107 results about "Production model" patented technology

The present invention discloses a digital production operation management method for data quality assessment and analysis, including: step 1: constructing a multi-level distributed data acquisition system; step 2: designing a multimodal data fusion framework, combining graph neural network and time series data processing technology to realize data fusion and multidimensional analysis; step 3: constructing an intelligent scheduling engine to realize dynamic adaptive scheduling strategy to cope with real-time changes in the production environment; step 4: designing a decentralized self-organizing production network to realize flexible adjustment and optimization of the production process; step 5: realizing the coordinated optimization of product design and equipment maintenance through topology optimization, multi-objective optimization and machine learning algorithms; step 6: constructing a virtual production network to realize real-time monitoring, dynamic prediction and optimization decision support of the production system. Through the present invention, enterprises can break the limitations of traditional production models, improve production efficiency, flexibly respond to changes in market demand, and optimize resource allocation.

The invention particularly relates to a hydrogeological analysis system and method based on big data, and relates to the technical field of hydrogeological analysis, and the method comprises the steps: placing a new model and an old model in an A / B test stage, and carrying out the parallel processing of real-time data; performing automatic judgment according to a preset performance index, and deciding whether to upgrade the candidate model to a new production model; and applying the model selected after decision making to hydrogeological analysis to solve the problem of long-term prediction precision under dynamic change of the hydrogeological system. According to the method, dynamic threshold monitoring, K-S outlier sample examination, sub-model optimization and A / B test smooth iteration processes are constructed, and a mechanism and machine learning coupling model and parallel calculation are combined, so that the model drift identification delay is reduced, the prediction precision is improved, scenes such as underground water over-mining prevention and control and pollution emergency are effectively supported, and the prediction accuracy is improved. And an accurate and efficient scientific decision basis is provided for water resource management.

The invention discloses an oil reservoir water content prediction method based on a graph neural network and Transform, and belongs to the technical field of oil and gas field development, and the method comprises the following steps: S1, converting an oil reservoir injection-production model at each time point into a graph structure, and constructing a connection network from a water injection well to a production well; and S2, constructing a GNT proxy model in combination with the graph neural network and Transform, training the GNT proxy model, and predicting the water content of each production well. According to the constructed GNT proxy model, an oil reservoir injection-production structure is converted into graph data through a GCN module, the information transmission strength between nodes is dynamically adjusted through an edge feature enhanced message transmission mechanism, and inter-well communication features are accurately captured; and the multi-head self-attention mechanism of the Transform encoder module is combined to process the time sequence dependence, so that the long-distance time dynamic relationship is effectively modeled, and an efficient and reliable agent model is provided for the dynamic analysis of the oil reservoir.

The invention discloses a multi-target flexible job shop scheduling method based on a graph network and reinforcement learning, which can effectively characterize key features of a production model by using features of a double-graph attention network deep mining process and a machine. The depth characterization capability can not only improve the performance of the model, but also enhance the adaptability of the model to different production models, so that the model can flexibly cope with various unseen production scenes, and shows excellent generalization performance. Besides, in the field of multi-objective optimization, how to balance the relationship between different objectives is an important and challenging problem, so that the model can find an optimal balance point among the multiple objectives by distributing different weights for different optimization objectives, and the method not only can improve the solving efficiency, but also can improve the optimal balance point among the multiple objectives. And moreover, a high-quality Pareto frontier solution set can be generated, and an effective solution is provided for a multi-target flexible job shop scheduling problem.

The invention discloses a data production and training method and a related device. A knowledge document, training parameters, a data production model and a pre-training model are obtained. And according to the knowledge document and the first cue word, processing through a data production model to obtain a seed data set. And according to the seed data set and the second cue word, performing expansion through a data production model to obtain an expanded data set including the seed data set. And according to a training mode indicated by the training parameters and the extended training set, performing fine tuning on the pre-training model to obtain a target model. And deploying the target model so that a user can use the target model. The data production model is rapidly manufactured and expanded to obtain the expanded data set, the efficiency of manufacturing the data set is improved, the prompt words can make the pre-training model clear own tasks during fine adjustment, and the accuracy of the target model is improved. Meanwhile, full-process automatic processing of data set making, pre-training model fine tuning and target model deployment is achieved, and the efficiency of the whole fine tuning process is improved.

The invention relates to a multi-cycle collaborative production scheduling method of an oil refining production and utility system under an uncertain condition. The method comprises the following steps: S1, obtaining operation data and price data of outsourcing power coal and electricity; s2, predicting the boundary of the uncertainty set according to the price data, and constructing a dynamic uncertainty set; s3, establishing a multi-cycle collaborative production scheduling model of a refinery plant production system and a public system under the dynamic uncertainty set; s4, introducing the model by adopting a distribution robust optimization method based on a dynamic uncertainty set; and S5, solving the multi-cycle collaborative production scheduling model of the oil refining production and utility system under the uncertain condition by adopting a robust dual reconstruction mode, and outputting a production plan. Compared with the prior art, the method has the advantages that the robustness and the conservative property of the production plan output by the model are improved, and the like.

The invention discloses a digital twinborn simulation optimization system and method for mining dilution rate and loss rate, relates to the technical field of digital mines, and solves the problem of isolated operation of geology, production and automation subsystems in a traditional system by fusing a geologic model and a production model in a unified platform. And an integrated intelligent system with data intercommunication and business collaboration is formed. And the management of the dilution rate and the loss rate is changed from the traditional post statistics to the instantaneous perception and the abnormal diagnosis based on the real-time data, so that the online monitoring and the active intervention of the production process are realized, and the resource waste is reduced. Parallel deduction and multi-objective optimization are carried out on multiple schemes through a simulation engine, and a global optimal scientific decision scheme under multiple objectives can be provided. Through real-time feedback of an execution result, the twin mining area model is continuously calibrated and optimized, the defect that the precision of a traditional static model is attenuated along with time is overcome, and a reliable basis is provided for long-term optimization.

The invention discloses a fault prediction method, system and device for credible features of power transmission and transformation equipment and a medium, and the method comprises the steps: collecting multi-source data of the power transmission and transformation equipment, carrying out the preprocessing of the multi-source data, obtaining noise reduction data, and extracting time dimension feature data and space dimension feature data from the noise reduction data; extracting vibration signal data from the noise reduction data, calculating probability distribution of the vibration signal data, and extracting entropy feature data from the vibration signal data through the probability distribution; performing dimension reduction visualization processing on the entropy feature data, the time dimension feature data and the space dimension feature data to obtain a sensor feature set; and inputting the operation state data into the equipment fault probability production model, outputting a production prediction result, and tracking and obtaining fault equipment through the production prediction result. According to the method, a complete technical closed loop is formed from data noise reduction, multi-dimensional feature fusion to fault source tracing, and the accuracy and reliability of power transmission and transformation equipment fault prediction are 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.

Task assignment for multi-robot systems (MRSs) in a smart factory (Industry 4.0) is described. Aspects are directed to a hypergraph based MRS and production model facilitating the cooperation among robots and serving frequent reconfiguration desired in Industry 4.0. Aspects are directed to a time complexity friendly search algorithm for real-time application using a hypergraph model to get task assignment(s). Parameters are provided for a tradeoff between solution optimality and time complexity. In an implementation, an example system can include a MRS including robots, wherein the MRS is configured to perform a manufacturing task, and a computing device configure to perform a multi-robot task allocation (MRTA) for the MRS. In an implementation, an example method can include generating task assignments, using MRTA, for robots of a MRS including the robots, wherein the MRS is configured to perform a manufacturing task, and providing the task assignments to the MRS.

The invention relates to the technical field of industrial Internet of Things, edge calculation, silk-screen and the like, and provides a substrate silk-screen method and equipment based on an intelligent silk-screen unit, and the method comprises the steps: configuring a feeding mechanism, a dust removal mechanism, a pre-printing alignment mechanism and a printing mechanism which are connected in sequence, and establishing an industrial Internet of Things information sensing layer and an edge gateway which are in two-way communication with the feeding mechanism, the dust removal mechanism, the pre-printing alignment mechanism and the printing mechanism. Order and formula data are received through a gateway, multi-source sensing point location real-time data are fused, a production model with a unified time reference and batch identification is constructed, and process parameters are dynamically issued. And during feeding, the model and the warping degree of the substrate are automatically recognized, and dust removal parameters are synchronously adjusted. In the printing process, screen tension, scraper stress, motor current and printing ink rheological property are monitored in real time and fused to obtain a printing ink comprehensive rheological characteristic value, scraper parameters and an ink return time sequence are cooperatively regulated according to the printing ink comprehensive rheological characteristic value, and a printing quality estimated value is output. According to the method, collaborative optimization and quality estimation of the whole printing process are achieved, and the production adaptability and consistency of product printing are improved.

The present disclosure provides a method for processing a substrate that can maintain a production amount even when a production model is changed. The method for processing a substrate comprises: disposing the substrate on a levitation stage; measuring a distance between the substrate and an inkjet head module; and changing a discharging speed of ink to be discharged from the inkjet head module based on the measured distance.

The invention relates to the technical field of industrial control, in particular to a synthetic ammonia production control method, system and equipment, a medium and a product. The method comprises the following steps: acquiring production modeling data of a synthetic ammonia field section, performing unsupervised analysis on the production modeling data through a preset big data model, determining key features and an analysis result of the key features in a synthetic ammonia production process, performing visualization processing on the key features, generating a visual chart, and displaying the visual chart on a database. Therefore, semantic analysis and causal reasoning are carried out on the visual chart and the analysis result through a preset large language model, a target optimization strategy is generated, optimization control is carried out on a synthetic ammonia production site, and the production efficiency of synthetic ammonia is improved.

The present invention relates to a method for constructing digital ship intelligent manufacturing. The construction method comprises the following steps: digital dock construction; digital weight and center of gravity control; hull response monitoring system construction; operation and management integrated digital management platform construction: building a digital overall framework, mainly including a basic layer, an equipment resource layer, an execution basic layer, a coordination business layer, an operation management layer and a decision management layer; digital inspection management platform construction. The advantages of the present invention are: with the help of digital transformation, the components are based on engineering decomposition, with planning management as the command, and an integrated solution for overall design, materials, production, quality and cost management is constructed. Through the construction of the industrial Internet and the integrated application of the information system, a digital shipyard based on digital twins is constructed, facilities, equipment, resources, etc. are interconnected, early warning and analysis are carried out from multiple angles, a big data operation system is explored, a digital operation model is constructed, and finally a new digital production model is formed.

The invention discloses an acquisition and analysis method and system based on intelligent data of a smart factory, and relates to the technical field of intelligent data acquisition and analysis, and the method comprises the steps: S10, analyzing the dynamic production data and basic production data of each production line of the smart factory; s20, analyzing the real-time risk coefficient of the production plan executed by the smart factory; s30, analyzing the dynamic production data regulation and control value of each production line of the smart factory in the remaining working time period; and S40, carrying out intelligent management and control on the real-time work plan of each production line of the smart factory. According to the method, the dynamic production model of the intelligent factory is constructed according to the analyzed production data, the real-time completion degree of the intelligent factory on the production plan can be rapidly analyzed, the influence of the number of unqualified products and the equipment aging condition on the analysis result does not need to be considered in the process, the analysis time is shortened, and meanwhile the production efficiency is improved. And the analysis precision is improved.

The present disclosure relates to techniques for privacy-preserving computing to protect a subject's privacy while using the subject's data for secondary purposes such as training and deploying artificial intelligence tools. Particularly, aspects are directed to receiving, at a local server, subject data regarding a first subject, performing, by the local server, a de-identifying operation, an anonymizing operation, or both on the subject data, sending the subject data to a remote server, receiving a production model from the remote server, the production model including parameters derived in part from the processed subject data, receiving, at the local server, subsequent data regarding a second subject, inputting, by the local server, the subsequent data into the production model to analyze the subsequent data and generate an inference or prediction from the analysis of the subsequent data; and sending, by the local server, the inference or the prediction to a computing device.

An example system may include an energy storage unit and various processors configured to communicate with components of the energy storage unit. An example system may determine a consumption model that predicts future consumption of a node based on local power consumption of the node. The system may receive weather forecast data for the node and determine a power production model for the node that predicts future power production using the weather forecast data. The system may calculate a predicted power difference using the consumption model, the production model, and the current environment of the node. The system may perform automated operations using the predicted power difference, such as charging or discharging an energy storage unit or controlling one or more electrical loads.

This invention provides a low-abnormality horizontal electroplating control system for diamond wire magnetic materials, belonging to the field of diamond wire electroplating technology. This invention uses a real-time pre-control compensation mechanism to capture and suppress early-stage anomalies, while dynamic optimization at the meso-level continuously adjusts process parameters to their optimal state. These two mechanisms work collaboratively within a macro-steady-state baseline framework, fundamentally changing the passive nature of traditional production models and enabling proactive management of production anomalies, significantly reducing the probability of defects such as agglomerates and incomplete plating. By establishing a learning loop that feeds micro-level intervention experience back to the meso-level optimization model, and by incorporating slow-time-varying factors such as plating solutions into the macro-baseline modeling, the system can continuously absorb knowledge from production practice, automatically adapting to long-term changes such as plating solution aging and raw material batch differences, reducing reliance on manual experience.

The application provides a design method of a light truck tire, relates to the technical field of tire design, and comprises the following steps: collecting the use environment data of the light truck tire, and generating the working parameter requirement of the light truck tire; then generating the contour data of the light truck tire; further generating the size data and preliminary material selection of each part structure of the light truck tire; finally generating a preliminary model; adjusting the preliminary model after simulation analysis to obtain a production model; selecting the tire pattern and the use air pressure range of the light truck tire based on the working parameter requirement of the light truck tire; adding the tire pattern and the use air pressure range of the light truck tire to the production model; producing a sample tire based on the production model, and detecting and verifying the design effect on site. Through the investigation on the use environment of the light truck tire, the accuracy of the working parameter requirement of the light truck tire is improved, through the comprehensive consideration of multiple factors such as the material, structure and pattern of the tire, and the mutual influence relationship among each part structure, the comprehensive improvement of the performance of the tire is realized.

The application belongs to the technical field of new energy comprehensive utilization, and discloses a wind-solar-storage hydrogen-ammonia-methanol capacity optimization configuration method and system. The method firstly calculates the electricity consumption of wind-solar-storage hydrogen-ammonia-methanol according to target basic data of the project location and determines the traversal condition, then builds a production model, and finally traverses the production model according to the traversal condition, so that the configuration scheme of different wind, light, hydrogen, ammonia and methanol can be quickly calculated. Then, combined with the pre-constructed financial estimation model, the economy of different configuration schemes can be estimated and sorted as the target, and the optimal scheme of the project economy is found out.

The present invention relates to the technical field of composite cloth production management, and specifically to a composite cloth production management optimization method and system based on a data module, comprising: collecting quality parameters such as air permeability, tensile strength, water absorption and color fastness of the composite cloth, constructing a quality assessment model, and generating a comprehensive quality score; predicting the demand for composite cloth by combining external market factors such as seasonal factors, price fluctuations, random events and historical demand; evaluating internal production factors such as process difficulty, production efficiency and working environment, generating production constraints, and constructing a production model by combining workload parameters and process parameters of production equipment to calculate production volume; and optimizing the workload and process parameters by using a particle swarm optimization algorithm based on the production balance rate and the comprehensive quality score, thereby achieving efficient optimization of the production process and improvement of product quality, thereby increasing the flexibility of production management.

The invention relates to a power motor component intelligent assembling and optimizing method in the new generation information technology field, and the method comprises the steps: adjusting a test protocol according to a potential defect position, the test protocol comprises a sensor monitoring rule, real-time sensor signal data is acquired, and if the fluctuation of the sensor signal data exceeds a preset range, the power motor component intelligent assembling and optimizing method is started; if yes, marking as an abnormal event to obtain a performance evaluation index; working condition adaptive characteristics are extracted from the performance evaluation indexes, the working condition adaptive characteristics reflect the response capability of the motor to environmental changes, the stability of the motor under complex working conditions is evaluated by adopting a classification algorithm, a service life prediction result is judged, and an optimization suggestion sequence is obtained; according to the optimization suggestion sequence, production parameters are updated, the production parameters relate to assembly and test configuration, feedback cycle data are obtained, and if the feedback cycle data indicate that consistency is improved, parameter configuration is locked, and a final production model is obtained.

The present invention relates to a hierarchical time-series production simulation method for integrated main, distribution, and micro-grid based on partition aggregation, which comprises the following steps: Step S1: Define the aggregation partitions of the main, distribution, and micro-grid; Step S2: Establish the upper-layer time-series production model; Step S3: Establish the lower-layer time-series production model; Step S4: Set the constraint conditions; Step S5: Based on Steps S2-S4, use Cplex for solution to obtain the time-series production simulation process. The present invention improves the power supply reliability and new energy utilization rate of the main, distribution, and micro-grid on the basis of optimizing the power balance of the main, distribution, and micro-grid, and ensures the safe and efficient operation of the main, distribution, and micro-grid.

The present invention relates to a computer-implemented method and a computing device. The method includes obtaining a second dataset including a set of sensor data sequences with associated annotations generated by a first machine learning model trained to perform a perception. Each sensor data sequence includes sensor data samples depicting a physical environment over a plurality of time instances. Then training a second machine learning model, using the second dataset, to perform an augmented perception task. The method also includes fine-tuning, using a third dataset, the second machine learning model, to perform the perception task, wherein the third dataset includes sensor data samples depicting a physical environment and that are annotated for the perception task. The method also includes providing the fine-tuned second machine learning model as a model for annotating training data for subsequent training of a production model, of an automated driving system, to perform the perception task.

The embodiments of the present invention disclose a method and system for verifying a KDM production model based on a national secret algorithm. The method first verifies that a KDM file generated by a KDM production model based on an internationally accepted cryptographic algorithm is correct. The method then compares the decryption results of KDM files generated by the internationally accepted cryptographic algorithm and the KDM production model based on the national secret algorithm. If the decryption results match, the KDM file generated by the KDM production model based on the national secret algorithm is determined to be correct. This method effectively verifies the correctness of KDM files generated by the KDM production model based on the national secret algorithm.

The present invention proposes an industrial production model parameter adjustment method and control system, which includes: real-time monitoring of internal or external production conditions that affect the production quantity of products; when any of the internal or external production conditions changes, determining the affected target production model parameters and their change ranges based on the changed internal or external production conditions; determining the associated production model parameters of the target production model parameters; judging whether the change range of the target production model parameters is greater than the upper limit of the dynamic parameter range of the target production model parameters; when the change range of the target production model parameters is not greater than the upper limit of the dynamic parameter range, keeping the associated production model parameters unchanged; otherwise, determining the change range of the associated production model parameters to adjust the production model according to the change range of the associated production model parameters, and being able to adaptively adjust the parameters of the industrial production model according to changes in internal and external production conditions.

The invention discloses a starting pressure gradient-based oil reservoir differential use and strata series recombination optimization method, which comprises the following steps of: collecting a rock core, and carrying out a starting pressure gradient experiment; cleaning the test data, and removing abnormal values in the data set; starting pressure gradient and permeability data standardization; establishing a characterization model of the starting pressure gradient and the permeability; establishing a multilayer heterogeneous oil reservoir single-layer liquid production capacity model considering the starting pressure gradient; judging whether each oil layer of the target well can be effectively used or not; establishing a strata series recombination chart considering the starting pressure gradient; and based on the strata series recombination chart, carrying out strata series recombination and the like. According to the method, the matching relation between the maximum permeability and the minimum permeability which can be effectively utilized under different pressure difference conditions can be determined, more small layers can be started under the same production pressure difference condition of the same set of layer series, the reservoir which can be started under the same production pressure difference serves as one set of layer series, effective utilization of the small layers in the longitudinal direction is achieved through layer series recombination, and the stability of the reservoir is improved. And the water drive development effect is improved.

The invention discloses a building structure blasting demolition vibration characteristic simulation experiment system, which comprises a blasting vibration generation model, a loading test device, a collapse body grounding vibration production model and a monitoring measurement system, and is characterized in that the blasting vibration generation model comprises an electronic detonator; the loading test device comprises a connecting hook and a disconnecting device, and the disconnecting device is located on the connecting hook; the connecting end of the collapse body grounding vibration production model is detachably connected with the other end of the connecting hook, and the electronic detonator and the disconnecting device are respectively connected with the monitoring and measuring system. The invention further discloses a simulation method of the building structure blasting demolition vibration characteristic simulation experiment system. Compared with the prior art, the building structure blasting demolition vibration characteristic simulation experiment system studies the interaction mechanism of blasting vibration and ground contact vibration caused in the blasting demolition process of a tall building structure and the vibration characteristics of the blasting vibration and the ground contact vibration.

The application discloses a cigarette material matching method and device, electronic equipment, medium and product, and belongs to the technical field of cigarette manufacturing. The method comprises the following steps: acquiring the number information and model information of a machine; querying a production scheduling system based on the number information and model information to obtain in-production model information; acquiring the required raw and auxiliary material information according to the in-production model information; acquiring label information; obtaining the raw and auxiliary material information to be used based on the label information; comparing the required raw and auxiliary material information with the raw and auxiliary material information to be used; if the required raw and auxiliary material information is the same as the raw and auxiliary material information to be used, and if the raw and auxiliary material information to be used is the raw and auxiliary material information of a packaging machine, then the raw and auxiliary material to be used is matching material; and storing the comparison information in a corresponding database. The application can effectively avoid material misuse.

The invention discloses a laser cutting method for stator and rotor steel sheets, and belongs to the technical field of laser machining, and the laser cutting method for the stator and rotor steel sheets comprises the steps that a clamping tool and a workbench are manufactured according to the preset size of a stator and / or a rotor; manufacturing a production model diagram according to the preset size of the stator and / or the rotor; the production model diagram is imported into a visual positioning laser cutting system, and the machining sequence and the machining path of the production model diagram are formulated to form a machining scheme; the clamping tool is positioned and connected to the workbench, the machined material piece is positioned and connected with the clamping tool, and laser cutting is conducted on the machined material piece according to the machining scheme to obtain a machined finished product; measuring the actual size of a processed finished product; when the deviation between the actual size and the preset size exceeds the preset value, the production model diagram is adjusted or remanufactured according to the deviation, machining is conducted again according to the adjusted or remanufactured production model diagram, and the laser machining efficiency and the finished product machining precision of the stator and / or the rotor can be improved.