Material forming press control system based on multi-parameter feedback

Abstract

The application discloses a material forming and pressing control system based on multi-parameter feedback and belongs to the technical field of processing control, aiming to solve the problems of the traditional material forming and pressing, such as the incapability of static parameter setting to adapt to dynamic changes of working conditions, the lag of quality control, and the low precision of parameter and quality demand adaptation. According to the historical record of material forming and pressing, the independent interval of production decision parameters is divided, the interval combination set is generated, and the initial parameter mapping table is constructed; the current batch production decision parameters are collected in real time, the initial processing process parameters are matched, and the dynamic changes are monitored to update the parameters; the quality detection results of finished products and intermediate products are acquired in real time, the parameter correction is judged and triggered to generate the sensitive parameter correction value; and the initial parameter mapping table is optimized in combination with the quality results. The application realizes real-time adaptation of working conditions and parameters, and significantly improves the product quality stability and the production automation level.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of processing control, more particularly to a material forming and pressing control system based on multi-parameter feedback. BACKGROUND

[0002] In the field of processing control production, product quality and production stability directly depend on the accurate setting and dynamic adaptation of processing parameters. The industry has increasingly stringent requirements for the scientific nature of parameters, the timeliness of working condition response, and the timeliness of working condition response. Currently, the parameter setting methods commonly used in the industry rely on manual experience or fixed standard values, and lack the ability to dynamically adapt to the complexity of production conditions. Dynamic changes such as fluctuations in environmental temperature and humidity, material performance differences, and mold wear often cause the preset parameters to deviate from the actual working conditions, leading to processing deviations. At the same time, the existing quality control mode is mainly based on post-detection, i.e., quality inspection after product forming. When size deviations, fusion defects, and other problems are found, the parameters are adjusted back, which not only responds late and is difficult to recover the loss of unqualified products, but also lacks precise direction to the root cause of quality defects, making it easy to cause production fluctuations due to blind adjustment. In addition, most production systems do not have a continuous optimization mechanism for parameters, and the parameter adjustment results are not systematically accumulated and iterated. In the long run, the parameter system is difficult to adapt to the gradual changes in production conditions, leading to insufficient production process stability and poor product quality consistency, seriously affecting production efficiency and market competitiveness. Therefore, in order to overcome these limitations, the present application provides a material forming and pressing control system based on multi-parameter feedback. SUMMARY

[0003] In view of the deficiencies of the prior art, the purpose of the present application is to provide a material forming and pressing control system based on multi-parameter feedback, which solves the problem of how to adapt the parameter setting to the dynamic changes of the working conditions and respond to quality problems in time to improve the adaptation accuracy of parameters and quality requirements, and at the same time realizes the continuous optimization of the parameter system to ensure the production stability and product quality consistency. In order to achieve the above purpose, the present application provides the following technical solutions:

[0004] The material forming and pressing control system based on multi-parameter feedback comprises a database management module, a production monitoring module, an intelligent correction module, and an iterative optimization module.

[0005] The database management module is used to divide the independent intervals of the production decision parameters according to the production decision parameter values and the corresponding processing parameter values of each production link in the historical pressing records of the material forming and pressing, generate a set of production decision parameter interval combinations, match the processing parameter values of each interval combination, and construct an initial parameter mapping table.

[0006] The production monitoring module is configured to collect real-time production decision parameter values of a current production batch, match initial processing parameter values of the current production batch based on an initial parameter mapping table, monitor dynamic changes of the real-time production decision parameter values, and determine whether to trigger processing parameter updating to regulate and control updated processing parameter values.

[0007] The intelligent correction module is configured to obtain quality detection results of finished products and intermediate products of the current production batch in real time, determine whether to trigger processing parameter correction based on a product quality pass rate of the current production batch and a frequency of each quality unqualified type, identify sensitive processing parameters, and generate correction values of the sensitive processing parameters.

[0008] The iterative optimization module is configured to perform initial parameter mapping table iterative optimization operation in combination with the quality detection results when an initial processing parameter interval combination does not exist in the initial parameter mapping table or the processing parameter correction is triggered.

[0009] Specifically, the step of generating the production decision parameter interval combination set comprises:

[0010] The historical pressing records in the material forming and pressing process are obtained, and historical pressing records with product quality pass rates meeting preset pass standards are screened to construct a decision data set. Each historical pressing record contains production decision parameter values, processing parameter values of each production link, and product quality pass rates.

[0011] Based on the historical pressing records in the decision data set, the historical values of each parameter are extracted according to the categories of the production decision parameters to construct production decision parameter subsets.

[0012] The historical values of each production decision parameter subset are statistically analyzed to calculate the distribution range of the historical values of each production decision parameter, determine the initial value interval range of the production decision parameter, and correct the initial value interval range based on multi-dimensional division basis to obtain the standard interval range of each production decision parameter.

[0013] The fluctuation characteristics of each production decision parameter in the standard interval range are counted, and for each production decision parameter, an association curve of the fluctuation characteristics and the corresponding product quality pass rate is generated.

[0014] By fitting and analyzing the association curve, a continuous value segment in the association curve is identified as an independent interval, and the upper and lower boundaries and the product quality pass rate range are labeled for each independent interval.

[0015] The independent intervals of each production decision parameter are combined in full to generate a production decision parameter interval combination set.

[0016] Specifically, the step of constructing the initial parameter mapping table comprises:

[0017] Each interval combination in the production decision parameter interval combination set is compared with the decision data set, interval combinations with historical suppression records are screened, and the corresponding product quality pass rate is counted. Interval combinations with product quality pass rates greater than a preset quality pass threshold are screened to generate an effective production decision parameter interval combination set;

[0018] For each interval combination in the effective production decision parameter interval combination set, all processing process parameter values corresponding to the interval combination are extracted from the decision data set and grouped by production batch;

[0019] The processing process parameter values of each group of production batches are analyzed for fluctuations, the fluctuation amplitudes of each processing process parameter within each group are calculated, and the parameter fluctuation levels of each production batch are summarized;

[0020] The product quality pass rate of each production batch is counted, and the product quality pass rates and parameter fluctuation levels of all production batches under the same interval combination are compared and contrasted to screen target production batches;

[0021] The processing process parameter values corresponding to the target production batches are extracted to form a processing process parameter set associated with the interval combination;

[0022] Each interval combination in the effective production decision parameter interval combination set is established in a one-to-one correspondence with the corresponding processing process parameter set as an initial parameter mapping table.

[0023] Specifically, the step of matching the initial processing process parameters of the current production batch includes:

[0024] The initial values of the production decision parameters at the start of the current production batch are collected, the initial parameter mapping table is retrieved through interval matching, and the collected initial values of the production decision parameters are compared one by one with the independent interval boundaries of each production decision parameter to form an initial interval combination of the production decision parameters;

[0025] If the initial interval combination exists in the initial parameter mapping table, the processing process parameter values associated with it are used as the initial processing process parameters of the current production batch;

[0026] If the initial interval combination does not exist in the initial parameter mapping table, the initial values of each production decision parameter in the initial interval combination are extracted, and the production decision parameter distances of all interval combinations in the initial parameter mapping table are calculated;

[0027] According to the production decision parameter distance, the interval combinations in the initial parameter mapping table are screened to obtain a reference combination, the processing process parameter values associated with the reference combination are used as the initial processing process parameters of the current production batch, and the suppression records under the reference combination are marked as unmatched interval combinations.

[0028] Specifically, the step of regulating and updating the processing parameters comprises:

[0029] According to the initial processing parameters, initial regulation instructions are sent to the execution devices of each production link to drive the execution devices to start production operations according to the initial regulation instructions;

[0030] The production decision parameter real-time values of the current production batch are continuously collected at a preset frequency, and are time-stamped, associated with the current production batch number, production station identifier and production progress node;

[0031] The production decision parameter real-time values are compared with the original independent interval boundaries corresponding to each production decision parameter in the initial interval combination one by one to determine whether the production decision parameter real-time values are still within the original independent interval; if yes, it is determined that the processing parameter update is not needed; otherwise, the production decision parameter is marked as an abnormal production decision parameter, and production decision parameter switching monitoring is performed;

[0032] If it is determined that the processing parameter update is needed, a new real-time production decision parameter interval combination is formed according to the new independent interval to which each production decision parameter real-time value belongs, and the processing parameter value associated with the new real-time production decision parameter interval combination is called as the updated processing parameter.

[0033] Specifically, the step of performing production decision parameter switching monitoring comprises:

[0034] Within a preset monitoring time range, the exceeding amplitude of the abnormal production decision parameter real-time value and the original independent interval boundary is calculated, and the fluctuation variance of the abnormal production decision parameter real-time value is calculated.

[0035] If the duration for which the abnormal production decision parameter real-time value continuously exceeds the original independent interval reaches a preset duration threshold, and the fluctuation variance is less than a preset variance threshold, it is determined that the processing parameter update is needed; otherwise, it is determined that the processing parameter update is not needed.

[0036] Specifically, the step of identifying sensitive processing parameters comprises:

[0037] Quality detection data of the intermediate products and finished products in the current production batch are collected in real time, and are stored according to the classification of intermediate products and finished products and the classification of detection items;

[0038] The total number of detections and the number of qualified products of the current production batch are counted to calculate the product quality qualification rate of the current batch; all unqualified products are classified according to a preset unqualified type division standard, the number of products of each unqualified type is recorded, and the occurrence frequency of each quality unqualified type is calculated.

[0039] a preset quality pass threshold and a frequency threshold corresponding to each type of unqualified type; if the current batch product quality pass rate is lower than the preset quality pass threshold, or the occurrence frequency of any unqualified type exceeds the corresponding frequency threshold, it is determined that the processing process parameter correction is triggered; otherwise, it is determined that the correction is not needed to be triggered;

[0040] If the processing process parameter correction is triggered, the historical pressing records are searched and filtered according to the current production decision parameter interval combination and the quality unqualified type, and a historical correction reference data set is constructed;

[0041] The processing process parameters related to the current quality unqualified type are extracted from the historical correction reference data set as candidate processing process parameters; the deviation value of the real-time processing process parameter of the current production batch from the initial processing process parameter is calculated, and the unqualified degree of the current quality unqualified type is calculated;

[0042] The correlation coefficient method is used to quantitatively calculate the correlation between the unqualified degree and the deviation value of each candidate processing process parameter, and the processing process parameters with an absolute value of the correlation coefficient greater than a preset correlation threshold are selected as sensitive processing process parameters affecting the current quality unqualified type.

[0043] Specifically, the step of generating the correction value of the sensitive processing process parameter includes:

[0044] The correction direction of the sensitive processing process parameter is determined according to the correlation analysis result; if the unqualified degree and the parameter deviation value are positively correlated, the correction direction is to adjust the parameter in the opposite direction, and if they are negatively correlated, the correction direction is to adjust the parameter in the same direction;

[0045] From the historical correction reference data set, the historical correction amplitudes corresponding to the same sensitive processing process parameter and the same quality unqualified type are extracted, as well as the historical unqualified degree and the historical parameter deviation value corresponding to each historical correction amplitude; the ratio of the historical unqualified degree to the historical parameter deviation value is calculated to obtain the historical ratio data;

[0046] The current ratio of the unqualified degree of the current quality unqualified type in the current production batch to the corresponding sensitive processing process parameter deviation value is calculated; the historical correction amplitudes in the historical ratio data whose absolute value of the difference from the current ratio is less than a preset ratio threshold are selected to form a candidate correction amplitude set;

[0047] The arithmetic mean of the historical correction amplitudes in the candidate correction amplitude set is calculated to obtain a basic correction amplitude; at the same time, the parameter distance between the current production decision parameter interval combination and the historical interval combination corresponding to the basic correction amplitude is calculated; the weight distribution rule is set according to the parameter distance, and the adjusted correction amplitude is calculated by weighting; according to the correction direction and the correction amplitude, the correction value of the sensitive processing process parameter is calculated.

[0048] Specifically, the step of performing the initial parameter mapping table iterative optimization operation comprises:

[0049] If the initial interval combination does not exist in the initial parameter mapping table in the current production batch, the relevant pressing record is extracted according to the unmatched interval combination label to obtain the quality pass rate of the production batch corresponding to the reference combination associated processing process parameter;

[0050] The quality pass rate of the reference combination is compared with the preset quality pass threshold value, if the quality pass rate is higher than the preset quality pass threshold value, the actual value of the production decision parameter of the current production batch is collected and is included in the value range of the reference combination, and the interval boundary of the reference combination is updated;

[0051] Otherwise, the reference combination is removed from the effective production decision parameter interval combination set, and the associated processing process parameter record and the historical pressing record of the reference combination in the initial parameter mapping table are deleted.

[0052] Specifically, the step of performing the initial parameter mapping table iterative optimization operation further comprises:

[0053] If the processing process parameter associated with the interval combination in the initial parameter mapping table triggers the correction in the current production batch, all processing process parameter correction values corresponding to the interval combination are extracted, are classified and summarized according to the production batch number of the correction occurrence, and a correction value statistical account book is established;

[0054] The cumulative number of correction values in the correction value statistical account book is counted, if less than the preset number threshold value, the optimization operation is not performed, otherwise the arithmetic mean, standard deviation and frequency distribution histogram of all correction values in the correction value statistical account book are calculated;

[0055] According to the frequency distribution histogram, the correction value distribution characteristics are judged, if the standard deviation is less than the preset concentration determination threshold value, it is determined that the correction value is concentratedly distributed, and based on the frequency interval distribution in the frequency distribution histogram, the new associated processing process parameter value of the interval combination is selected;

[0056] Otherwise, it is determined that the correction value is dispersedly distributed, a plurality of subintervals are divided according to the correction value range corresponding to the peak value, and the original interval combination is split to form a plurality of new interval combinations in combination with the fluctuation range of the corresponding production decision parameter;

[0057] For each new interval combination after splitting, the processing process parameter data under the corresponding working condition is extracted from the historical pressing record and the correction value statistical account book, the parameter fluctuation amplitude and the product quality pass rate are calculated according to the production batch, and the processing process parameter is screened as the associated processing process parameter of the new interval combination.

[0058] The beneficial effects of the present application are:

[0059] The application realizes real-time dynamic adaptation of processing process parameters and production conditions by systematic integration, dynamic monitoring and targeted optimization of production related parameters and quality data, effectively avoids the problem that traditional parameter setting is disconnected with actual working conditions; can quickly and accurately locate the core influencing factors of quality defects and adjust parameters in time, solves the production fluctuation problem caused by quality control lag and blind parameter adjustment; at the same time, through continuous iteration and improvement of the parameter system, the adaptation accuracy of parameters and quality requirements is significantly improved, the stability of long-term production process is guaranteed, the consistency of product quality is greatly improved, and the production efficiency and market competitiveness are improved. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 FIG. 1 is a structural schematic diagram of the material forming and pressing control system based on multi-parameter feedback of the application;

[0061] Figure 2 FIG. 2 is a flowchart for generating a production decision parameter interval combination set of the application;

[0062] Figure 3 FIG. 3 is a flowchart for constructing an initial parameter mapping table of the application;

[0063] Figure 4 FIG. 4 is a flowchart for matching the initial processing process parameters of the current production batch of the application;

[0064] Figure 5 FIG. 5 is a flowchart for regulating and updating the processing process parameters of the application. DETAILED DESCRIPTION

[0065] Please refer to Figure 1 The embodiment introduces a material forming and pressing control system based on multi-parameter feedback, which includes a database management module, a production monitoring module, an intelligent correction module and an iterative optimization module;

[0066] The database management module is used to extract the production decision parameter values and corresponding processing parameter values of each production link in the historical compression records of qualified quality according to the historical compression records in the material forming and compression process; wherein, the production decision parameter refers to the input parameter directly affecting the setting of the material forming and compression processing parameter, including the environmental parameter, the material parameter and the mold parameter; interval division is performed on the production decision parameter based on multi-dimensional division basis, wherein, the multi-dimensional division basis includes: product quality critical threshold, which is the numerical boundary of the product quality problem such as size deviation and fusion defect when the production decision parameter exceeds the threshold; forming and compression process constraint condition, which is the parameter range meeting the core process requirements such as filling uniformity, pressure densification and forming fusion degree; processing equipment running limit parameter, which is the maximum and minimum values of the parameter that the processing equipment can withstand in the safe and stable running state; and the interval boundary values of each production decision parameter are determined by the following way: the product quality pass rate corresponding to different parameter values in the historical compression records is counted, the correlation curve of parameter fluctuation and pass rate is drawn, and the parameter value range in which the pass rate remains stable in the curve is taken as a single interval to ensure that the parameter fluctuation in the single interval will not lead to product quality defects; the production decision parameter interval combination is established, which is an effective combination of the environmental parameter interval, the material parameter interval and the mold parameter interval; the production decision parameter interval combination and the corresponding processing parameter value are associated and mapped to construct an initial parameter mapping table, and the processing parameter value associated with the same production decision parameter interval combination in the initial parameter mapping table is the parameter set with the optimal quality stability under the combination in the historical compression records, wherein the optimal quality stability is defined as the highest product pass rate and the smallest parameter fluctuation amplitude in the same batch under the combination; a dynamic updating interface is set to receive the effective correction parameter fed back by the intelligent correction module and the optimization parameter output by the iterative optimization module, replace and update the processing parameter value of the corresponding interval combination in the initial parameter mapping table, and record the batch quality feedback results after parameter updating, including product pass rate, key size deviation value, fusion strength, etc.

[0067] In the embodiment, the database management module divides the production decision parameter interval based on multi-dimensional basis, breaks through the limitation of dividing the interval based on only a single empirical threshold or equipment rated value in the prior art, determines the boundary based on the correlation curve of parameter fluctuation and quality pass rate, and makes the interval division scientific and quality-constrained; by constructing the dynamic association mapping of the production decision parameter interval combination and the processing parameter, and continuously updating the initial parameter mapping table based on the effective correction parameter and the optimization parameter, the problem that the existing static database cannot adapt to the interaction between production decision parameters and the dynamic change of working conditions is solved, and the adaptation accuracy of the initial processing parameter and the actual production demand is significantly improved.

[0068] Preferably, the step of generating the production decision parameter interval combination set comprises:

[0069] Referring to Figure 2 , obtain all historical pressing records in the material forming and pressing process, each historical pressing record contains production decision parameter values, process parameter values of each production link and product quality pass rate; check the product quality result of each historical pressing record one by one, screen historical pressing records with product quality pass rate meeting the preset qualified standard, and eliminate records with product quality result not meeting the qualified standard; aggregate all screened historical pressing records, and construct a decision data set with unified structure and complete parameters.

[0070] Based on the historical pressing records in the decision data set, the historical values of each parameter are extracted according to the categories of production decision parameters, and production decision parameter subsets are constructed, including environmental parameter subset, material parameter subset and mold parameter subset; wherein the environmental parameter subset contains the environmental related parameter values in all historical pressing records, the material parameter subset contains the material related parameter values in all historical pressing records, and the mold parameter subset contains the mold related parameter values in all historical pressing records.

[0071] Statistical analysis is performed on the historical values of each production decision parameter subset, the distribution range of the historical values of each production decision parameter is calculated, and the initial value interval range of the production decision parameter is determined; based on multi-dimensional division basis, the initial value interval range is corrected step by step, for example, combined with the product quality critical threshold, the part of the initial value interval range exceeding the threshold is eliminated, and the value segment that will not cause product quality defects is retained; then according to the forming and pressing process constraint conditions, the value segment in the initial value interval range that meets the core process requirements such as filling uniformity, pressure densification and forming welding degree is selected; finally, referring to the running limit parameters of the processing equipment, the part of the initial value interval range exceeding the safe and stable running bearing range of the equipment is eliminated; the intersection of the value segments after the above three steps of screening is taken, and the standard interval range of each production decision parameter is obtained.

[0072] Statistical analysis is performed on the historical values of each production decision parameter subset, the distribution range of the historical values of each production decision parameter is calculated, and the initial value interval range of the production decision parameter is determined; based on multi-dimensional division basis, the initial value interval range is corrected step by step, for example, combined with the product quality critical threshold, the part of the initial value interval range exceeding the threshold is eliminated, and the value segment that will not cause product quality defects is retained; then according to the forming and pressing process constraint conditions, the value segment in the initial value interval range that meets the core process requirements such as filling uniformity, pressure densification and forming welding degree is selected; finally, referring to the running limit parameters of the processing equipment, the part of the initial value interval range exceeding the safe and stable running bearing range of the equipment is eliminated; the intersection of the value segments after the above three steps of screening is taken, and the standard interval range of each production decision parameter is obtained.

[0073] The independent intervals of each production decision parameter are combined, that is, all independent intervals of the environment parameters, all independent intervals of the material parameters, and all independent intervals of the mold parameters are combined to generate all possible production decision parameter interval combination forms, and the production decision parameter interval combination set is integrated.

[0074] Preferably, the specific steps of constructing the initial parameter mapping table include:

[0075] Referring to Figure 3 Each interval combination in the production decision parameter interval combination set is compared with the decision data set to check whether there is a corresponding historical pressing record for each interval combination, and the interval combination with a historical pressing record is selected; for the interval combination with a corresponding historical pressing record, the corresponding product quality pass rate is counted, the interval combination with a product quality pass rate greater than a preset quality pass threshold is screened, the interval combination without a corresponding historical pressing record or unstable production process is removed, and the effective production decision parameter interval combination set is generated.

[0076] For each interval combination in the effective production decision parameter interval combination set, all processing process parameter values corresponding to the interval combination are extracted from the decision data set and grouped according to production batches; the processing process parameter values of each group of production batches are analyzed for fluctuations, the fluctuation amplitudes of each processing process parameter in each group are calculated, and the parameter fluctuation levels of each production batch are summarized; the product quality pass rate of each production batch is also counted, and the product quality pass rates and parameter fluctuation levels of all production batches under the same interval combination are comprehensively compared and contrasted, and the production batch with the highest product quality pass rate and the smallest parameter fluctuation level is selected as the target production batch; the processing process parameter values corresponding to the target production batch are extracted to form a processing process parameter set associated with the interval combination and having the optimal quality stability.

[0077] Each interval combination in the effective production decision parameter interval combination set is established in one-to-one correspondence with the corresponding processing process parameter set; a retrieval index is set for the corresponding relationship table, the boundary information combination of each independent interval of the production decision parameter is used as a retrieval keyword to ensure the retrieval efficiency; the structured corresponding relationship table with the index set is defined as the initial parameter mapping table, stored in the database in a hierarchical encryption storage mode, and a data calling authority management mechanism is established to allow only the system authorized module to access and call the data in the initial parameter mapping table.

[0078] The production monitoring module is used to collect production decision parameter values of the current production batch in real time, including real-time values of environmental parameters, real-time values of material parameters, and real-time values of mold parameters; the initial parameter mapping table in the database management module is searched through interval matching, the production decision parameter values collected in real time are compared with the independent interval boundaries of each production decision parameter, the environment parameter interval, the material parameter interval, and the mold parameter interval to which the production decision parameter values belong are determined, and a real-time production decision parameter interval combination is formed; the processing process parameter values associated with the real-time interval combination in the initial parameter mapping table are called as initial processing process parameters of the current production batch; control instructions are sent to the execution devices of each production link according to the initial processing process parameters, and the execution devices are driven to perform material filling, press molding, drying and cooling operations according to the instructions; the dynamic changes of the production decision parameter values of the current production batch are continuously monitored during the production process, and it is judged whether the processing process parameter updating is triggered, if yes, the initial parameter mapping table is searched again through the interval matching algorithm, the new real-time production decision parameter interval combination and the corresponding processing process parameter values are determined, the processing process parameters are updated and controlled, and the updated control instructions are sent to the execution devices, so that the processing process parameters of the current production batch are dynamically updated until the production batch is completed.

[0079] In the embodiment, the production monitoring module accurately calls the adaptive processing process parameters in the initial parameter mapping table by collecting production decision parameters in real time and combining the interval matching algorithm, ensures the accurate adaptation of the initial processing process parameters to the current production conditions, breaks through the limitation that the static parameter setting in the traditional technology cannot respond to the dynamic changes of the conditions, realizes the real-time dynamic updating of the processing process parameters through the continuous monitoring and switching judgment of the dynamic changes of the production decision parameters during the production process, avoids the production defects caused by the mismatch between the parameters and the conditions, and completes the adaptive adjustment of the parameters without manual intervention, which not only improves the automation degree and response efficiency of the production process, but also guarantees the continuous adaptability of the processing process parameters and the conditions within the entire production batch, and significantly enhances the stability of the production process and the consistency of the product quality.

[0080] Preferably, the specific steps of obtaining the initial processing process parameters of the current production batch include:

[0081] Please refer to Figure 4 , the initial values of the production decision parameters when the current production batch is started are collected, including the initial values of the environmental parameters, the initial values of the material parameters, and the initial values of the mold parameters; the collected initial values of the production decision parameters are compared with the independent interval boundaries of each production decision parameter one by one, the environment parameter interval, the material parameter interval, and the mold parameter interval to which the initial values of the production decision parameters belong are determined, and the initial interval combination of the production decision parameters is formed;

[0082] If the initial interval combination exists in the initial parameter mapping table, the associated processing process parameter value is taken as the initial processing process parameter of the current production batch; if the initial interval combination does not exist in the initial parameter mapping table, the initial values of the production decision parameters in the initial interval combination are extracted, the production decision parameter distances of the initial values of the production decision parameters in the initial interval combination and all interval combinations in the initial parameter mapping table are calculated, and the production decision parameter distance is the sum of the absolute values of the differences between the initial values of the production decision parameters and the boundary values of the corresponding interval combinations;

[0083] According to the production decision parameter distance, the effective interval combination with the smallest distance is screened out as a reference combination, the processing process parameter value associated with the reference combination is taken as the initial processing process parameter of the current production batch, and the pressing record under the reference combination is marked as an unmatched interval combination.

[0084] Preferably, the step of regulating and updating the processing process parameter comprises:

[0085] Referring to Figure 5 , according to the initial processing process parameter, initial regulation instructions are sent to the execution devices of each production link, the instructions clearly indicate the processing process parameter values and operation requirements of each production link; instruction confirmation signals returned by each execution device are received to ensure that all execution devices successfully receive and respond to the initial regulation instructions; the execution devices are driven to start material filling, pressure forming, drying and cooling production operations according to the initial regulation instructions, and the current production batch is officially started.

[0086] During the production process, the real-time values of the production decision parameters of the current production batch are continuously collected at a preset frequency, including the real-time values of the environmental parameters, the real-time values of the material parameters, and the real-time values of the mold parameters; each set of collected production decision parameter real-time values is time-stamped and associated with the current production batch number, the production station identifier and the production progress node; invalid parameter groups with signal interruption, data loss or exceeding the physical reasonable range are synchronously removed, and complete and valid real-time parameter data are retained.

[0087] The production decision parameter real-time values are compared with the original independent interval boundaries corresponding to each production decision parameter in the initial interval combination one by one, and it is judged whether the real-time values of each production decision parameter are still within the original independent interval; if all the real-time values of the production decision parameters are still within the original independent interval, it is determined that the processing process parameter update is not triggered, and the initial processing process parameter is maintained to continue execution; if any real-time value of the production decision parameter exceeds the original independent interval, it is marked as an abnormal production decision parameter, and production decision parameter switching monitoring is performed, that is:

[0088] In the preset monitoring time range, the duration and fluctuation characteristics of the abnormal production decision parameter real-time value are monitored, the exceeding amplitude of the abnormal production decision parameter real-time value and the original independent interval boundary in the preset monitoring time range is counted, the fluctuation variance of the abnormal production decision parameter real-time value is calculated for quantitative analysis. If the duration of the abnormal production decision parameter real-time value continuously exceeding the original independent interval reaches the preset duration threshold, and the fluctuation variance is less than the preset variance threshold, that is, the abnormal production decision parameter exceeds the stable state without large fluctuations, it is determined that the processing process parameter update needs to be triggered; otherwise, it is determined that the processing process parameter update does not need to be triggered. Wherein, the preset duration threshold is set according to the process response period of the production link, to ensure that the parameter fluctuation has enough time to affect the processing effect before determining; the preset variance threshold is determined based on the parameter fluctuation data under the stable working condition, to distinguish between temporary exceeding caused by transient disturbance and stable exceeding caused by working condition change, to avoid accidental fluctuation from triggering the processing process parameter update, and to ensure the continuity and stability of the production process.

[0089] If it is determined that the processing process parameter update needs to be triggered, all production decision parameter real-time values of the current production batch are compared with the independent interval boundaries of each production decision parameter one by one, to determine the new independent interval to which each production decision parameter real-time value belongs; the newly determined environment parameter interval, material parameter interval and mold parameter interval are integrated to form a new real-time production decision parameter interval combination. The processing process parameter value associated with the new real-time production decision parameter interval combination is called as the updated processing process parameter.

[0090] The processing process parameter update instruction is sent to the execution device of each production link, which contains the specific value of the final updated processing process parameter, the execution start time and the effective duration; the instruction receiving receipt returned by each execution device is received in real time, and for the execution device that has not successfully received, the instruction is repeatedly sent at a preset retry time interval until all execution devices confirm the reception; the instruction issuing time, the receiving state of each execution device and the production progress node of the updated processing process parameter are recorded.

[0091] The intelligent correction module is used to acquire quality detection results of finished products and intermediate products of the current production batch in real time, including product appearance integrity, size accuracy, welding strength, structural stability and other detection data, to count the quality pass rate of the products of the current production batch, to record the classification of product quality unqualified types, including size deviation, welding defect, appearance damage and other types, and to record the occurrence frequency of each quality unqualified type synchronously; to determine whether to trigger the correction of the processing parameters; if yes, to extract historical effective records consistent or similar to the current production decision parameter interval combination and the unqualified type by searching the initial parameter mapping table and the historical pressing records in the database management module, to analyze the sensitive processing parameters corresponding to the quality unqualified type, to quantitatively calculate the correlation between the unqualified degree and the deviation of the sensitive processing parameters in combination with the real-time processing parameters of the current production batch and the dynamic change data of the production decision parameters; to determine the correction direction and the correction range of the sensitive processing parameters according to the correlation analysis result, to refer to the forming and pressing process constraint conditions, the operating limit parameters of the processing equipment and the effectiveness feedback of the historical correction data, to generate the correction value of the sensitive processing parameters, and to push the correction value to the production monitoring module in real time to update the processing parameters of the current production batch, so as to quickly improve the product quality defects; and to synchronously transmit the correction value, the corresponding production decision parameter interval combination and the quality detection feedback data to the database management module through the dynamic update interface of the database management module, to replace the processing parameter values associated with the corresponding interval combination in the initial parameter mapping table, to continuously optimize the parameter adaptation logic, and to improve the parameter accuracy and the product quality stability in subsequent production.

[0092] In the embodiment, the intelligent correction module realizes instant perception and accurate quantification of production quality problems by collecting full-dimensional quality detection data of finished products and intermediate products in real time, breaks through the hysteresis limitation of traditional post-repair quality control, accurately locates the core processing parameters affecting the quality defects by searching historical effective records and quantitatively analyzing the correlation between the unqualified degree and the deviation of the sensitive processing parameters, avoids production fluctuations caused by blind correction, determines the correction direction and the range in combination with the forming and pressing process constraint conditions and the equipment operating limit parameters during the correction process, ensures that the correction value has effectiveness and safety, solves the problem of insufficient precision of traditional manual correction depending on experience, quickly improves the product quality of the current batch, reduces the unqualified rate, continuously optimizes the parameter adaptation logic through data feedback, significantly improves the matching accuracy of the processing parameters and the quality requirements in subsequent production, guarantees the stability and consistency of the product quality, and reduces the efficiency loss and human error caused by manual intervention.

[0093] Preferably, the specific steps of identifying the correction value of the sensitive processing parameter include:

[0094] The real-time butt joint quality detection device collects quality detection data of intermediate products and finished products in the current production batch, including appearance integrity related data, size precision related data, fusion strength related data, and structure stability related data; each quality detection data is labeled with corresponding production batch number, production station, processing time node, and corresponding processing process parameters; the verified quality detection data is stored according to the classification of intermediate products and finished products and the classification of detection items;

[0095] The total number of detections and the number of qualified products in the current production batch are counted, and the qualified rate of the current batch of products is calculated according to the ratio of the number of qualified products to the total number of detections; all unqualified products are classified according to the preset unqualified type division standard, specifically classified into size deviation type, fusion defect type, appearance damage type, and structure instability type; the number of products of each unqualified type is recorded, and the occurrence frequency of each quality unqualified type is calculated; an association table of unqualified types and corresponding quality detection data is established to clearly show the specific detection index deviation of each unqualified type.

[0096] A quality qualified threshold and a frequency threshold corresponding to each unqualified type are preset; the quality qualified rate of the current batch of products is compared with the preset quality qualified threshold, and the occurrence frequency of each unqualified type is compared with the corresponding frequency threshold; if the quality qualified rate of the current batch of products is lower than the preset quality qualified threshold, or the occurrence frequency of any unqualified type exceeds the corresponding frequency threshold, it is determined that the processing process parameter correction is triggered; if both conditions are not met, it is determined that no correction is needed.

[0097] If the processing process parameter correction is triggered, the historical suppression records are searched and filtered according to the current production decision parameter interval combination and the quality unqualified type, and a historical correction reference data set is constructed; for example, the search process sets the filtering conditions as the production decision parameter interval combination being consistent or similar to the current production decision parameter interval combination, the unqualified type being consistent with the current quality unqualified type, and the quality unqualified type being the unqualified type with the highest occurrence frequency; the determination standard for similar interval combinations is that the sum of the absolute values of the interval boundaries of the historical interval combination and the current interval combination is less than the preset similarity threshold; the invalid records with product quality qualified rate lower than the preset quality qualified threshold and incomplete processing process parameter records are excluded from the search results, and the historical records with clear quality improvement effect are retained as the historical correction reference data set.

[0098] extract the processing process parameters related to the current quality unqualified type from the historical correction reference data set as candidate processing process parameters; calculate the deviation value of the real-time processing process parameters of the current production batch from the initial processing process parameters, and at the same time calculate the unqualified degree of the current quality unqualified type; adopt the correlation coefficient method to quantitatively calculate the correlation between the unqualified degree and the deviation value of each candidate processing process parameter, and screen the processing process parameters with an absolute value of the correlation coefficient greater than a preset correlation threshold as sensitive processing process parameters affecting the current quality unqualified type.

[0099] Preferably, the step of generating the correction value of the sensitive processing process parameter comprises:

[0100] According to the correlation analysis result, the correction direction of the sensitive processing process parameter is determined, if the unqualified degree and the parameter deviation value are positively correlated, the correction direction is to adjust the parameter in the opposite direction, if negatively correlated, the correction direction is to adjust the parameter in the same direction;

[0101] From the historical correction reference data set, extract all historical correction amplitudes corresponding to the same sensitive processing process parameter and the same quality unqualified type, and synchronously extract the historical unqualified degree and the historical parameter deviation value corresponding to each historical correction amplitude; calculate the ratio of the historical unqualified degree to the historical parameter deviation value to obtain the historical ratio data;

[0102] Calculate the current ratio of the unqualified degree of the current quality unqualified type in the current production batch to the corresponding sensitive processing process parameter deviation value; screen the historical correction amplitudes in the historical ratio data with an absolute value of the difference from the current ratio less than a preset ratio threshold to form a candidate correction amplitude set;

[0103] Calculate the arithmetic mean of all historical correction amplitudes in the candidate correction amplitude set to obtain a basic correction amplitude; at the same time, calculate the parameter distance between the current production decision parameter interval combination and the historical interval combination corresponding to the basic correction amplitude, and the parameter distance is the sum of the absolute values of the interval boundary difference values of each production decision parameter;

[0104] Set a weight distribution rule according to the parameter distance, the smaller the parameter distance, the higher the weight proportion, distribute weights to each correction amplitude in the candidate correction amplitude set based on the rule, and calculate the adjusted correction amplitude by weighting; and limit the amplitude of the adjusted correction amplitude in combination with the forming and pressing process constraint condition and the processing equipment running limit parameter, eliminate the values exceeding the constraint range, and determine the final correction amplitude;

[0105] According to the correction direction and the final correction amplitude, calculate the correction value of the sensitive processing process parameter, the correction value is the result of the operation of the current real-time sensitive processing process parameter value and the final correction amplitude according to the correction direction; and perform secondary correction on the correction value and the processing equipment running limit parameter and the forming and pressing process constraint condition.

[0106] The iterative optimization module is configured to perform iterative optimization of the initial parameter mapping table when the initial interval combination does not exist in the initial parameter mapping table or the process parameter associated with the interval combination in the initial parameter mapping table triggers a correction. Specifically, for the case of an unmatched interval combination, the quality pass rate of the initial process parameter corresponding to the reference combination is determined. If the pass rate is high, it indicates that the matching logic is valid, and the current production decision parameter value is merged into the interval of the reference combination and the interval boundary of the reference combination is updated. If the pass rate is low, the reference combination is removed from the set of valid interval combinations to avoid subsequent matching deviation. For the case where the process parameter associated with the interval combination has a problem, the cumulative number of correction values of the process parameter corresponding to the interval combination is counted. After the cumulative number of correction values accumulates to a preset number threshold, the statistical law is analyzed. If the correction values are concentrated around a certain value, the concentrated value is taken as the new associated process parameter of the interval combination. If the correction values are distributed in a scattered manner, the original interval combination is split according to the distribution characteristics of the correction values and the corresponding actual process parameters, and the process parameter with the optimal quality stability is configured for each new interval combination after the split. Finally, all interval adjustments and parameter update results are synchronized to the database management module to complete the optimization and update of the initial parameter mapping table and record the optimization basis and process archive.

[0107] In the embodiment, the iterative optimization module breaks through the limitations of traditional static parameter mapping tables that cannot dynamically adapt to new working conditions and are difficult to accumulate correction experience by accurately responding to two core scenarios of unmatched interval combinations and parameter corrections. For unmatched interval combinations, the matching logic is verified based on the quality pass rate, and the interval boundary or validity of the reference combination is dynamically adjusted, which not only avoids subsequent matching deviation caused by invalid reference combinations, but also efficiently absorbs the parameter range of new working conditions, expanding the coverage dimension of the initial parameter mapping table. For interval combinations with problems in associated process parameters, the parameter is updated with a concentrated value or the interval is split to configure the optimal parameter according to the scattered characteristics, avoiding parameter fluctuations caused by single correction and ensuring the continuous improvement of the adaptation accuracy of interval combinations and process parameters, echoing the core logic of the database management module based on the stable range of pass rates and the quality closed-loop idea of the intelligent correction module. Finally, the initial parameter mapping table is updated through iteration, significantly reducing the triggering frequency of parameter update and correction in subsequent production, improving the stability of the production process and the consistency of product quality, and strengthening the long-term self-adaptation of the system to complex working conditions and new working conditions.

[0108] Preferably, the step of performing iterative optimization of the initial parameter mapping table comprises:

[0109] If the initial interval combination does not exist in the initial parameter mapping table in the current production batch, the relevant suppression record is extracted according to the unmatched interval combination label to obtain the quality pass rate of the production batch corresponding to the reference combination associated with the processing process parameter;

[0110] The quality pass rate of the reference combination is compared with the preset quality pass threshold value. If the quality pass rate is higher than the preset quality pass threshold value, the actual value of the production decision parameter of the current production batch is collected and is included in the value range of the reference combination. The upper and lower boundaries of the independent interval to which the reference combination belongs are recalculated. The upper boundary takes the larger value of the original upper boundary and the maximum value of the current production decision parameter, and the lower boundary takes the smaller value of the original lower boundary and the minimum value of the current production decision parameter. The interval boundary information of the reference combination is updated.

[0111] If the quality pass rate of the reference combination is lower than or equal to the preset quality pass threshold value, the reference combination is removed from the effective production decision parameter interval combination set, and the associated processing process parameter record and the historical suppression record of the reference combination in the initial parameter mapping table are deleted.

[0112] If the processing process parameter associated with the interval combination in the initial parameter mapping table triggers a correction in the current production batch, all processing process parameter correction values corresponding to the interval combination are extracted, classified and summarized according to the production batch numbers in which the corrections occur, and a correction value statistical account book is established. The correction value statistical account book contains the real-time value of the production decision parameter corresponding to each correction value, the correction time stamp and the quality pass rate.

[0113] The cumulative number of correction values in the correction value statistical account book is counted. If it is less than the preset number threshold, the new correction value is supplemented to the correction value statistical account book, and the optimization operation is not performed temporarily. Otherwise, the arithmetic mean, standard deviation and frequency distribution histogram of all correction values in the correction value statistical account book are calculated.

[0114] The distribution characteristics of the correction values are determined according to the frequency distribution histogram. If the standard deviation is less than the preset concentration determination threshold, it is determined that the correction values are concentrated, and the median of the interval with the highest frequency in the frequency distribution histogram is taken as the new associated processing process parameter value of the interval combination.

[0115] Otherwise, it is determined that the correction values are dispersed, a plurality of subintervals are divided according to the correction value range corresponding to the peak value, and the original interval combination is split to form a plurality of new interval combinations in combination with the fluctuation range of the corresponding production decision parameter.

[0116] For each new interval combination after splitting, the processing process parameter data under the corresponding working condition is extracted from the historical suppression record and the correction value statistical account book, the parameter fluctuation amplitude and the product quality pass rate are calculated according to the production batch, and the processing process parameter with the highest product quality pass rate and the smallest parameter fluctuation amplitude is selected as the associated processing process parameter of the new interval combination.

[0117] The interval boundary update result, the reference combination removal record, the interval combination split result and the new associated processing process parameter set are synchronized to the database management module, and the corresponding entry in the initial parameter mapping table is replaced;A unique identification number is generated for this iteration optimization operation, and the interval combination information, data source, judgment basis and parameter adjustment details involved in the operation are recorded and stored in association with the version information of the initial parameter mapping table to form a traceable optimization archive.

[0118] Working principle and effect:

[0119] The present application aims to solve the technical problems of traditional material forming and pressing, such as static parameter setting unable to respond to working condition changes, quality control lagging behind, and parameter system difficult to continue iteration, to realize accurate matching of processing process parameters with production conditions and quality requirements and long-term self-adaptive operation of the system.

[0120] In the data-based construction link, the database management module filters the historical pressing records of qualified quality, and performs interval division on the production decision parameters such as environment, material and mold based on product quality critical threshold, forming and pressing process constraints and multi-dimensional processing equipment operation limits;Combined with the correlation curve of parameter fluctuation and product quality pass rate, the parameter value segment with stable pass rate is extracted as an independent interval, and the processing process parameters with optimal quality stability are matched through interval full combination, to construct an initial parameter mapping table, which breaks through the limitation of traditional interval division relying only on experience threshold or equipment rated value, so that the initial parameters have scientificity and quality constraint, effectively improving the adaptation accuracy of initial processing process parameters to actual production requirements, and laying a reliable data foundation for subsequent real-time regulation and control.

[0121] In the real-time production regulation and control link, the production monitoring module collects the current batch production decision parameters in real time, retrieves the initial parameter mapping table through interval matching algorithm, calls the adapted initial processing process parameters to drive the execution device to start production;At the same time, the parameters are monitored at a preset frequency, and if any parameter continuously and stably exceeds the original interval, the processing process parameters corresponding to the new interval are matched and the regulation and control instruction is updated to ensure that the working condition and the parameter are real-time synchronized;If there is a quality problem, the intelligent correction module collects the quality data of finished products and intermediate products in real time, calculates the batch pass rate and unqualified type frequency, and triggers correction when the pass rate is lower than the threshold or the frequency of a certain unqualified type exceeds the limit, identifies the sensitive processing process parameters and determines the correction direction and amplitude, generates the correction value and pushes it to the production monitoring module to update the parameters, which realizes dynamic response of working condition and immediate correction of quality problems, avoids waste of unqualified products caused by traditional after-correction, reduces the blindness and error of manual experience correction, and improves the stability and automation of the production process.

[0122] In the parameter system optimization link, the iterative optimization module, for the unmatched interval combination scene, according to the quality qualified rate of the reference combination, the qualified current production decision parameter is included in the reference interval and the boundary is updated, or the unqualified reference combination is removed; for the parameter correction scene, after accumulating the correction value, if the correction value is concentrated, the processing process parameter of the corresponding interval is updated, if the correction value is dispersed, the original interval is split according to the correction value distribution, the optimal processing process parameter is configured for the new interval, and the initial parameter mapping table is updated synchronously, the process realizes the continuous iteration of the parameter system, not only expands the coverage dimension of the initial parameter mapping table, but also reduces the trigger frequency of parameter updating and correction in subsequent production, and strengthens the long-term self-adaptability of the system to complex working conditions and new working conditions.

[0123] In summary, the application organically integrates historical production experience, real-time working condition changes and quality feedback data, not only solves the core pain points of traditional material forming and pressing technology, but also realizes the upgrade of parameters from static setting to dynamic adaptation and then to self-optimization, finally significantly improves the stability of the production process, the consistency of product quality and the long-term adaptability of the system, and provides a reliable technical solution for efficient and high-quality production in the field of material forming and pressing.

[0124] The above is only the preferred embodiment of the application, the protection scope of the application is not limited to the above-mentioned examples, any technical scheme falling within the idea of the application belongs to the protection scope of the application. It should be noted that for ordinary technical personnel in the technical field, some improvements and decorations without departing from the principles of the application are also regarded as the protection scope of the application.

Claims

1. A material forming press control system based on multi-parameter feedback, characterized by, The database management module, the production monitoring module, the intelligent correction module and the iterative optimization module are included. The database management module is used for dividing independent intervals of production decision parameters, generating a production decision parameter interval combination set, and matching processing process parameter values of each interval combination according to production decision parameter values and corresponding processing process parameter values in historical pressing records of material forming and pressing, to construct an initial parameter mapping table. The production monitoring module is used for real-time acquisition of production decision parameter values of a current production batch, matching of initial processing process parameters of the current production batch based on the initial parameter mapping table, monitoring of dynamic changes of real-time values of production decision parameters, judgment of whether to trigger processing process parameter updating, and regulation and control of updated processing process parameters. The intelligent correction module is used for real-time acquisition of quality detection results of finished products and intermediate products of the current production batch, judgment of whether to trigger processing process parameter correction according to a product quality pass rate of the current production batch and frequencies of each unqualified quality type, identification of sensitive processing process parameters, and generation of correction values of the sensitive processing process parameters. The iterative optimization module is used for performing initial parameter mapping table iterative optimization operation in combination with the quality detection results when the interval combination of the initial processing process parameters does not exist in the initial parameter mapping table or the processing process parameter correction is triggered.

2. The multi-parameter feedback based material forming press control system of claim 1, wherein, The step of generating the production decision parameter interval combination set comprises: Historical pressing records in a material forming and pressing process are acquired, and historical pressing records with a product quality pass rate meeting a preset qualified standard are screened to construct a decision data set; each historical pressing record contains production decision parameter values, processing process parameter values of each production link and a product quality pass rate; Based on the historical pressing records in the decision data set, historical values of each parameter are extracted according to the categories of production decision parameters to construct production decision parameter subsets; Statistical analysis is performed on the historical values of each production decision parameter subset, the distribution range of the historical values of each production decision parameter is calculated, the initial value interval range of the production decision parameter is determined, and the initial value interval range is corrected in steps based on multi-dimensional division basis to obtain the standard interval range of each production decision parameter; The fluctuation characteristics of each production decision parameter in the standard interval range are counted, and for each production decision parameter, an association curve of the fluctuation characteristics and the corresponding product quality pass rate is generated; Through fitting analysis of the association curve, a continuous value segment in the association curve is identified as an independent interval, and the upper and lower boundaries and the product quality pass rate range are labeled for each independent interval; The independent intervals of each production decision parameter are combined in full to generate the production decision parameter interval combination set.

3. The multi-parameter feedback based material forming press control system of claim 2, wherein, The step of constructing the initial parameter mapping table comprises: Each interval combination in the production decision parameter interval combination set is compared with the decision data set, interval combinations with historical pressing records are screened, the corresponding product quality pass rates are counted, interval combinations with product quality pass rates greater than a preset quality pass threshold are screened to generate an effective production decision parameter interval combination set; For each interval combination in the effective production decision parameter interval combination set, extract all processing process parameter values corresponding to the interval combination from the decision data set, and group them by production batch; Perform fluctuation analysis on the processing process parameter values of each group of production batches, calculate the fluctuation amplitude of each processing process parameter within the group, and aggregate to obtain the parameter fluctuation level of each production batch; Statistically analyze the product quality pass rate of each production batch, and comprehensively compare the product quality pass rate and parameter fluctuation level of all production batches under the same interval combination to select the target production batch; Extract the processing process parameter values corresponding to the target production batch to form a processing process parameter set associated with the interval combination; Establish a one-to-one correspondence between each interval combination in the effective production decision parameter interval combination set and the corresponding processing process parameter set as an initial parameter mapping table.

4. The multi-parameter feedback based material forming press control system of claim 1, wherein, The step of matching the initial processing process parameters of the current production batch includes: Collect the initial values of the production decision parameters at the start of the current production batch, retrieve the initial parameter mapping table through interval matching, and compare the collected initial values of the production decision parameters with the independent interval boundaries of each production decision parameter one by one to form the initial interval combination of the production decision parameters; If the initial interval combination exists in the initial parameter mapping table, the associated processing process parameter values are used as the initial processing process parameters of the current production batch; If the initial interval combination does not exist in the initial parameter mapping table, extract the initial values of each production decision parameter in the initial interval combination, and calculate the production decision parameter distance with all interval combinations in the initial parameter mapping table; According to the production decision parameter distance, filter the interval combinations in the initial parameter mapping table to obtain a reference combination, and use the processing process parameter values associated with the reference combination as the initial processing process parameters of the current production batch; and record the pressing under the reference combination as an unmatched interval combination.

5. The multi-parameter feedback based material forming press control system of claim 1, wherein, The step of regulating and updating the processing process parameters includes: According to the initial processing process parameters, send initial control instructions to the execution devices of each production link to drive the execution devices to start production operations according to the initial control instructions; Continuously collect the real-time values of the production decision parameters of the current production batch at a preset frequency, and perform timestamp labeling, association of the current production batch number, production station identifier, and current production progress node; Compare the real-time values of the production decision parameters with the original independent interval boundaries corresponding to each production decision parameter in the initial interval combination one by one to determine whether each real-time value of the production decision parameter is still within the original independent interval; if so, it is determined that there is no need to trigger processing process parameter update; otherwise, mark it as an abnormal production decision parameter and perform production decision parameter switching monitoring; If it is determined that the processing process parameter update needs to be triggered, form a new real-time production decision parameter interval combination according to the new independent interval to which each production decision parameter real-time value belongs, and call the processing process parameter values associated with the new real-time production decision parameter interval combination as the updated processing process parameters.

6. The multi-parameter feedback based material forming press control system of claim 5, wherein, The step of performing production decision parameter switching monitoring includes: Within a preset monitoring time range, the exceeding amplitude of the abnormal production decision parameter real-time value beyond the original independent interval boundary within the preset monitoring time range is counted, and the fluctuation variance of the abnormal production decision parameter real-time value is calculated; If the abnormal production decision parameter real-time value continuously exceeds the original independent interval for a duration reaching a preset duration threshold, and the fluctuation variance is less than a preset variance threshold, it is determined that the processing process parameter update needs to be triggered; otherwise, it is determined that the processing process parameter update does not need to be triggered.

7. The multi-parameter feedback based material forming press control system of claim 1, wherein, The step of identifying the sensitive processing process parameter includes: Real-time acquisition of quality detection data of intermediate products and finished products in the current production batch, and storage according to the classification of intermediate products and finished products and the classification of detection items; Counting the total number of detections and the number of qualified products of the current production batch, calculating the quality qualified rate of the current batch product, classifying all unqualified products according to a preset unqualified type division standard, recording the number of products of each unqualified type, and calculating the occurrence frequency of each quality unqualified type; A preset quality qualified threshold and a frequency threshold corresponding to each unqualified type are set; if the quality qualified rate of the current batch product is lower than the preset quality qualified threshold, or the occurrence frequency of any unqualified type exceeds the corresponding frequency threshold, it is determined that the processing process parameter correction needs to be triggered; otherwise, it is determined that the correction does not need to be triggered; If the processing process parameter correction is triggered, the historical correction reference data set is constructed by searching and screening the historical suppression records according to the current production decision parameter interval combination and the quality unqualified type; The processing process parameters related to the current quality unqualified type are extracted from the historical correction reference data set as candidate processing process parameters; the deviation value of the real-time processing process parameter of the current production batch from the initial processing process parameter is calculated, and the unqualified degree of the current quality unqualified type is calculated; The correlation coefficient method is used to quantitatively calculate the correlation between the unqualified degree and the deviation value of each candidate processing process parameter, and the processing process parameters with an absolute value of the correlation coefficient greater than a preset correlation threshold are selected as the sensitive processing process parameters affecting the current quality unqualified type.

8. The multi-parameter feedback based material forming press control system of claim 7, wherein, The step of generating the correction value of the sensitive processing process parameter includes: The correction direction of the sensitive processing process parameter is determined according to the correlation analysis result; if the unqualified degree and the parameter deviation value are positively correlated, the correction direction is to adjust the parameter in the opposite direction, and if they are negatively correlated, the correction direction is to adjust the parameter in the same direction; From the historical correction reference data set, the corresponding historical correction amplitude of the same sensitive processing process parameter and the same quality unqualified type is extracted, as well as the historical unqualified degree and the historical parameter deviation value corresponding to each historical correction amplitude; the ratio of the historical unqualified degree to the historical parameter deviation value is calculated to obtain the historical ratio data; The current ratio of the unqualified degree of the current quality unqualified type in the current production batch to the corresponding sensitive processing process parameter deviation value is calculated; the historical correction amplitude with an absolute value of the difference from the current ratio less than a preset ratio threshold is selected from the historical ratio data to form a candidate correction amplitude set; An arithmetic mean of the historical correction amplitudes in the candidate correction amplitude set is calculated to obtain a basic correction amplitude; meanwhile, a parameter distance between the current production decision parameter interval combination and the corresponding historical interval combination of the basic correction amplitude is calculated; a weight distribution rule is set according to the parameter distance, and a weighted calculation is performed to obtain an adjusted correction amplitude; and a correction value of the sensitive processing parameter is calculated according to the correction direction and the correction amplitude.

9. The multi-parameter feedback based material forming press control system of claim 4, wherein, The step of performing the initial parameter mapping table iterative optimization operation comprises: If the initial interval combination does not exist in the initial parameter mapping table in the current production batch, the quality pass rate of the associated processing parameter corresponding to the production batch of the reference combination is obtained according to the unmatched interval combination label by extracting the related pressing record; The quality pass rate of the reference combination is compared with the preset quality pass threshold value, if the quality pass rate is higher than the preset quality pass threshold value, the actual value of the production decision parameter of the current production batch is collected and is included in the value range of the reference combination, and the interval boundary of the reference combination is updated; Otherwise, the reference combination is removed from the set of effective production decision parameter interval combinations, and the associated processing parameter record and the historical pressing record corresponding to the reference combination in the initial parameter mapping table are deleted.

10. The multi-parameter feedback based material forming press control system of claim 9, wherein, The step of performing the initial parameter mapping table iterative optimization operation further comprises: If the processing parameter associated with the interval combination in the initial parameter mapping table triggers correction in the current production batch, all processing parameter correction values corresponding to the interval combination are extracted, are classified and summarized according to the production batch number in which the correction occurs, and a correction value statistical account book is established; The cumulative number of correction values in the correction value statistical account book is counted, if the cumulative number is less than a preset number threshold value, no optimization operation is performed; otherwise, the arithmetic mean, the standard deviation and the frequency distribution histogram of all correction values in the correction value statistical account book are calculated; According to the frequency distribution histogram, the correction value distribution characteristics are judged, if the standard deviation is less than a preset concentration determination threshold value, it is determined that the correction values are concentratedly distributed, and a new associated processing parameter value of the interval combination is selected based on the frequency interval distribution in the frequency distribution histogram; Otherwise, it is determined that the correction values are dispersedly distributed, a plurality of subintervals are divided according to the correction value range corresponding to the peak value, and the original interval combination is split to form a plurality of new interval combinations in combination with the fluctuation range of the corresponding production decision parameter; For each new interval combination after splitting, the processing parameter data under the corresponding working condition are extracted from the historical pressing record and the correction value statistical account book, the parameter fluctuation amplitude and the product quality pass rate are calculated according to the production batch, the processing parameters are screened, and are used as the associated processing parameters of the new interval combination.

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