36650results about "Programme control" patented technology

The invention provides a numerical control machine tool wear automatic detection and compensation method based on artificial intelligence, and the method comprises the steps: collecting the cutting force data of a high-curvature region in real time through multi-sensor fusion, and obtaining the cutting force fluctuation characteristics; cutting temperature data of the high-curvature area are monitored and obtained, the cutting temperature change rate is extracted, whether the temperature exceeds a preset threshold value or not is judged, and if yes, an alarm mechanism is triggered, and cutting parameters are adjusted; predicting the tool wear rate in combination with the co-evolution relationship between wear and temperature, the online monitoring data and the processed time, and generating a wear prediction curve in a preset time period; and performing trend analysis and feature extraction on the wear prediction curve to obtain wear parameter changes of the cutter in a preset time, and if the prediction curve shows that the wear parameter changes at a certain time point in the future exceed a preset critical value, adjusting the cutting parameters and generating a target cutting parameter combination.

The invention discloses a swivel bridge attitude monitoring system based on dynamic feedback control, and belongs to the technical field of bridge construction. Comprising the following modules: a multi-source sensor integration module for constructing an omnibearing attitude monitoring network and realizing high-precision real-time monitoring of key parts of a bridge; the edge calculation and data fusion module realizes real-time on-site processing and fusion of sensor data, effectively reduces data transmission delay and improves monitoring stability; the multi-physics field coupling modeling module is used for constructing a multi-physics field coupling model comprising structural mechanics, a temperature field, contact friction and fluid action, and realizing accurate prediction of the attitude change of the swivel bridge; the self-adaptive control strategy module is used for realizing high-precision self-adaptive control on the attitude of the swivel bridge; the grading early warning and cooperative control module automatically matches a corresponding control strategy and realizes risk grading response and intelligent cooperative control; and the digital twinning interaction verification module is used for constructing a high-precision digital twinning model of the swivel bridge.

The invention relates to the technical field of numerical control machining, in particular to a tool cutting path machining error compensation method based on online monitoring, which comprises the following steps: establishing a cutting path data model; constructing a multi-modal sensor assembly, and collecting error data in real time; establishing an error state equation to carry out error calculation; constructing a double-layer compensation mechanism; and executing the compensation instruction by adopting a real-time control system. Through real-time synchronous monitoring of the multi-mode sensor, a mixed error resolving framework and a double-layer intelligent compensation mechanism, the influence of dynamic error sources such as cutter abrasion and machine tool thermal deformation can be effectively overcome, the machining precision and the surface quality are remarkably improved, and the high-precision machining requirements in the fields of aerospace, precise instruments and the like are met.

The invention provides an equipment group collaborative fault prediction analysis method and system for a complex industrial process, and relates to the technical field of fault prediction, and the method comprises the steps: collecting equipment group data, and reconstructing a multi-scale time sequence feature; constructing a quantum walking enhanced dynamic association map; extracting spatial-temporal features and mapping the spatial-temporal features to a Riemannian manifold space; identifying an abnormal mode and generating hidden variable probability distribution; constructing a fault evolution model driven by a non-equilibrium state statistical fluctuation theory; and solving the cooperative field equation set to obtain a control parameter sequence. The method can effectively improve the accuracy and reliability of equipment group collaborative fault prediction and reduce the maintenance cost.

The invention relates to the technical field of machine tool intelligent monitoring, and discloses a numerical control machine tool intelligent monitoring method and system based on the Internet of Things and a storage medium. The method comprises the steps of collecting machine tool multi-source data and transmitting the data to an edge node; original data is processed to obtain a working condition feature set, the working condition feature set is input into a recursive convolution hybrid memory network to construct a common dependency relation graph, and state parameter prediction is realized through time-frequency domain joint analysis; converting the two-dimensional feature map into a two-dimensional feature map, and extracting interaction features through SE-ResNet; fusing the two types of features to execute state classification; and predicting the life and generating a diagnosis report based on the health state judgment result in combination with the process parameters and historical data. According to the method, effective fusion analysis of multi-source heterogeneous data of the numerical control machine tool is realized, and a self-adaptive state recognition and fault early warning mechanism is constructed, so that accurate monitoring of the operation state of the machine tool, intelligent diagnosis of early faults and accurate prediction of residual life are realized.

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 AI intelligent diagnosis method and system based on an intelligent system, and the method comprises the steps: collecting a building electromechanical equipment operation data flow containing multi-source heterogeneous data, such as a vibration spectrum, a thermal imaging characteristic and an energy consumption fluctuation parameter, through an edge calculation node, and carrying out the state time sequence coding to generate a coding sequence; and inputting the sequence into a fault prediction neural network to obtain a multi-dimensional fault feature distribution matrix. And then obtaining a standard diagnosis report of the expert knowledge base and an emergency diagnosis report of the field operation and maintenance record. And then a composite training objective function is constructed based on the result and equipment operation characteristic hidden space mapping, a fault prediction neural network parameter space is optimized through a back propagation algorithm, and a target fault prediction neural network is generated. And finally, fault prediction and diagnosis are carried out on the operation data flow of the building electromechanical equipment by using the network, so that comprehensive, accurate and intelligent fault diagnosis is realized.

The invention relates to the technical field of industrial automatic monitoring, in particular to a weftless tape machine intelligent monitoring system and method based on real-time data acquisition. The system comprises a data acquisition module used for acquiring real-time data of a multi-heterogeneous sensor; the data processing module is used for preprocessing the real-time data; the intelligent analysis module covers a feature extraction unit, an anomaly detection unit, a fault diagnosis unit, a service life prediction unit and a data fusion unit and can extract data features, detect anomaly, diagnose faults, predict the service life of a part and fuse multi-unit output to make a decision; and the early warning response module triggers corresponding levels of alarms according to the analysis result and generates a diagnosis report. According to the invention, comprehensive real-time monitoring, early fault early warning, accurate fault diagnosis, residual life prediction, multi-source information fusion and the like of the operation state of the weftless tape machine are realized, the production efficiency and the product quality are effectively improved, and intelligent management of production of the weftless tape machine is assisted.

The invention relates to the technical field of the Internet of Things, and discloses a machine room equipment environment monitoring method and system based on the Internet of Things, and the method comprises the steps: constructing an edge calculation hardware system of the Internet of Things to obtain an integrated terminal which integrates a communication unit, an acquisition unit and a control unit; performing multi-channel parallel acquisition and edge calculation analysis on equipment and environment parameters in the machine room to obtain a standard data tuple and an analysis data packet; executing local hierarchical storage and logic control to obtain a control instruction and storage data; executing three-layer network communication interaction on the storage data to obtain a remotely accessible monitoring view; according to the method, intelligent alarm and system self-maintenance are achieved, graded and classified alarm information and a system state report are obtained, the data transmission amount is greatly reduced, meanwhile, the system response delay is reduced, the control decision accuracy is improved, and the misoperation risk is reduced.

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 discloses a power industry robot collaborative inspection and fault self-diagnosis system and method, and belongs to the technical field of power inspection, and the system comprises a management module which receives an inspection task instruction and obtains inspection task information according to the inspection task instruction; the environment identification module is used for acquiring inspection environment data and identifying obstacles; the path planning module is used for generating an inspection path set by adopting a multi-target particle swarm optimization algorithm according to the obstacle and inspection task information; the scheduling module is used for acquiring the state data of each robot and distributing the routing inspection paths in the routing inspection path set to each robot; the fault feature extraction module is used for acquiring multi-sensor data acquired by the robot and generating a fault feature vector; and the fault diagnosis module performs fault analysis on the fault feature vector to obtain a fault risk analysis report. The obstacle is recognized by acquiring the environment data, the inspection path set is generated by combining the inspection task information and adopting the multi-target particle swarm optimization algorithm, and the method can adapt to the complex inspection environment.

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 provides a thermal power generating unit performance optimization control system and method, and the system comprises a data collection module which collects various real-time data in the operation process of a thermal power generating unit; the load prediction module is used for establishing a load prediction model and predicting a load change trend by comprehensively considering a historical load condition, a load change rate, a load change acceleration and an external interference factor; the optimization control module is used for dynamically adjusting fuel, an air door and air quantity according to a control strategy in combination with the load prediction result and the unit operation state; the actions of the regulating valve, the air door actuator and the water supply regulating valve are correspondingly controlled; the monitoring feedback module is used for realizing closed-loop control and self-correction; according to the method, a load prediction model based on the combination of time sequence analysis and a physical mechanism and a control strategy comprehensively considering multi-parameter collaborative optimization are utilized, so that quick response to load change, balance consideration of steam quality and combustion efficiency, self-adaptive stable operation under complex working conditions and improvement of overall performance are achieved.

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.

Disclosed in the present invention are a multi-modal shared teleoperation system and method for a three-arm space robot. The system at least comprises a master-side teleoperation system, a communication module and a slave-side robot system, wherein the master-side teleoperation system at least comprises two force feedback hand controllers, a microphone array and upper computer software, and the slave-side robot system comprises two operating arms each equipped with a gripper at the end, an observation arm having a binocular camera mounted at the end, a vision unit, a force sensor and lower computer software. The method comprises: an operator controlling two operating arms of an extravehicular robot to execute a task, and controlling an observation arm to acquire a better local field of view. In the method, a multi-modal teleoperation method comprising pose control, voice control and force control is fused with autonomous control of a robot by means of a shared control algorithm, and thus, human-robot collaborative control over the position, orientation and contact force of a robotic arm can be realized on the basis of the requirements of the operator, and the robot autonomously executes other relatively simple tasks, thereby reducing the operation burden of operators, and improving the control efficiency.

The invention relates to the technical field of gold ore flotation full-process monitoring and control, and discloses a gold ore flotation full-process intelligent monitoring and optimal control method and system. The method comprises the following steps: collecting flotation process parameters, and carrying out abnormal value detection and grading treatment; the graded data are fused, and a digital twinborn model is established; designing a negotiation control framework based on the model, and adjusting parameters by using a contract network protocol; and constructing a soft measurement model and an optimization strategy, and optimizing control parameters through a self-learning mechanism. The technical problems that global optimization is difficult to achieve due to unit independent control in an existing gold ore flotation control system, control decision is inaccurate due to simple data processing, key quality indexes are difficult to estimate due to lack of soft measurement means, and adaptability is poor due to limited system learning ability are solved.

The present application provides a system energy efficiency optimization method and apparatus based on a comprehensive energy demand, an electronic device, and a storage medium. The method comprises: in response to a received target comprehensive energy demand, on the basis of comprehensive energy demand parameters, operation parameters of a target system, and environmental loss parameters of the target system, constructing an energy efficiency optimization function of the target system (S210); on the basis of a balance relationship between the energy supply and the energy consumption and a device operation demand of transmitting energy, constructing constraint conditions for optimizing the energy efficiency of the target system (S220); and on the basis of the energy efficiency optimization function and the constraint conditions, using a target algorithm to process operation parameters of a plurality of devices by taking the operation parameters of the plurality of devices in the target system as population individuals to obtain device parameters of the target system meeting the target comprehensive energy demand (S230).

The invention provides a comprehensive and intelligent mariculture net cage management and control system. The system integrates a multi-parameter sensor module, an underwater robot module, an underwater camera module, a central control module, a data storage and analysis module, an early warning module, a remote communication module and an automatic execution module. By monitoring multiple environmental parameters such as water temperature, salinity, dissolved oxygen and the like in real time and utilizing the underwater robot and the underwater camera to carry out autonomous inspection and fish behavior observation, the system can accurately master the culture environment condition. The AI algorithm and the intelligent model are combined, the system dynamically adjusts the bait casting amount, the oxygenation frequency and the net cage position, and the breeding benefits are effectively improved. Meanwhile, the early warning module carries out graded early warning on risk events such as typhoon and diseases, and the breeding safety is ensured. The remote communication and visual human-computer interface further enhances the remote monitoring and management capability of the system.

A multimodal shared telerobotic system and method for a three-arm space robot, the system at least includes a local-site system, a communication module, and a remote-site system, where the local-site system includes two force-feedback haptic devices for left and right hands, a microphone array, and upper computer software; the remote-site system includes two robotic arms provided with end-effectors, an observation arm with a stereo camera installed at an end thereof, two force sensors, a vision unit and lower computer software; an operator can control the two robotic arms of the robot outside a cabin for performing operations, and control the observation arm to obtain a better local view; and a multimodal telerobotic control method of pose control, voice control, and force control is integrated with the robot's autonomous control through a shared control algorithm.

The invention provides an automatic production line multi-parameter real-time monitoring and fault diagnosis early warning method and system, and relates to the technical field of automatic production line monitoring, and the method comprises the steps: obtaining the data of a multi-source sensor, carrying out the data quality evaluation and correction, carrying out the monitoring of the performance parameters of an equipment assembly, and calculating the degradation rate; fault types are analyzed and identified in combination with time-frequency domain features, and a fault propagation link is established based on a physical connection relation to determine a fault source. According to the invention, equipment abnormity can be found in time, the fault source component is accurately positioned, the accuracy and timeliness of fault diagnosis are improved, and the equipment maintenance cost is effectively reduced.

The present invention relates to the technical field of agricultural irrigation. Disclosed are an intelligent irrigation strategy formulation method and system. The method comprises: determining the current irrigation strategy on the basis of a preset irrigation decision-making model and a Q-learning intelligent agent and according to the current agricultural state; on the basis of the current irrigation strategy, performing irrigation simulation on a target growing region to obtain a corresponding irrigation reward value and the next agricultural state, wherein the irrigation reward value is determined on the basis of the crop yield, the crop water demand, and the annual economic cost after irrigation, the current agricultural state, the current irrigation strategy, the irrigation reward value, and the next agricultural state form an empirical four-tuple, and a plurality of empirical four-tuples form an experience pool; and randomly sampling the empirical four-tuple from the experience pool as a training sample to train the Q-learning intelligent agent so as to obtain an optimal irrigation Q-value table which is used for determining a corresponding optimal irrigation strategy on the basis of any agricultural state of the target growing region. The present invention can efficiently obtain an accurate irrigation strategy.

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 relates to the technical field of information, in particular to an error compensation method for multi-axis linkage machining of a mold blank hole system, which comprises the following steps: judging the time range of a tool changing window based on an error accumulation trend, if error accumulation reaches a preset threshold value, triggering a compensation action, otherwise, continuously monitoring error change; aiming at the complexity of a motion track, a nonlinear optimization algorithm is adopted, an execution path of a compensation action is optimized, and conflicts with a processing action are reduced; according to the optimized compensation path, the control parameters of the servo motor are adjusted in real time, and it is ensured that the compensation action is stably executed in the machining process; dynamically updating an error model through data of a main shaft temperature sensor and a tool wear monitoring device, and continuously optimizing a time point and an execution path of a compensation action; in the machining process, the error accumulation condition is monitored in real time, if the error exceeds the preset range, the compensation action is triggered again, and the machining precision is ensured.

A humanoid robot includes a torso, a left arm assembly coupled to the torso and having a first reference line, a left wrist coupled to the left arm assembly and including at least a rotational axis, and a left end effector coupled to the left wrist. The left end effector is configured to move about the rotational axis and includes a finger assembly with a second reference line and at least two degrees of freedom, and a thumb assembly with at least three degrees of freedom. A first angle is formed between the first and second reference lines when the left wrist is in a first configuration, and a second angle is formed when the left wrist is in a second configuration. Both the first and second angles are greater than 70 degrees, and the difference between the first and second angles is greater than 150 degrees.

A system and method for HVAC optimization is disclosed. The system may include a digital twin (112) of an HVAC system (114) installed in a premises (108). The digital twin (112) is based on design parameters, operational data, and PMV-based thermal comfort analysis. The system further includes an RL agent (202) that is configured to receive and process at least real-time environmental conditions, occupancy patterns, and PMV value associated with the premises (108) of the HVAC system (114), identifying one or more optimal actions to adjust one or more HVAC parameters. The digital twin (112), driven by the identified optimal actions, may dynamically simulate the HVAC system (114) and the RL agent (202) may optimize operations of the HVAC system (114) based on the identified optimal actions, ensuring seamless and adaptive thermal comfort and energy efficiency management within the premises (108).