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CN-122008340-A - Automatic correction and compensation method for blanking and weighing

CN122008340ACN 122008340 ACN122008340 ACN 122008340ACN-122008340-A

Abstract

The invention relates to an automatic correction and compensation method for blanking weighing, and belongs to the technical field of blanking processing. The method comprises the steps of obtaining multiple types of blanking related parameters, generating a standardized data set through noise filtering and outlier removing, calculating weight deviation based on the standardized data, judging deviation types through deviation characteristic self-adaptive identification and classification mechanisms, generating related characteristic data, matching corresponding compensation models according to different deviation types, generating fusion compensation quantity parameters by means of a multi-mode compensation model dynamic fusion mechanism, correcting the blanking parameters based on the compensation quantity parameters, issuing a control instruction, and periodically obtaining historical data to update compensation model regulation parameters in an iterative mode. The invention improves the control precision of the blanking weight, adapts to the dynamic change of material characteristics and equipment states, reduces the manual intervention cost, enhances the stability and reliability of blanking processing, and can be widely applied to various industrial processing scenes needing accurate blanking weighing.

Inventors

  • FENG SHAOJUN
  • Long Tanan

Assignees

  • 昆山佳龙科智能科技有限公司

Dates

Publication Date
20260512
Application Date
20260129

Claims (12)

  1. 1. The automatic correction and compensation method for blanking and weighing is characterized by comprising the following steps of: s1, acquiring preset blanking parameters, actual blanking weighing feedback data, material characteristic parameters and equipment operation state parameters, and performing noise filtration and outlier rejection to generate a standardized data set; S2, calculating weight deviation based on target blanking weight and actual blanking weighing feedback data in the standardized data set, introducing a deviation characteristic self-adaptive identification and classification mechanism, and carrying out type judgment on the weight deviation by combining a preset deviation judgment reference library, material characteristic parameter fluctuation conditions and equipment running state parameter change trend in the standardized data set to generate a deviation type judgment result and deviation characteristic data; S3, matching a corresponding weight deviation compensation model aiming at a deviation type based on the deviation type judging result and deviation characteristic data, wherein the weight deviation comprises a system inherent deviation, a material fluctuation deviation and a device abrasion deviation, and generating a self-adaptive compensation quantity based on a dynamic fusion mechanism of the multi-mode compensation model; And S4, correcting the preset blanking parameters based on the fusion compensation quantity parameters, generating corrected blanking control instructions and sending the corrected blanking control instructions to a blanking executing mechanism, periodically acquiring historical data, and carrying out iterative updating on the regulation and control parameters of the weight deviation compensation model by combining the standardized data set, the deviation type judgment result and the fusion compensation quantity parameters to generate a compensation model parameter set.
  2. 2. The method according to claim 1, wherein in S1, the noise filtering specific process includes extracting material viscosity data in the material characteristic parameter and cutter rotation rate data in the equipment operation state parameter, calculating linear correlation coefficients of the material viscosity data and the cutter rotation rate data through correlation analysis, dynamically adjusting a time span of a filtering window according to the size of the linear correlation coefficients, performing a sliding average smoothing process on the actual blanking weighing feedback data based on the adjusted filtering window, and filtering a high-frequency noise signal.
  3. 3. The method according to claim 1, wherein in S1, the specific implementation process of outlier rejection comprises the steps of carrying out statistical analysis on blanking related historical data corresponding to similar materials, calculating the mean value and standard deviation of each parameter, constructing a normal data distribution interval corresponding to each parameter, substituting each obtained parameter into the corresponding distribution interval respectively for comparison, carrying out secondary verification on the initially determined outlier by further combining with the parameter change trend of adjacent moments, and calculating the supplementary data corresponding to the abnormal moments according to the time interval and the numerical value difference of two nodes by taking effective data of adjacent moments before and after the outlier as interpolation nodes for the data finally determined as the outlier.
  4. 4. The method according to claim 1, wherein in S2, the specific setting process of the deviation judging standard comprises classifying and carding historical blanking data, dividing data subsets according to material types, dividing each data subset into different time period groups according to equipment operation time length, carrying out deviation statistics on qualified blanking data in each time period group, outputting a blanking deviation range of each combined grid according to engineering allowed blanking precision requirements, and sorting and archiving all the grouped material types, equipment operation time length and corresponding qualified blanking deviation ranges to construct the deviation judging standard library.
  5. 5. The method according to claim 1, wherein in the step S2, when the weight deviation is determined, the determining process for the fluctuation deviation of the material includes extracting the material characteristic parameter and the corresponding weight deviation data based on the standardized data set to form a material characteristic-deviation data sequence, calculating the variation difference values of two adjacent groups of material characteristic parameters in the material characteristic-deviation data sequence, respectively counting the continuous variation difference values of the material density, the material water content and the material viscosity, accumulating and summing, comparing the accumulated and summed result with a preset material fluctuation reference, and recording the type and the variation amplitude of the material characteristic parameter triggering the determination.
  6. 6. The method according to claim 1, wherein in the step S2, when the weight deviation is determined, the determining process for the equipment wear deviation includes extracting accumulated operation duration data and corresponding weight deviation data in the equipment operation state parameters from the standardized data set, sorting and constructing an equipment operation duration-weight deviation data set according to time sequence, analyzing the variation trend of the weight deviation, simultaneously comparing the accumulated operation duration of the equipment with a preset duration reference, and recording the current accumulated operation duration and deviation variation rate of the equipment.
  7. 7. The method according to claim 1, wherein in the step S3, the weight deviation compensation model implementation process for system inherent deviation matching comprises the steps of extracting real-time numerical values, deviation accumulation effects and deviation change rates of the weight deviation based on the deviation characteristic data, substituting the real-time numerical values, deviation accumulation effects and deviation change rates into a compensation model constructed based on proportional-integral-derivative regulation logic, calculating proportional regulation components according to the real-time numerical values, calculating integral regulation components by the deviation accumulation effects, calculating derivative regulation components by the deviation change rates, and superposing the three regulation components according to preset weights to obtain compensation amounts corresponding to the system inherent deviation.
  8. 8. The method of claim 1, wherein in the step S3, the weight deviation compensation model implementation process aiming at material fluctuation deviation matching comprises the steps of obtaining corresponding blanking weight deviation and compensation quantity data of different material characteristic parameters under different difference degrees, constructing an associated mapping table of each material characteristic parameter and compensation quantity, wherein the associated mapping table comprises parameter difference grades, corresponding basic compensation quantity and influence weights, extracting actual material characteristic parameters in the standardized data set, comparing the actual material characteristic parameters with preset standard material characteristic parameters, determining the difference degrees and dividing the difference grades, extracting corresponding basic compensation quantity from the associated mapping table according to the difference grades, extracting influence weights on blanking weight from the mapping table, calculating to obtain preliminary correction compensation quantities, and summing the preliminary correction compensation quantities corresponding to all the material characteristic parameters to obtain the compensation quantity of the material fluctuation deviation.
  9. 9. The method according to claim 1, wherein in S3, the implementation process of the dynamic fusion mechanism of the multi-mode compensation model comprises the steps of determining compensation amounts corresponding to inherent deviation, fluctuation deviation and equipment abrasion deviation of the system based on the deviation type judging result, determining weight coefficients corresponding to the deviation according to fluctuation conditions of material characteristic parameters and equipment operation state parameters in the standardized data set, multiplying the compensation amounts corresponding to the deviation by self weight coefficients to obtain weighted compensation amounts of the deviation, adding all the weighted compensation amounts to obtain initial fusion compensation amounts, and smoothing the initial fusion compensation amounts to generate final self-adaptive compensation amounts.
  10. 10. The method according to claim 1, wherein in the step S3, the setting implementation process of the compensation upper limit constraint comprises the steps of presetting a compensation upper limit constraint range to form a compensation upper limit constraint standard, comparing the self-adaptive compensation with the compensation upper limit constraint, and synchronously recording a comparison result of the compensation and a finally determined compensation value.
  11. 11. The method according to claim 1, wherein in S4, the implementation process of periodically acquiring the historical data includes triggering a historical data acquisition instruction according to a preset period, extracting a standardized data set, a deviation type judgment result, a fusion compensation amount parameter, historical blanking data, deviation data and compensation effect data in the period, classifying and sorting the extracted data, adding time, material and equipment identification information for the sorted data, and storing the time, material and equipment identification information in a historical database according to a preset format.
  12. 12. The method of claim 1, wherein in S4, the implementation process of iterative updating of the regulation and control parameters of the weight deviation compensation model comprises constructing a parameter updating data set by combining a standardized data set, a deviation type judgment result and a fusion compensation amount parameter generated in each stage based on the historical blanking data, the deviation data and the compensation effect data, adjusting the regulation and control parameters of the compensation model corresponding to the inherent deviation of the system, the fluctuation deviation of the material and the abrasion deviation of the equipment by adopting a data driving mode with the optimal compensation effect as a target, and collecting all updated regulation and control parameters to generate a compensation parameter set.

Description

Automatic correction and compensation method for blanking and weighing Technical Field The invention belongs to the technical field of blanking processing, and particularly relates to an automatic correction and compensation method for blanking weighing. Background In the field of material cutting processing, the accurate control of material cutting weight directly influences product quality, material utilization rate and production efficiency, especially in industries with higher requirements on material weight precision such as food processing, plastic molding, metal processing, the accuracy of material cutting weighing is vital. The existing blanking weighing compensation technology mostly adopts a fixed parameter compensation or simple deviation feedback adjustment mode, and has a plurality of defects. The prior art is rough in processing the collected blanking related parameters, and often high-frequency noise generated by equipment vibration and abnormal value interference in the data collection process are not fully considered, so that the quality of input data is poor, and the accuracy of deviation calculation is affected. The existing deviation judging modes are mostly single threshold judgment, and cannot distinguish deviations of different types such as inherent deviations of a system, fluctuation deviations of materials, abrasion deviations of equipment and the like, and the deviations are adjusted by adopting a unified compensation model, so that the deviation judging modes are difficult to adapt to individual compensation requirements of different deviation causes, and the compensation precision is limited. The existing compensation fusion mechanism is mostly fixed weight superposition, the weight of each deviation compensation quantity can not be dynamically adjusted according to the fluctuation condition of material characteristics and the running state of equipment, and the problems of excessive compensation or insufficient compensation are easy to occur. In addition, the prior art lacks an effective closed loop iterative optimization mechanism, the regulation and control parameters of the compensation model cannot be continuously updated according to historical processing data, when the material characteristics change or equipment is worn and aged, the compensation accuracy can be gradually reduced, the regulation parameters are required to be manually and frequently intervened, the production operation and maintenance cost is increased, and the stability of the production process is difficult to ensure. Therefore, aiming at the problems of low data processing precision, inaccurate deviation type identification, poor compensation fusion adaptability and lack of closed loop optimization in the existing blank weighing compensation technology, an automatic blank weighing correction and compensation method capable of realizing data preprocessing, self-adaptive deviation classification, dynamic compensation fusion and continuous iterative optimization is needed. Disclosure of Invention In order to solve the problems in the prior art, the invention provides an automatic correction and compensation method for blanking and weighing, The aim of the invention can be achieved by the following technical scheme: An automatic correction and compensation method for blanking and weighing comprises the following steps: s1, acquiring preset blanking parameters, actual blanking weighing feedback data, material characteristic parameters and equipment operation state parameters, and performing noise filtration and outlier rejection to generate a standardized data set; S2, calculating weight deviation based on target blanking weight and actual blanking weighing feedback data in the standardized data set, introducing a deviation characteristic self-adaptive identification and classification mechanism, and carrying out type judgment on the weight deviation by combining a preset deviation judgment reference library, material characteristic parameter fluctuation conditions and equipment running state parameter change trend in the standardized data set to generate a deviation type judgment result and deviation characteristic data; S3, matching a corresponding weight deviation compensation model aiming at a deviation type based on the deviation type judging result and deviation characteristic data, wherein the weight deviation comprises a system inherent deviation, a material fluctuation deviation and a device abrasion deviation, and generating a self-adaptive compensation quantity based on a dynamic fusion mechanism of the multi-mode compensation model; And S4, correcting the preset blanking parameters based on the fusion compensation quantity parameters, generating corrected blanking control instructions and sending the corrected blanking control instructions to a blanking executing mechanism, periodically acquiring historical data, and carrying out iterative updating on the regulation and control parameters of the