42 results about "Production optimization" patented technology

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 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 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 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 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 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 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.

The invention discloses an oil and gas reservoir production optimization method and device which can be used in the field of oil and gas field development, and the method comprises the steps: determining an initial test design method, and constructing an initial agent model according to a selected agent model; determining a sampling strategy of a proxy optimization algorithm by adopting a penalty function method and an augmented Lagrangian multiplier method based on a sorting grouping evolution algorithm; determining a potential candidate point of an optimal solution of the production optimization problem according to a sampling strategy, determining an objective function value of the potential candidate point by using a flow field diagnosis optimization program, and determining a new sampling range according to a sampling range of a previous iteration step and the objective function value of the potential candidate point; and replacing the sampling range of the last iteration step with a new sampling range and updating the proxy model, repeating the proxy updating step and the potential candidate point sampling step, and taking the potential candidate point reaching the preset standard as the optimal decision variable. The oil and gas reservoir production optimization efficiency can be improved, and the oil and gas reservoir production optimization cost can be reduced.

This application relates to the field of digital printing production line management technology, and provides a method and system for managing a digital printing production line for aluminum decorative parts. The method includes: acquiring multiple pieces of first information; these first pieces of information include order scheduling information of the production line producing aluminum printed products, status information of the ink supply device supplying ink to the aluminum printed products on the production line within a preset time period prior to the current moment, and ink information in the ink supply device; determining microscopic quality characteristic information of the aluminum printed products; the microscopic quality characteristic information is used to indicate defects in the aluminum printed products; determining defect cause information leading to the presence of microscopic quality characteristic information in the aluminum printed products based on the microscopic quality characteristic information, order scheduling information, and status information of the ink supply device; and determining production optimization decision information for the production line based on the defect cause information and the ink information. This solution can improve the printing efficiency of digitally printed aluminum decorative parts.

This application relates to a production optimization method, system, electronic device, and storage medium based on surplus fabrics. The method includes: acquiring inventory data of surplus fabrics; extracting fabric attribute parameters for each batch of surplus fabrics from the inventory data; wherein the fabric attribute parameters include fabric type, fabric specifications, color parameters, available quantity, batch constraint rules, and / or historical loss indicators; inputting the fabric attribute parameters into a surplus fabric production capacity model to determine the production quantity of each batch of surplus fabrics; generating new product production plans and best-selling product production plans when the production quantity exceeds a target threshold; and determining a target production execution plan based on the new product production plans and best-selling product production plans. This enables accurate quantitative assessment and efficient utilization of surplus fabrics, effectively reducing warehousing and capital occupation costs, while simultaneously considering production efficiency and feasibility, and improving the refinement and intelligence level of garment production management.

The invention discloses an assembly type component production optimization management system based on computer vision. The system comprises a multi-agent PPO operation distribution module, a component defect punishment loss calculation module, a fuzzy logic quality risk assessment module, an industrial vision component monitoring module, a production parameter acquisition module and a production process optimization control module. The production parameter acquisition module acquires parameters such as equipment operation, environment and raw materials; the industrial visual component monitoring module captures a component image and extracts defect information; the multi-agent PPO operation distribution module generates an operation scheme in combination with the parameters; a component defect penalty loss calculation module quantifies defect loss; the fuzzy logic quality risk assessment module assesses the quality risk; and the production process optimization control module integrates data and adjusts production parameters. The system realizes intelligent distribution of component production operation, accurate defect identification, scientific quality risk assessment and dynamic process optimization, and improves production efficiency and quality stability.

The invention discloses an anti-nasal allergy gel production optimization method based on data analysis, particularly relates to the field of resource allocation and production optimization, and is used for solving the problems that in the existing anti-allergic gel production process, response to regional and seasonal demand changes is delayed, and raw material supply constraint and production line switching cost are difficult to collaboratively balance. According to the method, raw material supply chain data, market sales records and environmental allergen time sequence data are collected to construct a unified space-time associated data basis, the recurrent neural network is utilized to predict the anti-allergic response intensity in a future production cycle, and product dosage form preference weights are formed based on association rule mining. On the basis, various executable candidate production schemes are generated in combination with raw material inventory and delivery cycle constraints, and decision selection is further performed on the schemes according to the real-time state of a production line and switching cost, so that refined configuration and efficient execution of the production process of the anti-nasal allergy gel are realized.

The invention provides a multi-device collaborative maintenance and production optimization method in a manufacturing system. Relates to the technical field of equipment maintenance. The method comprises the following steps: obtaining residual service life prediction values of all equipment in target manufacturing; when the residual service life prediction value is not smaller than the preset maintenance threshold value, optimizing the production upper limit of the corresponding equipment based on the residual service life prediction value; when the residual service life prediction value is smaller than a preset maintenance threshold value, the corresponding equipment triggers maintenance; if the residual service life prediction values of the other devices in the device group where the maintenance triggering device is located are within the neighborhood of a preset maintenance threshold value, synchronously maintaining all the maintenance triggering devices in the same device group; and if the residual service life prediction values of the other devices in the device group where the maintenance triggering device is located are not in the neighborhood of the preset maintenance threshold value, transferring the manufacturing task of the maintenance triggering device to the device which has the same type as the maintenance triggering device and has the maximum residual service life prediction value.

The application provides a whole milk powder production optimization method and device, electronic equipment and storage medium, relates to the dairy production technical field, and includes the following steps: acquiring the nutritional component data of raw milk in a plurality of milk cans; grouping the plurality of milk cans based on the complementarity between the nutritional component data of the raw milk in each milk can, obtaining the production batch corresponding to each milk can; determining the process adjustment parameter for adjusting the nutritional components of the raw milk of each production batch based on the difference between the nutritional component data of the raw milk of each production batch and the target nutritional component data; and producing whole milk powder based on the process adjustment parameter of each production batch. The method and device provided by the application convert the original production process relying on manual experience into an automatic production process through scientific complementarity grouping and accurate process parameter calculation, and consider both product quality and economic benefits in the whole milk powder production process.

The application relates to the technical field of prefabrication plants, and discloses a production optimization method based on a digital twin production line model, which comprises the following steps: establishing initial models of all processes, simulating the production line by adopting Monte Carlo sampling, discovering capacity and quality risks, putting forward production suggestions and management points, tracking worker and equipment production data in real time during the operation of the production process, updating model parameters, updating the model into a twin model, simulating by Monte Carlo sampling, predicting capacity and quality risks, putting forward a production optimization scheme, continuously updating model parameters and the twin model in the subsequent production process, and finally solidifying into a twin model highly matched with the existing production line. The existing production line twin technology focuses on data acquisition and display, a linear model is adopted for the prediction of capacity and quality, and the variation of samples is not considered enough. The application provides a nonlinear model and key parameters, and the matching of the production line is realized through dynamic updating, which is of great significance to actual production.

The invention discloses a well cementation admixture-oriented whole production flow quality tracing method and system, and relates to the field of data tracing, and the method comprises the steps: carrying out the directional preprocessing of a raw material, and building a digital technology file; monitoring a key process in real time in production and deeply binding the key process with traceability information; a correlation tracing matrix is generated after a finished product is qualified through specific working condition inspection; precise inventory management and control are realized based on intelligent warehousing and RFID; recording transportation environment data by using a dynamic monitoring technology and incorporating the transportation environment data into a tracing chain; matching verification is completed on site through a compatibility test, and link data are used in a closed mode; finally, a full-life-cycle after-sales tracing mechanism is established, a problem source is reversely positioned through a tracing code, and continuous improvement is driven. The method has the advantages that a full-life-cycle digital closed-loop traceability system from raw material preprocessing to after-sale is constructed, data of all links are connected through multi-code association, quality traceability, problem checkability and production optimization are achieved, and admixture working condition adaptability and use stability are guaranteed.

The invention relates to the technical field of intelligent control, in particular to a high-speed rail gearbox production intelligent management control system which comprises a production task planning module, a real-time monitoring execution module, a quality detection and feedback module and a production optimization instruction module. According to the invention, the task process is designed through accurate production demand and resource availability analysis, high matching and fine management of production line configuration are realized, the production efficiency is improved, and real-time monitoring is combined to realize real-time acquisition and analysis of data in the production process, so that the production efficiency is improved. The instant response capability of production line efficiency and quality control is obviously enhanced, the system can instantly identify production deviation and quality problems, rapidly feed back adjustment measures, reduce identification and response time, guarantee optimal configuration of resources and high efficiency of production scheduling through continuous optimization of production parameters, and improve production efficiency. The cost is obviously reduced, the overall quality of the product is improved, and the market competitiveness is enhanced.

The application discloses a parallel hyper-heuristic injection-production optimization method based on a proxy model and belongs to the technical field of oil reservoir injection-production optimization, and comprises the following steps: step 1, constructing a three-dimensional geological model and an oil reservoir injection-production model; step 2, constructing a water drive oil reservoir injection-production optimization model and an initial database; step 3, constructing a proxy model; step 4, generating a subpopulation by using four algorithms for parallel evolution, evaluating the subpopulation by using the proxy model, and obtaining a candidate solution of each evolution algorithm; step 5, constructing a hyper-heuristic guided search strategy and an online parameter returning method, adjusting the search behavior of an evolutioner based on the candidate solution; step 6, repeating steps 3 to 5 until a preset maximum optimization iteration number is reached; and step 7, outputting an optimal Pareto front scheme and a corresponding economic net present value. The method can improve the injection-production optimization efficiency of the water drive oil reservoir, and dynamically adjusts the search direction by using the hyper-heuristic guided search strategy and the online parameter returning method.

The invention belongs to the field of fruit juice production supervision, relates to a data analysis technology, is used for solving the problem that production control parameters of a production line supervision system in the prior art cannot be adaptively optimized according to changes of raw material basic parameters, and particularly relates to an NFC fruit juice production line operation supervision system based on the Internet of Things technology. Comprising a raw material supervision module, a production supervision module, a quality supervision module and a production optimization module which are connected in sequence, and the raw material supervision module, the quality supervision module and the production optimization module are in communication connection with a database; according to the method and the device, the raw material pairing data set with the best performance in historical production can be intelligently matched according to the actual picking interval value, the sugar degree and the acidity of the new batch of raw material fruits, so that the production optimization data set corresponding to the raw material pairing data set is called, the scheme realizes refined and self-adaptive control of the NFC fruit juice production process, and the production efficiency is improved. And it is ensured that different batches of raw material fruits can reach the optimal quality standard in the processing process.

The embodiment of the invention relates to the technical field of perfume manufacturing, and discloses a perfume manufacturing process parameter dynamic optimization method based on production big data, and the method comprises the steps: receiving the production big data from a plurality of heterogeneous data sources; dynamically calculating a pollution risk evaluation value required when the production of the first perfume is switched to the production of the second perfume in the production equipment based on the perfume formula compatibility data; processing the real-time process data detected by the sensor to obtain process state data; inputting the market demand data, the pollution risk evaluation value and the process state data into a trained production optimization model, and executing the production optimization model; and transmitting the production process parameter instruction to a production execution system for perfume production. According to the scheme of the embodiment of the invention, market changes, equipment conditions and process fluctuations can be responded in real time, and a traditional static and single-target production mode is converted into a dynamic, collaborative and self-adaptive intelligent manufacturing mode.

A system for controlling a production plant includes a plant automation unit for monitoring and control of the production process within the production plant. A production planning system has information concerning the products to be produced. A model generator generates at least one prediction model for products produced in the production plant. The model generator takes into account the results of the monitoring of the production plant when generating the at least one prediction model. A production optimizer determines an optimized production process within the production plant on the basis of data from the plant automation unit, the production planning system, and the prediction model generated by the model generator. The production optimizer takes into account the production-related specifications of the individual plant parts. A production plant control unit generates target specifications for the plant automation unit on the basis of the optimized production process determined by the production optimizer.

The invention provides a whole milk powder production optimization method and device, electronic equipment and a storage medium, and relates to the technical field of dairy product production, and the method comprises the following steps: obtaining nutritional ingredient data of raw milk in a plurality of milk tanks; based on complementarity among the nutritional ingredient data of the raw milk in each milk tank, grouping the plurality of milk tanks to obtain a production batch corresponding to each milk tank; based on the difference between the nutritional ingredient data of the raw milk of each production batch and the target nutritional ingredient data, determining process adjustment parameters for adjusting the nutritional ingredients of the raw milk of each production batch; and performing whole milk powder production on the raw milk of each production batch based on the process adjustment parameters of each production batch. According to the method and the device provided by the invention, through scientific complementarity grouping and accurate process parameter calculation, an original production process depending on artificial experience is converted into an automatic production process, and both product quality and economic benefits are considered in the production process of the whole milk powder.