2633 results about "Machine control" patented technology

The invention relates to the technical field of numerical control machine tools, and discloses a five-axis high-precision numerical control machine tool control method, device and equipment and a storage medium, and the method comprises the steps: collecting temperature data and displacement data of a five-axis high-precision numerical control machine tool, and carrying out state space modeling to obtain a machine tool motion state model; stress data and thermal error influence factors of the feed shaft are calculated; feeding shaft stress data and the thermal error influence factors are input into the compensation model, real-time compensation parameters are calculated, compensation control is conducted on the machine tool feeding shaft, and a compensation control result is obtained; according to the compensation control result, an initial machining track optimization model is constructed, and optimized machining parameters are generated; performing segmented machining control based on the optimized machining parameters to obtain real-time machining data; the initial machining track optimization model is optimized based on the real-time machining data, a target machining track optimization model is obtained, and the dynamic response performance and the control precision of the five-axis high-precision numerical control machine tool are improved through the method.

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 provides an intelligent stretching machine control analysis system for tension stringing construction, and relates to the field of power transmission line stringing construction and automatic control. According to the invention, the data acquisition module is used for acquiring the multi-dimensional data of the operation of the stretching machine in real time, the data transmission module transmits the multi-dimensional data to the data terminal and the data processing module, the data processing module processes the received data, and a Markov chain model is used for carrying out depth feature analysis, so that the accuracy of the deep feature analysis is improved. According to the method, the fault risk prediction is carried out, the corresponding early warning operation is carried out according to the prediction result, an accurate braking scheme is given, the wiring construction progress is prevented from being influenced by blind braking or delayed braking, and by integrating various technologies and combining real-time data acquisition, data processing and visual fault analysis, the wiring construction progress is ensured. Comprehensive monitoring and intelligent management of the construction process of the stretching machine are achieved, the operation state of equipment can be monitored in real time, potential faults can be warned in advance, and construction efficiency and safety are improved.

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 intelligent control system and method for the rotating speed of a centrifugal machine, relates to the technical field of centrifugal machine control, and solves the technical problem that in the prior art, when the rotating speed of the centrifugal machine is adjusted, the influence of equipment abrasion on the centrifugal machine is neglected, so that the adjusting speed cannot meet the requirement, and the rotating speed control efficiency of the centrifugal machine is low. Inputting the detection image into a material category identification model to obtain a category label; searching an initial rotating speed according to the category label and the material weight; calculating an equipment wear coefficient according to historical equipment data, calculating an equipment adjustment rotating speed according to the equipment wear coefficient, and assigning the equipment adjustment rotating speed to the equipment rotating speed; calculating a centrifugal machine abnormal coefficient after calculating a vibration abnormal coefficient according to the equipment data; the alarm signal is generated according to the abnormal coefficient of the centrifugal machine, equipment parameters of the centrifugal machine are accurately obtained through multivariate data, the rotating speed difference caused by equipment abrasion is considered, the rotating speed of the centrifugal machine is dynamically adjusted, the state of the centrifugal machine is monitored in real time, and the rotating speed control efficiency of the centrifugal machine is improved.

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 discloses a numerical control grinding machine intelligent control method and device combined with real-time feedback, and relates to the technical field of grinding machine control. The method comprises the steps that historical machining records of a plurality of numerical control grinding machines in a target factory are collected and divided, a plurality of grinding machine domains of the same kind are determined, machining deviation analysis is conducted, and a plurality of static compensation coefficient sets are obtained; primary compensation is conducted on the initial machining parameters, and the multiple numerical control grinding machines are controlled to conduct grinding machining; performing fixed-point compensation on the primary compensation parameter set to obtain a secondary compensation parameter set; and the multiple numerical control grinding machines are controlled to conduct grinding till the equipment stops continuous operation or grinding wheel accessories are replaced. The technical problem that in the prior art, in the same workshop, due to the fact that the equipment states are inconsistent, and the machining consistency of batch products is poor along with the change of the machining time and the grinding wheel state is solved, and the technical effects that the machining process of the numerical control grinding machine is accurately controlled in an omnibearing mode, and the machining consistency of the batch products is poor are achieved.

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.

Disclosed are systems and methods to control a robotic device using natural language commands. A natural language command may be received by a system. The system may convert the command into low-level machine controls and logic for implementation by a robotic device to achieve a desired action. In some instances, an API module may include mapping data to associate high-level commands with low-level machine controls. A language model may process the natural language command (input), high-level commands, and / or other information, such as system state, sensor observation data, parameters, etc., to determine one or more commands to execute by a robotic device and possibly logic for execution by the robotic device. The robotic device may receive the low-level machine controls and logic to cause the robotic device to perform the requested actions.

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.

A resistance training machine, a method of providing custom workouts, and a method of providing feedback on user form and user balance are disclosed. The resistance training machine includes a left and right motor, a left and right pulley system, a left and right cable, a machine controller, and an HMI. Advantageously, the resistance training machine can provide isotonic exercises, isokinetic exercises, and feedback on form and balance for a user. The first method includes a user calibrating one or more exercises, selecting an exercise, selecting an exercise mode, and performing the exercise. For each exercise, a motor may ramp up to and ramp down from a selected velocity or force level. The second method includes a user performing an exercise, a machine controller receiving position data, torque data, and force data throughout the exercise, the machine controller generating user feedback, and an HMI displaying the feedback to the user.

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 discloses a shield tunneling machine driving attitude adjusting method and system under multi-sensor monitoring, and relates to the technical field of shield tunneling machine control. Interactive engineering planning is carried out, a tunnel center line is identified, a driving reference axis is determined, a tunnel section is subjected to triangular grid division, and an attitude adjusting module is trained by setting the attitude position relation of a shield tunneling machine; and collecting measurement data, carrying out shield attitude misalignment measurement and adjustment decision of each shield single ring in the tunneling operation of the shield tunneling machine, determining an attitude adjustment strategy, and responding to a control center of the shield tunneling machine to carry out tunneling attitude adjustment management and control. The method and the device are used for solving the technical problems that in the prior art, decision consideration is not comprehensive enough, the technical support degree is insufficient, instantaneity and adaptivity of tunneling posture adjustment cannot be guaranteed, and consequently accuracy and reliability of an adjustment strategy are insufficient. High-precision real-time adjustment and control of the posture of the shield tunneling machine are achieved, and construction safety and efficiency are effectively improved.

A virtual machine controller service of a service provider network may provide a virtual machine controller environment where applications associated with machine controllers (e.g., programmable logic controllers (PLCs), programmable automation controllers (PACs), etc.) can execute and data can be provided as input to the executing applications. An example process may include receiving a request to create a virtual controller instance, and creating the virtual controller instance in a service provider network based at least in part on the request. The example process may further include executing, on the virtual controller instance, an application that is associated with a machine controller that is operable within a facility, and sending data as input to the application to elicit a response from the application. This example process can be used for training personnel and / or for validating machine controller applications prior to their deployment on a physical machine controller in a facility.

The invention relates to the field of wind turbine generator control, in particular to a wind turbine generator operation control optimization method and system based on deep learning, and the method comprises the steps: obtaining the vibration, bearing temperature and generator winding temperature of a wind turbine generator, and the wind speed, wind direction and air density of the wind turbine generator; inputting all acquired data into a control parameter acquisition model, and outputting an ideal control parameter set of the current wind turbine generator; calculating an accurate rated wind speed; adjusting the control frequency based on the wind speed and the wind direction; each control parameter is adjusted; the wind turbine generator operation control optimization system comprises a data monitoring module, a control parameter acquisition module, a control frequency adjustment module and a pitch angle adjustment module. The method has the advantages that limitation of a traditional single-machine control strategy is broken through, especially when the wind speed is lower than the rated wind speed, the ideal increment of the pitch angle is actively adjusted, and therefore the total generating capacity of the wind power plant is improved.

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.

According to the metal plate self-adaptive straightening method and technology based on the line laser contourgraph, the laser scanning contourgraph is arranged above a feeding part of a straightening machine, a line laser transmitter and an image sensor are arranged in the laser scanning contourgraph, and the line laser transmitter projects laser lines to a metal plate; the image sensor is used for capturing a plate image with a laser reflection light strip to assist the straightening control system to adjust the pressing roller, straightening of the metal plate can be adjusted in real time, and the straightening precision is improved; image data collected by an image sensor is transmitted to a data processing module, and the data processing module analyzes an image to obtain actual contour data of a plate before straightening; the actual contour data are compared with a preset ideal straightened plate contour model, the deviation value of each point is calculated, and a deviation matrix is generated; the straightening machine control system controls the position conditions of all the pressing rollers independently according to the deviation matrix, the accuracy of plate straightening can be greatly optimized, and continuous adjustment can be achieved.

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.

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 relates to the technical field of cutting machine control, in particular to a wire cutting machine feeding speed control system and method.The wire cutting machine feeding speed control method comprises the steps that a multi-mode signal recognition material module extracts current and vibration signal features through the self-adaptive feature selection technology, and the current and vibration signal features are obtained on the basis of an improved random forest model; a processing material is identified by combining dynamic weight voting and a weighted fusion method, and online learning and model updating are supported so as to adapt to processing requirements of different materials. The double-loop self-adaptive control module comprises outer-loop initial compensation and inner-loop fuzzy PID adjustment, real-time fine adjustment parameters are optimized according to historical machining records and double-target error values, machining precision and stability are ensured, and machining quality is improved. And the energy consumption management optimization module intelligently regulates and controls the energy consumption of the system by identifying non-processing and processing periods, so that the overall energy efficiency is improved, and energy-saving production is realized. According to the invention, the efficiency and the accuracy of the wire cutting machine in machining of multi-variety and small-batch dies are improved.