59 results about "Production model" patented technology

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.

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 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 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 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 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 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 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.

The application discloses a kind of methods, devices, computing equipment and storage medium for data configuration to vehicle, belong to vehicle technical field.The method comprises: obtaining the position information of vehicle, obtaining the configuration data packet corresponding to the position information from the configuration database stored in advance, and the configuration data packet at least includes map data packet, and then the data configuration of each configuration item of vehicle is carried out based on the configuration data packet.Above-mentioned method is configured according to the position information of vehicle for the configuration data of vehicle export area, reduces the configuration difficulty, improves the configuration flexibility;And, the method does not need to distinguish the configuration data of different export area, vehicle material number and spare parts material software and hardware, avoid producing excessive vehicle configuration data and spare parts material number, reduce production division and production model control problem, to integrate development resources, reduce production, vehicle, material control and so on cost.

A computer-implemented method for algorithmic generation of musical compositions may include (i) receiving a musical composition and listener preference data associated with at least one listener, (ii) extracting from the musical composition a first set of tracks and a second set of tracks, (iii) generating, by at least one generative machine learning model, based at least in part on the listener preference data, a new set of tracks, and (iv) combining, by a music production model, the first set of tracks extracted from the musical composition with the new set of tracks generated by the generative machine learning model to create a new musical composition based at least in part on the listener preference data. Various other methods, systems, and computer-readable media are also disclosed.

The invention discloses an agricultural product sales method, device and equipment, a storage medium and a program product, and relates to the technical field of agricultural product sales, and the disclosed agricultural product sales method comprises the following steps: constructing an agricultural product production model; determining a target agricultural product corresponding to the ordering demand of the consumer in the agricultural product production model based on the received selective purchasing instruction; a first virtual selling space is generated according to the agricultural product production model and the ordering demand, the first virtual selling space is used for displaying a matched farm virtual model containing the target agricultural product in the agricultural product production model, and the matched farm virtual model comprises a virtual image and product information of the agricultural product to be sold in the matched farm; and determining first ordering data based on an ordering instruction received in the first virtual selling space, and performing agricultural product selling processing according to the first ordering data. The agricultural product sales efficiency can be improved, the overstock and waste of agricultural products are reduced, and the healthy development of agricultural economy is promoted.

A method for automated quality control in film extrusion processes using a production model-based control system. The method involves monitoring quality parameters of produced film and comparing them against target values to determine deviations. Using a production model, the system establishes relationships between these quality deviations and physical operating parameters of the extrusion line. Based on these relationships, the system automatically generates and implements control value adjustments to the relevant physical operating parameters. The adjusted parameters are then used to continue film production, creating a closed-loop control system that maintains product quality. This automated approach enables real-time quality optimization and reduces the need for manual intervention in the film extrusion process.

The application discloses an embedded numerical control electro-processing system and relates to the technical field of numerical control electro-processing.The application comprises a processing unit, a power management unit, a console, a planning module and a coordination module, wherein the processing unit comprises an ARM unit, a DSP unit, a five-axis motor control module, an HDMI module, an RJ45-HUB, a USB-HUB, an FPGA unit, a hand wheel serial port, an RS485 serial port and an IO signal processing module; the power management unit comprises a protection module and a filtering module; the port of the console is in communication with the port of the planning module; the output end of the planning module is connected with the input end of the coordination module; the output end of the ARM unit is connected with the input end of the planning module; and the port of the coordination module is in communication with the port of the ARM unit.The planning module of the application can automatically generate a production plan and automatically update and optimize the production plan according to the establishment of a general production model, so that the production plan is more reasonable, and the coordination module can intelligently schedule the processing tasks and graphic calculation.

The invention discloses a health care product production control method and system based on twinborn simulation and a medium, and relates to the related field of industrial process control, and the method comprises the steps: carrying out the twinborn simulation of technological parameters and production quality on a production line of a target health care product, and constructing a twinborn production model; configuring a raw material detection network at a raw material input end of the production line, wherein the network specifically comprises effective component detection equipment and a detection mechanism; and performing dynamic detection on effective components of the raw materials through the raw material detection network, inputting a detection result and current process parameters of the production line into the twin production model for production quality simulation, and when a production quality index does not meet a preset quality range, adjusting and optimizing the current process parameters by using the twin production model. The technical problems that production factor changes cannot be mastered in real time, technological parameters are difficult to accurately adjust and quality defects cannot be effectively prevented in existing health care product production control are solved, and the technical effects of accurately controlling the production process in real time and improving the product quality stability are achieved.

The invention discloses a tunnel steel ring reinforcing method and system based on the tunnel hospital idea, and relates to the technical field of tunnel steel ring reinforcing, and the tunnel steel ring reinforcing method based on the tunnel hospital idea comprises the following steps that a tunnel hospital is established; according to the data information of the to-be-reinforced tunnel structure, a production model of the steel ring structure is generated, and an auxiliary steel ring structure is synchronously produced; splicing the auxiliary steel ring structure in a tunnel hospital, testing the steel ring spliced structure through the tunnel hospital, and synchronously collecting corresponding installation test information; optimizing a production model of the steel ring structure through the obtained installation test information; according to the technical scheme, through establishment of the tunnel hospital, the production model of the auxiliary steel ring structure can be optimized, so that the main steel ring structure, the whole system structure and the reinforcing scheme which can be directly and rapidly installed in the tunnel to be reinforced are formed, and the actual installation efficiency of the steel ring structure can be effectively improved.

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

This invention relates to a vibrator assembly line; it includes a frame platform, on which a moving platform is mounted, a rotating platform is mounted, a tilting platform is mounted, and a housing is mounted on the tilting platform. A four-column, three-beam hydraulic workstation is located at the front of the moving platform. Bearing clamping and feeding mechanisms, spindle clamping and feeding mechanisms, and gear clamping and feeding mechanisms are respectively mounted on both sides of the frame platform below the four-column, three-beam hydraulic workstation. At the front of the gear clamping and feeding mechanism, an oil baffle ring assembly unit, a shaft assembly unit, an automatic glue application unit, a through-hole cover assembly unit, an automatic bolt assembly unit, a labyrinth cover assembly unit, a spacer assembly unit, an eccentric block assembly unit, and a discharge point are sequentially arranged. This line has the advantages of saving significant space, conforming to an intensive production model, freeing up manpower, occupying a small footprint, and improving work efficiency.

The invention relates to a residual network-based feature decoupling self-encoding polypropylene production prediction model, and the method comprises the steps: obtaining the original data of polypropylene production, and extracting the trend features and periodic features of the original data through a trend period long-short term memory neural network, spatial features of the original data are extracted through a dynamic self-attention convolutional neural network; splicing the trend features, the periodic features and the spatial features by using feature decoupling self-coding to obtain decoupled multi-source features; and establishing a relationship between the decoupled multi-source features and the output by using a residual network, and constructing a polypropylene production model according to the relationship between the decoupled multi-source features and the output. Therefore, the problems that in the related technology, a data-driven modeling method assumes that the production process is in a stable working condition, and the polypropylene production process is a non-static and non-linear complex process, so that the model is difficult to adapt to complex dynamic changes, the generalization performance of the model is reduced and the like are solved.

The application discloses a kind of based on depth reinforcement learning and course learning's optimization method of fragment connection small molecule compound, it is related to artificial intelligence and drug molecule optimization field.The method comprises: establishing supervised training set;Establish initial prior model based on graph neural network, determine several prior models for different drug design tasks using supervised training set;In the course learning framework, reinforcement learning is carried out using prior model, and production model with target attribute is obtained;Large-scale molecular generation is carried out using production model.Compared with prior art, the method disclosed in the application solves the problem of generating more invalid molecules and non-real structured compounds by previous models, and can generate drug molecules with complex desired attributes at a relatively small computational cost.

This invention provides an intelligent production system and sintering equipment for alumina ceramics, including manufacturing equipment, sensor modules, and a smart terminal. The manufacturing equipment produces materials and is equipped with sensor modules. The sensor modules are networked with the smart terminal for data transmission. The smart terminal analyzes and optimizes the data obtained from the sensor modules and feeds back the optimized structure to the manufacturing equipment. Through the combination of sensor modules and the smart terminal, the system can monitor key parameters in real time. Based on time series analysis, a production model is established to dynamically adjust equipment parameters, particularly using a GARCH model to control the temperature of the sintering equipment. This enables precise temperature control, reduces the impact of temperature fluctuations, thereby improving product quality and stability while reducing energy consumption and maintenance costs.

The utility model relates to the technical field of split rubber covered rollers, and discloses a split rubber covered roller of a coating machine, which comprises a main body mechanism, a sleeving mechanism is sleeved on the surface of the main body mechanism, and a butt joint mechanism is arranged on the surface of the main body mechanism. The rubber covered roller is split, the effective utilization rate of the rubber covered roller is improved, the rubber covering renovation price can be effectively reduced due to the fact that the rubber covering area is small, the rubber covered roller can be subjected to shaft sleeve rubber covering treatment according to an existing production model and avoid a tab area, and therefore the tab area is prevented from being crumpled, a through shaft and a shaft sleeve are arranged in a separated mode in the non-installation state, and assembly is started. Shaft sleeve rubber coating treatment is carried out according to the model of a battery produced on site, corresponding fixing screws are prepared, after preliminary assembly is completed, the distance between shaft sleeves is determined according to the model of a produced pole piece, the screws are fixed, after assembly is completed, a rubber roller is installed on a coating machine, the compression degree of the rubber roller is debugged, and production can be carried out after it is confirmed that the screws are fastened and the installation positions are correct.

The application provides a crude oil selection and purchase production data processing method, device and equipment and a storage medium. An electronic device obtains crude oil selection and purchase production parameters, corresponding data and selection and purchase production constraint conditions. According to a constraint condition for limiting the upper limit of crude oil purchase types in the selection and purchase production constraint conditions, an integer constraint equation about 0-1 variables is generated. After a relaxation operation, a constraint equation about p-norm is obtained. According to the crude oil selection and purchase production parameters, the Lagrange multiplier and the constraint equation about p-norm, a target function in a crude oil selection and purchase production model is generated. In combination with a constraint equation group of the crude oil selection and purchase production model generated based on other constraint conditions in the selection and purchase production constraint conditions and the data corresponding to the selection and purchase production parameters, the crude oil selection and purchase production model is iteratively solved, and a crude oil purchase production result is obtained. The model convergence speed is accelerated, the model oscillation and non-convergence problem is solved, and the accuracy of solving the crude oil selection and purchase result is improved.

The invention provides a new energy consumption potential assessment method and device for a direct supply polycrystalline silicon production line, and relates to the technical field of new energy consumption potential assessment. The method comprises the following steps: on the basis of production parameters of a polycrystalline silicon production line, constructing a corresponding production model for each link of a technological process of the polycrystalline silicon production line, and associating the production models of all links to obtain a polycrystalline silicon full-process production model; determining a new energy power generation power reference curve of the polycrystalline silicon production line based on historical power generation power data of a wind and light power generation station near the polycrystalline silicon production line; and constructing constraint conditions based on the polycrystalline silicon full-process production model and the new energy power generation power reference curve, and constructing a target function by taking the maximum new energy installed capacity which can be consumed by the polycrystalline silicon production line as an optimization target to obtain a new energy consumption potential evaluation model. According to the new energy consumption potential evaluation model, accurate evaluation of the new energy consumption potential of the direct supply polycrystalline silicon production line can be realized, and investment waste and power generation resource loss are avoided.