526 results about "Material flow" patented technology

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 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 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 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 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 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 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%.

The invention relates to the technical field of coal mining, in particular to a dry coal preparation control system based on data analysis, which comprises a real-time data acquisition module, a data analysis module, a self-adaptive control module and a feedback optimization module. The real-time data acquisition module acquires information such as granularity, density, images and moisture of coal particles through a granularity sensor, a density sensor, an image sensor and a humidity sensor, and transmits data to the central processing unit. And the data analysis module analyzes the collected data based on a random forest algorithm to generate a sorting precision prediction value. The self-adaptive control module dynamically adjusts key parameters of the coal dressing equipment according to the predicted value by utilizing a PID control algorithm, and the key parameters comprise the wind speed, the screen inclination angle, the material flow speed and the like. The system not only improves the coal dressing precision and stability, but also improves the self-adaptive capacity of the equipment through intelligent control and optimization, and the system is suitable for popularization and application. The method has remarkable economic benefits and wide application prospects.

The invention discloses a material tracking system for battery production, and particularly relates to the technical field of production data traceability, a process topology constraint model is constructed based on a production process, and a material flow expected path is generated; collecting behaviors of the materials in each station to form a material circulation time sequence track; matching the time sequence track with the process topology constraint model to generate a process legality state; controlling association establishment of process data and material identifiers according to a process legality state, and isolating and re-checking data which do not meet constraints; a source identifier is introduced into the process data and is bound with the process legality state, and finally a material production resume record is generated; according to the invention, through process topology constraint and behavior time sequence matching, accurate tracking of material circulation in a dynamic production scene is realized; abnormal path data is prevented from entering associative storage through legality judgment and associative gating; through source identification and resume integration, the traceability and the rechecking reliability capability of the material production process are improved.

The invention provides a method and device for determining steelmaking-continuous casting production multi-process collaborative demand response, and relates to the technical field of manufacturing, and the method comprises the steps that target parameters in the steelmaking-continuous casting production process are obtained; based on a production plan, each work starting moment, each work ending moment, each unit energy consumption and time-of-use electricity prices of different time periods included in the target parameters, by taking the minimum energy cost and the minimum waiting duration as targets and based on an MILP method, establishing a heat coordinated optimization material flow mathematical model in the steelmaking-continuous casting production process and solving the heat coordinated optimization material flow mathematical model; and a coordinated optimization demand response strategy is obtained. According to the method, the coordinated optimization demand response strategy in the steelmaking-continuous casting production process is quickly obtained by solving the heat coordinated optimization material flow mathematical model, so that the determination efficiency of the heat distribution strategy is improved, the internal flexibility adjustment potential of iron and steel enterprises can be mined by the obtained coordinated optimization demand response strategy, and the economic benefit of the enterprises is improved. And friendly interaction between iron and steel enterprises and a power grid is promoted.

The invention discloses a material granularity monitoring method, device and equipment and a storage medium, and relates to the technical field of granularity monitoring, and the method comprises the following steps: obtaining material flow data and an original material vibration signal, and filtering the original material vibration signal to obtain a pure material vibration signal; extracting multi-dimensional vibration characteristic signals of the pure material vibration signal in a time domain dimension, a frequency domain dimension and a nonlinear dimension; inputting the multi-dimensional vibration characteristic signal and the pure material vibration signal into a granularity inversion system for deduction to obtain an initial material granularity proportion; and calculating based on the initial material granularity proportion, the pure material vibration signal and the material flow data to obtain a compensation coefficient, and calibrating the initial material granularity proportion based on the compensation coefficient to determine the target material granularity proportion. The system can adapt to severe industrial environments with high dust and the like, hardware cost and maintenance difficulty are reduced, the whole process from signal processing to granularity calibration is automatic, the response speed is high, and the precision is high.

The invention discloses a safety and environmental protection management system and method based on multi-pollutant cooperative treatment, and relates to the technical field of environmental management. According to the invention, multi-pollution data is collected in real time through a multi-parameter sensor network; optimizing and constructing an optimized pollutant relevance model by adopting a graph neural network and a Nelder-Mead simplex method, analyzing a collaborative / antagonistic relationship among pollutants, and generating a multi-pollutant processing list; multi-target optimization is carried out on a three-dimensional target function containing the removal rate, the cost and the by-product amount through an improved genetic algorithm, an optimal scheme is selected, balance between the environment and economic benefits is achieved, a dynamic multi-pollution map is constructed, and a pollution migration path is accurately tracked; a by-product material flow network analysis system is established, and resource recycling is achieved through a directional separation and reprocessing technology; according to the method, the combined pollution analysis precision is improved, the treatment cost is reduced, and the occurrence rate of secondary pollution is reduced.

The invention discloses a thermosetting mold machining method and system based on artificial intelligence, and relates to the technical field of mold machining. The method comprises the following steps: constructing a three-dimensional model of a product through a drawing or 3D scanning; establishing a thermosetting material curing reaction kinetic model; material flow characteristics under different temperature and different pressure combinations are predicted through deep learning; adjusting parameters of a mold temperature controller in real time to carry out optimal control on the curing rate; mold surface defect detection is carried out based on computer vision; and an automatic compensation mechanism is arranged, and shrinkage deformation is automatically compensated in subsequent machining. By establishing a thermosetting material curing reaction kinetic model and combining material flow characteristics under different temperature / pressure combinations, parameters of a mold temperature controller are adjusted in real time to achieve optimal control over the curing rate, defect detection is conducted on the surface of a product, shrinkage deformation is automatically compensated, the mold machining efficiency and detection efficiency are improved, and the product quality is improved. And the defective product probability of product production is reduced.

The invention discloses a multi-process energy consumption dynamic optimization control system for a precious metal processing production line, and relates to the technical field of precious metal processing, and the system comprises a data collection module which is used for collecting energy consumption data, process parameters and material flow data of equipment in real time; the optimization decision module comprises an energy consumption modeling unit and a dynamic optimization unit; the energy consumption modeling unit is used for establishing an energy consumption digital twin model for key equipment on a production line; the dynamic optimization unit is used for receiving and calculating a current optimal process parameter set value and an optimization instruction for adjusting the operation power and rhythm of equipment; and the driving execution module is used for realizing collaborative energy consumption optimization control of multiple processes. The method has the advantages that accurate energy consumption perception is achieved by constructing the energy consumption digital twinborn model, meanwhile, order merging is intelligently decided based on the material flow state and the order delivery time, and double optimization of overall energy efficiency improvement of the production line and comprehensive energy consumption cost is achieved.

The utility model relates to the technical field of material detection, in particular to a material flowing state detection device. The device comprises a chute, an impeller and a rotating speed detection structure, wherein a channel for materials to flow through from top to bottom is formed in the chute; the impeller is rotationally connected into the chute, and blades on one side of the impeller can make contact with materials in the material flowing process; the rotating speed detection structure is connected to the impeller and used for detecting the rotating state of the impeller in real time. Materials can push the impeller to rotate while flowing; the rotating speed detection structure can detect the rotating state of the impeller in real time to obtain the rotating speed of the impeller; a worker judges whether materials exist in the chute or not and judges the flowing state of the materials by observing the detection result of the rotating speed detection structure. The detection device can stably and reliably work for a long time, does not need to be maintained frequently, and is not easily interfered by sundries, and the detection result can accurately reflect the flowing state of the material, so that the smoothness of the production process can be improved, and accidents are avoided.

The invention relates to the technical field of valves, in particular to a high-temperature material flow adjusting device and a control method thereof.The high-temperature material flow adjusting device comprises a device body, a spring hanging bracket fixed to the device body, a water inlet and return system, a control system, a monitoring device and a connecting pipe fixed to a discharging port of the device body. By means of the curved surface fitting design of the valve head and the valve seat, linear adjustment of the high-temperature material flow is achieved, the defects that small opening is sensitive and large opening is slow are thoroughly overcome, the micro-adjustment requirement is accurately met, material level and temperature signals are collected in real time through a monitoring device, closed-loop control is constructed through a PLC control system, and the control precision of the high-temperature material flow is greatly improved. The electric actuating mechanism is automatically driven to adjust the position of the valve rod, manual intervention is not needed, manual judgment errors are avoided, potential safety hazards of operation in a high-temperature scene are eliminated, flow control accuracy and stability are greatly improved, and the problem that a traditional control loop is unstable is solved.

The invention relates to the technical field of intelligent manufacturing and industrial internet of things, in particular to an intelligent real-time production line regulation and control system and method based on digital twinning, and the system comprises a state sensing and collection module which is used for obtaining the real-time operation parameters of production equipment, material flow state information and environment disturbance characteristics of a production line; the digital twin modeling module is used for fusing multi-source data, constructing accurate mapping of a production line physical entity and a virtual model through a twin model driven by dynamic data, and predicting a performance degradation trend and a disturbance propagation path of production line equipment in real time; the intelligent decision-making module is used for generating a production line real-time scheduling strategy through a distributed collaborative reinforcement learning algorithm and outputting a production line regulation and control instruction set; and the real-time regulation and control feedback module executes production line regulation and control actions through a multi-target dynamic balance algorithm and automatically generates a production line production optimization report with equipment health degree compensation. Therefore, the problems of static model construction, lagging of a decision mechanism, lack of regulation and control feedback and the like in the prior art are solved.

The invention discloses a three-dimensional visual material dynamic management scheduling system for power station storage, and relates to the technical field of material dynamic management. The system comprises a material circulation data management module, an approval path performance analysis module, a material perfectness rate statistics and early warning module, a demand response and influence calculation module and a three-dimensional visual display module. The material circulation data management module constructs a whole-process database; the examination and approval path efficiency analysis module reveals the relationship between examination and approval time consumption and emergency degree and the path efficiency difference; the material perfectness rate statistics and early warning module forms correlation between the circulation frequency and the perfectness rate and sets an early warning threshold value; the demand response and influence calculation module calculates an aging value and provides an optimization suggestion; and the three-dimensional visual display module integrates and displays the key information to form a dynamic visual management interface, so that efficient, accurate and intelligent management and scheduling of the warehouse materials of the power station are realized.

The invention relates to the technical field of blister thermoforming, and discloses a high-precision controllable thermoforming method of a blister machine, which comprises the following steps: extracting a feature recognition material type, and calculating to obtain a thermal diffusion coefficient; a temperature field is collected in real time, temperature distribution at the next moment is predicted, a fuzzy rule base is established to dynamically adjust PID parameters, a falling risk coefficient is calculated based on the actually measured temperature, and whether airflow compensation is started or not is judged; a mold cavity structure is constructed, and a vacuum degree curve is set in stages; material flow monitoring is conducted on the material, and the vacuum degree is corrected through fuzzy control; analyzing to obtain a coordinate deviation, and executing dynamic compensation; performing real-time correction according to the coordinate deviation, establishing an inter-station coupling coefficient matrix through multi-station cooperative control, and performing temperature compensation; and constructing a multi-dimensional quality evaluation model, performing analysis to obtain a comprehensive quality index, judging whether to trigger a key parameter optimization process or not based on the comprehensive quality index, and performing real-time feedback optimization.

The invention provides a sewage plant water-energy-drug collaborative optimization method and system, and the method comprises the steps: obtaining the data of a technological process and a material transfer relationship of a sewage plant, constructing a graph network structure model with a technological unit as a node and material flow as an edge, and carrying out the preprocessing, thereby obtaining dynamic coupling graph structure data; a dynamic coupling graph neural network model containing a node feature coding layer, a time sequence coding module, a space message passing layer and an attention mechanism layer is constructed based on the data, and a prediction model capable of representing the dynamic coupling relation of the process unit is obtained through historical data training; then, a multi-objective optimization function which takes the lowest ton water treatment cost as an objective and covers water quality standard reaching, energy consumption and medicament dosage constraints is constructed; and in combination with the prediction model and the optimization function, the optimal operation parameters are solved through an optimization algorithm, and a whole-plant collaborative optimization decision scheme is generated. According to the invention, the dynamic coupling GNN prediction model and the virtual element multi-domain parallel optimization technology are integrated, and intelligent operation management of the sewage treatment plant is realized.

The present invention discloses a real-time linkage method for digital twin models driven by information-physical fusion, which specifically relates to the technical field of digital twin collaborative control of industrial automation production lines. It is used to solve the problems of instruction conflicts and control failures caused by the mismatch between virtual model parameters and the dynamic capabilities of physical equipment in the existing technology. It collects equipment status and material flow data in real time, extracts equipment dynamic degradation parameters to generate capability attenuation feature vectors, combines implicit process conflict path detection and resource preemption probability simulation, builds a collaborative optimization model of equipment health and process scheduling, and generates a control instruction set containing dynamic capability constraints. After verifying and screening instructions through physical constraint matching, the parameters are closed-loop corrected based on the execution results and the model is iterated to achieve adaptive matching between virtual instructions and physical equipment capabilities. It significantly improves the reliability of digital twin control instructions and the stability of the production line, and avoids the risk of abnormal shutdown caused by over-limit instructions.

The invention relates to the technical field of production line scheduling, in particular to a production line resource scheduling control method and system, and the method comprises the steps: connecting end equipment of each production line through a parallel pipeline and a controllable valve to form a shared equipment resource pool, and obtaining the operation data information of each production line when a production request is received, the operation data information comprises the material flow and the equipment load rate of the end equipment, performing calculation based on the material flow and a preset target yield to obtain a demand calculation result, and dynamically scheduling the end equipment in the shared equipment resource pool based on the equipment load rate and the demand calculation result. According to the technical scheme of the invention, by constructing the'shared equipment resource pool ', the barrier that the resources of the traditional production lines are isolated is broken, and the elastic allocation and sharing of the resources across the production lines are realized, so that the resource utilization rate of the end equipment is improved, and the comprehensive output efficiency of the whole production system is improved.