642 results about "Material flow" patented technology

The invention provides a real-time transmission method and system based on internet-of-things sensing data in a digital twinborn scene, and the method comprises the steps: collecting and processing vibration spectrum data, pressure gradient change data and temperature distribution data of equipment in an industrial production line, generating a preprocessing data package, carrying out the analysis to obtain an equipment state fingerprint feature and an environment working condition label, and carrying out the real-time transmission of the environment working condition label. A target virtual control parameter and a target error compensation amount are generated by using a dynamic parameter correction loop of the digital twin, an actual material flow state is generated in combination with material flow rate data, an optimization strategy sequence of multi-device collaborative operation is generated by using a distributed computing architecture, and the optimization strategy sequence is converted into a track compensation parameter of an end effector in the device; in combination with the target error compensation amount, data closed-loop transmission is carried out; according to the invention, millisecond-level time domain matching of vibration, pressure and temperature data and material flow velocity is realized through a dynamic timestamp alignment mechanism of 5G edge computing nodes, and the problem of synchronization error accumulation of traditional internet-of-things sensing data is solved.

The invention provides an intelligent factory production scheduling method and system based on digital twinning, and the method comprises the steps: firstly obtaining a real-time monitoring data set, including equipment operation states, material flow tracks and environment sensing data, of a physical workshop; generating a three-dimensional digital twinborn model containing an equipment dynamic operation topology, a material real-time distribution thermodynamic diagram and an environment state simulation layer, then performing operation state correlation analysis on the three-dimensional digital twinborn model, and generating a feature set of equipment operation efficiency, material flow bottleneck, environment interference and the like; generating a dynamic scheduling parameter set containing equipment load balancing, material priority distribution and environment adaptation compensation parameters, and finally calling a preset scheduling strategy optimization algorithm to perform multi-objective optimization processing on the dynamic scheduling parameter set to generate an equipment scheduling, material distribution and environment regulation and control instruction set. And synchronizing to a physical workshop to execute real-time scheduling operation, thereby realizing intelligent and efficient production scheduling.

The invention discloses a digital twinborn model real-time linkage method driven by information physics fusion, particularly relates to the technical field of digital twinborn cooperative control of an industrial automation production line, and is used for solving the problems of instruction conflict and control failure caused by mismatching of virtual model parameters and dynamic capability of physical equipment in the prior art. According to the method, equipment state and material flow data are collected in real time, equipment dynamic degradation parameters are extracted to generate capability attenuation feature vectors, hidden process conflict path detection and resource preemption probability simulation are combined, a collaborative optimization model of equipment health and process scheduling is constructed, and a control instruction set containing dynamic capability constraints is generated. After the screening instruction is verified through physical constraint matching, parameters are corrected in a closed loop mode based on an execution result, a model is iterated, and self-adaptive matching of the virtual instruction and the physical equipment capacity is achieved; the reliability of the digital twinning control instruction and the stability of a production line are remarkably improved, and the risk of abnormal shutdown caused by an overrun instruction is avoided.

The invention relates to the technical field of process optimization, and discloses an optimization method and system for a graphite sagger compression molding process. The method comprises the following steps: determining and analyzing particle size distribution, specific surface area, porosity and viscosity-temperature characteristics of graphite powder and a binder to obtain raw material characteristic parameter data; modeling is conducted on the migration behavior of the binder and the deformation characteristic of the graphite powder in the pressing process, a material rheological model is obtained, meanwhile, a multi-point pressure sensor system is arranged on the surface of a pressing mold, and target pressure data are collected; constructing a pressure distribution feature vector and a material flow state prediction result; performing adaptive adjustment on the initial pressing parameters to obtain a segmented pressure curve and a local pressure compensation strategy; and performing multi-objective optimization on the graphite sagger pressing path to obtain an optimal pressing process parameter combination. The method is combined with material flow state prediction, accurate control over the pressing process is achieved, and the intelligent level of the graphite sagger pressing forming technology is improved.

The invention discloses a control system and method of an intelligent screw grouting extruder, and belongs to the technical field of wire and cable production, and the control system comprises the following steps: a flow control module monitors the material flow in real time, and adjusts the feeding amount through fluctuation detection and frequency analysis; the temperature control module monitors and predicts temperature deviation through a temperature sensor and adjusts heating power; the rotating speed control module initializes the rotating speed of the screw according to material characteristics and monitors the load of the screw through the torque sensor so as to dynamically adjust the rotating speed of the screw; the pressure adjusting module monitors and adjusts the pressure of the inner cavity of the extruder in real time, and adjusts the distance between the die orifice and the screw rod by calculating the pressure deviation; the quality detection module detects the physical and visual quality of the product on line through a laser range finder and an image recognition technology, and carries out comprehensive quality evaluation; according to the invention, the problem of how to dynamically adjust the running state of the extruder according to real-time parameters such as temperature, flow, pressure and rotating speed is solved, so that the consistency of the thickness of a cable sheath layer and the product quality is ensured.

The invention provides an intelligent actual measurement and quantity management method and system for a building. The method comprises the following steps: capturing environmental parameters, material circulation paths and spatial geometric information of key parts of a building in a construction process in real time to obtain a measurement data set; a distributed ledger technology is combined with a homomorphic encryption algorithm, a graph theory analysis technology is applied to evaluate a dependency relationship among operation points of a construction site, and a data log is generated; according to the data log, using an augmented reality technology to superpose a BIM model, introducing a support vector machine algorithm to carry out automatic classification and risk assessment processing on deviation data between measured data and preset standard data, and generating a rectification suggestion report; and a closed-loop quality management process is formed by continuously collecting rectification effect feedback. According to the technical scheme, high transparency and efficient quality control in the construction process are ensured.

The invention relates to the technical field of control or regulation systems, and discloses a cooperative control method for a photovoltaic intelligent manufacturing equipment production line, and the method comprises the steps: a control unit collects the stock and change rate of materials in a physical cache region in real time; establishing and mapping the physical cache region into a virtual viscoelastic dynamic model with non-Newtonian fluid characteristics; calculating a virtual elastic restoring force enabling the stock to return to a balance point and a virtual viscous damping force preventing the stock state from changing based on the model; wherein an asymmetric anisotropic damping generation strategy is executed, and a damping coefficient is dynamically split according to a material flowing trend; and finally, superposing the virtual adjustment correction after vector synthesis to the basic transmission speed to generate a dynamic speed instruction, and by constructing a virtual dynamic field with rheological characteristics and an asymmetric damping mechanism, the problem of nonlinear cascade oscillation in discrete logistics transmission is solved, and differential self-adaptive suppression of accumulation and evacuation risks is realized.

The invention relates to the technical field of intelligent garbage sorting, and discloses a garbage sorting method based on artificial intelligence. According to the method, heterogeneous data fusion is performed on a multi-source garbage feature data set through a semantic alignment module, feature dimensions are unified, redundant items are eliminated, and a standardized feature set is output. And extracting a spatial position identifier and a physical state identifier of the junk entity, and dynamically dividing time slices by adopting a transfer learning model to generate a periodic junk flow data set. The coordinates of the sorting starting point and the sorting ending point are converted into a three-dimensional space grid to construct a migration track topological graph, flow data and physical state identifiers are combined, sorting paths are clustered and analyzed according to material categories, the material flow intensity in a specific period of a target area is quantified, and a dynamic index matrix is generated. According to the method, space-time correlation analysis of multi-modal junk data is realized, and the adaptability of sorting path planning in a complex environment is improved.

The invention discloses a tunnel intelligent primary support construction wet spraying over-consumption analysis method and system, and the method comprises the steps: obtaining spraying track space distribution data through a three-dimensional motion capturing system installed on a spraying unit, and combining a material flow fluctuation curve generated by a dynamic weighing system, constructing a three-dimensional feature tensor comprising a material flow state feature, an injection process feature and an environment interference feature; a theoretical volume of a primary support structure is extracted based on a geological BIM model, surrounding rock back break data obtained by point cloud scanning are fused, a dynamic use amount reference model considering rebound loss is established through fluid mechanics simulation, and a theoretical use amount interval value with a confidence interval is generated. According to the method, the three-dimensional characteristic spectrum and the closed-loop optimization system are constructed by fusing multi-physical-field dynamic data, the problems that dynamic adaptation of geological conditions is not accurate and a multi-factor coupling super-consumption mode cannot be analyzed in wet spraying construction are solved, and dynamic accurate control over the material consumption and explainable diagnosis of super-consumption causes are achieved.

The invention discloses an L-shaped conveyor belt path dynamic planning and congestion prediction system, which relates to the technical field of automatic control, and constructs a conveying path node diagram by collecting material weight, speed and spacing information of each node on a conveying path, and calculates a congestion index of each path section; the method comprises the following steps: combining a light-mass material jumping sensitivity model, identifying a high-risk node and marking the high-risk node as a congestion prediction area, constructing a dynamic optimization model based on path risk distribution and a material flow direction, generating and evaluating a plurality of path adjustment strategies, executing an optimal strategy according to priority, and realizing path speed adjustment, standby path switching and feeding rhythm control; through a feedback mechanism, a strategy effect is evaluated in real time, a path state is updated, operation data is synchronized to a historical database, a learnable conveying operation file is formed, the stability and the intelligent response capability of a conveying system can be effectively improved, and the method is suitable for high-speed sorting and automatic packaging scenes.

The invention relates to an agricultural microbial preparation production line multi-device collaborative control method and system, and the method comprises the steps: calculating a device health score based on operation state information, combining the device health score with a biological risk index to generate a task release score, and generating a production task instruction when the task release score meets a preset release threshold value, constructing a cooperative interlocking matrix between the multiple types of process equipment and the material handling equipment based on the production task instruction, distributing a control execution instruction to the multiple types of process equipment and the material handling equipment, respectively obtaining equipment feedback state information and material circulation information, executing consistency verification on the equipment feedback state information and the material circulation information, and sending a verification result to the material handling equipment; a consistency verification result is obtained, during manual modification, a revision audit chain corresponding to the control event information is generated, and when the running state information or the control event information triggers a preset abnormal threshold value, a disposal control instruction is generated. The method has the effect of improving the production safety.

The unmanned material transportation system comprises an intelligent equipment cluster and a remote centralized dispatching system, and the intelligent equipment cluster is connected with the remote centralized dispatching system; the intelligent equipment cluster comprises horizontal transportation intelligent equipment used for same-layer transportation of materials, an intelligent construction elevator used for cross-layer transportation of the materials and a stacking robot used for sorting, taking and placing of the materials. The remote centralized control system comprises a front-end interface and a rear-end server, wherein the front-end interface is used for issuing tasks and monitoring material processes and equipment states, the rear-end server is used for task scheduling, task path planning, task conflict monitoring and management and transmitting collected information to the front-end interface for display, and the front-end interface is connected with the rear-end server. And the intelligent equipment cluster and the back-end server are in two-way communication through the real-time communication module. Material sorting and transporting can be automatically completed, manual operation on a construction site can be reduced, the equipment efficiency is improved, and the transporting cost is reduced.

The invention discloses an injection molding equipment anomaly detection method based on a graph neural network, and the method comprises the following steps: collecting multi-dimensional monitoring data in the operation process of injection molding equipment, and carrying out the normalization, denoising and missing value filling, and obtaining structured monitoring sequence data; dividing the injection molding production process into a plurality of process stages based on timestamps and process stage labels in the structured monitoring sequence data, and constructing a corresponding stage sub-graph set; based on the stage sub-graph set, the energy transfer path, the material flow path and the historical abnormal propagation path, constructing a nested graph structure; inputting the nested graph structure into an improved dynamic graph convolutional network for feature extraction to obtain a time-aware graph embedding representation; and performing anomaly detection on the time perception type graph embedded representation to generate an anomaly detection result of the injection molding equipment. According to the method, nested graph structure modeling and the dynamic graph convolutional network are adopted, and high-precision detection and rapid positioning of the abnormal state of the injection molding equipment are achieved.

The invention discloses a discrete coating manufacturing method and system which are suitable for small-batch and multi-variety production scenes. The system comprises an MES system, an AGV intelligent logistics device, an RFID identification module, various processing devices and a quality inspection terminal. The method comprises the following steps: an MES system issues a production task and process parameters, a material distribution worker endows each box of materials with a unique identifier and binds a process template, an AGV conveys the materials to a matching device according to a path template and carries out processing, and the device automatically reports a processing state when the materials flow in or out to realize single-box-level tracing; if the equipment bottleneck exists, the materials are fed into a cache stereoscopic warehouse and wait for redistribution; and after machining is completed, the AGV conveys the materials to a quality inspection station, quality evaluation is completed in a machine vision or manual quality inspection mode, and a detection result is uploaded to an MES system for filing. The system has the capabilities of flexible scheduling, precise process control and whole-course tracing, and the coating production efficiency and the quality control level can be remarkably improved.

The invention relates to the technical field of industrial production process control, and provides a production process dynamic scheduling method and system based on energy consumption optimization. The method comprises the steps that the power waveform of an internal mixer and temperature control energy consumption data of a vulcanizing machine are collected in real time, and a process chain topological relation is constructed based on a material circulation path; generating an energy consumption coupling coefficient through a partial derivative of the power change of the internal mixer to the energy consumption of the vulcanizing machine according to the topological relation; generating a decoupling instruction when the coupling coefficient exceeds a threshold value, modifying the starting time of the operation plan of the internal mixer according to the decoupling instruction, and determining an energy consumption peak time period needing to be avoided by the vulcanizing machine; and after it is verified that the power peak value and the energy consumption peak value of the internal mixer are staggered, cooperative control is conducted on equipment. The system comprises a data acquisition module, a coupling coefficient generation module, a decoupling instruction generation module, a peak time period determination module and an equipment cooperative control module. According to the method, the process chain topological relation and the energy consumption coupling dynamic model are constructed, so that accurate energy consumption optimization of multi-device cooperation is realized.

The invention relates to the technical field of soil remediation, in particular to a soil remediation formula optimization method based on solid waste raw material charcoal improvement, which comprises the following steps: acquiring a coal gangue powder material flow and a sludge material flow, respectively performing component detection on a soil sample set, the coal gangue powder material flow and the sludge material flow to obtain a component content report, performing multi-factor combination on the plurality of biochar, the plurality of microbiota and the plurality of enrichment plants to obtain a plurality of first initial raw material combinations; and constructing a plurality of unit soil remediation experiment groups based on the component content report, the plurality of first initial raw material combinations, the soil sample set, the coal gangue powder material flow and the sludge material flow, and performing a soil remediation experiment on the plurality of unit soil remediation experiment groups based on a preset test pause condition to obtain an optimal scheme. According to the invention, the formula optimization of targeted soil remediation can be realized by utilizing the charcoal, the coal gangue and the municipal sludge.

The invention provides a cutting process carbon emission boundary adaptive division method based on # imgabs0 # balance, and relates to the technical field of numerical control machine tool carbon emission, system boundary division is carried out according to a # imgabs1 # theory, a high # imgabs2 # loss point in a system is identified, the system boundary is readjusted around a high # imgabs3 # loss subsystem, # imgabs4 # loss of each part of different systems is analyzed and compared, and the system boundary is determined according to the # imgabs0 # balance. According to the method, the energy flow and the material flow in the operation process of the machine tool are included in the unified standard to be analyzed, the situation that the energy flow or the material flow is subjectively analyzed is avoided, a more comprehensive division standard is provided for system boundary division, and the system boundary division efficiency is improved. According to the method, boundary division of the carbon emission system of the machine tool can be carried out according to different requirement levels, personnel in different roles can input own requirements, and division of the carbon emission boundary of the machine tool is completed in a self-adaptive mode.

The invention relates to the technical field of engineering machinery, in particular to a material weight control method and device, engineering machinery and a storage medium. Comprising the steps of obtaining material weight measured by a weighing device; determining material flow according to the material weight; inputting the material flow into a fall prediction model to obtain a fall prediction value; determining a metering stop value according to the fall prediction value and the target material weight; and under the condition that the error between the material weight measured by the weighing device and the metering stop value is within the preset range, the screw conveyor is controlled to stop, so that the control precision of the screw conveyor is improved, the screw conveyor can operate timely and accurately, and the metering precision of the material is improved.

The utility model discloses a multi-dimensional staggered auger lifting and feeding device. The multi-dimensional staggered auger lifting and feeding device comprises a transverse feeding mechanism, a vertical feeding mechanism and an inclined gathering pipe, the transverse feeding mechanism comprises a transverse pipe, and the vertical feeding mechanism comprises a vertical pipe; packing auger conveying mechanisms are fixedly arranged in the transverse pipe and the vertical pipe respectively; the auger conveying mechanism blocks the transverse pipe and the vertical pipe so that a transverse conveying channel and a vertical conveying channel can be formed in the transverse pipe and the vertical pipe. According to the utility model, a horizontal and vertical staggered extension lifting structure is adopted, so that the feed port and the discharge port are staggered in multiple dimensions. Through the staggered layout, the position space for feeding and discharging is staggered, so that smooth and efficient flowing of the materials is ensured, the space layout of a working area is optimized, and when the materials are conveyed to the end of the transverse conveying pipe, due to the rotary pushing effect of the auger feeding device, the materials can fall and converge together through the inclined converging pipe, so that the materials are conveyed to the end of the transverse conveying pipe. And the condition that materials are loose and are not beneficial to lifting and conveying is avoided.

The invention relates to the technical field of order management, in particular to an intelligent material order management method and system, and the method comprises the following steps: based on collected order data, analyzing the feature expression of the order data through an auto-encoder, comparing the feature data with newly input order data, and calculating a difference value; and marking the order with the difference value exceeding a threshold value to generate an abnormal order identifier. According to the invention, the self-encoder is used to deeply analyze order data, automatically identify orders having significant differences with normal data, and carry out abnormal marking in real time, so that execution errors are effectively prevented and fraud risks are reduced, and the application of the long and short term memory network further enhances the prediction capability of abnormal order behaviors. Through the analysis of the time sequence data, the potential order execution problem can be tracked and predicted, the constructed dynamic relation graph between the materials and the orders provides a global view for the material flow and inventory management, and the optimization of the material use and the cost control are promoted.

The invention discloses a material level intelligent sensing and early warning system in the silo unloading process, and relates to the technical field of silo material level intelligent sensing. The method comprises the following steps: a data acquisition module used for acquiring a multi-modal data stream; the model construction module is used for constructing a normal unloading model based on the multi-modal data flow and a preset physical mechanism constraint; the state judgment module is used for extracting a current feature vector from the multi-modal data stream; comparing the current feature vector with a feature distribution range in a multi-dimensional feature space determined by a normal unloading model, and judging a current material flow state; according to the invention, material flow abnormal symptoms can be captured in time, early warning of the blocking risk is realized, the production rhythm is prevented from being disturbed by material flow interruption, the potential safety hazard caused by manual entering into the silo for troubleshooting is reduced, the grain loss and equipment damage risks caused by long-term retention and mildew of materials are reduced, and the storage safety and the operation stability of a grain storage system are guaranteed.

The invention relates to the technical field of detection, and particularly discloses an automatic sampling and detecting device and method for automobile transported materials, the device comprises a sampling mechanism arranged at the bottom of a position adjusting mechanism and used for taking out materials from a transport vehicle, the sampling mechanism comprises semi-cylinders, and plate bodies are fixedly arranged on the sides, away from each other, of the semi-cylinders; the semi-cylinders are separated from each other and then inserted into the material, and then the semi-cylinders are close to each other, so that the material enters the storage tank for sampling, and the situation that the material flows downwards in the traditional sampling process, so that more materials in the upper layer of the sampled material are generated, and the subsequent detection precision is influenced is reduced; the semi-cylinders are close to each other, so that the materials can enter the storage tank conveniently, the situation that the sampling amount is small and even sampling cannot be performed due to the fact that the materials enter a sampling mechanism only through the mobility of the materials traditionally is avoided, actual sampling use is facilitated, follow-up detection is facilitated, and the detection efficiency is prevented from being reduced.

The embodiment of the invention provides an intelligent monitoring method and system based on multi-source heterogeneous data fusion. The intelligent monitoring method based on multi-source heterogeneous data fusion comprises the steps that production data sets in different production scenes are collected and stored in a classified mode; mining association features of equipment operation behavior records and material circulation track information through semantic association analysis, and extracting influence features of production environment perception data in combination with environmental factor mapping to obtain multi-source qualitative features; generating a node relation chain according to the business process association relation, and configuring a matching algorithm of qualitative scene features; screening feature subsets conforming to association rules, and combining and associating the feature subsets to obtain a scene feature combination; creating a scene exception reasoning model by using the scene feature combination and performing exception feature evolution analysis to obtain an exception reasoning result; and the abnormal propagation path is traced, the root cause node is positioned, and the abnormal monitoring label is generated, so that the accuracy of abnormal root cause positioning and the scene adaptability are improved.

The invention discloses an automatic monitoring method for an industrial solid waste treatment process, and the method comprises the steps: synchronously collecting a temperature distribution matrix, a gas concentration spectrogram, a kiln body vibration spectrum and a material rheological image in the solid waste treatment process, constructing a multi-source heterogeneous data set, and carrying out the time-space alignment preprocessing; based on a preset solid waste component feature code, calling a process parameter reference value, and performing self-adaptive correction in combination with the material rheological image features to generate a personalized reference set; fusing the multi-modal monitoring data to generate a solid waste reaction process three-dimensional state model, extracting core feature parameters, comparing the core feature parameters with a personalized reference set, and screening abnormal features; inputting the abnormal features into a pre-trained abnormal diagnosis model, and when an abnormal probability value exceeds a preset threshold value, triggering early warning and generating an abnormal identifier; according to the method and the system, intelligent monitoring and regulation of the industrial solid waste treatment process are realized, and the stability, the safety and the treatment efficiency of the treatment process are comprehensively improved.

The embodiment of the invention discloses a spectrum-based substance mixing degree detection method and device. The intelligent generated data processing method comprises the following steps: acquiring first hyperspectral data of a to-be-modeled material in a stirring process state and final-state hyperspectral data of the to-be-modeled material in a uniform stirring state; establishing a final state spectrum difference curve, an intra-group spectrum difference curve and a large particulate matter distribution curve; determining a multi-dimensional mixing degree judgment index according to the final-state spectrum difference curve, the intra-group spectrum difference curve and the large particulate matter distribution curve; when it is detected that the to-be-detected material flows into the detection pipeline and is located in the to-be-detected area, starting a hyperspectral acquisition device to acquire a plurality of second hyperspectral data of the to-be-detected material; when it is detected that the multiple second hyperspectral data meet the preset validity condition, the multi-dimensional mixing degree judgment index and the preset batch qualification condition, it is judged that the to-be-detected material reaches the uniform mixing state. According to the embodiment of the invention, accurate detection of the substance mixing uniformity is realized.

The invention relates to a material loading control method, which comprises the following steps of: acquiring volume data of loading materials in a time period, calculating volume according to the acquired volume data, and calculating a mapping relation by utilizing a fitting algorithm; installing a camera device, capturing a material flow image in real time, and calculating a fluidity parameter in a flow velocity vector field by using an algorithm; substituting the calculated fluidity parameters into the mapping relation, correcting the mapping relation, and establishing a corrected volume mapping relation model; the prediction result is compared with the volume of the material monitored in real time, the control parameters are dynamically adjusted according to the comparison result, and the deviation in the loading process can be found and corrected in time by monitoring the volume and fluidity parameters of the material in real time and dynamically adjusting the control parameters according to the corrected volume mapping relation model; and the control precision of material loading is improved.

The invention discloses an intelligent operation control system for an automatic batching all-in-one machine, relates to the technical field of intelligent operation, and can quickly and accurately identify and deal with the blockage condition in a pipeline of the batching all-in-one machine through real-time data acquisition, accurate analysis and automatic judgment, thereby effectively improving the stability and safety of equipment. The system not only realizes dynamic monitoring of material flow by collecting key data such as pressure and flow speed, but also establishes a steady-state model based on historical data, so that the system can evaluate and quantify the blockage risk in real time, and can send out an early warning signal at the initial stage of blockage, thereby reducing the maintenance intervention frequency and downtime; in addition, the automatic continuous monitoring and alarming functions integrated in the system can intelligently trigger the unblocking operation, the scouring force is dynamically adjusted according to real-time monitoring feedback, and it is ensured that the unblocking process is efficient and the pipeline is safe.

The invention discloses a process mass production capability collaborative evaluation method and system, and belongs to the technical field of process management. The method comprises the following steps: collecting parameter data of each process link in each time period in real time, identifying and analyzing process cycle characteristics and material flow characteristics in the parameter data, evaluating the influence effect of each process link on the production capacity in each time period, carrying out grade division on the influence effect, judging the equipment operation state in the high-influence process link, and judging the equipment operation state in the high-influence process link. Whether hidden defects occur or not is determined, and prediction and early warning are carried out in advance; according to the method, general influencing process links are further analyzed, a potential optimization space is identified according to an analysis result, and parameter setting and process steps of the potential optimization space are dynamically adjusted, so that the overall production capacity and efficiency are improved, the efficiency and productivity of the production process are comprehensively improved, the downtime and resource waste are reduced, and the production cost is reduced. The high-quality mass production capability is ensured, the bottleneck and optimization space in the production process can be comprehensively reflected, and potential production problems are effectively prevented and solved.

The embodiment of the invention discloses an adjustable capacity calculation method based on long-process steel production process modeling, which comprises the following steps: analyzing a long-process steel production process, and determining the material flow, equipment start-stop and energy consumption relationship of each link; drawing a resource task network block diagram by using a resource task network method, and depicting a coupling relationship among the links; based on the resource task network method, an optimization scheduling model is established, and the maximum adjustable capacity is calculated; the method can analyze the whole long-process steel production process, fully considers the incidence relation between links based on a resource task network method, determines key elements such as material flow, equipment start and stop, energy consumption and the like, considers various requirements, establishes a long-process steel production process model, can determine the adjustable maximum capacity of steel enterprises in each time period, and improves the production efficiency. And theoretical support is provided for iron and steel enterprises to participate in power grid dispatching.

The invention provides a friction stir welding digital twinning system and method based on multi-sensing data fusion, and belongs to the technical field of advanced welding manufacturing and industrial digitization crossing. The twinning system comprises a physical sensing layer, a data acquisition and fusion layer, a digital twinning model layer and a twinning interaction and service platform; the physical perception layer is connected with the data acquisition and fusion layer, the data acquisition and fusion layer is connected with the digital twinborn model layer, and the digital twinborn model layer is connected with the twinborn interaction and service platform. According to the method, heat-force-material flow multi-dimensional information is covered on the basis of multi-sensor fusion, and the defect detection rate is increased to 98% and is increased by 13% compared with a traditional method; the error of the mixed twin model is reduced by 40% compared with that of a pure data driving model, and the real-time performance reaches a millisecond level; the transformation from post detection to online prediction-real-time intervention is realized, and the rejection rate is reduced by 60%.