Cutting positioning method and device for laser cutting metal based on big data

Abstract

The invention discloses a cutting positioning method and device for laser cutting metal based on big data, and relates to the technical field of laser cutting. The invention discloses a cutting positioning method and device for laser cutting metal based on big data. The cutting positioning method comprises the steps that S1, thermal working condition data, process path spatial feature data and historical working condition data are preprocessed; s2, based on time sequence thermal fluctuation analysis, initial calibration is conducted on multi-point thermal expansion, thermal fluctuation and disturbance coupling space offset before cutting, and a fine compensation process is entered; s3, performing fine adjustment and high-precision track correction on a path node space through thermal coupling compensation theory analysis; and S4, performing real-time monitoring and graded intervention on time sequence response fluctuation and abnormal risks in the compensation process based on time sequence fluctuation and abrupt change criteria. The problems that complex coupling drifting is generated due to multi-source high-temperature instantaneous thermal expansion and deformation of a laser head and a workpiece under the limit cutting working condition, real-time accurate compensation is difficult to achieve through an existing positioning method, and the high-precision cutting stability is seriously affected are solved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of laser cutting, in particular to a cutting positioning method and device for laser cutting of metal based on big data. BACKGROUND

[0002] With the rapid development of intelligent manufacturing and high-end equipment machining technology, laser cutting has become an important means for metal material forming and complex structure machining due to its high precision, high efficiency and flexible processing advantages. Modern laser cutting systems are widely used in aerospace, automobile manufacturing, rail transportation, engineering machinery and electronic manufacturing industries, and the requirements for cutting quality and positioning accuracy are increasingly improved. In order to realize high-precision cutting of complex curves and special-shaped profiles, laser cutting equipment is usually equipped with multi-axis numerical control platform, high-performance laser and multiple types of sensors, which can obtain process parameters such as temperature, position, speed and acceleration in real time. These technical means promote the continuous improvement of the intelligentization and automation level of laser cutting process, and promote the wide application of high-precision and high-efficiency metal machining.

[0003] For example, the patent for invention with publication number CN119609395A discloses a metal pipe laser cutting device with positioning and cutting functions, which includes a support frame, a conveying plate, a mounting seat, a mounting plate, a clamp base and a laser cutting machine, etc. The mounting plate is provided with a second servo motor and a third servo motor, which respectively drive a first rotating rod and a second gear, realizing multi-axis linkage and fine adjustment. The clamp base is provided with a bidirectional screw rod, a clamping block, an elastic telescopic head and a cylinder, etc. Through the cooperation of gear meshing and screw transmission, the clamp limiting sliding groove and the L-shaped fixed plate can realize the clamping and positioning of metal pipes and efficient laser cutting, and are suitable for automatic precision machining of metal pipes of different sizes.

[0004] For example, the utility model patent with publication number CN222359467U discloses a metal machining cutting positioning structure, which includes a workbench, supporting legs and an adjusting assembly. The adjusting assembly includes a transmission shaft, a pinion, a ring gear, a placement disc, a driving pulley, a driven pulley, a reciprocating screw rod, a lifting frame, an adjusting slide rail, an adjusting cylinder and a laser cutting device. The structure drives the placement disc to rotate through the meshing of the pinion and the ring gear. The driving pulley and the driven pulley drive the reciprocating screw rod and the lifting frame to realize lifting adjustment through belt transmission. The adjusting slide rail is provided with an adjusting cylinder, which drives the laser cutting device to realize cutting positioning adjustment. The structure design realizes multi-dimensional positioning of the workpiece and accurate movement of the laser cutting device, improving the cutting positioning precision and efficiency of metal machining.

[0005] Although the existing metal processing cutting positioning and laser cutting device can realize multi-dimensional positioning of the workpiece and a certain degree of automatic cutting operation, in actual application, there are still problems such as insufficient precision of space offset and deformation response under complex working conditions, limited dynamic compensation capability, insufficient depth of multi-source process data fusion, and the like, which are difficult to continuously guarantee cutting precision and stability under extreme environments such as high temperature, high speed and dynamic disturbance. In addition, the response strategy of the prior art in risk early warning, process abnormality monitoring and intelligent intervention is relatively single, and cannot meet the higher requirements of high-precision, intelligentization and full-process closed-loop control in the field of high-end equipment manufacturing.

[0006] Therefore, in view of the above problems, there is an urgent need for a cutting positioning method and device for laser cutting of metal based on big data. SUMMARY

[0007] Technical problems solved In view of the deficiencies of the prior art, the present application provides a cutting positioning method and device for laser cutting of metal based on big data, which solves the problem that the laser head and the workpiece produce complex coupling drift due to multi-source high-temperature instantaneous thermal expansion and deformation under extreme cutting conditions, and the existing positioning method cannot compensate in real time and accurately, which seriously affects the stability of high-precision cutting.

[0008] Technical scheme To achieve the above purpose, the present application is implemented by the following technical scheme: a cutting positioning method for laser cutting of metal based on big data, comprising S1, collecting structure thermal condition data and process path space feature data, obtaining disturbance historical condition data, and preprocessing the thermal condition data, process path space feature data and historical condition data; S2, based on time series thermal fluctuation analysis, predicting and initially calibrating the trend of multi-point thermal expansion, thermal fluctuation and disturbance coupling space offset before cutting, and entering the detailed compensation process; S3, for dynamic thermal drift and disturbance in the cutting process, the path node space is fine-tuned and high-precision trajectory corrected through thermal coupling compensation theory analysis; S4, based on time series fluctuation and mutation criterion, real-time monitoring and hierarchical intervention on the time series response fluctuation and abnormal risk of the compensation process.

[0009] Further, the specific process of collecting structure thermal working condition data and process path space characteristic data and obtaining disturbance historical working condition data is as follows: collecting structure thermal working condition data, which includes compensation structure quantity, temperature length change data, geometric size data, real-time temperature data, initial temperature variable, surface temperature sequence data, current time, three-axis acceleration data and angle change data; collecting process path space characteristic data, which includes main path space coordinate point data, compensation vector direction data, length change data, current space coordinate, monitoring start time and monitoring end time; obtaining disturbance historical working condition data and establishing a disturbance historical working condition database, which includes historical three-axis acceleration data and historical angle change data.

[0010] Further, the specific process of preprocessing thermal working condition data, process path space characteristic data and historical working condition data is as follows: through outlier rejection and multi-source time alignment algorithm, the missing value of structure thermal working condition data is completed and the data is synchronized and arranged to ensure that various thermal parameters correspond at the same time; through space coordinate normalization and node distance equalization algorithm, the path resampling and space partitioning of process path space characteristic data are processed to extract the key node features of the main path; through window mean filtering and disturbance amplitude normalization algorithm, multi-channel data denoising and smoothing of disturbance historical working condition data are performed to strengthen the effective expression of disturbance change trend; through distribution standardization and linear normalization algorithm, the standardization and normalization of structure thermal working condition data, process path space characteristic data and disturbance historical working condition data are performed.

[0011] Further, the specific process based on the time sequence thermal fluctuation analysis is: obtaining the compensation structure quantity, temperature length change data, geometric size data, real-time temperature data, initial temperature variable, surface temperature sequence data, current time, three-axis acceleration data and angle change data; obtaining the thermal expansion coefficient of the temperature length change data through the least square linear regression algorithm; obtaining the effective feature length of the geometric size data through the three-dimensional distance calculation algorithm; obtaining the real-time temperature variable of the real-time temperature data through the temperature mean value algorithm; obtaining the thermal fluctuation amplitude factor of the surface temperature sequence data through the range calculation; obtaining the thermal fluctuation angular frequency of the surface temperature sequence data through the fast Fourier transform analysis of the main frequency, and calculating 2π times the main frequency; obtaining the disturbance quantity of the three-axis acceleration data and the angle change data through the Euclidean norm fusion and variance threshold detection algorithm; obtaining the process disturbance space offset value of the three-axis acceleration data and the angle change data through the Euclidean norm fusion, vector projection and absolute value operation; calculating the product of the thermal expansion coefficient, the effective feature length and the difference between the surface temperature and the static temperature to obtain the thermal expansion offset item of the structure; calculating the product of the thermal fluctuation amplitude factor, the thermal fluctuation angular frequency and the sine value of the current time to obtain the thermal fluctuation offset item of the structure; adding the thermal expansion offset item and the thermal fluctuation offset item of the structure to obtain the space offset prediction value of the structure; adding the space offset prediction values of all structures and adding the process disturbance space offset values after adding to obtain the space offset prediction value.

[0012] Further, the specific process of the trend prediction and initial calibration of the multi-point thermal expansion, thermal fluctuation and disturbance coupled space offset before cutting and entering the detailed compensation process is: real-time comparison of the space offset prediction value and the space offset prediction threshold: when the space offset prediction value is less than the space offset prediction threshold, it is determined as a normal initial offset state; through the three-dimensional geometric projection and vector superposition algorithm, the thermal sensitive space offset prediction value is projected to the three-dimensional coordinate axis to convert into a preliminary three-dimensional coordinate compensation to obtain the thermal sensitive space offset prediction three-dimensional value, which acts on the CNC zero point and path offset compensation, and enters the detailed compensation process; when the space offset prediction value is greater than or equal to the space offset prediction threshold, it is determined as an initial abnormal offset state; the preheating time is prolonged, the zero seeking and edge finding are repeated, and the workpiece static waiting process is executed until the space offset prediction value is less than the space offset prediction value threshold.

[0013] Further, for the dynamic thermal drift and disturbance in the cutting process, the specific process of thermal coupling compensation theoretical analysis is: obtaining the thermal sensitive space offset prediction three-dimensional value, the main path space coordinate point data, the compensation vector direction data, the real-time temperature data, the length change data, the current space coordinate, the three-axis acceleration data, the angle change data, the historical three-axis acceleration data and the historical angle change data; obtaining the compensation vector and the cutting main path included angle by vector included angle calculation on the main path space coordinate point data and the compensation vector direction data; obtaining the workpiece main material thermal expansion coefficient by least square linear regression algorithm on the real-time temperature data and the length change data; obtaining the remaining feature length by three-dimensional Euclidean distance calculation algorithm on the main path space coordinate point data and the current space coordinate; obtaining the disturbance response amplification factor by extreme value algorithm and sensitivity calibration method on the three-axis acceleration data and the angle change data; obtaining the overall space drift increment by weighted Euclidean norm fusion algorithm and window accumulation operation on the three-axis acceleration data and the angle change data; obtaining the historical space drift increment by weighted Euclidean norm fusion algorithm and window accumulation operation on the historical three-axis acceleration data and the historical angle change data, and obtaining the disturbance smoothing coefficient by sliding window average algorithm on the historical space drift increment; calculating the product of the thermal sensitive space offset prediction three-dimensional value and the cosine value of the compensation vector and the cutting main path included angle to obtain the space compensation term; calculating the product of the workpiece main material thermal expansion coefficient, the remaining feature length and the difference between the real-time temperature variable and the initial temperature variable to obtain the thermal expansion offset term; adding the space compensation term and the thermal expansion offset term to obtain the path offset term; calculating the product of the disturbance response amplification factor and the overall space drift increment to obtain the disturbance drift term; adding the path offset term and the disturbance drift term to take the absolute value as the numerator; calculating the sum of the safety drift amount, the absolute value of the difference between the real-time temperature variable and the initial temperature variable and the disturbance smoothing coefficient as the denominator; dividing the numerator by the denominator, and summing the results of all structural parts to obtain the thermal drift response value.

[0014] Further, the specific process of fine-tuning the path node space and high-precision trajectory correction is: comparing the thermal drift response value with the thermal drift response threshold in real time, the thermal drift response threshold including a first response threshold and a second response threshold, and executing a detailed compensation process: when the thermal drift response value is greater than or equal to the first response threshold, the execution of the subsequent uncut path is suspended, the current partition is regionally inspected, the compensation parameters are reviewed, and an abnormal area report is output; when the thermal drift response value is greater than or equal to the second response threshold and less than the first response threshold; based on the dynamic analysis of the items, it is detected whether the thermal drift response value fluctuation belongs to temperature rise and disturbance, if it belongs to temperature rise and disturbance, the feed speed, laser pulse width and energy density are reduced to slow down the local drift development; the path node space compensation algorithm based on vector projection, the item compensation amount of the thermal drift response value greater than the risk threshold is calculated in real time, the item compensation amount is superimposed on the original path node coordinates according to the path direction component, the dynamic vector fine-tuning of the cutting trajectory is performed, the corresponding correction numerical control instruction is generated in real time, the numerical control instruction is synchronously sent to the motion controller, and the path is updated online in real time; when the thermal drift response value is less than the second response threshold, the cutting path execution continues to advance according to the current state, and the thermal drift response value is continuously monitored along the remaining path.

[0015] Further, the specific process based on time series fluctuation and mutation criterion is: obtaining the current time, the compensated thermal drift response value sequence data, the monitoring start time and the monitoring cutoff time; obtaining the response sliding window mean value of the compensated thermal drift response value sequence data through the sliding window mean value algorithm; obtaining the response sliding window standard deviation of the compensated thermal drift response value sequence data through the sliding window standard deviation algorithm; obtaining the time derivative of the response value of the compensated thermal drift response value sequence data through the numerical differentiation algorithm; obtaining the response sliding window complexity of the compensated thermal drift response value sequence data through the sample entropy algorithm; calculating the absolute value of the difference between the compensated thermal drift response value and the response sliding window mean value, multiplying it by the response sliding window standard deviation to obtain the response deviation fluctuation term; calculating the absolute value of the derivative of the compensated thermal drift response value with respect to time plus one to obtain the response change rate term; dividing the response deviation fluctuation term by the response change rate term to obtain the response fluctuation value; calculating the response complexity adjustment term by adding one to the response sliding window complexity; multiplying the response fluctuation value by the response complexity adjustment term, and taking the maximum value in the monitoring period to obtain the compensation risk value.

[0016] Further, the specific process of real-time monitoring and hierarchical intervention on the timing response fluctuation and abnormal risk of the compensation process is: real-time comparison of the compensation risk value and the compensation risk threshold, the compensation risk threshold includes a first risk threshold and a second risk threshold: when the compensation risk value is less than the second risk threshold, it is determined that the process is in a healthy zone, a process health database is established, and the structural thermal working condition data and the process path space characteristic data are dynamically updated and archived to the process health database, and no special action is required; when the compensation risk value is greater than or equal to the second risk threshold and less than the first risk threshold, it is determined that the process is in an attention zone, the sampling period is encrypted, and the attention subarea node is digitally marked; when the compensation risk value is greater than or equal to the first risk threshold, it is determined that the process is in a warning zone, and physical intervention such as spray cooling and vibration isolation is implemented on the subarea, and if the compensation risk value is still greater than or equal to the first risk threshold after the physical intervention, the process is temporarily determined, multi-dimensional abnormal parameters are pushed to generate a process abnormality report, and it is suggested that the operator inspects.

[0017] The second aspect of the present application provides a cutting positioning device for laser cutting metal based on big data, comprising a collection and preprocessing module for collecting structural thermal working condition data and process path space characteristic data, obtaining disturbance historical working condition data, and preprocessing the thermal working condition data, the process path space characteristic data and the historical working condition data; an initial thermal drift space compensation and verification module for predicting and initially calibrating the trend of the coupling space offset of thermal expansion, thermal fluctuation and disturbance at multiple points before cutting based on time series thermal fluctuation analysis, and entering a detailed compensation process; a thermal drift compensation strategy module for fine tuning and high-precision trajectory correction of path node space through thermal coupling compensation theory analysis for dynamic thermal drift and disturbance in the cutting process; and a compensation risk monitoring and intervention module for real-time monitoring and hierarchical intervention on the timing response fluctuation and abnormal risk of the compensation process based on time series fluctuation and mutation criteria.

[0018] Advantages The present application has the following advantages: (1) The present application realizes real-time high-precision identification and self-adaptive correction of thermal expansion, structural deformation and other multi-physical field factors in the laser cutting process by fusing multi-source structural thermal working condition data and process path multi-dimensional characteristics, thereby realizing precise compensation effect of spatial dynamic offset under complex thermal coupling working conditions, and effectively solving the technical bottleneck of difficult timely correction of high-temperature transient deformation in the prior art.

[0019] (2) The present application relies on multi-point heterogeneous sensor network and historical disturbance big data analysis to build a dynamic risk monitoring and intelligent early warning system throughout the whole process, thereby realizing rapid perception and subarea self-adaptive intervention effect of cutting path abnormalities and process disturbances, and effectively solving the problems of lagging risk identification and insensitive response in the prior art.

[0020] (3) The present application implements partition space correction and trajectory dynamic optimization on the cutting main path and key nodes, thereby realizing the adaptive dynamic regulation effect of high-precision cutting trajectory, and effectively solving the problems of coarse path offset correction granularity and insufficient control precision in the prior art.

[0021] (4) The present application realizes multi-module collaborative closed-loop scheduling of cutting process space compensation, risk monitoring and path correction, thereby realizing the system-level improvement effect of the whole process of high-precision and high-reliability intelligent cutting, and effectively solving the core problems of compensation and control link separation and limited response capability in the prior art.

[0022] Of course, any product implementing the present application does not necessarily need to achieve all the advantages described above at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 A flowchart of a cutting positioning method for laser cutting metal based on big data of the present application; Figure 2 A structure diagram of a cutting positioning device for laser cutting metal based on big data of the present application; Figure 3 A structure part thermal drift response component accumulation distribution diagram of the present application; Figure 4 A compensation risk value partition dynamic prevention and control flowchart of the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0025] Please refer to Figures 1-4 The embodiments of the present application provide a technical solution: a cutting positioning method for laser cutting metal based on big data, which comprises the following steps: S1, collecting structure thermal working condition data and process path space characteristic data, obtaining disturbance historical working condition data, and preprocessing the thermal working condition data, the process path space characteristic data and the historical working condition data; S2, based on time series thermal fluctuation analysis, predicting and initially calibrating the trend of multi-point thermal expansion, thermal fluctuation and disturbance coupling space offset before cutting, and entering the detailed compensation process; S3, for dynamic thermal drift and disturbance in the cutting process, fine tuning and high-precision trajectory correction are performed on the path node space through thermal coupling compensation theory analysis; S4, based on time series fluctuation and mutation criteria, real-time monitoring and hierarchical intervention are performed on the time series response fluctuation and abnormal risk of the compensation process.

[0026] Specifically, the specific process of collecting structure thermal working condition data and process path space characteristic data to obtain disturbance historical working condition data is as follows: collecting structure thermal working condition data, the structure thermal working condition data including: compensation structure quantity, temperature length change data, geometric size data, real-time temperature data, initial temperature variable, surface temperature sequence data, current time, three-axis acceleration data and angle change number; the compensation structure quantity refers to the number of key structure units that need to be monitored and dynamically compensated by CAD partitioning and process analysis, the heated and easily deformed parts of the laser cutting workpiece are selected, and corresponding sensors are distributed and arranged; the temperature length change data is measured by the cooperation of the grating ruler with the accuracy of microns and the sampling rate of ≥50 Hz installed at the key nodes of the workpiece and the K-type thermocouple with the accuracy of ±0.5℃; the geometric size data is collected by the three-dimensional laser scanner with the accuracy of 0.01 mm once before workpiece processing; the real-time temperature data and the initial temperature variable are both collected by the distributed thermocouple array fixed on the surface of the workpiece and the cutting heat affected zone, the real-time temperature is high-frequency sampled, and the initial temperature is the starting baseline; the surface temperature sequence data is continuously obtained by the infrared thermal imager with the frame rate of ≥25 Hz above the work station; the current time is recorded by the clock module of the main control system; the three-axis acceleration data and the angle change data are collected in real time by the IMU installed on the laser head and the clamping device part, reflecting the dynamic disturbance.

[0027] Collecting process path space characteristic data, the process path space characteristic data including: main path space coordinate point data, compensation vector direction data, length change data, current space coordinate, monitoring start time and monitoring cutoff time; the main path space coordinate point data is output by the numerical control system and is corrected by the laser range finder at the end of the laser head, the compensation vector direction data and the length change data are generated by path planning and sensors, the current space coordinate is output in real time by the positioning sensors on the laser head and the clamp, and the monitoring start time and the monitoring cutoff time are both recorded and archived by the control system.

[0028] Obtaining disturbance historical working condition data and establishing a disturbance historical working condition database, the disturbance historical working condition data including: historical three-axis acceleration data and historical angle change data; the historical three-axis acceleration data and the historical angle change data are both long-term collected by the IMU module, archived into the historical working condition database, and support high-frequency sampling and process stage and partition query.

[0029] In this embodiment, through the integration of multi-source high-precision sensors and intelligent control system, the key data of the whole process of laser cutting process is comprehensively collected and standardized managed. Through the collection and archiving of structural thermal working condition data, process path space feature data and disturbance historical working condition data, not only can the multi-dimensional information of workpiece heating, deformation, movement and path be obtained in real time and accurately, but also a solid data foundation is provided for subsequent space compensation, dynamic control and risk identification. This step effectively improves the integrity, accuracy and traceability of process data, and lays a solid foundation for realizing high-precision and intelligent laser cutting process control.

[0030] Specifically, the specific process of preprocessing the thermal working condition data, process path space feature data and historical working condition data is as follows: through the abnormal value elimination and multi-source time alignment algorithm, the missing value completion and data synchronization arrangement of the structural thermal working condition data are carried out, the data accuracy is improved by using high-precision clock and sensor self-checking function, and it is ensured that various thermal parameters correspond at the same time; through the space coordinate normalization and node spacing equalization algorithm, the path resampling and space partitioning processing of the process path space feature data are carried out, the node distribution density is optimized to facilitate subsequent fine compensation of trajectory, and the key node features of the main path are extracted; through the window mean filtering and disturbance amplitude normalization algorithm, the multi-channel data denoising and smoothing of the disturbance historical working condition data are carried out, the smoothness and discrimination stability of the disturbance data are improved, and the effective expression of the disturbance change trend is strengthened; through the distribution standardization and linear normalization algorithm, the structural thermal working condition data, process path space feature data and disturbance historical working condition data are standardized and normalized, so that various data have unified dimension and comparability in subsequent analysis.

[0031] In this embodiment, through data cleaning, alignment and normalization processing, the integrity, accuracy and time sequence consistency of multi-source process data are effectively improved. By eliminating abnormal values, completing missing information, and using multi-source time alignment and space resampling algorithm, the efficient synchronization and standardized expression of structural thermal working condition, path feature and historical disturbance data are realized. After the data are processed by unified dimension and scale, the key node features and disturbance change trend can be better reflected, which provides a reliable data foundation for subsequent space compensation, trajectory optimization and risk analysis, significantly enhances the scientificity of data fusion, feature extraction and intelligent analysis, and effectively supports the high-precision decision and dynamic control ability.

[0032] Specifically, the specific process based on the time sequence thermal fluctuation analysis is: obtaining the compensation structure quantity, temperature length change data, geometric size data, real-time temperature data, initial temperature variable, surface temperature sequence data, current time, three-axis acceleration data and angle change data, laying the original data foundation for subsequent thermal coupling analysis and dynamic compensation; the thermal expansion coefficient is obtained by the least square linear regression algorithm on the temperature length change data, the algorithm has strong anti-noise ability, and can effectively fit the linear relationship between multiple groups of temperature and length change; the effective feature length is obtained by the three-dimensional distance calculation algorithm on the geometric size data, the key size of the structure is quickly extracted by using the space distance matrix, and the efficiency is improved; the real-time temperature variable is obtained by the temperature mean value algorithm on the real-time temperature data, and the sliding window mean value is used to further reduce the influence of measurement fluctuation on the analysis result; the thermal fluctuation amplitude factor is obtained by the range calculation on the surface temperature sequence data, the range algorithm can sensitively capture the maximum dynamic interval of temperature change, which is used for thermal disturbance intensity characterization; the main frequency is analyzed by the fast Fourier transform on the surface temperature sequence data, the thermal fluctuation angular frequency is obtained by multiplying 2π by the main frequency, the FFT algorithm can effectively extract the dominant frequency of complex thermal field change, and supports periodic fluctuation modeling; the disturbance item quantity is obtained by the Euclidean norm fusion and variance threshold detection algorithm on the three-axis acceleration data and the angle change data, the Euclidean norm fusion improves the multi-dimensional signal expression ability, and the variance threshold detection enhances the discrimination accuracy of abnormal disturbance; the process disturbance space offset value is obtained by the Euclidean norm fusion, vector projection and absolute value operation on the three-axis acceleration data and the angle change data, and the comprehensive space vector algorithm can realize the normalization and spatial quantitative analysis of multiple source disturbances.

[0033] The product of the thermal expansion coefficient, the effective feature length and the difference between the surface temperature and the static temperature is calculated to obtain the thermal expansion offset item of the structure, and the multi-parameter normalization realizes the quantitative decomposition of the thermal expansion effect; the product of the thermal fluctuation amplitude factor, the thermal fluctuation angular frequency and the sine value of the current time is calculated to obtain the thermal fluctuation offset item of the structure, and the time sequence sine modeling can reflect the periodic characteristics of the thermal fluctuation; the thermal expansion offset item and the thermal fluctuation offset item of the structure are added to obtain the space offset prediction value of the structure, and the multi-factor superposition improves the comprehensive accuracy of the space offset prediction; the space offset prediction values of all structures are added, and the process disturbance space offset values are added to obtain the space offset prediction value, and finally the quantitative output of the spatial comprehensive offset under the global thermal-force-disturbance coordination is realized, and the specific calculation formula is: ; In the formula, The space offset prediction value represents the global space offset after the comprehensive output of the multi-source thermal force working condition and the disturbance; The compensation structure quantity represents the total number of structure units participating in the compensation calculation; It represents the coefficient of thermal expansion, reflecting the expansion characteristics of a material after it is heated; Indicates the effective feature length, which is the initial length parameter of the structural component; This represents the real-time temperature variable, indicating the actual temperature of the structural component at the current moment. This represents the initial temperature variable, indicating the reference temperature of the structural component at the initial moment; This represents the thermal fluctuation amplitude factor, used to measure the magnitude of temperature changes. It represents the angular frequency of thermal fluctuations, used to characterize the periodicity of temperature changes; This indicates the current moment, the point in time at which the judgment and calculation are performed; This indicates the number of perturbation terms, representing the number of perturbations that need to be considered during the cutting process; This represents the spatial offset value of the process disturbance, used to reflect the degree of spatial offset of each disturbance.

[0034] In this implementation scheme, quantitative analysis and collaborative modeling of various thermal and disturbance factors in the laser cutting process are achieved through data fusion and hierarchical feature extraction. A multi-algorithm collaborative processing approach, employing linear regression, spatial distance calculation, moving average, range, and fast Fourier transform, not only accurately decomposes and expresses various offset components of the structural components under thermal expansion, thermal fluctuations, and dynamic disturbances, but also realizes spatial offset prediction under the superposition of multiple factors. The final output global spatial offset comprehensively reflects the coupling effects of complex physical fields in the cutting process, providing a scientific and reliable criterion and data foundation for subsequent high-precision compensation, dynamic path correction, and risk warning, significantly improving the intelligent and refined control level of the laser cutting process.

[0035] Specifically, the process of predicting and initially calibrating the trend of spatial displacement coupled with multi-point thermal expansion, thermal fluctuations, and disturbances before cutting, and then proceeding to a detailed compensation process, involves: real-time comparison of the predicted spatial displacement value and the predicted spatial displacement threshold to ensure that the compensation decision can be dynamically adjusted according to the current actual working conditions. When the predicted spatial offset is less than the predicted spatial offset threshold, it is determined to be in a normal initial offset state. The preliminary compensation process will be initiated based on the real-time working conditions to ensure data closure. Through three-dimensional geometric projection and vector superposition algorithms, the predicted thermal spatial offset is projected onto the three-dimensional coordinate axis in parts, and converted into the preliminary three-dimensional coordinate compensation to obtain the predicted three-dimensional value of thermal spatial offset. The compensation value will be directly synchronized to the CNC zero point correction and path offset adjustment to ensure the dynamic accuracy of the spatial reference and enter the detailed compensation process.

[0036] When the space offset prediction value is greater than or equal to the space offset prediction threshold, it is determined that the initial abnormal offset state; the preheating time is prolonged, and the preheating time is dynamically adjusted according to the space offset prediction value exceeding the range of 5-20 minutes to ensure the thermal steady state of the structure, repeat the zero search, 2-3 times, each time combined with high-precision position detection calibration zero, execute workpiece static waiting process, static time is not less than 10 minutes, until the space offset prediction value is less than the space offset prediction value threshold.

[0037] In the embodiment, by predicting and real-time correcting the trend of multi-point thermal expansion, thermal fluctuation and disturbance space offset before cutting, the accurate discrimination and intelligent compensation of the space position of the workpiece in the initial state are realized. By real-time comparison of the space offset prediction value and the threshold value, according to different working conditions, preliminary compensation, three-dimensional space projection adjustment, CNC zero correction and path fine tuning and other fine operations are triggered to ensure that the cutting reference point and the compensation amount always maintain dynamic consistency. When the initial abnormal offset is detected, the preheating time is prolonged, the workpiece is repeatedly zeroed and searched, and high-precision calibration is performed, and at the same time, the static waiting strategy is adopted, and the space offset is continuously monitored and until the space offset is restored to within the set threshold. Through this series of dynamic discrimination and multi-link compensation process, not only the space compensation accuracy under the condition of thermal-mechanical coupling and disturbance is significantly improved, but also the self-adaptive ability to complex starting conditions and environmental fluctuations is enhanced, which lays a solid foundation for the high-precision start and subsequent stable operation of the laser cutting process.

[0038] Specifically, for the dynamic thermal drift and disturbance in the cutting process, the specific process of thermal coupling compensation theoretical analysis is as follows: obtaining the thermal sensitive space offset prediction three-dimensional value, the main path space coordinate point data, the compensation vector direction data, the real-time temperature data, the length change data, the current space coordinate, the three-axis acceleration data, the angle change data, the historical three-axis acceleration data and the historical angle change data, providing full-quantity original data support for dynamic compensation and multi-parameter response modeling, and ensuring high data synchronization in each link; the main path space coordinate point data and the compensation vector direction data are calculated by the vector angle to obtain the compensation vector and the cutting main path angle, and the angle is used to quantitatively describe the spatial relationship between the compensation direction and the actual cutting path, and provides a key criterion for compensation accuracy; the real-time temperature data and the length change data are obtained by the least square linear regression algorithm to obtain the thermal expansion coefficient of the workpiece main material, which improves the dynamic identification ability of material characteristic parameters, and makes the compensation parameters adaptive to the actual working condition; the main path space coordinate point data and the current space coordinate are calculated by the three-dimensional Euclidean distance calculation algorithm to obtain the remaining feature length, which reflects the spatial distance change of the to-be-cut path in real time, and assists in adjusting the dynamic compensation range; the three-axis acceleration data and the angle change data are obtained by the extreme value algorithm and the sensitivity calibration method to obtain the disturbance response amplification factor, which significantly improves the rapid response ability to sudden disturbance and vibration change; the three-axis acceleration data and the angle change data are obtained by the weighted Euclidean norm fusion algorithm and the window cumulative operation to obtain the overall space drift increment, which takes into account the disturbance intensity and duration, and realizes the cumulative expression of the drift trend; the historical three-axis acceleration data and the historical angle change data are obtained by the weighted Euclidean norm fusion algorithm and the window cumulative operation to obtain the historical space drift increment, and the disturbance smoothing coefficient is obtained by the sliding window average algorithm, so that the compensation algorithm can fully utilize the historical disturbance trend, and improve the smoothness and stability of the overall compensation.

[0039] The product of the cosine value of the included angle between the space offset directionality and the main path of cutting and the space offset value is calculated to obtain a space compensation term, the space offset directionality is directly taken into account in the compensation amount calculation, and offset misjudgment is effectively prevented; the product of the thermal expansion coefficient of the main material of the workpiece, the residual feature length and the difference between the real-time temperature variable and the initial temperature variable is calculated to obtain a thermal expansion offset term, the thermal expansion characteristics and the length change of the process path are comprehensively considered, and the precision of thermal compensation is improved; the space compensation term and the thermal expansion offset term are added to obtain a path offset term, and the path offset term comprehensively integrates the directionality and the thermal deformation influence; the product of the disturbance response amplification factor and the overall space drift increment is calculated to obtain a disturbance drift term, the weight of strong disturbance on the overall compensation is further amplified, and the risk response capability is improved; the path offset term and the disturbance drift term are added, and the absolute value is taken as the numerator to ensure the consistency of the offset compensation direction and improve the robustness of the compensation criterion; the sum of the safety drift amount, the absolute value of the difference between the real-time temperature variable and the initial temperature variable and the disturbance smoothing coefficient is calculated as the denominator, the safety margin, the process temperature difference and the disturbance smoothing are fully integrated, and the compensation stability and safety are enhanced; the numerator is divided by the denominator, and the results of all structural parts are summed to obtain the thermal drift response value, and the specific calculation formula is as follows: ; In the formula, The thermal drift response value represents the current thermal coupling and disturbance compensation effect of the system and is an important index for dynamic adjustment and risk criterion. The thermal-sensitive space offset prediction three-dimensional value represents the overall space dynamic change characteristics. The included angle between the compensation vector and the main path of cutting represents the spatial relationship between the compensation direction and the main path of the cutting process. The thermal expansion coefficient of the main material of the workpiece represents the volume and length expansion characteristics of the material under the action of temperature change. The residual feature length is the path length to be processed in the cutting process. The real-time temperature variable represents the actual temperature of the structural part at the current time. The initial temperature variable represents the reference temperature of the structural part at the initial time. The disturbance response amplification factor weights and amplifies the response to sudden disturbance and non-steady-state motion, and improves the influence weight of the disturbance signal on the compensation result. The overall space drift increment is used to describe the cumulative space drift amount under each disturbance source. The disturbance smoothing coefficient reflects the smoothing and trend characteristics of the disturbance historical data. The safety drift amount is obtained by the denominator minimum value robustness constraint algorithm on the absolute value of the difference between the initial temperature variable and the real-time temperature variable, and the compensation constant is in the range of 0.005 to 0.01.

[0040] In this embodiment, Table 1 is a data table of the structural part item thermal drift influence quantity, which records in detail the thermal drift response value components of 8 structural parts under the core components of the space compensation item, the thermal expansion offset item and the disturbance drift item, for quantifying the multi-dimensional response performance of different structural parts in the laser cutting positioning compensation process. Among them, the space compensation item of structural part 1 is 0.14, the thermal expansion offset item is 0.06, the disturbance drift item is 0.08, and the thermal drift response value component is 0.28; the space compensation item of structural part 3 is 0.18, the thermal expansion offset item is 0.08, the disturbance drift item is 0.12, and the thermal drift response value component reaches 0.38, which is the highest in the group, showing that its comprehensive drift influence is the most significant; the three components of structural part 4 are 0.07, 0.06 and 0.05 respectively, and the thermal drift response value component is only 0.14, which is the lowest in this group, reflecting that its compensation pressure is the smallest. The denominator parameter not involved in the formula is uniformly fixed as 1.0, which is used to eliminate the influence of other variables on the response value and ensure the transverse comparability between components.

[0041] Table 1 Data table of structural part item thermal drift influence quantity As shown in Figure 3 the structural part thermal drift response component accumulation distribution diagram, the difference of the structural part thermal drift response component accumulation distribution diagram is shown in the space compensation, thermal expansion offset and disturbance drift, from which it can be seen that the thermal drift response value component of structural part 3 and structural part 5 is the highest, and the space compensation and disturbance drift contribute significantly to the overall drift; while the response value components of structural parts 2, 4 and 8 are relatively low, and the item influence is balanced. Overall, the comparison of the item response between structural parts not only reveals the risk level of each structural part in the actual compensation process, but also provides a quantitative basis for subsequent optimization of compensation strategy and dynamic partition management.

[0042] In this embodiment, through real-time acquisition and dynamic fusion of multi-dimensional data, precise modeling and response compensation of dynamic thermal drift and multi-source disturbance in the cutting process are realized by using thermal force coupling compensation theory. After parameter processing by multi-layer algorithm, the compensation criterion can consider the complex action of thermal, force and disturbance physical quantities. By comprehensively considering the factors of directionality, thermal deformation, disturbance dynamics, historical trend and safety margin, each structural unit can be calculated for item compensation, and finally the overall thermal drift response value is output. The response value not only reflects the space offset intensity under complex thermal-force-disturbance working conditions, but also provides a quantitative basis for real-time path adjustment, compensation decision and risk classification and control in the cutting process. Overall, this process greatly improves the adaptive compensation and intelligent control capability in high dynamic and complex process environment, and provides theoretical support for realizing high-precision, stability and safety of laser cutting process control.

[0043] Specifically, the specific process of fine-tuning the path node space and high-precision trajectory correction is: comparing the thermal drift response value with the thermal drift response threshold in real time, the thermal drift response threshold includes a first response threshold and a second response threshold, executing a detailed compensation process, continuously monitoring the thermal drift response state of each partition, and ensuring that the compensation strategy dynamically switches according to the real-time working condition: When the thermal drift response value is greater than or equal to the first response threshold, the execution of the subsequent uncut path is suspended, the abnormal partition is immediately locked, the current partition is regionally inspected, the compensation parameters are reviewed, and an abnormal area report is output, providing decision support for subsequent manual intervention and parameter optimization.

[0044] When the thermal drift response value is greater than or equal to the second response threshold and less than the first response threshold; based on dynamic analysis, it is detected whether the thermal drift response value fluctuation belongs to temperature rise and disturbance, the fluctuation type is determined based on historical trends, the temperature rise and disturbance causes are dynamically identified, if it belongs to temperature rise and disturbance, the feed speed, laser pulse width and energy density are reduced, the process parameters are adjusted to reduce the system load, and the local drift development is slowed down; based on the path node space compensation algorithm of vector projection, the compensation amount of the item with the thermal drift response value greater than the risk threshold is calculated in real time, the item compensation amount is added to the original path node coordinates according to the path direction component, the dynamic vector fine-tuning of the cutting trajectory is performed, the compensation amount is accurately applied to each key node according to the actual spatial direction, the corresponding correction numerical control instruction is generated in real time, the numerical control instruction is synchronously sent to the motion controller, the path is updated online in real time, and the intelligent level of compensation and numerical control linkage is improved.

[0045] When the thermal drift response value is less than the second response threshold, the real-time monitoring of the remaining path is maintained, the response value change trend is dynamically recorded, the cutting path execution continues to advance according to the current state, the thermal drift response value is continuously monitored along the remaining path, and the coherence and safety of the process operation are ensured.

[0046] In the embodiment, by monitoring the thermal drift response value and its relationship with the multi-level response threshold in real time, high-precision fine-tuning and dynamic trajectory correction of the path node space are realized. The hierarchical response strategy ensures that when the deviation anomaly occurs, the abnormal partition can be immediately locked and a detailed report can be output, quickly assisting manual intervention and parameter optimization; in the critical fluctuation interval, the thermal drift causes are identified based on dynamic analysis and historical trend judgment, the process parameters are intelligently adjusted, the local thermal drift development is slowed down, and the key nodes of the cutting path are finely space-fined tuned through the vector projection compensation algorithm, the correction numerical control instruction is generated and sent in real time, and the high coordination of the trajectory and the compensation is ensured. When the thermal drift response is in the normal interval, the remaining path is continuously monitored and data is recorded at a high frequency, and the stability and safety of the cutting process are dynamically ensured. The process effectively improves the trajectory self-adaptation and high-precision control capability of laser cutting in a complex thermal disturbance environment, greatly enhances the intelligence, stability and risk controllability of the process.

[0047] Specifically, the specific process based on the time series fluctuation and mutation criterion is: obtaining the current time, the compensated thermal drift response value sequence data, the monitoring start time and the monitoring cutoff time, ensuring that all key data are strictly collected according to a unified time sequence, laying a foundation for time consistency for subsequent dynamic risk discrimination; obtaining the response sliding window mean value of the compensated thermal drift response value sequence data through the sliding window mean value algorithm, using an adaptive window length to make the mean value calculation take into account the real-time and trend smoothing; obtaining the response sliding window standard deviation of the compensated thermal drift response value sequence data through the sliding window standard deviation algorithm, improving the ability to sensitively capture the local fluctuation intensity change; obtaining the response value time derivative of the compensated thermal drift response value sequence data through the numerical differentiation algorithm, reflecting the change speed and mutation dynamics of the response value in real time; obtaining the response sliding window complexity of the compensated thermal drift response value sequence data through the sample entropy algorithm, quantifying the complexity and abnormality of the response sequence in the local period, and improving the risk discrimination accuracy.

[0048] The absolute value of the difference between the compensated thermal drift response value and the response sliding window mean value is calculated, multiplied by the response sliding window standard deviation, to obtain the response deviation fluctuation term, which sensitively represents the deviation and fluctuation intensity of the current response value relative to the historical mean value; the absolute value of the derivative of the compensated thermal drift response value with respect to time is calculated and added to one to obtain the response change rate term, ensuring stability and discrimination for periods of rapid change and mutation; the response deviation fluctuation term is divided by the response change rate term to obtain the response fluctuation value, which dynamically balances the deviation amplitude and the change rate, and realizes a more robust risk quantitative criterion; the response sliding window complexity is calculated and added to one to obtain the response complexity adjustment term, and the introduction of the complexity adjustment term can adaptively amplify the influence of high-complexity abnormal intervals on risk; the response fluctuation value and the response complexity adjustment term are multiplied, and the maximum value in the monitoring period is taken to obtain the compensation risk value, and the specific calculation formula is as follows: ; In the formula, The compensation risk value represents the overall risk level of the compensated thermal drift response in the monitoring period; The current time represents the unified time index for sampling, discrimination and calculation; The compensated thermal drift response value represents the dynamic reflection of the compensation effect and spatial state at t time, and intuitively reflects the compensation accuracy of the current working condition; The response sliding window mean value represents the smooth trend of the response value in the local period; The response sliding window standard deviation represents the fluctuation amplitude of the response value in the same window; The response value time derivative represents the rate and trend of the response value change with time; The response sliding window complexity represents the local complexity and uncertainty of the response sequence, and is used to highlight the risk impact of high-entropy abnormal intervals. The monitoring start time represents the starting point of the monitoring interval, and ensures that the risk calculation interval strictly corresponds to the actual process flow. The monitoring end time defines the termination point of the monitoring interval, and is helpful for process archiving and periodic risk evaluation.

[0049] In this embodiment, the quantitative analysis and risk discrimination of the dynamic fluctuations and mutation behavior of the compensated thermal drift response sequence are comprehensively realized. The sliding window mean and standard deviation algorithm is used to realize real-time tracking of the local trend and fluctuation intensity of the response sequence. Through numerical differentiation and sample entropy calculation, the multi-dimensional features of the response sequence in terms of change rate, mutation and complexity are effectively extracted. The calculation results jointly construct a hierarchical criterion that can not only sensitively capture instantaneous deviations and local anomalies, but also dynamically adjust the risk amplification of high-complexity intervals, realizing comprehensive monitoring and quantitative risk discrimination of potential anomalies and trend mutations under different working conditions. The final output of the compensation risk value can comprehensively quantify the risk level after compensation in the entire monitoring period, providing a scientific and reliable decision basis for hierarchical warning, dynamic intervention and continuous optimization in the process, and improving the safety, stability and intelligent management capability of the laser cutting process.

[0050] Specifically, the specific process of real-time monitoring and hierarchical intervention of the timing response fluctuations and abnormal risks of the compensation process is as follows: real-time comparison of the compensation risk value and the compensation risk threshold, the compensation risk threshold including a first-level risk threshold and a second-level risk threshold, to realize dynamic prevention and control in different zones: As shown in Figure 4 The compensation risk value partition dynamic prevention and control flowchart is shown in FIG. 6. When the compensation risk value is less than the second-level risk threshold, it is determined to be a healthy process zone, and the routine data archiving process is entered. The process health database is established and the structural thermal working condition data and process path space feature data are dynamically updated and archived to the process health database. No special action is required, and the historical data accumulation and process tracing capability are continuously ensured.

[0051] When the compensation risk value is greater than or equal to the second-level risk threshold and less than the first-level risk threshold, it is determined to be a process attention zone. The sampling period is encrypted and shortened to half of the current period. The attention zone node is digitally marked to facilitate subsequent dynamic backtracking and accurate monitoring.

[0052] When the compensation risk value is greater than or equal to the first risk threshold value, it is determined that the process is in a process warning zone, and physical intervention such as spray cooling and vibration isolation is implemented in the zone. During the spray cooling process, the cooling flux and the spray duration are adjusted according to the actual thermal drift degree, the cooling intensity per unit area and the time are adjusted, the temperature of the zone is effectively reduced rapidly, and the structural warping and thermal deformation are inhibited. If the compensation risk value is still greater than or equal to the first risk threshold value after the physical intervention, the process is temporarily suspended, multi-dimensional abnormal parameters are pushed to generate a process abnormality report, and it is suggested that the operation and maintenance personnel perform an inspection to ensure that the risk is controllable and the abnormal event is closed loop processed.

[0053] In the embodiment, intelligent hierarchical control of the compensation process timing response fluctuation and abnormal risk is achieved. In the process health zone, various process and structure thermal data are continuously archived to ensure the integrity and traceability of the process data. In the process attention zone, the sampling frequency is increased and digital markers are implemented in key zones to enable more fine dynamic monitoring and process recording of the risk nodes. In the process warning zone, physical intervention measures such as spray cooling and vibration isolation are intelligently started, and the cooling intensity and duration are dynamically adjusted according to the actual thermal drift status of the zone to significantly improve the local temperature drop rate and the structural stability to prevent thermal deformation and warping. When the physical intervention cannot eliminate the high-risk state, the process is temporarily suspended, abnormal parameters are pushed to generate a report, and it is suggested that the process be manually inspected to ensure that the high-risk event is closed loop processed in time, greatly enhancing the timeliness and fineness of the compensation risk identification, hierarchical response and intelligent intervention, and providing a strong guarantee for the safety, stability and intelligent control of the laser cutting process.

[0054] As shown in Figure 2 The second aspect of the present application provides a cutting positioning device for laser cutting metal based on big data, which comprises a collection and preprocessing module for collecting structure thermal working condition data and process path space feature data, obtaining disturbance historical working condition data, and preprocessing the thermal working condition data, the process path space feature data and the historical working condition data; an initial thermal drift space compensation and verification module for predicting and initially calibrating the trend of the multi-point thermal expansion, thermal fluctuation and disturbance coupling space offset before cutting based on timing thermal fluctuation analysis, and entering a detailed compensation process; a thermal drift compensation strategy module for fine tuning and high-precision trajectory correction of path node space through thermal coupling compensation theory analysis for dynamic thermal drift and disturbance in the cutting process; and a compensation risk monitoring and intervention module for real-time monitoring and hierarchical intervention of the timing response fluctuation and abnormal risk of the compensation process based on time series fluctuation and mutation criteria.

[0055] In the embodiment, through full-process big data acquisition and efficient preprocessing, multi-dimensional data fusion and dynamic management of structural thermal working conditions, process path space characteristics and disturbance history are realized. Before cutting, the spatial deviation caused by multi-point thermal expansion, thermal fluctuation and disturbance coupling can be accurately predicted and calibrated. During cutting, dynamic thermal drift and path node fine-tuning are continuously self-adaptively and high-precisely corrected. Full-process compensation combines time sequence fluctuation and mutation criterion to identify abnormal risks and dynamically intervene according to hierarchical strategies, ensuring the stability of cutting positioning and the safety and reliability of the process. The device greatly improves the intelligent level and dynamic risk response capability of metal laser cutting positioning, effectively ensuring the cutting precision and process stability in high-end manufacturing scenarios.

[0056] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0057] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details of the application, nor limit the application to the specific embodiments described. Obviously, many modifications and variations can be made to the application according to the content of the specification. The specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. A cutting positioning method for laser cutting of metal based on big data, characterized in that, Includes the following steps: S1. Collect structural thermodynamic condition data and process path spatial characteristic data, obtain historical disturbance condition data, and preprocess the thermodynamic condition data, process path spatial characteristic data, and historical condition data. S2, based on time-series thermal fluctuation analysis, predicts and initially calibrates the trend of multi-point thermal expansion, thermal fluctuation and disturbance coupled spatial displacement before cutting, and then enters a detailed compensation process; S3, targeting the dynamic thermal drift and disturbance during the cutting process, uses thermal coupling compensation theory to fine-tune the path node space and correct the trajectory with high precision. S4, based on time series fluctuation and mutation criteria, performs real-time monitoring and graded intervention on the time series response fluctuation and abnormal risk of the compensation process.

2. The cutting positioning method for laser cutting metal based on big data according to claim 1, characterized in that: The specific process for acquiring historical disturbance data by collecting structural thermal condition data and process path spatial characteristic data is as follows: Collect structural thermal condition data, which includes: the number of compensating structural components, temperature length variation data, geometric dimension data, real-time temperature data, initial temperature variables, surface temperature sequence data, current time, triaxial acceleration data, and angle variation data; Collect process path spatial feature data, which includes: main path spatial coordinate point data, compensation vector direction data, length change data, current spatial coordinates, monitoring start time, and monitoring end time; Acquire historical disturbance data and establish a historical disturbance data database. The historical disturbance data includes historical triaxial acceleration data and historical angle change data.

3. The cutting positioning method for laser cutting metal based on big data according to claim 1, characterized in that: The specific process for preprocessing thermal condition data, process path spatial feature data, and historical condition data is as follows: By using outlier removal and multi-source time alignment algorithms, missing values ​​are filled in and data is synchronized for structural thermal condition data to ensure that various thermal parameters correspond at the same time. By using spatial coordinate normalization and node spacing balancing algorithms, path resampling and spatial partitioning are performed on the spatial feature data of the process path to extract the key node features of the main path. By using window mean filtering and disturbance amplitude normalization algorithms, multi-channel data denoising and smoothing are performed on historical disturbance data to enhance the effective expression of disturbance change trends. By using distribution standardization and linear normalization algorithms, structural thermal condition data, process path spatial feature data, and historical disturbance data are standardized and normalized.

4. The cutting positioning method for laser cutting metal based on big data according to claim 1, characterized in that: The specific process based on time-series thermal fluctuation analysis is as follows: The system acquires data on the number of compensation structural components, temperature length variation, geometric dimensions, real-time temperature, initial temperature variables, surface temperature sequence data, current moment, triaxial acceleration, and angular variation. It then uses a least-squares linear regression algorithm to obtain the coefficient of thermal expansion from the temperature length variation data; a three-dimensional distance calculation algorithm to obtain the effective feature length from the geometric dimensions; a temperature mean algorithm to obtain the real-time temperature variable from the real-time temperature data; a range calculation to obtain the thermal fluctuation amplitude factor from the surface temperature sequence data; and a fast Fourier transform analysis of the surface temperature sequence data to determine the dominant frequency, multiplying 2π by the dominant frequency to obtain the thermal fluctuation angular frequency. The number of disturbance terms is obtained from the triaxial acceleration data and angle change data through Euclidean norm fusion and variance threshold detection algorithms; the spatial offset value of process disturbance is obtained from the triaxial acceleration data and angle change data through Euclidean norm fusion, vector projection and absolute value operation. The thermal expansion offset term of the structural component is obtained by calculating the product of the coefficient of thermal expansion, the effective feature length, and the difference between the surface temperature and the static temperature. The thermal fluctuation offset term of the structural component is obtained by multiplying the thermal fluctuation amplitude factor by the thermal fluctuation angular frequency and the sine value at the current moment. The thermal expansion offset term and the thermal fluctuation offset term of the structural component are added together to obtain the predicted value of the spatial offset of the structural component. The predicted spatial offset values ​​of all structural components are summed up, and the predicted spatial offset values ​​of all process disturbances are summed up again to obtain the predicted spatial offset value.

5. The cutting positioning method for laser cutting metal based on big data according to claim 1, characterized in that: The specific process of predicting and initially calibrating the trend of multi-point thermal expansion, thermal fluctuation and disturbance coupled spatial offset before cutting, and then entering the detailed compensation process is as follows: Real-time comparison of spatial offset prediction values ​​and spatial offset prediction thresholds: When the predicted spatial offset is less than the predicted spatial offset threshold, it is determined to be a normal initial offset state. Through three-dimensional geometric projection and vector superposition algorithm, the predicted thermal spatial offset is projected onto the three-dimensional coordinate axis, which is converted into the preliminary three-dimensional coordinate compensation to obtain the predicted three-dimensional value of thermal spatial offset. This value is then applied to the CNC zero point and path offset compensation, and the detailed compensation process begins. When the predicted spatial offset value is greater than or equal to the predicted spatial offset threshold, it is determined to be an initial abnormal offset state. Extend the preheating time, repeat the zero-finding process, and execute the workpiece static waiting process until the spatial offset prediction value is less than the spatial offset prediction value threshold.

6. The cutting positioning method for laser cutting metal based on big data according to claim 1, characterized in that: The specific process of analyzing the dynamic thermal drift and disturbance during the cutting process using thermo-mechanical coupling compensation theory is as follows: The system acquires the following data: thermal spatial offset prediction 3D value, main path spatial coordinate point data, compensation vector direction data, real-time temperature data, length change data, current spatial coordinates, triaxial acceleration data, angle change data, historical triaxial acceleration data, and historical angle change data. It calculates the angle between the compensation vector and the main cutting path using the vector angle calculation based on the main path spatial coordinate point data and compensation vector direction data. It obtains the workpiece's main material thermal expansion coefficient using the least squares linear regression algorithm based on the real-time temperature data and length change data. It obtains the remaining feature length using a 3D Euclidean distance calculation algorithm based on the main path spatial coordinate point data and current spatial coordinates. Finally, it obtains the disturbance response amplification factor using an extremum algorithm and sensitivity calibration method based on the triaxial acceleration data and angle change data. The overall spatial drift increment is obtained by using a weighted Euclidean norm fusion algorithm and window accumulation operation on triaxial acceleration data and angle change data; the historical spatial drift increment is obtained by using a weighted Euclidean norm fusion algorithm and window accumulation operation on historical triaxial acceleration data and historical angle change data; and the disturbance smoothing coefficient is obtained by using a sliding window averaging algorithm on the historical spatial drift increment. The spatial compensation term is obtained by multiplying the predicted three-dimensional value of the thermal spatial offset and the compensation vector with the cosine of the angle between them and the main cutting path. The thermal expansion offset term is obtained by multiplying the workpiece's main material thermal expansion coefficient, remaining characteristic length, and the difference between the real-time temperature variable and the initial temperature variable. The spatial compensation term is added to the thermal expansion offset term to obtain the path offset term. The disturbance response amplification factor is calculated and the overall spatial drift increment is calculated to obtain the disturbance drift term. The path offset term and the disturbance drift term are added together, and the absolute value is taken as the numerator. The sum of the safety drift amount, the absolute value of the difference between the real-time temperature variable and the initial temperature variable, and the disturbance smoothing coefficient is calculated and taken as the denominator. The numerator is divided by the denominator, and the results for all structural components are summed to obtain the thermal drift response value.

7. The cutting positioning method for laser cutting metal based on big data according to claim 1, characterized in that: The specific process of fine-tuning the path node space and correcting the high-precision trajectory is as follows: Real-time comparison of thermal drift response values ​​and thermal drift response thresholds, including primary and secondary response thresholds, and execution of a detailed compensation process: When the thermal drift response value is greater than or equal to the first-level response threshold, the execution of subsequent uncut paths is paused, the current partition performs a regional inspection, verifies the compensation parameters, and outputs an abnormal area report. When the thermal drift response value is greater than or equal to the second-level response threshold and less than the first-level response threshold, the thermal drift response value fluctuation is detected based on sub-item dynamic analysis to determine whether it belongs to temperature rise and disturbance. If it belongs to temperature rise and disturbance, the feed rate, laser pulse width and energy density are reduced to slow down the development of local drift. Based on the path node space compensation algorithm of vector projection, the sub-item compensation amount for thermal drift response values ​​greater than the risk threshold is calculated in real time. The sub-item compensation amount is superimposed on the original path node coordinates according to the path direction component, and the cutting trajectory is dynamically vector fine-tuned. The corresponding correction CNC command is generated in real time, and the CNC command is synchronously sent to the motion controller, and the path is updated online in real time. When the thermal drift response value is less than the secondary response threshold, the cutting path execution continues to proceed according to the current state, and the thermal drift response value is continuously monitored along the remaining path.

8. The cutting positioning method for laser cutting metal based on big data according to claim 1, characterized in that: The specific process based on the time series fluctuation and mutation criterion is as follows: Acquire the current time, the compensated thermal drift response value sequence data, the monitoring start time, and the monitoring end time; obtain the sliding window mean of the compensated thermal drift response value sequence data using the sliding window mean algorithm; obtain the sliding window standard deviation of the compensated thermal drift response value sequence data using the sliding window standard deviation algorithm; obtain the time derivative of the response value using the numerical differentiation algorithm. The sliding window complexity of the response is obtained by using the sample entropy algorithm on the compensated thermal drift response value sequence data. Calculate the absolute value of the difference between the compensated thermal drift response value and the mean of the response sliding window, multiply it by the standard deviation of the response sliding window to obtain the response deviation fluctuation term; calculate the absolute value of the derivative of the compensated thermal drift response value with respect to time and add one to obtain the response change rate term. Divide the response deviation fluctuation term by the response change rate term to obtain the response fluctuation value; Calculate the response sliding window complexity plus one to obtain the response complexity adjustment term; Multiply the response volatility value by the response complexity adjustment term, and take the maximum value within the monitoring period to obtain the compensated risk value.

9. The cutting positioning method for laser cutting metal based on big data according to claim 1, characterized in that: The specific process for real-time monitoring and tiered intervention of the time-series response fluctuations and abnormal risks in the compensation process is as follows: Real-time comparison of compensation risk value and compensation risk threshold, including primary risk threshold and secondary risk threshold: When the compensation risk value is less than the secondary risk threshold, it is determined to be a process health zone. A process health database is established and the structural thermal condition data and process path spatial characteristic data are dynamically updated and archived into the process health database without any special action. When the compensation risk value is greater than or equal to the secondary risk threshold and less than the primary risk threshold, it is determined to be a process concern area, the sampling cycle is increased, and the concern area nodes are digitally marked. When the compensated risk value is greater than or equal to the first-level risk threshold, it is determined to be a process warning zone. Physical interventions such as spray cooling and vibration isolation are implemented in the zone. If the compensated risk value is still greater than or equal to the first-level risk threshold after physical intervention, the process is temporarily suspended, multi-dimensional abnormal parameters are pushed to generate a process abnormality report, and maintenance personnel are advised to conduct inspections.

10. A cutting positioning device for laser cutting metal based on big data, employing the cutting positioning method for laser cutting metal based on big data as described in any one of claims 1-9, comprising: The acquisition and preprocessing module is used to acquire structural thermodynamic condition data and process path spatial characteristic data, obtain historical disturbance condition data, and preprocess the thermodynamic condition data, process path spatial characteristic data, and historical condition data. The initial thermal drift spatial compensation and verification module is used to predict and initially calibrate the trend of multi-point thermal expansion, thermal fluctuation and disturbance coupled spatial offset before cutting based on time-series thermal fluctuation analysis, and then enter the detailed compensation process. The thermal drift compensation strategy module is used to fine-tune the path node space and correct the trajectory with high precision by analyzing the dynamic thermal drift and disturbance during the cutting process through thermal coupling compensation theory. The compensation risk monitoring and intervention module is used to monitor and intervene in the time-series response fluctuations and abnormal risks of the compensation process in real time based on time-series fluctuation and mutation criteria.

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