2015 results about "Machine control" patented technology

The invention provides an intelligent toy injection molding process optimization method which comprises the following steps: aiming at a key temperature control area, calculating an optimized cooling time value and a cooling water temperature set value through a correlation model of thermodynamic attribute parameters of toy materials, cooling time and cooling water temperature, and issuing the cooling time value and the cooling water temperature set value to an injection molding machine control system, meanwhile, according to the temperature fluctuation amplitude of the key area, the forming pressure value is finely adjusted, so that the temperature fluctuation of the key area is reduced; in the injection molding process, temperature data of multiple positions on the surface of the mold are collected in real time and transmitted to the central processing unit, the central processing unit conducts filtering and denoising on the collected temperature data and then judges whether the temperature data exceed the optimized temperature control range or not, if yes, an alarm mechanism is triggered, and meanwhile operation parameters of the injection molding machine are automatically adjusted. And the time for deviating from the temperature control range is shortened to the minimum.

The invention relates to the technical field of machining control, and discloses a forging part machining process control method and system based on digital twinning. The method comprises the following steps: acquiring temperature field and stress field data streams of large oil press equipment and forgings at multiple points in different areas; performing organization-defect association mode recognition on the data stream to generate a forging process database; a constitutive unit nesting method is adopted to construct a digital twinborn model, and a microstructure evolution unit is embedded in a macroscopic unit; performing nonlinear progressive simulation to obtain a critical deformation condition and a defect threshold value; and carrying out reverse solution on the process window to obtain an optimal process parameter combination. According to the method, the forging digital twin model is established through the constitutive unit nesting method, bidirectional coupling of macroscopic deformation and microstructure evolution is achieved, a stable process parameter combination is determined through the process window reverse solution technology, and therefore the quality stability and production efficiency of forged parts are improved.

The invention relates to the technical field of intelligent manufacturing and numerical control machining control, and discloses a machine tool dynamic characteristic sensing and intelligent machining control method and system based on a knowledge graph and a large model, and the method comprises the following steps: extracting frequency domain parameters through a vibration sensor, obtaining vibration displacement in combination with laser displacement, and comparing the vibration displacement with modal data to construct a graph; processing parameters and displacement are synchronously sampled, time sequence characteristics are extracted to generate tensors, dynamic characteristics are predicted and corrected, and frequency response adjustment rotating speed is matched to generate optimized track control parameters which are converted into G code instructions. According to the method, a dynamic characteristic map is constructed by fusing vibration signals and displacement, a sliding window synchronizes processing parameters and vibration data, LSTM extracts joint characteristics, GRU predicts rigidity and damping ratio, an attention mechanism dynamically corrects weight, characteristic coupling analysis and prediction precision is improved, nonlinear modeling captures dominant frequency offset and harmonic distribution, response speed is enhanced, and the method has the advantages of being high in precision and high in precision. Cutting vibration is inhibited, and the process stability is guaranteed.

The invention belongs to the technical field of radial forging machine control, and particularly relates to a high-precision radial forging machine control system and a control method thereof.The method comprises the steps that a technological parameter database is established, dynamic parameters in the forging process are synchronously collected through a multi-source heterogeneous sensor, and workpiece surface temperature field distribution is obtained through an infrared thermal imager; measuring radial deformation through a laser displacement sensor group, and capturing internal defect characteristics of the material by using an acoustic emission sensor; the optimal forging frequency is calculated according to real-time material rheological characteristics, and a model predictive control algorithm is adopted to generate hammerhead motion trail correction; dynamic compensation control is implemented, and the back pressure of the hydraulic system is adjusted through strain rate feedback; and closed-loop quality evaluation is executed, online metallographic analysis is carried out in the forging intermittent period, process parameters are reversely corrected according to the grain size detection result, the fatigue life of the workpiece is predicted based on a digital twin model, and the effects that the whole forging process is dynamically controlled in real time, errors and deviation are effectively eliminated, and the forging precision is improved are achieved.

A predictive incident management system, consisting of: a sensor input module configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical and cyber sources; a temporal correlation control unit operationally coupled to the sensor input module, wherein the temporal correlation control unit is configured to normalize received data into a uniform time series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context markers, and is further configured to compute sliding window-cross-sensor correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities; a scalable rule processor that is communicatively linked to the control unit for temporal correlation, wherein the rule engine includes an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules that reference primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; A historical repository that is communicatively connected to both the temporal correlation control unit and the rule engine. The repository is configured to store tagged event histories, correlation motif dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions; and An incident response interface is operationally connected to the rule engine. The incident response interface is configured to trigger automated workflows, including the generation of tickets for IT service management, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols. the system is configured to perform predictive analyses based on temporal correlations between sensors and to execute context-aware, rule-based incident management in real time.

The invention relates to the technical field of intelligent control, in particular to a full-automatic plug-in testing machine control system and method, and the system comprises a multi-dimensional data collection module, an abnormal state recognition module, an action termination judgment module, a parameter correction module and a trajectory optimization execution module. According to the invention, based on multi-sensor array fusion, multi-physical quantity cooperative acquisition in equipment operation is realized, vibration, temperature, path and plugging force data are comprehensively calibrated by using a time axis, and through spectral analysis and three-dimensional deviation comparison, fine abnormity and trend change are dynamically revealed, and operation fluctuation and potential abnormity are identified in real time. According to the method, the reasonable interval of the insertion and extraction force is automatically calculated, the action duration and path trend are adjusted, the operation parameters are continuously corrected, equipment abnormity early warning, accurate intervention and multi-parameter linkage adjustment are achieved in the whole process, the test continuity, the execution sensitivity and the prevention and control capacity are improved, misoperation is effectively restrained, abrasion is reduced, and the high-density intelligent test requirement is met.

The invention discloses an electronic jacquard machine control method and system based on multi-channel collaboration, and the method comprises the steps: calculating a delay compensation value of each channel in real time according to the hardware performance difference and signal transmission delay data of each channel, and dynamically adjusting the time sequence offset of a control instruction of an electronic jacquard machine by using the delay compensation value, generating a multi-channel cooperative control signal; according to input fabric pattern data, decomposing the fabric pattern data to generate a layered instruction set, and binding the layered instruction set to a multi-channel cooperative control signal to form a time-space synchronization instruction set; according to channel operation state data acquired in real time, identifying an abnormal channel and a fault type thereof, and generating a reconstruction instruction set after fault immunization; and dynamically allocating computing resources and storage bandwidth according to the reconstruction instruction set, generating an optimization execution instruction set, and issuing the optimization execution instruction set to each channel for execution in real time. By utilizing the embodiment of the invention, the control capability of the electronic jacquard machine and the fabric production efficiency can be improved, and the intelligent development of the textile industry is promoted.

The invention discloses a pipe roof construction optimization control system and method suitable for a water-rich soft soil stratum, and relates to the technical field of data processing, and the system comprises a pipe jacking area modeling module which is used for modeling the water-rich soft soil stratum; the steel pipe jacking simulation construction module is used for simulating the regional model through a pipe jacking machine; the risk induction characteristic analysis module is used for analyzing construction simulation data and determining a pipe jacking path and a pipe jacking machine control risk factor; the predetermined scheme optimization module is used for optimizing a pipe roof pipe jacking construction scheme according to the risk factors; and the scheme execution module is used for executing pipe roof pipe jacking construction. The technical problems of high construction risk and low efficiency caused by lack of risk prediction and scheme dynamic optimization capabilities under complex conditions in water-rich soft soil layer pipe roofing construction in the prior art are solved, and the purposes of realizing refined control and risk avoidance through multivariate risk analysis and dynamic optimization control and improving the construction efficiency are achieved. And the construction safety and efficiency are improved.

The invention relates to the technical field of deep learning, and discloses an earphone mold injection molding parameter optimization method, device and equipment, and the method comprises the steps: collecting temperature distribution data and mold cavity pressure data in an earphone mold cavity, and carrying out earphone shell injection molding dynamic analysis to obtain an earphone shell material flow curve; carrying out topological characteristic analysis on the earphone shell material flow curve, and constructing an earphone shell injection molding process parameter model; based on the earphone shell injection molding process parameter model, the mold cavity temperature and the mold cavity pressure serve as state variables, the heating power and the injection pressure serve as control variables, and an earphone shell injection molding optimization controller is constructed; the earphone shell injection molding optimization controller is connected with the injection molding machine control system, the heating power and the injection pressure of the earphone mold are adjusted in real time, the optimal technological parameters of the earphone mold are solved, a real-time feedback and parameter fine adjustment mechanism is adopted, the injection molding machine control system can quickly respond to technological fluctuation, and the consistency of product quality is guaranteed.

The invention relates to the technical field of arc discharge control, in particular to an arc discharge machine control method based on multi-parameter collaborative optimization, which comprises the following steps of: generating a first regulation and control instruction by adopting a fuzzy PID (Proportion Integration Differentiation) algorithm based on a deviation value between a dynamic parameter feedback signal and a preset threshold value, the axial feeding speed of the servo motor, the power output curve of the arc generator and the coolant spraying time sequence are synchronously adjusted, so that the arc energy density gradient and the workpiece heat conduction rate form dynamic balance, the mechanical resonance frequency in the machining process is monitored through the vibration spectrum analysis unit, and when it is detected that the resonance intensity exceeds a safety threshold value, the coolant spraying time sequence is started. And a second regulation and control instruction is generated by triggering a frequency compensation algorithm, and the driving frequency of the servo motor is corrected in real time, so that the control precision is improved, the arc discharge process is more stable, the response speed is increased, the arc discharge time is shortened, the adaptability is enhanced, the requirements of different materials and processes can be met, the energy consumption is reduced, and the production efficiency is improved.

The invention provides a numerical control machining defect real-time detection method and system based on AI image recognition and a medium, and belongs to the technical field of intelligent manufacturing and machine vision crossing. The method comprises the following steps: collecting part surface image data according to a preset frequency, and separating a part from a background through noise reduction, contrast enhancement and a dynamic threshold algorithm of a data preprocessing module; inputting the processed image into a multi-modal feature fused AI detection model, and extracting and fusing multi-modal features to judge the type and position of a defect; and processing the image by using a parallel computing architecture, feeding back a detection result to a numerical control processing control system in real time, and visually displaying the detection result on a monitoring interface. According to the method, real-time and accurate detection and feedback control of numerical control machining defects are realized, and the machining quality and efficiency are effectively improved.

The invention discloses a shield tunneling intelligent control method and system based on a ground-tunnel-machine-information-human adaptation level, and relates to the crossing field of underground engineering intelligent construction and information technology. Setting a first-level index and a second-level index; dividing the shield construction state into three levels of intelligent adaptation levels by combining the total score of the construction state with a dynamic threshold value; setting four targets of attitude intelligent control, settlement intelligent control, tunneling efficiency optimization and construction abnormity diagnosis; a machine learning prediction model is constructed, and the construction state parameters corresponding to the targets are predicted in real time; according to the shield intelligent adaptation level, the man-machine cooperation modes are divided into three classes of A level, B level and C level; and comparing actual data with a prediction result of the machine learning model, and dynamically correcting the control target parameters. Through real-time sensing of geological, mechanical and environmental multi-dimensional construction states and in combination with a dynamic weight distribution strategy, precise evaluation of the shield construction state is achieved, and meanwhile self-adaptive switching of the man-machine control right is achieved.

The invention provides a humanoid machine control method and system based on model prediction, and the method comprises the steps: firstly obtaining joint sensing data (hip joint movement angle sequences and knee joint stress signals) and environment interaction data (depth image flow data and sole contact pressure data) of a humanoid machine, and generating a dynamic state feature set through feature correlation processing; the feature matrix comprises a joint cooperation feature vector and an environment constraint feature matrix; calling a pre-training model to carry out spatio-temporal joint prediction on the feature set, and outputting a future motion control sequence containing gait cycle features; then, a joint synchronous control model is constructed on the basis, and a driving control instruction set containing time coordination constraint is generated; and finally, the driving control instruction set is transmitted to the distributed execution unit, and joint state feedback data is collected for next round of processing, so that the motion control performance of the humanoid machine in a complex environment is improved.

The invention discloses a numerical control machining intelligent monitoring and self-adaptive control method and a related device, and the method comprises the steps: collecting a multi-mode original data set in a high-speed rotation state of a main shaft; performing time-frequency domain conjoint analysis on the data set to generate a time-varying feature matrix; reconstructing an inner hole state digital twinborn model based on the time-varying feature matrix; performing multi-scale analysis on the model, identifying macroscopic geometric deviation and microscopic material change, and generating a layering abnormal index; historical data and abnormal indexes are combined, a deep learning algorithm is applied to analyze the performance evolution trend of the spindle inner hole, and a multi-time-scale state prediction result is output; and according to the prediction result and the current processing task requirement, dynamically generating a decision scheme including processing parameter optimization and a customized maintenance plan, and feeding back the decision scheme to a processing control system in real time. According to the invention, real-time dynamic monitoring of the inner hole of the main shaft can be realized, accurate performance analysis and prediction are provided, and the stability and the machining precision in the machining process are ensured.

The invention provides a multi-machine cooperative scheduling system of a man-machine control shelter, and belongs to the technical field of multi-machine cooperative scheduling control. The environment feature intelligent classification and curved surface modeling module is connected with the task semantic understanding and planning module; the task semantic enhancement and risk assessment module is connected with the environment feature intelligent classification and curved surface modeling module; and the multi-machine collaborative decision module is connected with the task semantic enhancement and risk assessment module. An organic whole is formed through tight cooperation of functions such as task semantic understanding, environment modeling, risk assessment and decision making, different types of machines can efficiently and cooperatively complete complex tasks under unified scheduling of the system, the limitation of isolated operation of modules in a traditional system is broken through, and the reliability of the system is improved. And the harsh requirements on multi-machine collaborative operation in multiple fields and multiple scenes are met.

The invention relates to the technical field of mechanical automation control, in particular to a bearing pressing machine control method and system. The method comprises the following steps: acquiring multi-dimensional monitoring data in a press fitting process; based on the difference between the monitoring data at the current moment and the data at each moment in the preset window, constructing a deviation degree sequence of each dimension at the current moment; adjusting the length of a preset window based on the correlation of the deviation degree sequence to obtain a target window at the current moment; determining a truncation distance of clustering analysis according to the data point distribution sparseness of the monitoring data in the target window; taking the truncation distance as a parameter, and performing clustering analysis on the data points in the target window through a density clustering algorithm to identify abnormal data points; and when the monitoring data at the current moment is abnormal, the bearing press-fitting machine is controlled to execute a preset response action. According to the method, the analysis window and the clustering truncation distance are adaptively adjusted, so that the accuracy of abnormal state recognition under the multi-stage working condition is improved.

The embodiment of the invention provides a turning and milling combined machining deformation control method and system for a special-shaped part, and is used for the technical field of numerical control machining. The turning and milling combined machining deformation control method for the special-shaped part comprises the steps that structural feature analysis and process association marking are conducted on design data of the special-shaped part to generate a feature association data set, and clamping and process planning are conducted according to a machining constraint database and the feature association data set to generate initial turning and milling combined machining process data; cutting parameter multi-objective optimization solution is carried out on the initial turning-milling combined machining process data to obtain a turning-milling cutting parameter set, and multi-axis linkage analysis and instruction collaborative planning are carried out on the turning-milling cutting parameter set according to the equipment operation feature set to generate a combined machining control instruction set. According to the method, through part key feature recognition and risk marking, combined machining control instructions are generated after layered digital twinning verification in combination with process multi-objective optimization, and the one-time qualified rate of special-shaped part machining and turn-milling combined machining process stability are improved.

The invention relates to the technical field of automatic driving, in particular to a man-machine control right switching method and system considering a driving style and driving ability. The method comprises the steps that vehicle data are collected, and the driving style and driving ability of a driver are evaluated in real time; when the driving control right needs to be switched, a control right switching strategy is made according to the driving style and the driving ability, and changes of the control weight of the automatic driving system and the control weight of the driver are adjusted; the sum of the control weight of the automatic driving system and the control weight of the driver is always 1, and the change trend is influenced by the control weight function # imgabs0 # of the automatic driving system. According to the technical scheme, a flexible pipe connecting strategy can be provided, and therefore the vehicle can be stably transited from automatic driving to manual driving.

The invention relates to the technical field of fastener process production, in particular to a process parameter control method and system applied to fastener production. The method comprises the following steps: acquiring three-dimensional cutting force of a tool-workpiece contact area in a fastener thread machining process in real time, and generating cutting force dynamic fluctuation characteristic data; the real-time abnormal situation of the current machining process is recognized according to the cutting force dynamic fluctuation characteristic data, a tool wear mode is judged, thread ring machining deviation prediction is conducted, and a predicted thread geometric deviation value is generated; and adjusting multiple machining control parameter values according to the predicted thread geometric deviation value, and carrying out real-time machining feedback monitoring so as to realize fastener production self-adaptive control. According to the method, intelligent recognition of the tool wear mode and accurate prediction of the machining deviation are achieved through cutting force sensing, and it is ensured that the thread machining precision of the fastener is stable and controllable through multi-parameter cooperative self-adaptive regulation and control.

The invention discloses a virtual synchronous machine control-based impedance modeling and virtual impedance design method for a network construction type MMC (Modular Multilevel Converter). The method comprises the following steps: firstly, establishing a mathematical model of a network construction type MMC converter based on virtual synchronous machine control through a control strategy, and establishing a sequence impedance model of the MMC by adopting a harmonic linearization method; secondly, designing virtual impedance control to optimize traditional VSG control, and proposing a sequence impedance model after introduction of the virtual impedance control; and finally, analyzing impedance characteristics and grid-connected stability of the MMC converter after virtual impedance control is introduced. According to the method, the MMC sequence impedance model based on virtual impedance control can be effectively established, the grid-connected stability of the MMC sequence impedance model is analyzed, and the method has economical and practical values.

The invention discloses an intelligent pressure maintaining control method for a thin-wall product of an injection molding machine based on mold pressing feedback. The intelligent pressure maintaining control method comprises the following steps: (1) constructing a hardware layer; (2) configuring a software layer; (3) a mold cavity pressure acquisition module acquires original pressure data in the injection mold cavity in real time through a pressure sensor; (4) the data preprocessing module carries out filtering processing on pressure signals generated by the pressure data, and (5) the dynamic pressure maintaining control module dynamically adjusts pressure parameters and pressure maintaining time by receiving the real-time pressure data, product wall thickness parameters, mold structure parameters and injection molding process parameters, generates a pressure maintaining control instruction and sends the pressure maintaining control instruction to an injection molding machine control system; driving the injection molding machine to execute pressure maintaining action; (6) the dynamic pressure maintaining control module associates and stores the pressure change curve and the parameter adjustment record in the pressure maintaining process in a feedback learning module, and optimization calculation is carried out; and (7) the man-machine interaction interface displays the pressure change curve, the pressure maintaining control parameters and the forming state of the injection mold cavity in real time.

The invention provides a numerical control machining control method and device, a numerical control machine tool, a medium and a program product, and relates to the technical field of numerical control machining. The numerical control machining control method comprises the steps that in response to a received first setting instruction of a self-adaptive learning state, a main shaft of a numerical control machine tool is controlled to machine a part based on a fixed feeding rate under a preset machining condition, and a corresponding main shaft power curve is generated; performing adaptive learning based on the main shaft power curve and the configured power range to obtain a processing model; and in response to a received second setting instruction of the self-adaptive control state, in the machining process of the part, the sensed load of the main shaft is input into the machining model, so that the machining model outputs the feeding rate of the main shaft based on the self-adaptive adjustment operation, and the main shaft operates based on the changing feeding rate. According to the technical scheme, the machining model is used for adjusting the feeding rate of the machine tool spindle in real time based on the detected real-time load, and the machining efficiency is higher.

The invention provides a full-automatic vacuum oxygen-free oven, belongs to the technical field of wafer processing, and solves the problems that an existing wafer oven is low in feeding, discharging, heating and baking efficiency; and the automation degree is low. The full-automatic vacuum oxygen-free oven comprises an oven frame assembly, an EFEM frame assembly, two vacuum pump assemblies and four wafer loaders which are evenly distributed at equal intervals, four cavity assemblies and four automatic door assemblies are arranged in the oven frame assembly, and a robot transplanting assembly and a machine control box are arranged in the EFEM frame assembly. A main control computer is arranged on the left side of the EFEM frame assembly, the cavity assemblies are connected with the vacuum pump assemblies on the same side through exhaust pipelines, and the cavity assemblies are connected with an external high-purity nitrogen source. According to the invention, through cooperation of multiple sensors, mechanical linkage and a gas-liquid cooling system in the whole process, full-automatic stable grabbing and loading, vacuumizing treatment, heating, constant-temperature process baking treatment, dual-mode cooling and taking treatment of wafers in an oxygen-free and high-precision temperature control environment are realized.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

The invention discloses a wheel-legged robot whole-body motion control system and method considering slippage steering, and relates to the technical field of wheel-legged machine control. The system comprises an input module used for receiving an expected motion track or a motion instruction from an upper layer planning system; the trajectory optimization module takes the motion trajectory output by the input module as a tracking target, so that the platform can be optimized in real time along an offline trajectory; the tracking control module is used for solving a joint torque through inverse dynamics by adopting a total rigid body dynamic model, realizing motion tracking through a whole body control WBC method, and solving a motion and contact force priority task by using a hierarchical optimization method; and the state estimation module is used for estimating all current state quantities of model predictive control MPC and information required by whole body control WBC through sensor information. By means of the system, high-dynamic stable motion control over the wheel-legged robot under the complex terrain can be achieved.

The invention particularly relates to a discharge machining simulation control system for a nuclear power plant in a high-radiation boron-containing water environment, which belongs to the technical field of special maintenance in the high-radiation environment of the nuclear power plant and comprises a man-machine interaction system, a discharge machining control system and subsystems. Each subsystem comprises a pulse power supply system, a servo power system, a washing system, a purification system, a grounding system and a tool system; the man-machine interaction system is used for man-machine interaction and displaying the discharge machining implementation data fed back by the subsystems; the electric discharge machining control system is used for generating an electric discharge machining instruction according to the man-machine interaction information and controlling the subsystems to cooperate to perform electric discharge machining work; and each subsystem is used for implementing electric discharge machining work according to the electric discharge machining instruction and feeding back electric discharge machining implementation data. According to the invention, simulation control of discharge processing in the high-radiation boron-containing water environment of the nuclear power plant is realized.

The invention relates to a mold injection molding speed control method, device and equipment, a medium and a program product. The method comprises the following steps: acquiring dynamic behavior information in a mold cavity of the injection mold and screw displacement data of injection molding equipment; carrying out fusion processing on the screw displacement data and the dynamic behavior information to obtain fusion data; inputting the fusion data into a pre-constructed melt front edge prediction model, and generating a target injection molding speed control curve by using preset target molding strategy parameters; wherein the target injection molding speed control curve is used for controlling the injection molding equipment to execute corresponding injection molding speeds in different injection molding stages; the target injection molding speed control curve comprises at least two speed change control sections so as to adapt to mold filling requirements of different stages; and the target injection molding speed control curve is input into an injection molding machine control system, so that the injection molding machine control system executes injection molding action according to the target injection molding speed control curve, and the melt is pushed along the mold cavity. By adopting the method, the accuracy of the forming speed can be improved.

A policy map is obtained that includes of prescribed machine settings values for controlling a machine to perform an operation at different locations in the field. Sensor signals indicating conditions that will be encountered by the machine in the future, as it moves through the site. A predictive model provides an expected machine response, based upon the a future condition and a prescribed machine setting value. Adjusted machine setting value is generated based on the expected machine response. Control signals are generated, based on the adjusted machine setting value, to control a set of controllable subsystems.

The invention discloses a machine control system and method based on large language model structured data output, and relates to the technical field of artificial intelligence and robot control. Voice information and image information are acquired through a multi-mode acquisition module, and based on a Gemma 3 large model, a text instruction and a coding image are combined, so that a multi-mode model is obtained; a decision output module generates a structured instruction conforming to predefined JSON Schema, the structured instruction is analyzed by an execution control module to drive a mechanical arm, a memory feedback module is utilized to store interaction data and execute feedback, dynamic switching between a dialogue mode and an action mode is supported, and the mechanical arm is controlled to operate. On the basis of large language model structured output and a multi-module cooperation mechanism, the ambiguity and unreliability of natural language control are effectively solved, and the man-machine interaction precision, the execution reliability and the complex scene adaptation intelligence level are improved.

The invention relates to the technical field of machine tool control, and provides a machine tool machining control method and system.The method comprises the steps that in the machine tool machining process, microenvironment parameters of a cutting area are obtained and monitored; obtaining a cutter type, a workpiece material attribute and a preset cutting parameter corresponding to the machine tool; based on the tool type, the workpiece material attributes and the preset cutting parameters, an initial judgment basis of the tool wear speed is established; according to the deviation between the microenvironment parameters and a preset standard value, the initial judgment basis is corrected, and the corrected tool wear speed is obtained; according to the actual tool abrasion loss, the rule for subsequently correcting the initial judgment basis is optimized; and based on the corrected tool wear speed, the tool remaining life is updated, and when tool wear reaches a preset threshold value, a compensation strategy of the cutting parameters or the tool path is decided and executed in combination with the value of the machined workpiece and the remaining machining task. The method has the effect of improving the prediction accuracy of the tool wear speed.