86 results about "Production optimization" patented technology

A method and device for optimizing comprehensive production of an oil reservoir. The method comprises: on the basis of historical production data, performing regional level recognition, single-well level recognition, and production optimization model recognition, and updating oil reservoir dynamic recognition; on the basis of the historical production data, using the following steps to perform cyclic historical fitting on model parameters of a plurality of production optimization models: performing historical fitting on the model parameters, and performing well-to-well communication relationship calibration on the plurality of production optimization models; determining, on the basis of the updated oil reservoir dynamic recognition, a constraint condition corresponding to each production optimization model, solving an objective function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; obtaining a comprehensive development regulation scheme of an oil reservoir; and updating the oil reservoir dynamic recognition on the basis of a regulation effect obtained by real-time monitoring of a development operation.

The invention relates to the technical field of automobile part manufacturing, and discloses an automobile injection molding part production process control system and method, and the system comprises the following modules: a data collection module which is used for collecting technological parameters, molds, raw materials and equipment operation original data, attaching timestamps, and storing the data in a database; the process parameter prediction module is used for reading original data to construct a time sequence data set, inputting the time sequence data set into a TFT model to obtain a pre-training model, and predicting a short-term process parameter fluctuation range in combination with current production working condition parameters; and the quality risk index acquisition module is used for inputting the process parameter data and the mold data into a quality risk index calculation formula to obtain a quality risk index. Through the system, data-driven comprehensive production optimization is realized, the process control accuracy and adaptability are improved, the quality control scientificity and reliability are enhanced, the intelligent level of the production process is improved, the production efficiency is effectively improved, the defective rate is reduced, and the product quality is stabilized. The problem that process control lacks system intelligence is solved.

The invention provides an intelligent factory production optimization method and system based on AI scheduling, and belongs to the technical field of intelligent factory production management.The method comprises the steps that firstly, process disassembling is conducted on batch production orders, an order task atlas is obtained, a dynamic resource pool is constructed according to the real-time resource state of a factory, the order task atlas is input into a pre-trained scheduling AI model, and a scheduling AI model is established; a dynamic resource pool is combined to generate multiple groups of initial scheduling paths, conflict detection and conflict point identification are performed on the initial scheduling paths, an adaptive adjustment module is called to correct parameters based on conflict point types and influence ranges, a final production optimization scheme is generated and imported into a factory execution system, and intelligentization and high efficiency of production scheduling are realized. And the production efficiency and benefits are improved.

The invention discloses an oil reservoir comprehensive production optimization method and device, and the method comprises the steps: carrying out the region level recognition, single well level recognition and production optimization model recognition according to the production historical data, and updating the oil reservoir dynamic recognition; based on production historical data, performing cyclic historical fitting on model parameters of the various production optimization models by adopting the following steps: performing historical fitting on the model parameters, and performing inter-well communication relation calibration on the various production optimization models; determining a constraint condition corresponding to each production optimization model according to the updated reservoir dynamic cognition, solving a target function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; an oil reservoir comprehensive development regulation and control scheme is obtained; and according to the regulation and control effect obtained through real-time monitoring of the development operation, oil reservoir dynamic understanding is updated. According to the invention, the efficiency and precision of oil reservoir production optimization can be improved.

The invention provides a gas reservoir gas storage injection-production optimization method based on block pressure balance control. The method comprises the following steps: acquiring target gas reservoir basic data; establishing a gas reservoir gas storage production operation dynamic optimization model objective function; constructing a gas reservoir gas storage production operation dynamic optimization model constraint condition; and solving the gas reservoir gas storage production operation dynamic optimization model, and generating a gas reservoir gas storage optimization injection-production scheme. On the basis of the injection-production scheduling optimization problem of the multi-block gas reservoir type underground gas storage, the corresponding optimization model is established with the purpose of minimizing the pressure difference degree between the gas reservoir blocks in the injection-production process, and the injection-production scheduling scheme is successfully solved through the established optimization model and the solving method. According to the method, the deviation degree of the formation pressure between the blocks can be effectively reduced under the condition that the requirement for the gas injection and production amount of the gas storage is met, extreme high-pressure blocks can be effectively avoided, and safe and stable operation of the gas storage is further guaranteed.

The invention discloses a multi-agent collaborative production optimization method and system based on an industrial large model, and relates to the technical field of production control, and the method comprises the steps: connecting an industrial knowledge base platform, carrying out the platform retrieval and interpretation reconstruction of a target production scene, and determining a production line interaction chain; aiming at a production line interaction chain and a driving agent construction interface, calling and splicing optimization of a code fragment library are carried out, lightweight construction of agents is carried out, multi-agent integration is carried out by introducing an intelligent interaction contract, and an industrial large model is established; and deploying the industrial large model in an industrial production system, establishing interaction with production line equipment, triggering the industrial large model along with the start of a target production scene, and performing automatic production management of the production line equipment through multi-agent cooperative control driven by an intelligent interaction contract. The technical problem that in the prior art, an industrial production system lacks intelligent and multi-dimensional collaborative optimization is solved, and the technical effects of improving the production efficiency, the resource utilization rate and collaborative work of production line equipment are achieved.

The invention discloses a part flexible dynamic production optimization method and device based on a low-carbon target, and relates to the field of intelligent machining. The method comprises the following steps: firstly, aiming at an autonomous production station process and an outsourcing production station process of part production, constructing a multi-objective optimized Petri net model; secondly, respectively calculating the production cost, the carbon emission and the production cycle of each path in the Petri network model based on the decision variable group; and finally, performing multi-objective optimization through a marine predator algorithm according to the priorities of the three objectives of the production cost, the carbon emission and the production cycle. On the basis of a traditional part production and manufacturing organization mode, multiple targets such as the product production cycle, the production cost and the carbon emission in the production process are considered, a part flexible dynamic production organization optimization model is constructed, and dynamic flexible distribution of autonomous production and outsourcing production is achieved. The carbon emission in the production process can be estimated in the product design stage, and effective measures are taken to reduce the carbon emission when production tasks are distributed.

The invention relates to the technical field of production, in particular to an intelligent batch production optimization method, device and equipment. The method comprises the following steps: establishing a simulation scheduling model of a welded pipe production process, and generating an initial batch amount scheme based on historical scene offline training; inputting the scheme into a digital twinning simulation environment for high-precision simulation, and collecting whole-process data; analyzing feedback and dynamically adjusting a reward function by using a large language model in combination with the data, and generating a new batch amount scheme after optimizing the model; the new scheme is put into a digital twinborn environment for multi-round closed-loop verification, and a verification result and a dispatcher natural language instruction are analyzed into structured constraints; and finally, the constraint is injected into the optimization model to generate a production scheduling optimization scheme. Dynamic optimal configuration of production resources and precise closed-loop management and control of the manufacturing process are achieved, and the effect in intelligent scheduling of the welded pipes is remarkable.

The invention discloses a crowd-sourcing scheduling method and system for photovoltaic module multi-process collaborative production optimization, and the method comprises the steps: constructing a multi-agent system, and determining the acquaintance relationship between agents; the warehouse agent is used for acquiring order information in real time and disassembling the processing task according to the order information; the scheduling agent adjusts priority parameters of the machining tasks, and allocates the adjusted machining tasks to a workpiece agent; the workpiece intelligent agent sends a bid invitation book to all production intelligent agents with an acquaintance relationship, and the production intelligent agents judge whether to respond to the bid invitation book or not according to the states of the production intelligent agents; the scheduling agent performs bid evaluation on the production agent responding to the bid invitation book based on deep reinforcement learning, and the task acquaintance relationship degree with the workpiece agent is considered in the state space of the production agent; and carrying out production scheduling according to the bid evaluation result. By adopting the acquaintance library priority screening, the formatting bidding book and the bidding strategy based on the perception coefficient and the credibility, the communication redundancy is reduced, and the production efficiency is improved.

The invention discloses a parallel hyper-heuristic injection-production optimization method based on an agent model, which belongs to the technical field of oil reservoir injection-production optimization, and comprises the following steps: step 1, constructing a three-dimensional geologic model and an oil reservoir injection-production model; step 2, constructing a water-drive reservoir injection-production optimization model and an initial database; step 3, constructing an agent model; 4, generating a filial generation population through parallel evolution by using four algorithms, and evaluating filial generation individuals by using an agent model to obtain respective candidate solutions of each evolution algorithm; 5, constructing a hyper-heuristic guide search strategy and an online parameter return method, and adjusting the search behavior of the evolver based on the candidate solution; step 6, repeating the step 3 to the step 5 until a preset maximum optimization iteration number is reached; and step 7, outputting an optimal Pareto frontier scheme and a corresponding economic net present value. According to the method disclosed by the invention, the search direction is dynamically adjusted by utilizing a hyper-heuristic guide search strategy and an online parameter return method while the injection-production optimization efficiency of the water-drive reservoir is improved.

The invention relates to the technical field of corrugated board production, and discloses a corrugated board production optimization method and system based on data mining and a medium. The method comprises the steps that corrugated board production full-link multi-dimensional data are collected and stored in a classified mode; performing cleaning, integration and feature engineering processing on the multi-dimensional data in sequence to obtain a standardized data set; key features and rules influencing quality and efficiency are mined based on the data set; constructing a quality and efficiency prediction model by adopting a gradient lifting decision tree algorithm; by taking model output as a constraint, optimizing through an NSGA-II algorithm to obtain an optimal process parameter, and issuing and executing the optimal process parameter; and acquiring an execution result in real time and comparing the execution result with a threshold value, and if the execution result exceeds the threshold value, triggering secondary optimization. Through data mining and closed-loop optimization, the problems of existing production parameter solidification and optimization passivity are solved, and the production quality and efficiency are improved.

The invention relates to the technical field of concrete electric pole manufacturing, and discloses an optimized steel fiber anti-crack ultra-high performance concrete centrifugal electric pole manufacturing method and system. The method comprises the following steps: dividing an electric pole manufacturing process into a plurality of manufacturing units according to production batches, and collecting material component data, process setting data and online detection data in each manufacturing unit to form a multi-source production data set; calculating an anti-cracking performance index and a strength performance index based on a multi-source production data set, and dynamically outputting an initial process correction value through a dual-threshold monitoring mechanism and a performance adjustment model; performing adaptive adjustment on the initial process correction value of each manufacturing unit according to the production correlation between the adjacent manufacturing units to generate an adjusted process correction value; and carrying out serialization processing on the adjusted process correction value, and outputting a production optimization instruction which is applied to a manufacturing execution process. According to the method, electric pole manufacturing whole-process data integration and dynamic process optimization are achieved, and the production efficiency and the product quality stability can be improved.

The invention discloses an intelligent factory data monitoring method and system based on distributed collaborative sampling, belongs to the field of intelligent factory data monitoring, and aims to solve the problems of real-time monitoring, quality control, equipment maintenance and production optimization in the production process. The system is composed of a data acquisition module, a production control module, a local processing module, a data center module and a user interaction module. And efficient data processing and storage are realized through the distributed collaborative sampling network, and the expandability and flexibility of the system are optimized. The production management and control module is responsible for distribution and sampling detection of production tasks, ensures product quality and realizes production continuity when equipment is abnormal. The local processing module analyzes equipment data, establishes a prediction model, and timely finds and early warns deviation in equipment and production. The data center module analyzes early warning production data in real time, transmits an early warning signal through a central node, and drives a production line to operate. And the user interaction module provides dynamic information updating, so that a user can obtain and feed back data in real time.

The invention discloses a product quality tracing method for fish flour-wrapped products, and belongs to the technical field of product quality tracing, and the method specifically comprises the steps: extracting a production order of the fish flour-wrapped products; searching historical quality data and external factor data of the fish wrapped flour product according to the raw material batch identifier and the production time identifier of the production order; based on the historical quality data and the external factor data, a quality risk prediction model is constructed, and the model outputs defect probability and key influence factors; monitoring a real-time production process of the fish flour-wrapped product, wherein the real-time production process comprises raw material input parameters, processing temperature parameters and equipment operation parameters; according to the quality risk prediction model and the real-time production flow data, dynamically adjusting a production plan, modifying process parameters or redistributing production resources; and integrating the prediction result and the production flow data to generate a quality tracing report of the fish flour-wrapped product, including a risk early warning identifier and a production optimization suggestion.

The utility model discloses a heptagonal tool positioning structure for special alignment, and belongs to the technical field of engine alignment positioning tools. A heptagonal tool positioning structure is arranged on the camshaft body, the heptagonal tool positioning structure comprises a surface A, a surface B and a surface C, the surface A and the surface B are parallel to each other, the surface C is adjacent to the surface A and the surface B, and a two-dimensional code used for tracing the machining process is arranged on the surface C. According to the heptagonal tool positioning structure for special alignment, through the advantages of accurate positioning, reliable assembly, traceable quality, production optimization, product reliability, high adaptability and the like, the product quality, the production efficiency and the market competitiveness are effectively improved, and a powerful guarantee is provided for related production of camshafts.

The invention discloses a heat treatment process parameter deviation intelligent correction method and system and a storage medium, and belongs to the technical field of production optimizing.The method comprises the steps that data expansion is conducted on original production data, a first prediction model is trained locally based on the expansion data, and a second prediction model is obtained, calculating the deviation of the network parameters at the same position of the different second prediction model of each factory, determining an adjustment value based on the deviation, correcting the network parameters based on the adjustment value, defining the corrected second prediction model as a third prediction model, and inputting the real-time production data into the third prediction model. A dynamic adjustment mechanism in the third prediction model adjusts the number of network layers according to the product type, and outputs a process adjustment amount and a corresponding confidence coefficient; and when the confidence coefficient is smaller than a second threshold value, the historical case library is called to correct the process adjustment amount, and real-time dynamic adjustment of the heat treatment process parameters is achieved through a cooperative correction mechanism of the local and cloud models.

The invention discloses an agricultural product whole-course traceability tracking method and system based on a block chain technology, and relates to the technical field of agricultural informatization and block chain fusion application, and the method comprises the steps: initializing an e-commerce traceability block chain platform, calculating the production carbon footprint of crops, employing gray-DEMATEL-ISM to analyze a green toughness score, and constructing a mean field game model. The Nash equilibrium optimization problem is converted into a minimum weighted objective function of a nonlinear equation set, and a quadratic sub-problem is used for solving; calculating a whole-course carbon footprint, and carrying out error correction and recording on crop data by using chameleon hash; performing whole-course tracing on the abnormal data; according to the method, the mean field game model is constructed, the Nash equilibrium problem is converted into the minimization problem of the nonlinear weighted objective function, the problems of resource allocation and production optimization under multi-agent collaboration are solved, and the intelligent collaboration efficiency of agricultural product circulation is improved.

The invention provides a production plan scheduling optimization method for a polyester fiber product, a production plan scheduling optimization system for the polyester fiber product, and a computer readable storage medium. The production plan scheduling optimization method for the polyester fiber product comprises the steps of establishing a polyester fiber production optimization scheduling model for a polyester fiber production process based on a polyester fiber production scheduling period; setting order demand quantity, product scheduling parameters and device scheduling parameters of the polyester fiber production optimization scheduling model; and solving the polyester fiber production optimization scheduling model by using an improved grey wolf algorithm to obtain a polyester fiber production scheduling scheme. According to the production plan scheduling optimization method for the polyester fiber product, the production task can be accurately arranged, the production efficiency is improved, the resource waste is reduced, the production cost is reduced, and the fine management of the production process is realized.

The invention belongs to the technical field of automobile manufacturing, and particularly discloses a production optimization method and system based on workshop key in-process monitoring deduction after order production scheduling, and the method comprises the following steps: taking an actual time point as a collection instantaneous time point, and automatically collecting in-process data of each order of a workshop; setting production rhythm data of each line body in a future production day to obtain a pre-production plan; presetting the maximum value and the minimum value of a working point based on the working data of each sequence and the pre-production plan, comparing with each working point of the pre-production plan, judging whether the placement of each working point of the pre-production plan is reasonable or not, and if the working point is not reasonable, feeding back the result to the production scheduling system to formulate a reasonable production plan; data in the production process are analyzed in real time, abnormal conditions and potential problems are detected, and if the abnormal conditions are detected, an early warning mechanism is triggered. According to the technical scheme, transparent in-process deduction is provided, in-process changes of joints are obtained, and a reasonable production plan is made.

The application discloses an anti-nasal allergy gel production optimization method based on data analysis, and particularly relates to the field of resource allocation and production optimization, and is used for solving the problem that the response to regional and seasonal demand changes in the existing anti-allergy gel production process is lagging behind, and the raw material supply constraint and the production line switching cost are difficult to balance. The method collects raw material supply chain data, market sales records and environmental allergen time series data, constructs a unified space-time correlation data basis, predicts the anti-allergy response intensity in the future production cycle by using a recurrent neural network, and forms product dosage form preference weights based on association rule mining. On this basis, combined with the raw material inventory and the delivery cycle constraint, a plurality of executable candidate production schemes are generated, and further, the schemes are selected according to the real-time state of the production line and the switching cost, so as to realize the fine configuration and efficient execution of the anti-nasal allergy gel production process.

The invention relates to the technical field of digital printing production line management, and provides an aluminum decorative part digital printing production line management method and system, and the method comprises the steps: obtaining a plurality of pieces of first information; the multiple pieces of first information comprise order scheduling information of a production line for producing the aluminum printed products, state information of an ink supply device for supplying ink to the aluminum printed products on the production line within a preset duration before the current moment and ink information of ink in the ink supply device; microscopic quality characteristic information of the aluminum printed product is determined; the microcosmic quality characteristic information is used for indicating defects of the aluminum printed product; according to the microscopic quality feature information, the order scheduling information and the state information of the ink supply device, defect cause information causing the microscopic quality feature information to exist in the aluminum printed product is determined; and determining production optimization decision information of the production line according to the defect cause information and the ink information. According to the scheme, the printing efficiency of the digital printing aluminum decorating part can be improved.

The invention provides an industrial internet intelligent factory production optimization method based on deep learning. The method comprises the steps of obtaining a multi-dimensional data vector of an intelligent factory; inputting the multi-dimensional data vector into a deep reinforcement learning model to obtain a basic scheduling strategy; taking the production scheduling optimization strategy and the multi-dimensional data vector as input, inputting the production scheduling optimization strategy and the multi-dimensional data vector into a constructed deep learning model, automatically learning and extracting risk features, and generating a safety risk score; performing online correction on the basic scheduling strategy according to the security risk score to generate a joint optimization strategy; collecting feedback data of the intelligent factory, and designing a target optimization function in combination with the security risk score; the joint optimization strategy is sent to the intelligent factory, so that the intelligent factory executes the joint optimization strategy according to the target optimization function, and feedback data of the intelligent factory is collected in real time.

The invention discloses a production and processing control method of a connecting block assembly for hoisting, and relates to the field of machining and manufacturing. The method comprises the following steps: according to a bearing demand of a target hoisting connection assembly in a target processing order; generating K production processing parameter spaces; a plurality of production processing parameter combinations are obtained through multiple times of random extraction; performing quality prediction on the K parts to obtain a plurality of part quality prediction value sets; the machining precision and the assembly precision are recognized, fitness analysis is conducted on production and machining parameter combinations, and multiple fitness degrees are obtained; performing combination variation optimization to obtain a target production processing parameter combination; and carrying out production processing control on the target connecting assembly for hoisting. The technical problems that in the prior art, a production method cannot flexibly adjust machining parameters according to requirements and lacks an optimization mechanism, so that a connecting assembly cannot meet the quality requirement, and the production efficiency is low are solved, and the technical effects of customized production and production and machining parameter optimization are achieved.

The invention relates to a multi-stage production process decision and cost optimization problem based on sampling detection, and the method comprises the following steps: obtaining the product inspection requirements of a certain product production stage, and determining the main decision links involved in the production process. And a cost minimization dynamic model based on a genetic algorithm is provided, so that the defective rate is reduced, and the total cost is reduced. In addition, the invention provides a dynamic model for multi-stage production optimization, so that inferior-quality products entering an assembly link are reduced, and the market loss and the disassembly cost are reduced. Finally, a multi-stage production decision and cost optimization model based on sampling detection is constructed, and the detection and processing decision problems in different production stages are solved.

The invention discloses a feed production whole-process intelligent monitoring method and device based on the Internet of Things and a medium, and relates to the technical field of industrial Internet of Things, and the method comprises the steps: carrying out Internet of Things sensing configuration on raw material crushing, ingredient mixing and granulation forming workshops, and building a multi-stage monitoring network; the network communication link is optimized through an RS485 communication system, a dynamic monitoring network is obtained, and a feed production monitoring data group is obtained through real-time monitoring; mining production accident big data to establish an accident category matrix, predicting an accident in combination with an accident adversarial iterative learning architecture, and obtaining a multi-level accident map; and obtaining a feed production optimization strategy according to the production control scheme of the optimization workshop. The technical problems that multi-link monitoring of feed production is disjointed, an industrial communication link is easy to interfere and a production accident is difficult to pre-judge in advance in a traditional monitoring means are solved, and the technical effects of comprehensively and accurately obtaining and utilizing data of the whole process of feed production and improving the accident pre-judgment and production optimization level are achieved.

The invention belongs to the field of production and manufacturing optimization, and particularly relates to a production scheme optimization method and device, equipment and a medium, and the method comprises the steps: generating a filial generation production scheme through the crossover and mutation operation of a genetic algorithm, so as to increase the diversity; dividing the combined candidate schemes into non-dominated layers with different quality levels according to the dominated relationship of the plurality of cost parameters; the production schemes are selected according to the priorities of the non-dominated layers from high to low, the distributivity of a solution set is guaranteed through crowding degree calculation in the same layer, the population is made to continuously approach the real optimal leading edge through iterative evolution, and finally the optimal production scheme is output. According to the method, under the condition that multiple targets are not artificially weighted into a single target, a batch of optimization schemes capable of achieving good balance among multiple competitive targets can be automatically found. Compared with a traditional optimization method, the method not only can ensure that the quality of the solution is quickly converged to the Pareto frontier, but also can maintain the diversity of the solution set through a crowding degree mechanism, and effectively avoids falling into local optimum.

A nesting and shear cutting preprocessing method for a defective plate, comprising the following steps: acquiring plate information of a plate, wherein the plate information includes defect information and plate dimension information; making an electronic defect map according to the defect information and the plate dimension information, and partitioning out a region available for nesting and shear cutting; and partitioning the plate to obtain small plates according to the region available for nesting and shear cutting. A nesting and shear cutting layout method for a defective plate, a nesting and shear cutting production optimization method for a defective plate, and a system, a computing device, a storage medium therefor. The preprocessing method and the system can reduce the scrap rate of plate processing.

The application discloses a kind of processing clamps of liquid storage tank pipe products and processing method thereof, belong to processing clamp technical field, including support big bottom plate;The upper end of the support big bottom plate is fixedly connected with four-axis rotary table and four-axis tailstock;Multiple groups of profiling support components, multiple groups of compression components and multiple groups of end positioning components are installed on the clamp bridge plate;The airtight detection hole that passes through up and down is formed in the profiling support component, when the movable end of end positioning component moves to the lowermost end, the movable end of end positioning component blocks airtight detection hole.By the above mode, the application realizes the traditional three sequence flow production, optimizes the finished product of one sequence processing by one equipment, greatly reduces the processing procedure, greatly saves the use of manpower, avoids the cumulative error of product scratch and size repeated positioning caused by multiple clamping and taking, and is beneficial to improve processing stability.

This application provides a method and system for optimizing forging production driven by digital twins and real-time data feedback, belonging to the field of process optimization technology. The method includes: parsing the contextual data chain of related parameters based on forging production target optimization parameters; constructing a digital twin space and establishing a real-time data interaction path; updating the digital twin space using the real-time data interaction path; acquiring pre-processed product variables; simulating the production process and processing parameters through the digital twin space; and outputting a forging production process optimization strategy that achieves the optimization target, used for deploying the production process of pre-processed products. This application solves the technical problem in the prior art where the lack of simulation and optimization of process parameters before processing makes it difficult to detect and adjust process problems in a timely manner during processing, further affecting production efficiency. By using digital twins for full-process production pre-simulation, optimization before production is achieved, reducing product defect rates and improving production efficiency.

The invention relates to a production optimization control method and system for thermoplastic elastomer coated ABS in the field of new materials, and the method comprises the following steps: obtaining physicochemical characteristic data of a multi-component system from a thermoplastic elastomer formula database, and extracting epoxy group reaction activity and chain segment flexibility control parameters by adopting a principal component analysis algorithm; a compatibility feature vector between the components is obtained; if the interface bonding force distribution data is lower than a preset threshold value, new formula parameters are generated by adjusting the proportion of polyester chain segments and the addition amount of maleic anhydride SEBS, and an optimized multi-component system formula is obtained; and extracting the processing dynamic balance characteristics of the pre-dispersed crosslinking points and the nano-enhanced synergy from the stable die head pressure distribution data, and judging whether a preset processing stability threshold value is met or not to obtain a processing process stability evaluation result.