segment is determined according to the starting segment length range; The number of corners per unit length is obtained by counting the number of corners in the path characteristic data to obtain the corner density; The corner density is multiplied by the preset temperature influence coefficient to obtain the temperature reference adjustment amount; The temperature reference value is added to the temperature reference adjustment amount to obtain the preliminary temperature constant value; The least square method is used to curve fit the preliminary temperature constant value along the path coordinates to obtain the temperature distribution sequence; The sum of the squares of the temperature difference between adjacent sampling points is calculated by the temperature distribution sequence, and the total number of sampling points is divided to obtain the temperature fluctuation variance. If the temperature fluctuation variance is greater than the preset variance threshold, the preliminary temperature constant value is adjusted downward by a fixed amplitude to obtain an adjusted temperature upper limit value, otherwise the preliminary temperature constant value is directly used as the temperature upper limit value; further comprising: analyzing the path characteristics and corner density data in combination with the path coordinates and length range, and obtaining accurate judgment of the stable starting position and constant value through temperature distribution and fluctuation variance calculation, specifically including: step 1: obtaining path characteristic data and corner density information, and combining path coordinate data for preliminary arrangement to determine a basic information set through data integration; step 2: according to the basic information set, processing the path coordinates and range analysis, if the path coordinate coverage range exceeds the preset threshold, the range boundary is segmented to obtain a preliminary division region; step 3: analyzing the relationship between corner density and temperature distribution through the preliminary division region, using statistical tools to segment the temperature distribution data and determine the fluctuation interval of the temperature distribution; step 4: calculating the specific value of the fluctuation variance according to the fluctuation interval of the temperature distribution, if the fluctuation variance exceeds the preset standard, layering sampling is performed on the fluctuation interval to determine the fluctuation control range; step 5: analyzing the influence of path characteristics on the starting position by combining the data of stable starting and position judgment through the fluctuation control range, and obtaining the specific coordinate point of the stable starting; step 6: according to the specific coordinate point of the stable starting, combining the constant value and difference calculation processing, using the least square method to smooth the value to obtain the final constant reference value; step 7: associating and storing the path characteristics and temperature distribution data through the final constant reference value, combining the result output and data integration process to determine the complete path analysis result.

5. The intelligent metal cutting process method of claim 1, wherein, The end point of the stable segment temperature distribution sequence is obtained, and it is judged whether the number of corners in the path complexity classification result exceeds the preset threshold, if it exceeds, the temperature drop rate of the termination segment is reduced, otherwise the temperature drop rate is maintained, and the temperature gradient curve of the termination segment is determined, including: The specific coordinates of the end point are extracted from the temperature distribution data of the stable segment, and the starting position of the termination segment is obtained by using a data reading tool to determine the initial boundary of the termination segment; The number of corners in the path complexity classification result is obtained for the starting position of the termination segment, and it is judged whether the number of corners exceeds the preset threshold by using a data comparison tool to obtain the judgment result of the path complexity; If the judgment result of the path complexity shows that the number of corners exceeds the preset threshold, the temperature drop rate of the termination segment is adjusted, a new drop rate value is calculated by using a rate adjustment tool, and the adjusted rate parameter is determined; If the judgment result of the path complexity shows that the number of corners does not exceed the preset threshold, the original temperature drop rate of the termination segment is maintained, the current rate value is recorded by using a data retention tool, and the stable rate parameter is obtained; According to the adjusted rate parameter or the stable rate parameter, a curve generation tool is used to construct the temperature gradient curve of the termination segment to obtain preliminary gradient distribution data; For the preliminary gradient distribution data, local fluctuations of the temperature gradient curve are processed by a data smoothing tool to obtain smoothed gradient curve data; According to the smoothed gradient curve data, the starting position of the termination section is associated and saved by using a data storage tool to determine the final temperature gradient distribution record; also includes: according to the starting position and boundary point data of the termination section, the corner number and complexity analysis are used, the temperature drop and the falling rate are adjusted, the gradient and distribution number are constructed, the fluctuation area is processed to obtain the smoothing value, and the final temperature gradient distribution record is determined, which specifically includes: according to the starting position of the termination section and the end coordinate of the stable section, the initial temperature value of the termination section and the number of path corners are obtained; according to the number of path corners and the preset threshold, the path complexity level is judged, if the number of corners exceeds the preset threshold, the path complexity level is marked as high, otherwise the path complexity level is marked as low; if the path complexity level is high, the temperature falling rate is reduced, the adjusted rate value is calculated, otherwise the original rate value is directly recorded; according to the adjusted rate value or the original rate value, the termination section length is combined to generate a termination section temperature gradient sequence; the temperature gradient sequence is scanned by a sliding window to detect local mutation points and mark all fluctuation interval positions; for the marked fluctuation interval position, a low-pass filter is applied to process the temperature gradient sequence to obtain a smoothed gradient distribution; according to the smoothed gradient distribution and the starting position of the termination section, the final temperature gradient curve is constructed and stored in the temperature distribution record.

6. The intelligent metal cutting process method of claim 1, wherein, The starting section length range and the stable section temperature distribution sequence are combined with the termination section temperature gradient curve to generate full-range temperature trajectory data, real-time feedback sensors are used to monitor temperature deviations in the trajectory data, and an adjusted temperature trajectory optimization version is obtained, including: Generate an initial full-range trajectory sequence according to the starting section length, the stable section sequence, and the termination section curve; A continuous full-range trajectory sequence is obtained by smoothing and connecting three pieces of data through cubic spline interpolation, real-time feedback sensors are used to collect actual temperature sequence data points at a fixed sampling period to obtain real-time temperature data point sequence, the initial full-range trajectory sequence is subtracted from the corresponding position of the real-time temperature data point sequence to obtain the temperature deviation sequence at the current time, if the absolute value of the temperature deviation sequence exceeds the preset deviation threshold, the deviation compensation amount calculation is started, otherwise the current full-range trajectory sequence remains unchanged, the proportional-integral-derivative controller is used to calculate the real-time deviation compensation amount sequence by taking the temperature deviation sequence as the input, and the real-time deviation compensation amount sequence is added to the corresponding position of the initial full-range trajectory sequence to obtain the compensated trajectory sequence. The Kalman filter is used to fuse the compensated trajectory sequence and the real-time temperature data point sequence as observation inputs to obtain the optimized temperature trajectory sequence.

7. The intelligent metal cutting process method of claim 1, wherein, The adjusted temperature trajectory optimization version is extracted to determine whether the preheating time of the initial high-temperature stage is less than the proportion threshold of the stable constant temperature stage, if it is less than, the starting section length is extended, otherwise the termination cooling stage length is shortened to obtain the adaptive proportion adjustment result, including: The proportion of each stage is obtained from the optimized version of the temperature trajectory, and the data of the initial high-temperature stage and the stable constant-temperature stage are compared to determine whether the preheating time length meets the preset threshold standard; If the preheating time length is lower than the proportion threshold of the stable constant-temperature stage, the initial length of the initial high-temperature stage is adjusted incrementally to generate adjusted initial segment data; If the preheating time length is higher than the proportion threshold of the stable constant-temperature stage, the length of the terminal cooling stage is adjusted decrementally to obtain adjusted terminal segment data; According to the adjusted initial segment data and the adjusted terminal segment data, the full-length data sequence of the temperature trajectory is reconstructed to obtain an updated trajectory version; The updated trajectory version is smoothed by using the Kalman filtering method to obtain a smoothed temperature trajectory sequence; Through the smoothed temperature trajectory sequence, whether the proportion parameters of each stage match the adaptability requirement is analyzed, and whether the final length result meets the business requirement is determined; According to the final length result, the temperature trajectory data after adaptive adjustment is generated to determine the final full-length trajectory configuration.

8. The intelligent metal cutting process method of claim 1, wherein, According to the adaptive proportion adjustment result, the full-length temperature trajectory data is updated, and the control module is used to transmit the updated trajectory data to the processing equipment to realize dynamic matching of the temperature in the material processing process, including: According to the adaptive proportion adjustment result, an updated full-length temperature trajectory sequence is generated, the updated full-length temperature trajectory sequence is issued to the processing equipment through the control module, an actual temperature sampling point sequence returned by the processing equipment is obtained, a deviation value is calculated point by point for the actual temperature sampling point sequence and the updated full-length temperature trajectory sequence to obtain a deviation sequence, it is judged whether there is a deviation point exceeding the preset threshold range in the deviation sequence, if the deviation sequence exceeds the preset threshold range, a trajectory correction instruction is generated and the exceeding position is marked, according to the trajectory correction instruction, the temperature trajectory sequence of the corresponding interval of the exceeding position is scaled in the proportional fine adjustment mode to obtain a corrected temperature trajectory sequence, the corrected temperature trajectory sequence is transmitted to the processing equipment to cover the original trajectory sequence, a new round of actual temperature sampling point sequence after the processing equipment executes the corrected temperature trajectory sequence is obtained, the deviation sequence is recalculated for the new round of actual temperature sampling point sequence and the corrected temperature trajectory sequence, until the deviation sequence falls within the preset threshold range to obtain a temperature real-time tracking matching state.

9. An intelligent metal cutting processing system, characterized by, The system comprises: a path data acquisition and grouping module for acquiring material processing path data through a scanning device, obtaining path length and corner number as path feature data, and grouping the path feature data by using a data grouping method to obtain a path complexity classification result; a starting segment proportion adjustment module for extracting a starting segment proportion threshold according to the path complexity classification result, judging whether the path length exceeds a preset threshold, increasing the starting segment proportion if it exceeds, otherwise maintaining the starting segment proportion, and determining a starting segment length range; a stable segment temperature calculation module for obtaining a stable segment starting point from the starting segment length range, calculating a temperature constant value of the stable segment by using an optimization calculation method, adjusting an upper limit of stable segment temperature through the corner number in the path feature data, and obtaining a stable segment temperature distribution sequence; a termination segment temperature gradient determination module for obtaining a termination segment starting position for the end point of the stable segment temperature distribution sequence, judging whether the corner number in the path complexity classification result exceeds a preset threshold, reducing the termination segment temperature drop rate if it exceeds, otherwise maintaining the temperature drop rate, and determining a termination segment temperature gradient curve; a whole-process temperature trajectory generation and adjustment module for generating whole-process temperature trajectory data by combining the starting segment length range, the stable segment temperature distribution sequence, and the termination segment temperature gradient curve, monitoring temperature deviation in the trajectory data by using a real-time feedback sensor, and obtaining an adjusted temperature trajectory optimization version; an adaptive proportion adjustment module for extracting each stage proportion parameter from the adjusted temperature trajectory optimization version, judging whether the preheating time of the starting high-temperature stage is less than the proportion threshold of the stable constant temperature stage, lengthening the starting segment length if it is less, otherwise shortening the termination cooling stage length, and obtaining an adaptive proportion adjustment result; and a temperature trajectory updating and control module for updating whole-process temperature trajectory data according to the adaptive proportion adjustment result, transmitting the updated trajectory data to a processing device by using a control module, and realizing temperature dynamic matching in the material processing process.

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