23425results about "Computer control" patented technology

The invention belongs to the technical field of intelligent machining path control, and discloses an intelligent numerical control machine tool automatic programming path optimization method based on workpiece modeling, which comprises the following steps: acquiring a CAD model, machine tool sensor data, tool wear data and historical machining logs, generating a workpiece characteristic parameter set, and fusing a three-level compensation mechanism to generate a dynamic error parameter set; then, dividing a preliminary risk level of the processing area, and performing secondary risk assessment to generate a comprehensive risk level; extracting a risk level conflict area, and determining a final risk level; constructing a static / dynamic cost matrix to obtain a path priority map; thirdly, generating an initial path, smoothing an optimized path trajectory, and performing multi-objective optimization to generate an optimized path planning table; cutting parameters are adjusted in real time, the path feasibility is verified, and a real-time control instruction set is generated; and finally, constructing a quality-process correlation model, generating a global strategy packet, forming closed-loop iteration, and completing system self-evolution.

The invention provides a filling control method and system based on online parameter identification. According to the method, the synchronous error data of each servo shaft in the multi-shaft servo system is acquired, the error time sequence matrix is constructed by the synchronous error data according to the time sequence, and the displacement feedback data of each servo shaft in the multi-shaft servo system is acquired in real time through the high-precision rotary encoder; a dynamic compensation signal is generated according to the deviation between the displacement feedback data and a preset track, online estimation is carried out on mechanical vibration disturbance of the multi-axis servo system based on the error time sequence matrix, a disturbance parameter estimation value is obtained, the dynamic compensation signal and the disturbance parameter estimation value are fused, and a servo synchronous compensation amount is generated; according to the technical scheme, through fusion of real-time parameter identification and dynamic compensation, the filling precision and efficiency are remarkably improved, meanwhile, the manual debugging complexity is reduced, and a reliable solution is provided for high-stability production under multiple working conditions.

The invention discloses an automatic control method and system for secondary granulation of a high-voltage zinc oxide resistor disc, relates to the technical field of intelligent manufacturing of power equipment, and solves the problems of out-of-control particle morphology caused by dynamic coupling parameter identification lag and control instability caused by multi-physical field parameter coupling in an existing method. According to the invention, a dynamic physical property parameter matrix is generated in real time based on multi-band dielectric relaxation spectrum analysis and terahertz wave tomography; predicting a fluidized phase change threshold value and an energy gathering area through multi-physics field coupling modeling; a time sequence attention deep reinforcement learning algorithm is adopted to generate a multi-field cooperative adjustment instruction; positioning a parameter conflict source and triggering decoupling compensation by combining a high-frequency vibration and acoustic emission combined monitoring module; performing closed-loop correction on the control network weight based on the laser spectrum data and a partial least squares regression model; the real-time performance of fluidization parameter identification, the stability of multi-field coupling control and the recovery efficiency of abnormal working conditions are remarkably improved, and meanwhile the batch consistency of the electrical performance of the resistor discs is guaranteed.

The invention provides an intelligent factory monitoring method and system based on multi-sensor fusion, and the method comprises the steps: firstly obtaining real-time monitoring data streams of multiple types of sensors in an intelligent factory, including operation state data collected by an equipment state sensor and scene state data collected by an environment state sensor; performing basic synchronization processing on the real-time monitoring data stream to obtain a standardized monitoring data stream, calling a pre-trained multi-sensor association analysis model to perform cross-source feature fusion processing on the standardized monitoring data stream to generate a fusion feature sequence, and performing abnormal mode detection processing based on the fusion feature sequence to generate an abnormal detection result; and finally, generating a monitoring intervention instruction containing equipment positioning information according to an anomaly detection result, and sending the monitoring intervention instruction to a factory control system to trigger a state adjustment operation, thereby effectively improving the monitoring precision and anomaly processing efficiency of the intelligent factory.

The invention relates to a digital twinning-based bogie numerical control machining error compensation method and system, and the method comprises the steps: constructing an initial digital twinning model comprising material characteristics and geometric constraints through fusing the historical machining data of a bogie and the structural characteristics of a workpiece; according to the real-time cutting force and temperature data dynamic correction model, time-varying stiffness parameters are updated, and a deformation prediction result is generated; identifying an error exceeding area based on the precision requirement of the bearing seat assembly surface; and the feed rate compensation amount is calculated according to the thin-wall area deformation gradient, and a pre-compensation parameter scheme used for adjusting the cutting path is generated. The method overcomes the industrial problems that a traditional compensation technology cannot dynamically suppress instantaneous deformation, multi-source error coupling is difficult to decouple, and parameter adjustment depends on trial cutting verification, and the machining precision and efficiency of the bogie are remarkably improved.

The invention relates to the technical field of large language models, and discloses a voice data interaction feedback control processing method based on a large language model. The method comprises the following steps: acquiring an industrial voice instruction through an AMR main controller, and obtaining a standardized vector through industrial lexicon matching and intention classification; inputting an industrial large language model for reasoning processing, and generating an AMR execution scheme; carrying out distributed coordination and task allocation on the AMR cluster to form a control instruction sequence; and the motion controller executes monitoring, processes exceptions and outputs a feedback strategy. Accurate classification and semantic understanding of complex industrial instructions are achieved, the processing capacity of professional knowledge in the industrial field is improved, and meanwhile high-precision real-time state monitoring is achieved.

The invention discloses a concrete mixing plant automatic control system based on intelligence, and belongs to the technical field of automatic control. Comprising a multi-modal sensing data acquisition module, an intelligent batching optimization module, a digital twin simulation module, a self-adaptive energy consumption management module, a fault self-diagnosis and predictive maintenance module, a dynamic quality tracing module and a multi-target collaborative scheduling module. Real-time synchronization of sensor data and a virtual model is realized in combination with an edge computing technology, dynamic and visual technical support is provided for full-flow simulation of the concrete mixing plant, and complex working conditions in production are reflected more truly; the system predicts a potential problem through a machine learning algorithm, triggers an early warning signal based on a multi-dimensional threshold rule, and generates a preventive maintenance plan in advance; the digital twin platform supports AR and VR interaction interfaces, so that an operator can visually observe the operation states of a virtual model and actual equipment.

The invention discloses a modularized full-process high-quality direct drinking water treatment system and a control method, and belongs to the technical field of direct drinking water treatment.The modularized full-process high-quality direct drinking water treatment system is characterized in that an online sensor is deployed at a water inlet of a flocculation basin, raw water turbidity, temperature and flow parameters are collected in real time, and qualified samples are screened to construct a standardized water quality characteristic data set in combination with historical water quality and agent adding records; based on the data set, performing multi-dimensional clustering on historical water quality parameters, generating a characteristic working condition cluster, calculating a three-dimensional efficiency index, generating a dynamic dosage reference interval by using a sliding window confidence interval and a time decay factor, establishing a mapping relation between water quality characteristics and medicament dosage, and generating a graded dosage strategy; the transmembrane pressure difference and the membrane flux change rate are continuously monitored at the water inlet end in the membrane treatment stage, the membrane pollution trend is predicted through time sequence analysis, the prediction result is corrected according to the agent adding deviation, the precipitation effluent turbidity and the membrane inflow COD data, a cleaning early warning signal is generated, and graded response is performed according to the pollution degree.

The invention relates to the technical field of numerical control machine tools, in particular to a five-axis machining center real-time thermal error compensation system based on digital twinning and a medium. Firstly, a data acquisition unit is used for acquiring physical actual measurement data in real time; then, the digital twin processing unit constructs a model set used for representing the thermal dynamic behavior of the five-axis machining center, and the model set is composed of a plurality of virtual thermal model members; thirdly, fusing physical measured data into the model set through a data assimilation algorithm to dynamically correct the state of each model member and predict the thermal error of the machine tool; the prospective compensation decision-making unit receives the thermal error prediction result output by the digital twinning processing unit and generates a prospective compensation instruction; and finally, the compensation execution unit issues the prospective compensation instruction to a numerical control system of the five-axis machining center for real-time adjustment. The control precision of real-time thermal error compensation of the five-axis machining center can be improved.

The invention discloses a hydropower station unit state online monitoring system, relates to the technical field of hydropower station unit monitoring control, and adopts a hybrid digital twin modeling technology combining a physical mechanism main model and a liquid neural network residual compensation model to construct a high-fidelity unit operation state model. The system comprises a data acquisition module, a digital twin modeling module, a fault evolution prediction module, a multi-target optimization module and an adaptive control generation module. Multi-source heterogeneous data fusion is realized through a space-time adaptive weight distribution algorithm, and residual compensation modeling is performed by using dynamic time constant characteristics of a liquid neural network. Virtual fault injection and fault evolution trajectory prediction are realized, and passive fault response is converted into active fault prediction. A two-stage optimization strategy is adopted to realize'safety-efficiency-life 'three-dimensional target collaborative optimization, and a continuous and smooth adaptive control parameter trajectory is generated by controlling a liquid neural network. The modeling precision is improved, and the fault early warning time is advanced.

The invention provides a high-precision five-axis rotary table cooperative control method and system based on multi-closed-loop feedback, and the method comprises the steps: obtaining a real-time acceleration signal of a five-axis rotary table, carrying out the spectrum analysis of the acceleration signal, determining a current resonant frequency drift range and a main frequency component, and carrying out the frequency spectrum analysis; dynamically simulating the turntable structure by adopting a pre-established finite element model, and predicting the vibration mode change trend of the structure by comparing the deviation between the modal frequency obtained by simulation and the actually measured signal spectrum; according to the vibration mode change trend, matching similar vibration mode modes from a pre-established rotary table vibration mode database, obtaining corresponding vibration energy transmission path distribution, and determining an energy transmission abnormal region by analyzing the distribution of energy on each transmission path to obtain vibration energy transmission characteristics; according to an optimized motion instruction of a five-axis turntable, the dynamic response of the turntable is estimated, vibration frequency domain characteristics are extracted, and frequency domain compensation parameters are generated in combination with a preset frequency domain error matrix.

The invention relates to the technical field of program control, in particular to a servo position control method and system applied to an engraving and milling machine. The method comprises the following steps that real-time temperature data and real-time load data of lead screws of all shafts in the engraving and milling machine are obtained; collecting multi-node temperature distribution data of the engraving and milling machine; obtaining cutting force data and resonant frequency characteristic values in the engraving and milling machine; constructing a screw rod reverse clearance prediction model based on the real-time temperature data and the real-time load data of the screw rod, and generating a clearance compensation value; performing pre-compensation control before the servo position direction of each axis is changed based on the clearance compensation value to obtain a correction position instruction; and performing thermal deformation simulation on the engraving and milling machine based on the multi-node temperature distribution data to generate thermal deformation compensation amounts of all shafts. Through multi-source data fusion and intelligent control, comprehensive optimization of the servo position system of the engraving and milling machine is achieved, the machining precision, dynamic response and stability are effectively improved, and efficient and reliable operation of high-speed and high-precision machining is guaranteed.

The invention relates to the technical field of machining precision control, in particular to a laser carving precision control method and system.The laser carving precision control system comprises a feature collecting unit, a model building and analyzing unit, a dynamic threshold value adjusting unit and an online incremental learning unit, and the feature collecting unit collects vibration, current and temperature data through a multi-source sensor array; the model construction analysis unit realizes dynamic prediction of processing parameters by combining a bidirectional long-short-term memory network with an attention mechanism, and the dynamic threshold adjustment unit dynamically updates parameters of a numerical control system based on a material hardness real-time detection and thermal coupling model. The online incremental learning unit automatically generates training samples through error data, continuously optimizes model parameters and constructs a'data acquisition-intelligent modeling-dynamic compensation-model evolution 'closed loop, so that accurate prediction and adaptive adjustment of machining parameters are realized, and the adaptability of the manufacturing process to multi-variety and small-batch working conditions is remarkably improved.

The present invention provides a tool wear state monitoring method and system based on multiple types of signals, and relates to the technical field of data processing. The method includes: obtaining data of a cutting force, an acoustic emission signal and a vibration signal, and extracting a plurality of statistical features from data of the cutting force and the acoustic emission signal; extracting a singularity feature from the vibration signal by combining a singularity analysis with a wavelet transform; building a tool wear state monitoring model based on a random forest, using an obtained feature to perform preliminary training, and outputting a wear prediction result; and based on the real-time data of the cutting force, the acoustic emission signal and the vibration signal, monitoring the wear state of the tool through the refined model.

The invention provides a method and system for obtaining optimal parameters of a machine tool based on an industrial mechanism AI data model, belongs to the technical field of industrial machine tool application, and solves the problems that an existing machine tool cannot obtain the optimal parameters in real time, autonomous intelligent control of the machine tool cannot be achieved and the like. Comprising the following steps: multi-dimensional high-frequency data fusion acquisition and monitoring; performing data synchronization and storage management; analyzing and mining data; mixing intelligent decision engines; constructing an industrial knowledge base and a model base; training an industrial knowledge base and a model base; intelligent decision making and optimization; equipment is automatically controlled; user visual interaction is carried out; carrying out on-line adaptive parameter optimization; continuously optimizing and iterating; and the production business system works cooperatively. According to the method, online self-adaptive parameter optimization can be realized, a closed-loop control system with rapid sensing, intelligent decision-making and accurate execution capabilities is constructed, the method develops in the direction of sensing-decision-execution full-link autonomous optimization, and finally autonomous intelligent control of the machine tool is realized.

The invention provides an automatic driving agricultural machinery marshalling operation control method and system, and relates to the technical field of agricultural machinery control, and the method comprises the steps: determining the function type and number configuration of needed agricultural machinery according to the farming task type, farmland scale and terrain parameters; based on the agricultural machinery operation capability parameter set and the farmland space features, generating agricultural machinery cooperation groups; planning an operation path containing space-time collision avoidance constraint for each agricultural machine group, and establishing a time sequence scheduling strategy matched with the task load; based on farmland environment data, agricultural machinery working condition data and operation quality data which are acquired in real time, agricultural machinery marshalling topology and operation parameters are dynamically reconstructed, through dynamic grouping, intelligent path planning and real-time state feedback and reconstruction, the flexibility, efficiency and robustness of agricultural machinery collaborative operation are remarkably improved, and the development requirements of precision and intelligence of modern agriculture are met.

The invention relates to the technical field of smart park management, in particular to an analysis monitoring method and system based on a smart park operation state, and the method comprises the following steps: obtaining park equipment operation data parameters and associated environment parameters, analyzing the equipment operation difference, recognizing the equipment operation state, and predicting the state change trend. And calculating an abnormal event propagation path and rate, determining a response priority, constructing early warning information, and obtaining a smart park monitoring record. According to the method, the accuracy of park equipment state identification is improved by combining the fluctuation analysis of the equipment operation data and the environmental parameters, early warning of abnormal states is realized by combining the time sequence correlation analysis and equipment state prediction, the influence of equipment faults on park operation is reduced, and the accuracy of park equipment state identification is improved through the topological structure and propagation probability calculation. The method achieves the prediction of an abnormal event propagation path, enhances the accuracy and timeliness of emergency response, improves the real-time monitoring and fault prediction capability, and guarantees the efficient operation and safety of a park.

The invention relates to the field of building automation, discloses a control method and system for a low-carbon energy-saving building system, and aims to solve the problems of insufficient multi-source data integration, dynamic response lag and the like, a distributed sensor network is adopted to collect environment, equipment and energy data in real time, a dynamic energy efficiency index matrix is generated through spatial-temporal feature fusion, and the dynamic energy efficiency index matrix is subjected to dynamic energy efficiency analysis. In combination with a deep reinforcement learning model, carbon emission, energy consumption cost and comfort are collaboratively optimized, and the system adopts a cloud edge collaboration architecture: an edge terminal realizes equipment-level millisecond response, cloud digital twin global optimization is performed, and a heterogeneous gateway and an energy router complete multi-protocol equipment linkage and energy scheduling. The innovative technology comprises air conditioner variable air volume control, illumination self-adaptive dimming, elevator colony and ant colony scheduling and actual measurement display, the comprehensive energy consumption of the system is reduced by 32.7%, the demand response reaches the second level, the PMV thermal comfort index is stabilized to be + / -0.5, and a building cluster is supported to participate in a virtual power plant. And an intelligent building low-carbon integrated scheme is provided.

The invention discloses a numerical control machine tool machining environment monitoring system, and belongs to the technical field of intelligent monitoring. Comprising the following modules: a multi-dimensional sensing monitoring module for realizing omnibearing data acquisition of an interaction state of a cutter and a workpiece in a machining process; the feature extraction module is used for converting the time sequence data into a key feature set for representing the state of the cutter; the wear type identification module is used for accurately identifying and classifying the wear type of the cutter based on a deep learning classifier and judging the specific wear type; the wear progress prediction module calls a corresponding special prediction model according to the identified specific wear type, quantifies the wear progress rate and estimates the residual life of the cutter; the decision support module is used for integrating the tool wear state and the prediction result, balancing the production efficiency, the machining quality and the tool cost, and providing optimization suggestions of parameter adjustment and tool changing opportunities; and the self-learning optimization module is used for continuously collecting actual production data to carry out model evaluation and incremental learning so as to realize multi-machine knowledge sharing.

A system for breakage early warning of high-voltage transmission lines based on acoustic prints, including a data acquisition unit, an edge processing unit, a cloud server, a warning execution unit, and a communication unit. The data acquisition unit is configured to collect environmental information of a high-voltage transmission tower in real time through a multimodal sensing device. The edge processing unit includes an adaptive filtering module configured to eliminate wind noise interference in the environmental information and compensate for frequency response deviation. The cloud server can generate a warning instruction based on the environmental information. The warning execution unit can perform warning and alarming actions based on the warning instruction. The communication unit is configured to enable data and information transmission among the units. A method implemented by such system is also provided.

The invention relates to the technical field of gate control, and discloses a gate control method and system based on dynamic cooperative scheduling and a storage medium. The method comprises the following steps: carrying out fatigue quantification on gate torque change and adjustment times through a strain sensor to obtain a comprehensive health index; establishing an adjustment frequency and equipment degradation correlation model according to the health index to obtain degradation prediction data; coupling and fusing the water level flow data and the degradation prediction data to obtain a water regimen-equipment coupling prediction result; a coupling prediction result is optimized through an adaptive weight algorithm, and a differentiated scheduling strategy of the healthy gate and the aged gate is obtained; and performing corresponding control processing on the healthy gate and the aging gate to generate a cooperative control instruction sequence. The technical problem that in an existing gate control technology, water regimen changes and equipment state evolution are independently processed, and differentiated collaborative scheduling based on equipment health constraints cannot be achieved is solved.

The invention discloses an intelligent discharge port dynamic regulation and control system and method based on multi-source time sequence fusion, and relates to the technical field of intelligent water affairs, and the system comprises a data collection module which is used for obtaining the multi-source data of a target area in real time; the space-time fusion module is used for mapping the meteorological radar data to the topological space of the drainage pipe network through a space-time alignment encoder, dynamically fusing the meteorological radar data, the surface runoff sensor data and the historical drainage data by adopting a multi-head attention mechanism, and generating a space-time fusion tensor; the prediction model module is used for constructing a TCN-LSTM hybrid model, taking the space-time fusion tensor as input and taking future drainage flow as output; and the dynamic regulation and control module is used for optimizing the gate opening degree and the pump station power control sequence in a rolling manner based on a model predictive control algorithm, and performing dual-target optimization by combining real-time electricity price data and overflow risks. According to the system, the prediction precision of the drainage flow can be improved, and double-target balance of the overflow risk and the energy consumption cost can be achieved.

The invention provides a self-adaptive error compensation method and system for focused ion beam equipment, and the method comprises the steps: obtaining a real-time error signal set generated by the focused ion beam equipment in a machining process, training a self-adaptive error compensation model according to the real-time error signal set, and generating an error compensation parameter set; inputting the error compensation parameter set into a control unit of the focused ion beam equipment, and generating a real-time compensation control instruction set; adjusting an ion source parameter and a deflection electrode voltage parameter of the focused ion beam equipment according to the real-time compensation control instruction set, and outputting an adjusted ion beam path parameter set; and performing ion beam processing on the target processing area according to the adjusted ion beam path parameter set, and monitoring morphology feature data of the processed area in real time to verify an error compensation effect. According to the invention, harmful effects caused by thermal effects, electric fields or magnetic fields can be eliminated, and the self-adaptability and long-term reliability of a compensation strategy are ensured.

An autonomous environmental regulation and behavioral monitoring system is disclosed, configured to adapt temperature, lighting, and acoustic conditions based on real-time emotional and physiological data. The system includes a dual-redundant central processor, hierarchical communication networks, multi-angle visual acquisition units, infrared thermometers, and modular environmental subsystems. It detects posture, gestures, facial expressions, and thermal signals to classify user states and apply individualized airflow, light, and sound modulation without relying on external internet connectivity. The system also monitors connected appliances using voltage-based pressure analysis to forecast device degradation. With integrated gesture recognition, privacy-preserving data handling, and predictive adaptation, the invention enables multi-user personalization, long-term learning, and uninterrupted operation within residential, administrative, or healthcare infrastructures.

The invention relates to the technical field of body-equipped intelligent robots, in particular to a multi-mode body-equipped intelligent robot control method and device, and the method comprises the steps: synchronously collecting visual, auditory, tactile, force sense and body perception information, and unifying the information to the same time-space reference through a cross-mode time-space stamp alignment mechanism; hierarchical feature extraction and fusion are carried out on the multi-modal information, and unified multi-modal scene state representation is generated; reasoning a decision based on the representation by using a body agent framework, and outputting a control instruction; motion planning and control, visual servo tracking in a non-contact stage and dynamic parameter correction in a contact stage are executed according to instructions; optimizing the multi-modal strategy network through an incremental strategy distillation mechanism based on the interactive data flow; the problem of space-time asynchronization of multi-modal sensing information is solved through a cross-modal space-time stamp alignment mechanism.

The invention relates to the technical field of data acquisition and processing, and provides a real-time data acquisition and processing method and system for a distributed control system, and the method comprises the steps: collecting a data flow in real time through a terminal sensor, analyzing the probability distribution of a data change rate through a sliding window, and obtaining a data change rate; and a sensitivity threshold is intelligently generated by combining dynamic factors such as equipment electric quantity and network load. When data mutation exceeds a threshold value, the system automatically extracts a key change section and generates time, period and variable quantity three-dimensional features, and meanwhile, the frequency domain analysis result is fused to enhance the anti-interference performance. And the event emergency degree is calculated based on the time-space correlation of the spectrum energy entropy and the characteristic parameters, and finally a processing queue sorted according to priorities is formed and the bandwidth is dynamically allocated. According to the method, through multi-dimensional data fusion and resource adaptive adjustment, the response speed of the system to emergencies, the data discrimination precision and the stability in a complex environment are improved.

The invention provides a smart home management method and system based on the Internet of Things. The method comprises the steps that indoor and outdoor environment parameters, user physiological data, home equipment operation states and energy consumption data are collected; based on the data, learning behavior preferences of the user in different time and environments, and establishing a personalized behavior prediction model; deploying the model at an edge computing node, combining big data analysis of a cloud server to form a hierarchical intelligent decision-making architecture, and outputting a preliminary decision-making result; the real intention of the user is understood and an intention confidence evaluation mechanism is established; when the consistency of the identification results of the multiple modes is lower than a threshold value, confirmation is actively carried out on the user, and a primary intention is obtained; according to the intention, a control strategy is dynamically generated in combination with the environment state and the equipment capacity; and based on the operation data and the energy consumption data, establishing an equipment health degree evaluation model, and predicting a fault risk and a maintenance demand. Through the scheme of the invention, the intention of the user can be accurately identified, and accurate personalized services are provided, so that the user experience is improved.

The invention provides a multi-stage embedded control equipment state sensing and energy cascade scheduling system. Comprising a master control decision center module, a distributed edge embedded node module, an equipment full-dimension state sensing module, an energy dynamic optimization scheduling module, a fault prediction and self-healing control module, a cross-protocol communication interconnection module and a man-machine cooperative command module. According to the invention, through constructing a three-layer time domain control chain of edge node nanosecond-level signal processing, cloud second-level optimization scheduling and equipment hour-level strategy presetting, seamless cooperation of turbine bearing pedestal micro-vibration monitoring and a power grid peak regulation strategy is realized, and real-time wavelet noise reduction preprocessing of embedded nodes is combined with cloud LSTM life prediction. A taboo search algorithm is driven to dynamically reconstruct a power supply scheme, and the pain point of control response lag in a high-fluctuation scene is solved.

The invention discloses an intelligent household electrical appliance interaction control method and system, and the method comprises the following steps: collecting the operation state parameters, environment perception data and user behavior characteristics of each household electrical appliance in a living space in real time, and generating an environment perception data packet through a multi-modal data fusion model; constructing a user habit preference model library, generating a household appliance control strategy decision tree in combination with historical interaction records, and matching an optimal control strategy set according to a real-time scene type; and when a user active intervention signal or an environment sudden change event is detected, a dynamic strategy reconstruction mechanism is triggered, an instruction updating request carrying a priority identifier is broadcasted to the associated household appliance group, and a redistribution control instruction set is generated. The method has the following advantages and effects: multi-source environment data can be deeply fused, the user habit model is constructed in real time, and the method has a dynamic strategy reconstruction capability so as to solve the problems of response lag, strategy stiffness and energy efficiency imbalance in a complex home environment in the prior art.

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.